Snehasish CHINARA, Author at SimTrade blog

About the author

The article was written in July 2025 by Snehasish CHINARA (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2025).

Optimal capital structure with corporate and personal taxes: Miller 1977

July 21, 2025July 20, 2025 by Snehasish CHINARA

In this article, Snehasish CHINARA (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2025) explores the optimal capital structure for firms, which refers to the balance between debt and equity financing. This post focuses on how the impact of personal taxes on the firm capital structure. The author unpacks Miller’s 1977 proposition, which presents a formula for calculating the right tax advantage of debt, and explains how it helps reconcile theory with what we actually observe in practice.

Introduction

When Modigliani and Miller introduced their capital structure theory in 1958, they shook the foundations of corporate finance. They argued that, in a perfect market with no taxes, no bankruptcy costs, and no frictions, a firm’s value is completely independent of how it is financed. In other words, it doesn’t matter whether a firm uses debt, equity, or a combination of both—the total firm value remains the same.

In 1963, Modigliani and Miller revised their theory to incorporate corporate taxes. With this adjustment, interest payments on debt are tax-deductible, and then provide firms with a “tax shield” that effectively reduces the cost of debt. This made debt financing more attractive than equity, leading to the conclusion that firms should increase their leverage to maximize their value (ideally reaching a 100 debt ratio). In the extreme, this version of the theory suggested that firms should be financed entirely with debt to benefit from the maximum tax advantage.

However, the real world tells a different story. Very few firms rely solely on debt. In fact, most maintain a balanced mix of debt and equity. If debt is supposedly so advantageous under corporate tax rules, why don’t we see more of it being used? This is where Merton Miller’s 1977 work offers a crucial refinement to the theory.

Miller introduced a critical yet often overlooked component into the capital structure discussion: personal taxes. While interest payments are tax-deductible at the corporate level, the income received by investors—whether as interest or dividends—is also subject to personal taxation. Importantly, interest income is often taxed at a higher rate than equity income (like capital gains or dividends). This means the supposed advantage of debt at the corporate level may be offset—or even completely nullified—by the higher tax burden borne by investors.

Modigliani-Miller 1963 Theorem (M&M 1963)

Let us first remind you about the main findings of Modigliani and Miller (1963). In their revision of their first article published a few years earlier (1958), their theory about the firm capital structure introduced corporate taxes, which has a crucial impact on their earlier conclusions which found that the capital structure was irrelevant. They recognized that, in most economies, governments impose corporate income tax, but companies can deduct interest payments on debt from their taxable income. This interest tax-shield increases the after-tax profits of a firm and thereby raises its overall value.

The tax shield refers to the reduction in taxable income that results from interest payments on debt. Since interest expenses are tax-deductible, they effectively reduce the amount of taxes a company owes. This provides a direct financial benefit to firms that use debt financing, making it a valuable tool for optimizing capital structure.

The formula for the tax shield is:

This means that, under the M&M (1963) proposition, the value of a leveraged firm is given by:

where:

V_L is the value of a levered firm using debt.

V_U is the value of a unlevered firm not using debt but only equity

T_c is the Corporate tax rate

D is the amount of debt of the firm

This formula shows that the value of a firm increases by the amount of tax shield (Tc⋅D) when debt is introduced into the capital structure. The more debt a company takes on, the greater the tax benefit, making debt financing more attractive than equity financing.

Miller (1977): The Role of Personal Taxes in Capital Structure

Modigliani and Miller’s 1963 revision made a powerful case for debt: because interest payments are tax-deductible, firms enjoy a tax shield that reduces their cost of capital. The logical (but extreme) implication of this idea was that firms should maximize debt in their capital structure. However, the theory still fell short of explaining reality—most firms do not load up on debt. Why?

This is where Merton Miller’s 1977 paper brought a major refinement. While M&M (1963) focused on corporate taxes, Miller highlighted the crucial role of personal taxes paid by investors. Specifically, he noted that:

Interest income (from bonds or loans) is typically taxed at a higher personal rate (T_Pi),

While equity income (via dividends or capital gains) is often taxed at a lower rate (T_Pe).

Thus, although the firm saves taxes through debt, the investor receiving interest income may lose part of that advantage due to higher personal taxes. Miller argued that the tax benefit of debt is not universal—it depends on the relative tax positions of the firm and its investors.

Miller quantified the net tax advantage of debt with the following formula:

where:

T_Pi is the personal tax rate on interest income

T_Pe is the personal tax rate on equity income (dividends/capital gains)

T_c is the corporate tax rate

This expression compares the after-tax returns from debt and equity financing, from both the firm’s and investor’s perspectives.

Value of a Levered Firm according to Miller (1977)

In Miller (1977), the value of the firm incorporates both:

1. The corporate tax shield (from M&M 1963), and

2. The personal tax disadvantage from investor taxation on interest income.

Unlike M&M 1963 (which assumed value keeps increasing with leverage due to tax shields), Miller showed that the firm’s value plateaus at an equilibrium level, reflecting the offsetting effect of personal taxes.

There isn’t a single formula as elegant as in M&M 1963 because Miller focuses on market equilibrium, not firm-level maximization. But we can express the adjusted value of a levered firm relative to the unlevered firm as:

that is,

where:

V_L is the value of a levered firm using debt.

V_U is the value of a unlevered firm not using debt but only equity

T_c is the Corporate tax rate

T_Pi is the personal tax rate on interest income

T_Pe is the personal tax rate on equity income (dividends/capital gains)

D is the amount of debt of the firm

Figure 1. Firm Value vs Debt according to Miller 1977 Theorem

where:

T_c is the Corporate tax rate

T_Pi is the personal tax rate on interest income

T_Pe is the personal tax rate on equity income (dividends/capital gains)

The Equilibrium Capital Structure Across Firms

One of the most insightful—and often misunderstood—contributions of Miller (1977) is that there is no single “optimal” capital structure for all firms. Instead of recommending that every company should maximize debt (as M&M 1963 might suggest), Miller argued that the optimal mix of debt and equity depends on the broader market, not just individual firm decisions. His approach introduced a market-level equilibrium perspective, which helps us understand the diverse financing strategies we observe in the real world.

Miller recognized that not all investors are taxed equally. Some investors—like pension funds, endowments, or individuals in low tax brackets—are less affected by taxes on interest income. These investors prefer debt because they can earn stable interest income without facing significant tax penalties. On the other hand, investors in higher tax brackets might favour equity, particularly because capital gains and dividends are often taxed at lower rates than interest income.

This diversity in investor preferences (from different personal tax rates) creates a kind of natural balance in the financial markets. Some firms will issue more debt to attract income-focused investors, while others will rely more on equity to appeal to investors who value capital gains. Over time, this leads to a market equilibrium in which different firms adopt different capital structures based on the preferences of the investors they attract.

In reality, we do not see all firms aggressively using debt to lower their tax bills. Instead, we see some firms—like utilities or financial institutions—using higher levels of debt, while others—like tech startups or growth firms—rely more on equity. This variation observed in practice aligns perfectly with Miller’s theory. The aggregate tax advantage of debt is “used up” across the economy, so not every firm needs to (or should) leverage itself heavily.

Firms essentially compete for investor types, and their capital structure decisions reflect the marginal investor’s personal tax situation. In this way, the equilibrium is not found at the level of a single firm, but across the entire set of firms.

How Miller (1977) Redefined the Cost of Equity and WACC from Modigliani-Miller (1963)

In M&M (1963), the introduction of corporate taxes led to a crucial insight: because interest payments are tax-deductible, debt financing creates a tax shield that reduces the firm’s Weighted Average Cost of Capital (WACC). The model predicted that, as leverage increases, WACC decreases, and firm value rises—implying that a firm should use as much debt as possible to minimize its cost of capital.

This had a direct impact on the cost of equity as well. In M&M (1963), the cost of equity (rE) increases with leverage to compensate for the rising risk faced by shareholders:

where:

r_E is the cost of equity for a levered firm

r_U is the cost of equity for an unlevered firm

r_D is the cost of debt

D/E is the debt to equity ratio measuring leverage

Here, while the cost of equity increases due to higher financial risk, the overall WACC falls, thanks to the tax shield:

Where: V is the Value of the firm (V= D + E)

Miller (1977) introduced personal taxes into the equation—something that M&M (1963) completely ignored. He observed that investors are not only taxed at the corporate level but also at the personal level:

Interest income is taxed at the personal level (personal tax rate on interest income: T_Pi)
Equity dividends and capital gains are taxed at the personal level (personal tax rate on equity: T_Pe)

Crucially, interest income is taxed more heavily than equity dividends and capital gains: T_Pi > T_Pe. This is the case in the United States and most developed countries.

This alters the perceived tax advantage of debt as the benefit of corporate tax deductibility may be neutralized—or even outweighed—by the higher taxes on interest income.

While Miller (1977) didn’t give a neatly adjusted cost of equity formula like Modigliani and Miller (1963), he did show that the tax advantage of debt financing is not universal—it depends on both corporate and personal tax rates. This led to a redefinition of the net tax advantage of debt, which in turn affects WACC:

And so, the adjusted value of the tax shield, and by extension the impact of debt on WACC, becomes:

Using this expression, the WACC becomes:

where,

T_c is the Corporate tax rate

T_Pi is the personal tax rate on interest income

T_Pe is the personal tax rate on equity income (dividends/capital gains)

D/V is the proportion of debt in the capital structure

E/V is the Proportion of equity in the capital structure

r_E is the cost of equity for a levered firm

r_D is the cost of debt

This means that the WACC no longer declines indefinitely with debt. Instead, as the tax burden on interest income increases (via Ti ), the marginal benefit of debt diminishes. At market equilibrium, the advantage of debt disappears, and WACC flattens—explaining why we observe moderate, not extreme, debt usage in practice.

If Ti > Te and corporate tax Tc is high, debt still offers a net tax advantage, though smaller than in M&M (1963).

If the term in brackets equals zero, there is no net tax advantage—WACC remains flat regardless of leverage.

If the term becomes negative, equity becomes more tax-efficient, and adding debt raises the WACC.

Why Should I Be Interested in This Post?

In corporate finance, the debate around how much debt a firm should take on is far from settled. While traditional models like Modigliani-Miller (1963) emphasize the tax benefits of debt, they ignore the taxes investors pay. This post introduces the groundbreaking Miller (1977) framework, which shows how personal taxes can offset corporate tax advantages, reshaping our understanding of optimal capital structure. If you’re a finance student, investor, or aspiring professional, understanding this equilibrium-based view will give you a more realistic—and nuanced—perspective on how real-world firms decide between debt and equity.

Useful resources

About the author

The article was written in July 2025 by Snehasish CHINARA (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2025).

Optimal capital structure with taxes: Modigliani and Miller 1963

July 20, 2025 by Snehasish CHINARA

In this article, Snehasish CHINARA (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2025) explores the optimal capital structure for firms, which refers to the balance between debt and equity financing. This post dives into the article written by Modigliani and Miller (1963) which explores the case of corporate tax and a frictionless market (no bankruptcy costs).

Introduction to Modigliani and Miller Propositions

In 1958, Franco Modigliani and Merton Miller introduced a groundbreaking theory on capital structure, famously known as the M&M Proposition. Their research concluded that, under certain ideal conditions, the way a company finances itself—whether through debt or equity—does not affect its overall value. This result, known as the Capital Structure Irrelevance Principle, was based on assumptions such as no corporate taxes, no bankruptcy costs, and perfect capital markets. The intuition behind this idea is simple: if investors can create their own leverage by borrowing personally at the same rate as firms, then a company’s financing mix should not matter for its value.

According to M&M Proposition I (1958), in a frictionless world:

where:

V_L is the value of a levered firm using debt.

V_U is the value of a unlevered firm not using debt but only equity

Key Assumptions:

No taxes (in reality, firms pay corporate taxes).

No bankruptcy costs (in reality, firms pay costs if they go bankrupt).

No financial distress (in reality, too much debt can make investors nervous).

However, this initial model had a major limitation: it ignored the effect of corporate taxes. In reality, most governments tax corporate profits, but they allow firms to deduct interest expenses on debt from taxable income. This means that using debt provides a tax advantage, which was missing from the 1958 model. Recognizing this, Modigliani and Miller revised their original work in 1963, introducing the impact of corporate taxes. Their new findings dramatically changed the conclusion: debt financing increases firm value because interest payments reduce taxable income, creating a tax shield. This update laid the foundation for modern corporate finance by showing that, with corporate taxes, firms should prefer debt over equity.

Modigliani-Miller 1963 Theorem (M&M 1963)

Modigliani and Miller’s 1963 revision to their capital structure theory introduced the concept of corporate taxes, which has a crucial impact on their earlier conclusions. They recognized that, in most economies, governments impose corporate income tax, but companies can deduct interest payments on debt from their taxable income. This interest tax-shield increases the after-tax profits of a firm and thereby raises its overall value.

The formula for the tax shield is:

Since interest expense is calculated as:

Therefore, the tax shield for a single year becomes:

The Modigliani-Miller (1963) model assumes perpetual debt primarily for simplification and mathematical clarity. The use of perpetual debt helps in calculating the present value of the tax shield without the need for complex discounting over a finite period.

If the firm has perpetual debt, meaning it never repays the principal and continues paying interest forever, the total value of the tax shield is found by calculating the present value of all future tax shield benefits. Since the tax shield is received every year indefinitely, its present value is:

Using the cost of debt (rd) as the discount rate, we get:

The (rd) cancels out, simplifying to:

This means that, under the M&M (1963) proposition, the value of a leveraged firm is given by:

where:

V_L is the value of a levered firm using debt.

V_U is the value of a unlevered firm not using debt but only equity

T_c is the Corporate tax rate

D is the amount of debt of the firm

Figure 1. Firm Value vs Debt according to M&M 1963 Theorem

In simple terms, taxes make debt financing more beneficial because firms pay interest on debt before paying taxes, reducing their taxable income. On the other hand, dividends paid to equity shareholders are not tax-deductible, meaning that firms must pay taxes on their entire profit before distributing dividends.

Implication for Capital Structure Decisions:

Firms benefit from using debt due to the tax shield, leading to a preference for more leverage.

The Modigliani-Miller (1963) model with taxes suggests that because of the tax shield on debt, a firm’s value increases as it takes on more debt. The formula for value of a levered firm according to M&M(1963) shows that every additional unit of debt directly increases firm value by the tax savings it provides. In theory, this means that a firm should finance itself entirely with debt (100% debt financing) to maximize its value. This is a significant departure from M&M (1958), where capital structure had no effect on firm value.

Limitations

However, in real-world scenarios, firms do not rely solely on debt. This is because excessive debt increases the risk of financial distress and bankruptcy costs, which M&M (1963) did not initially consider.

Case Study: Implications of M&M 1963 (Optimal Capital Structure with corporate taxes)

Alpha Corp operates in an imperfect capital market (with taxes only). It has two financing options for the capital structure:

Option 1: equity only (100% equity, 0% debt)

Option 2: debt and equity (60% equity, 40% debt)

Each option funds a $100 million investment that generates an annual operating income of $10 million. The risk-free interest rate is 5%, and the corporate tax rate is 30%.

Figure 2. Simplified Balance Sheet of Alpha Corp

Table 1. M&M 1963: an Example

Based on Table 1, the key takeaways are as follows:

1.Debt Creates a Tax Shield:

Under Option 2 (40% debt, 60% equity), Alpha Corp pays €2 million in interest expense, reducing taxable income from €10 million to €8 million.

This results in a lower corporate tax payment (€2.4 million instead of €3 million), leading to a €600,000 tax shield benefit.

2.Net Income is Lower with Debt, But Firm Value Increases:

Despite reducing tax liability, net income under Option 2 (€5.6 million) is lower than Option 1 (€7 million) because of interest expenses.

However, the firm’s total value increases due to the tax shield, meaning equity holders still benefit from debt financing.

How Modigliani-Miller (1963) Redefined the Cost of Equity and WACC from Modigliani-Miller (1958)

In Modigliani-Miller (1958), the firm’s capital structure—the mix of debt and equity—was considered irrelevant to its overall cost of capital (WACC) and, by extension, its firm value. This proposition, based on ideal market conditions (no taxes, no bankruptcy costs), argued that whether a firm is financed by debt or equity, the overall cost of capital remains unchanged. The cost of equity increases with leverage because equity holders demand higher returns to compensate for the additional financial risk, but this increase in cost of equity was offset by the lower cost of debt. Therefore, WACC stayed constant regardless of a firm’s capital structure.

However, when Modigliani and Miller (1963) introduced corporate taxes into their model, they demonstrated a significant change in the cost of capital (WACC) and cost of equity dynamics. With the tax deductibility of interest payments on debt, the cost of debt is effectively reduced, which leads to a reduction in WACC. This creates a clear benefit for firms that use more debt in their capital structure, making debt financing a value-enhancing tool. Let’s explore these key differences in detail.

Impact on the Cost of Equity (r_E)

MM (1958) – Cost of Equity Increases with Leverage

Under the Modigliani-Miller (1958) framework, the cost of equity (rE) increases as a firm takes on more debt because equity holders demand higher returns for taking on additional risk due to leverage. The relationship between cost of equity and leverage is described by the following formula:

where:

r_E is the cost of equity for a levered firm

r_U is the cost of equity for an unlevered firm

r_D is the cost of debt

D/E is the debt to equity ratio measuring leverage

This formula shows that as a firm increases its debt, its cost of equity increases to compensate for the increased financial risk borne by equity holders. However, since debt is cheaper than equity, the overall WACC remains unchanged.

MM (1963) – Tax Shield Reduces the Impact on Cost of Equity In MM (1963), the introduction of corporate taxes changes the scenario. Since interest expenses on debt are tax-deductible, the effective cost of debt (r_D) becomes lower. This reduces the overall risk for the firm and, therefore, the increase in the cost of equity (r_E) is less severe than in MM (1958). The new formula for cost of equity becomes:

where Tc is the corporate tax rate. The (1 – Tc) term reduces the increase in cost of equity (r_E), because the firm’s debt is now partially subsidized by the tax shield. This shows that while leverage still increases the cost of equity (r_E), the effect is less pronounced in the presence of tax deductibility of interest payments.

Impact on the Weighted Average Cost of Capital (WACC)

M&M (1958) – WACC Remains Constant Regardless of Leverage

In MM (1958), because the increase in the cost of equity (rE) offsets the benefit of cheaper cost of debt (rD), the WACC remains constant no matter the debt-to-equity ratio. The formula for WACC in this model is:

where:

V=D+E is the total firm value

r_E is the cost of equity for a levered firm

r_D is the cost of debt

D is the total debt

E is the total equity

According to MM (1958), since debt and equity are in perfect balance (i.e., the increase in the cost of equity (r_E) is offset by the lower cost of debt (r_D)), the WACC stays constant. The capital structure—how much debt or equity a firm uses—has no effect on the overall cost of capital or the firm’s value in a world without taxes.

MM (1963) – WACC Declines as Debt Increases

With the introduction of taxes, MM (1963) shows that WACC decreases as a firm increases its debt. The tax shield created by the deductibility of interest payments lowers the effective cost of debt (rD), making debt financing more attractive.

The formula for after-tax WACC in MM (1963) is:

In this scenario, debt financing becomes more advantageous because the firm can lower its overall WACC by utilizing debt, which reduces the tax burden. The WACC decreases as a firm increases its leverage (debt) because the cost of debt (r_D) is reduced due to the tax shield, and the cost of equity (r_E) increases at a slower rate due to the reduced impact of debt on financial risk.

Figure 3. Modigliani-Miller View Of Gearing And WACC: With Taxation (MM 1963)

Case Study: Implications of M&M 1963 (Optimal Capital Structure with corporate taxes)

Alpha Corp operates in a capital market (no bankruptcy costs, and no market imperfections). It has two financing options:

Option 1: Fully equity-financed (No debt with Corporate Taxes of 30%)

Option 2: 40% Debt, 60% Equity (without Corporate Taxes)

Option 3: 40% Debt, 60% Equity (with Corporate Taxes of 30% )

Each option funds a $100 million investment that generates an annual operating income of $10 million. The risk-free interest rate is 5%, and the required return on equity is 10%.

Figure 4. Modigliani-Miller View Of Gearing And WACC: With Taxation (MM 1963)

Table 2. M&M 1963: an Example

Key takeaways from this example are as follows :

1. Corporate Taxes Make Debt Financing More Attractive by Reducing the Effective Cost of Debt

In a no-tax world (M&M 1958, Option 2), firms are indifferent between debt and equity, as capital structure does not affect WACC.

However, M&M (1963) proves that in a taxed environment (Option 3), debt financing creates value because interest payments reduce taxable income, leading to lower corporate taxes.

This is called the “tax shield” effect, where firms pay less in taxes by using debt, increasing after-tax cash flows available to shareholders.

2. WACC Declines with Leverage When Corporate Taxes Exist, Unlike in M&M (1958)

In M&M (1958) (no taxes, Option 2), WACC remains constant at 10%, regardless of leverage.

M&M (1963) (Option 3) introduces taxes, causing WACC to drop to 8.80% due to the tax shield.

Strategic Takeaway: Firms can reduce their cost of capital and increase firm value by incorporating moderate levels of debt into their capital structure.

3. Cost of Equity Increases with Debt, But the Tax Shield Reduces the Rate of Increase

Higher leverage increases financial risk for shareholders, leading to a higher required return on equity (r_E).

In Option 2 (M&M 1958, No Taxes), introducing 40% debt raises the cost of equity to 13.33% due to added risk.

In Option 3 (M&M 1963, With Taxes), the cost of equity only increases to 12.33%, because the tax shield offsets part of the financial risk.

4. After-Tax Cost of Debt is Lower than the Cost of Equity, Making Debt a Cheaper Financing Option

The cost of debt before taxes is 5%.

Due to the corporate tax rate (30%), the effective cost of debt is reduced: r_D^after-tax= r_D ×(1−T_c)

Comparing Financing Costs in Option 3:
- Cost of Equity (r_E) = 12.33%
- After-Tax Cost of Debt (r_D) = 3.5%
Debt financing is significantly cheaper than equity financing after adjusting for the tax shield.

Firms should utilize debt strategically to lower overall financing costs.

5. The Trade-Off Between Tax Benefits and Financial Distress Risk Determines the Optimal Capital Structure

M&M (1963) suggests using more debt to reduce WACC, but in reality, excessive debt increases financial distress risks.

While debt reduces WACC through the tax shield, too much debt leads to higher bankruptcy risks, credit downgrades, and operational constraints.

Most firms balance debt and equity to optimize WACC, using debt to take advantage of tax savings without excessive financial risk.

Takeaways on Optimal Debt Structure and Bankruptcy Costs from M&M 1963 Theorem

The Modigliani-Miller (1963) proposition demonstrated that the presence of corporate taxes fundamentally changes the implications of capital structure on firm value. Unlike their earlier 1958 proposition, where capital structure was deemed irrelevant, the 1963 revision highlighted the benefits of debt financing due to the tax shield effect. Since interest expenses on debt are tax-deductible, firms can reduce their taxable income and, consequently, their tax obligations. This finding suggests that, in a world with corporate taxes and no other frictions, firms should finance themselves entirely with debt to maximize their value.

The M&M (1963) proposition remains a cornerstone in understanding capital structure decisions, demonstrating that debt financing enhances firm value through tax savings. However, in practice, firms must carefully balance leverage to avoid excessive financial distress. The optimal capital structure is not purely debt-driven but rather a carefully calibrated mix of debt and equity that maximizes firm value while maintaining financial stability.

Why Should I Be Interested in This Post?

This post explains a key concept in corporate finance—how debt financing affects firm value through corporate tax benefits and financial risks. If you’re a student, finance professional, or investor, understanding the Modigliani-Miller (1963) proposition will help you grasp why companies use debt. With clear explanations, real-world examples, and Excel-based analysis, this post provides practical insights into optimal capital structure decisions.

Useful resources

About the author

The article was written in January 2025 by Snehasish CHINARA (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2025).

Optimal capital structure with no taxes: Modigliani and Miller 1958

July 20, 2025May 8, 2025 by Snehasish CHINARA

In this article, Snehasish CHINARA (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2025) explores the optimal capital structure for firms, which refers to the balance between debt and equity financing. This post dives into the article written by Modigliani and Miller (1958) which explores the case of no corporate tax and a frictionless market (no bankruptcy costs).

Introduction to Capital Structure

Capital structure refers to the mix of debt and equity financing that a company uses to fund its operations and growth. It is a critical component of corporate finance, as it directly impacts a firm’s cost of capital, financial risk, and overall valuation. The choice of capital structure affects a company’s ability to raise funds, weather economic downturns, and pursue strategic investments.

Debt (Liabilities) – Found under the Liabilities section, debt includes short-term borrowings, long-term loans, bonds payable, and lease obligations. Debt financing requires periodic interest payments and repayment of principal, increasing financial obligations but also benefiting from potential tax shields.

Equity (Shareholders’ Equity) – Located under the Shareholders’ Equity section, equity includes common stock, preferred stock, retained earnings, and additional paid-in capital. Equity financing does not require fixed interest payments but dilutes ownership among shareholders.

Table 1 below gives a simplified version of a balance sheet.

Table 1 – Simplified Balance Sheet Example

A higher reliance on debt can lead to increased financial risk due to interest obligations, while too much equity financing may dilute shareholder returns. Therefore, finding an optimal capital structure is crucial for maintaining a healthy balance between risk, return, and financial stability.

Capital structure is one of the most fundamental decisions in corporate finance, influencing a firm’s financial stability, cost of capital, and overall value. At the heart of this discussion lies the Modigliani-Miller (M&M) theorems (M&M 1958 and M&M 1963), which provides the foundational framework for understanding how a company’s choice between debt and equity affects its valuation. However, while MM’s initial work (1958) proposed that capital structure is irrelevant in a frictionless market, real-world complexities such as taxation, bankruptcy costs, and financial distress challenge this assumption, leading to more nuanced theories.

The Modigliani-Miller 1958 Theorem (M&M 1958)

The Modigliani-Miller theorem (M&M 1958), introduced in 1958 by Franco Modigliani and Merton Miller, is a cornerstone of modern corporate finance. It provides a theoretical framework for understanding the role of capital structure in determining a firm’s value. M&M 1958’s core argument is that in a perfect market, a firm’s value is independent of its capital structure, meaning that the choice between debt and equity financing has no impact on firm valuation.

M&M 1958 Proposition I: Capital Structure Irrelevance

For the problem of the determination of the optimal capital structure of the firm, we assume that the firm (and its managers) seek to maximize the financial or economic value of the shareholders’ equity.

M&M’s first proposition states that, in a world with no taxes, no transaction costs, and perfect information, the total value of a firm (V) is unaffected by its financing decisions. Whether a company is financed with 100% equity, 100% debt (almost), or any combination of both, its market value remains the same because investors can create their own leverage through homemade financing.

M&M’s first proposition says that a company’s value is determined by its business operations (profits, assets, and growth potential), not by how it finances those operations. Since in a perfect world, investors can create leverage on their own. If a company doesn’t use debt, an investor can borrow money separately to create the same effect. This means that whether the company uses debt or not, its overall value remains the same.

For a firm with market value V, total assets A, and financed by debt D and equity E:

According to M&M Proposition I, in a frictionless world:

where:

V_L is the value of a levered firm using debt.

V_U is the value of a unlevered firm not using debt but only equity

Key Assumptions:

No taxes (in reality, firms pay corporate taxes).

No bankruptcy costs (in reality, firms pay costs if they go bankrupt).

No financial distress (in reality, too much debt can make investors nervous).

Figure 1. Firm Value vs Debt Ratio according to M&M 1958: Proposition I

In Figure 1, according to M&M 1958 Proposition I, the firm value remains constant regardless of the debt ratio. The flat blue line represents the idea that whether a firm is 100% equity-financed or takes on debt, its total value does not change in a perfect world with no taxes, no bankruptcy costs, and no market imperfections.

M&M 1958 Proposition II: Cost of Equity and Leverage Relation

While M&M Proposition I states that firm value is independent of capital structure, Proposition II explains how leverage affects the cost of equity (and then then total cost of financing measured by the weighted average cost of capital or WACC). It shows that as a firm increases its debt, equity becomes riskier, leading to an increase in the cost of equity (r_E) to compensate for higher financial risk.

When a firm increases its leverage, its cost of debt (r_D) is typically lower than its cost of equity (r_E) due to the priority of debt holders in the capital structure and the fixed nature of interest payments. However, as leverage rises, the firm’s equity becomes riskier because debt obligations take precedence, amplifying the volatility of residual earnings available to shareholders. According to Modigliani-Miller Proposition II, this higher financial risk leads to an increase in the required return on equity (r_E), as shareholders demand greater compensation for bearing the amplified risk exposure.

where:

r_E = cost of equity for a levered firm

r_U = cost of equity for an unlevered firm

r_D = cost of debt

D/E = debt to equity ratio measuring leverage

This formula highlights that with higher leverage, the cost of equity increases, offsetting any benefit from the lower cost of debt. Thus, while leverage amplifies returns, it also raises financial risk, maintaining the firm’s overall cost of capital.

Shareholders bear more risk as leverage increases due to the following reasons –

Residual Claimants: Shareholders are last in line for cash flows, meaning higher debt increases fixed interest obligations, reducing the certainty of equity returns.

Earnings Volatility: With more debt, small fluctuations in operating profits cause larger swings in equity returns, making equity riskier.

Default & Financial Distress Risk: If debt levels rise too much, the firm faces a higher probability of default or financial distress, further increasing required equity returns.

WACC according to M&M 1958 Proposition II

The Weighted Average Cost of Capital (WACC) is a key financial metric that represents a firm’s overall cost of financing by combining the costs of equity and debt. Under Modigliani-Miller Proposition II (1958), the WACC is given by the formula:

Where:

WACC = Weighted Average Cost of Capital

E = Value of equity

D = Value of debt

r_E = Cost of equity (which increases with leverage)

r_D = Cost of debt (fixed by assumption)

M&M 1958 Proposition II states that as a firm increases its debt financing, its cost of equity rE rises to compensate for the additional financial risk. However, because debt is cheaper than equity, the lower cost of debt rD balances out the increase in rE, keeping WACC constant.

Figure 2. Modigliani-Miller View Of Gearing And WACC: No Taxation (MM 1958 Proposition II)

Based on Figure 2, implication for firms are as follows:

In a world with no taxes and bankruptcy costs, leverage does not create or destroy firm value.

Higher leverage increases equity risk, leading to higher required returns for shareholders.

The Weighted Average Cost of Capital (WACC) remains constant regardless of debt-equity mix.

If a company borrows money (takes on debt), it must pay interest no matter how well the business performs. If profits drop, shareholders get whatever is left after paying the debt, which makes equity riskier. Because of this extra risk, shareholders demand a higher return, which increases the cost of equity.

Case Study: Implications of M&M 1958 (Optimal Capital Structure with no taxes)

Alpha Corp operates in a perfect capital market (no taxes, no bankruptcy costs, and no market imperfections). It has two financing options:

Option 1: Fully equity-financed (No debt)

Option 2: 40% Debt, 60% Equity

Each option funds a $100 million investment that generates an annual operating income of $10 million. The risk-free interest rate is 5%, and the required return on equity is 10%.

Figure 3. Simplified Balance Sheet of Alpha Corp

Table 2. M&M 1958: an Example

Based on Table 2, the key takeaways are as follows:

1. Firm Value Remains Constant

In both financing scenarios (100% Equity vs. 40% Debt, 60% Equity), the total value of the firm remains $100M.

This aligns with Modigliani-Miller Proposition I (1958), which states that in a perfect capital market, capital structure does not impact firm value.

2. Cost of Equity Increases with Leverage

In the 100% equity scenario, the required return on equity (rE) is 10%.

When the firm takes on 40% debt, the cost of equity (rE) increases to 13%, reflecting the additional financial risk borne by equity holders.

This aligns with Modigliani-Miller Proposition II (1958), which states that as leverage increases, equity holders require a higher return due to increased financial risk.

3. WACC Remains Constant

Despite the change in capital structure, the Weighted Average Cost of Capital (WACC) remains at 10%.

This reinforces M&M Proposition II, which states that in a perfect market, using debt does not lower the firm’s overall cost of capital.

4. Impact on Cash Flows & Present Values

Equity holders receive lower cash flows ($8M) under 40% debt financing due to interest payments ($2M) to debt holders.

However, the present value of debt ($40M) + present value of equity ($60M) = $100M, meaning that the firm’s total value remains unchanged regardless of financing choices.

Computation of Cash Flows and the DCF Approach

Table 3. Cash Flow for shareholders using cost of equity

Table 4. Cash Flow for debt holders using cost of debt

The Discounted Cash Flow (DCF) approach is used to determine the value of equity (E) and debt (D) by discounting their respective cash flows.

1. Cash Flows to Shareholders (Equity Holders)

Formula: CF to Equity= Operating Income −Interest Payments

Computation:

100% Equity Case:10M−0=10M

40% Debt, 60% Equity Case:10M−2M=8M

2. Cash Flows to Debt Holders

Formula: CF to Debt= Interest Payment = Debt × r_D

Computation: 40% Debt, 60% Equity Case: 40M×5%=2M

3. Present Value (PV) of Equity and Debt Using DCF

DCF Formula for constant perpetual cash flows:

For Equity Holders:

100% Equity Case: PVE = 10M/10% = 100M

40% Debt, 60% Equity Case: PVE = 8M/13% = 60M

For Debt Holders (PV of Debt):

40% Debt, 60% Equity Case: PVD= 2M/5% = 40M

The firm’s total value is always equal to the sum of the present values of equity and debt. This confirms the Modigliani-Miller capital structure irrelevance theorem, which states that in a perfect market, the financing mix does not change the total value of the firm.

Takeaways on Optimal Debt Structure and Bankruptcy Costs from M&M 1958 Propositions

The Modigliani-Miller Theorem provides a foundational understanding of how capital structure affects firm value. Proposition I tells us that in a perfect market with no corporate tax, the choice between debt and equity does not impact a company’s total value. However, Proposition II highlights that increasing debt makes equity riskier, which raises the cost of equity and offsets the benefits of cheaper debt.

Why Should I Be Interested in This Post?

The Modigliani-Miller (MM) 1958 theorem revolutionized corporate finance by demonstrating that, in a perfect market without corporate taxes, a firm’s capital structure (debt vs. equity) does not affect its overall value. This insight laid the foundation for modern finance theory, influencing corporate decisions on capital budgeting, M&A, and risk management. While real-world factors like taxes and bankruptcy costs modify MM’s conclusions, its core principles remain central to understanding cost of capital, leverage effects, and financial strategy. MM 1958 theory provides a theoretical framework that underpins advanced topics in corporate valuation, investment strategy, and capital markets.

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Snehasish CHINARA

Grande Ecole

Solvency and Insolvency in the Corporate World

May 8, 2025May 7, 2025 by Snehasish CHINARA

In this article, Snehasish CHINARA (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2025) explores the vital difference between solvency and insolvency—where solvency signals long-term financial health, and insolvency marks a tipping point of distress. Understanding this divide is key to assessing corporate resilience and recovery.

Introduction to Solvency and Insolvency

Solvency refers to the ability of a company to meet its long-term financial obligations and sustain operations over time. It is a measure of financial stability that reflects whether an entity’s total assets exceed its total liabilities, providing a buffer to absorb financial shocks or downturns. A solvent company is one that not only meets its short-term obligations (liquidity) but also maintains a robust capital structure for the long haul.

Insolvency occurs when a company is unable to meet its financial obligations as they come due. This may stem from either insufficient liquidity (cash flow insolvency) or a situation where liabilities exceed assets (balance sheet insolvency). Insolvency is a critical financial distress signal and, if unresolved, can lead to bankruptcy, restructuring, or liquidation.

Key Indicators of Solvency

Assessing solvency requires robust financial metrics that provide insight into a company’s long-term financial stability and its ability to meet obligations. Here are the primary indicators used to evaluate solvency:

Solvency Ratios

Solvency ratios measure a company’s financial leverage and its capacity to sustain operations while servicing debt and other long-term obligations. These ratios are pivotal for stakeholders to evaluate financial resilience.

1. Current Ratio

Purpose: Measures the proportion of debt versus equity in a company’s capital structure.

Interpretation:

A higher ratio indicates higher reliance on debt, increasing financial risk.

A lower ratio suggests a more conservative and stable financial structure.

Example: A Debt-to-Equity ratio of 1.5 means the company has $1.50 in debt for every $1 of equity.

2. Interest Coverage Ratio:

Reflects the company’s ability to cover its interest payments using earnings before interest and taxes (EBIT).

Interpretation:

A ratio above 2 is generally considered healthy, indicating sufficient earnings to cover interest expenses.

A ratio below 1 signals that the company may struggle to meet interest obligations.

Example: A ratio of 3 means the company earns three times its interest expense, indicating financial stability.

Cash Flow Analysis

Cash flow analysis evaluates whether a company generates enough cash from its operations to sustain long-term commitments. Unlike profits, cash flows reflect the actual inflow and outflow of money, providing a clearer picture of financial health.

1. Operating Cash Flow (OCF):

Indicates the cash generated from core business operations.

Key Metric: Positive and consistent OCF suggests strong financial health.

Example: A manufacturing firm with consistent OCF can comfortably reinvest in growth or repay long-term debt.

2. Free Cash Flow (FCF):

Free Cash Flow = Operating Cash Flow−Capital Expenditure

Free Cash Flow = EBIT × (1−Tax Rate) + Depreciation and Amortization − Change in Working Capital − Capital Expenditures

Reflects the cash available for distribution to shareholders or debt repayment after maintaining capital assets.

Example: A company with growing FCF can fund expansion or pay down debt without raising additional capital.

Balance Sheet Strength

The balance sheet provides a snapshot of a company’s financial position, highlighting its solvency through the relationship between assets, liabilities, and equity.

1.Net Asset Value (NAV):

Net Asset Value=Total Assets−Total Liabilities

Purpose: Indicates the residual value of a company’s assets after all liabilities are settled.

Interpretation: A positive NAV reflects solvency, while a negative NAV signals financial distress.

Example: Companies with high NAV relative to liabilities are perceived as stable and creditworthy.

2.Asset Quality and Liquidity:

High-quality assets (e.g., cash, receivables) contribute to solvency by being easily convertible into cash during a crisis.

Illiquid or depreciating assets, such as specialized machinery, may erode financial strength.

3.Leverage and Capital Structure:

A balance sheet with excessive liabilities compared to equity may indicate solvency risks.

Strong equity reserves act as a cushion against unforeseen losses.

Types of Insolvency

Insolvency is a critical financial condition indicating that a company or individual is unable to meet its financial obligations. It is broadly categorized into Cash Flow Insolvency and Balance Sheet Insolvency, each reflecting distinct dimensions of financial distress. Understanding these types is essential for diagnosing financial health and determining appropriate remedies.

Cash Flow Insolvency

Cash flow insolvency occurs when an entity is unable to meet its immediate or short-term financial obligations as they come due, even though its assets may exceed its liabilities. This situation often arises from liquidity issues rather than an inherent lack of financial stability.

Key Characteristics:

The entity has sufficient assets but lacks the liquid resources to convert them into cash quickly enough to pay its debts.

Typically a temporary condition that can be resolved through effective liquidity management or short-term financing.

Causes:

Poor cash flow management (e.g., delayed collections, excessive inventory buildup).

Seasonal business cycles with uneven cash inflows and outflows.

Overreliance on credit for operational expenses without adequate cash reserves.

External factors such as economic downturns or disruptions in supply chains.

Balance Sheet Insolvency

Balance sheet insolvency arises when an entity’s total liabilities exceed its total assets, resulting in negative net worth. This form of insolvency reflects deeper financial distress, often signalling a fundamental mismatch between a company’s obligations and its overall financial resources.

Key Characteristics:

Indicates that the entity is technically insolvent and unable to repay its debts even if all assets are liquidated.

Unlike cash flow insolvency, this condition is structural and often requires extensive restructuring or bankruptcy proceedings.

Causes:

Persistent losses that erode retained earnings and equity over time.

Excessive borrowing relative to the company’s capacity to generate revenue.

Depreciation in the value of long-term assets, particularly in industries reliant on physical or specialized assets (e.g., real estate or heavy machinery).

External shocks such as regulatory changes, market collapses, or catastrophic events.

Causes of Insolvency

Insolvency arises from a combination of factors that undermine a company’s ability to meet its financial obligations over the long term. Below are the primary causes of insolvency:

Prolonged Financial Losses – Sustained operational losses over time erode a company’s equity and reduce its ability to generate profits. Businesses operating in highly competitive or declining markets may struggle to maintain profitability, leading to negative net income and a weakened financial position.

Excessive Leverage – Over-reliance on borrowed funds (debt) can strain a company’s financial stability. High leverage increases fixed costs in the form of interest payments, reducing financial flexibility. If the company’s revenues are insufficient to cover these obligations, insolvency becomes inevitable.
Poor Financial Management – Inadequate budgeting, weak internal controls, or mismanagement of resources can lead to insolvency. Companies that fail to monitor expenses, optimize revenue streams, or manage working capital effectively are at higher risk of insolvency.

Decline in Market Demand – Shifts in consumer preferences, technological advancements, or market disruptions can lead to reduced demand for a company’s products or services. Persistent declines in revenue can deplete reserves and make it difficult to cover fixed costs and debt obligations.

Legal and Regulatory Challenges – Ongoing legal disputes, fines, or changes in regulatory requirements can drain financial resources and disrupt operations. Companies facing substantial penalties or compliance costs may become insolvent if they lack sufficient reserves.

Poor Capital Structure – An imbalance in the capital structure, such as an over-reliance on short-term debt for funding long-term projects, can increase financial risk. Companies that fail to optimize their mix of debt and equity may struggle with rising interest payments and reduced operational flexibility.

Unanticipated Large Expenses – Unexpected financial burdens, such as lawsuits, product recalls, or natural disasters, can quickly deplete a company’s reserves and lead to insolvency.

Inefficient Business Model -Companies with outdated or inefficient business models may fail to generate sufficient returns to sustain operations, especially in competitive or innovative markets.

Consequences of Insolvency

Insolvency has far-reaching implications for businesses, creditors, employees, and other stakeholders. Below are the primary consequences of insolvency:

Bankruptcy Filings -Insolvency often leads to legal proceedings, with the most common being bankruptcy filings. Depending on the jurisdiction, companies may choose between different bankruptcy types:

Chapter 7 (Liquidation): The company ceases operations, and its assets are sold to pay creditors. This is common for businesses that have no viable path to recovery.

Chapter 11 (Reorganization): The company continues operations while restructuring its debts and obligations under court supervision. This allows businesses to renegotiate terms with creditors and emerge as a leaner, more viable entity.

Restructuring or Liquidation of Assets -Companies may undergo significant restructuring to restore financial stability. This can include renegotiating debt terms, cutting operational costs, or divesting non-core assets.

Restructuring: Focuses on reorganizing the company’s financial obligations to regain solvency while maintaining operations.

Liquidation: Involves selling off assets to repay creditors, often signalling the end of business operations.

Loss of Shareholder Value – Shareholders are often the last to be compensated in insolvency scenarios, and in many cases, they lose their entire investment. The market value of the company’s shares typically plummets during insolvency proceedings, reflecting the financial instability.

Reputational Damage – Insolvency erodes trust among stakeholders, including creditors, investors, customers, and suppliers. This damage to reputation can make it challenging for a company to secure future financing, partnerships, or business opportunities even after recovery.

Employee Layoffs and Salary Defaults – Insolvent companies often reduce their workforce to cut costs. Employees may face delayed salaries, loss of benefits, or sudden termination. This can create significant disruptions for the workforce and impact morale and productivity.

Legal and Regulatory Implications – Insolvency proceedings often involve legal scrutiny, with courts, regulatory bodies, and creditors closely examining the company’s financial activities. Non-compliance or mismanagement that contributed to insolvency can lead to fines, penalties, or criminal charges against executives.

Asset Seizure by Creditors – Creditors may take legal action to recover debts, resulting in the seizure or foreclosure of the company’s assets. Secured creditors typically have priority in claiming collateral, while unsecured creditors may receive partial or no repayment.

Impact on Creditors – Creditors may face financial losses due to unpaid debts. In bankruptcy, the repayment hierarchy often prioritizes secured creditors, leaving unsecured creditors with minimal recovery. This can lead to a ripple effect on creditors’ financial health.

Industry and Market Implications – Insolvency of a major company can disrupt the industry or supply chain it operates in. For example, the bankruptcy of a large supplier may affect dependent companies downstream, creating broader economic consequences.

Opportunities for Acquisition or Takeover – Insolvency often leads to opportunities for competitors or investors to acquire assets or the entire company at discounted valuations. This can result in consolidation within the industry.

Preventing Insolvency

Proactively managing financial health is the cornerstone of preventing insolvency. Businesses must employ strategic measures to anticipate potential risks, optimize resources, and build resilience against economic uncertainties.

Importance of Financial Forecasting and Stress Testing

Financial Forecasting: Regular financial forecasting allows businesses to predict future cash flows, revenue, and expenses. Accurate forecasts enable companies to identify potential shortfalls well in advance and implement corrective measures.

Key Actions:

Develop rolling forecasts that adjust for real-time changes.

Incorporate multiple scenarios to evaluate outcomes under varying conditions.

Example: A company anticipating seasonal revenue dips can arrange short-term financing or delay non-essential expenses.

Stress Testing: Stress testing simulates adverse economic scenarios—such as a market downturn, supply chain disruption, or rising interest rates—to evaluate the company’s ability to remain solvent under pressure.

Key Actions:

Assess liquidity under stress scenarios to determine if obligations can be met.

Use outcomes to refine contingency plans.

Example: A manufacturer testing the impact of a 20% raw material cost increase might discover a need for improved supplier contracts.

Effective Debt Management Strategies

Debt Structuring: Avoid excessive reliance on short-term debt, which can strain cash flows. Use a balanced mix of short-term and long-term debt to align with business cycles and asset lifespans.

Key Actions:

Renegotiate unfavourable loan terms.

Use fixed-rate loans during periods of volatile interest rates.

Debt Servicing Discipline:

Prioritize timely repayment of interest and principal to avoid compounding liabilities.

Example: Automating debt payments ensures consistency and avoids penalties.

Monitoring Debt Ratios:

Regularly analyse debt-to-equity and interest coverage ratios to ensure sustainable leverage.

Key Actions:

Reduce non-essential borrowing.

Use retained earnings or equity to finance expansion instead of debt.

Building Strong Liquidity Buffers

Liquidity Reserves:

Maintain cash reserves or liquid assets to manage unexpected shortfalls. A robust liquidity buffer acts as a financial safety net during crises.

Key Actions:

Allocate a percentage of revenue to a contingency fund.

Invest in low-risk, short-term instruments like treasury bills.

Credit Line Management:

Establish pre-approved credit facilities for emergency use.

Example: A revolving credit line ensures access to immediate funding without lengthy approval processes.

Working Capital Optimization:

Efficiently manage receivables, payables, and inventory to free up cash.

Example: Implementing stricter credit terms for customers and negotiating extended payment terms with suppliers.

Why Should I Be Interested in This Post?

Insolvency is not just a business concern; it’s a fundamental challenge that can impact investors, employees, and entire economies. This post equips you with a comprehensive understanding of how to anticipate, prevent, and address insolvency by exploring its causes, indicators, and solutions. Whether you’re a student aspiring to master corporate finance, an entrepreneur striving to protect your business, or a professional managing financial risks, the insights in this article empower you to navigate financial complexities with confidence. By understanding solvency dynamics and adopting proactive strategies, you can make informed decisions, safeguard financial stability, and capitalize on opportunities, even in the face of adversity.

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About the author

The article was written in January 2025 by Snehasish CHINARA (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2025).

Liquidity and Illiquidity in the Corporate World

May 7, 2025 by Snehasish CHINARA

In this article, Snehasish CHINARA (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2025) delves into liquidity and illiquidity, key concepts in the corporate world.

Introduction to Liquidity and Illiquidity

Liquidity and Illiquidity Definitions

Liquidity is an important economic concept that can be applied to individuals, companies and financial institutions. In this article, we deal with liquidity at the firm level, which involves the liquidity of the assets and the due date of the liabilities.

In a corporate context, liquidity refers to the ability and capacity of accompany to meet its short-term financial obligations using its available cash or easily convertible assets. Short-term financial obligations refer to the payment of the salary to employees, the invoices to providers, the interests of loans and bonds to the creditors, the taxes to the state, etc. Current assets refer to cash, marketable securities, accounts receivable, and inventories. They are categorized as liquid assets due to their relative accessibility and low conversion time.

To assess liquidity, we need to compare the liquidity of assets and the due date of liabilities. In practice, assets and liabilities can be found with their amount in the balance sheet of the firm. For an asset, liquidity is the ability to quickly convert the asset into cash at its fair market value (with relatively small market impact). For a liability, the due date is key.

Illiquidity refers to the inability of a company to convert assets into cash quickly enough to meet short-term financial obligations as they come due. This condition arises from a mismatch in the timing of cash inflows and outflows (illiquidity or a fundamental deficiency in overall financial health (insolvency). For instance, a firm might hold substantial non-liquid assets (e.g., accounts receivable or inventory) that are valuable but not immediately accessible for use in settling debts. The states of liquidity and illiquidity are generally viewed as a short-term liquidity risk and is often addressed through measures such as enhanced cash flow management, securing bridge financing, or leveraging credit facilities.

Causes of Illiquidity

The following are few causes of illiquidity:

Poor Cash Management – Inefficient management of cash flows, such as misaligning income and expenses, is a primary cause of illiquidity. Businesses that fail to maintain adequate liquidity reserves or do not accurately forecast future cash needs may face severe short-term financial strain. For instance, a company might overestimate receivables collections or underestimate operational expenses, leading to insufficient funds for immediate obligations.

External Shocks:
- Market Downturns: Economic recessions, sudden market volatility, or a decline in demand for products/services can significantly reduce cash inflows, creating liquidity stress.
- Seasonal Variations: Businesses with highly seasonal revenue streams, such as retail or tourism, may experience cash shortages during off-peak periods when income generation is low but fixed costs remain constant.
- Supply Chain Disruptions- Unexpected events like raw material shortages, logistical delays, or geopolitical risks can disrupt production cycles, leading to revenue delays and payment bottlenecks.

Over-Leverage – Excessive reliance on debt without adequate planning for repayments can strain liquidity. Companies that overextend themselves with short-term borrowing may face difficulties rolling over or refinancing debt, especially in tight credit markets.

Rapid Expansion- Aggressive growth strategies, such as entering new markets or launching new products, can deplete cash reserves if expenses outpace revenue generation. For example, increased capital expenditures or marketing costs may lead to liquidity shortages during the early stages of expansion.

Asset Illiquidity- Holding a significant portion of assets in non-liquid forms (e.g., real estate, long-term investments) reduces the ability to generate quick cash. While these assets contribute to the overall balance sheet value, their inability to be converted into liquid funds on short notice can exacerbate illiquidity during crises.

Contractual Obligations- Fixed payment schedules for rent, salaries, or interest on loans can pressure liquidity when cash inflows do not align with payment deadlines. Even profitable businesses can face illiquidity if contractual commitments are poorly synchronized with revenue cycles.

Credit Constraints – Difficulty accessing credit markets due to poor credit ratings, higher interest rates, or restrictive borrowing conditions can leave firms unable to secure short-term financing. Companies with a history of missed payments may struggle to find lenders willing to provide liquidity support.

Measuring Liquidity

Key Liquidity Metrics

Liquidity metrics provide a structured way to assess the ability of individuals, companies, or financial systems to meet short-term obligations. These metrics are foundational for understanding a financial entity’s operational efficiency and financial health.

Current Ratio

Purpose: Measures a company’s ability to cover its short-term obligations with its short-term assets.

Interpretation: A ratio above 1 indicates that a company has more current assets than current liabilities, signalling good liquidity. However, an excessively high ratio might indicate inefficiency in utilizing resources.

Example – Consider two (non- financial) companies A & B

Company A:

Current Assets = €200,000

Inventory = €50,000

Current Liabilities = €100,000

Quick Ratio (Company A) = (200,000 – 50,000)/100,000 = 1.5

Company B:

Current Assets = €80,000

Inventory = €30,000

Current Liabilities = €100,000

Quick Ratio (Company B) = (80,000-30,000)/100,000 = 0.5

Here, Company A maintains sufficient liquidity even after excluding inventory, while Company B faces liquidity concerns.

Cash Conversion Cycle (CCC)

Purpose: Evaluates the efficiency of a company in managing its working capital by measuring the time it takes to convert investments in inventory and receivables into cash.

Interpretation: A shorter cycle indicates faster liquidity, suggesting effective operational management.

Example – Consider two (non- financial) companies A & B

Company A:

DIO = 40 days

DSO = 30 days

DPO = 45 days

CCC(A)=40+30−45=25 Days

Company B:

DIO = 80 days

DSO = 50 days

DPO = 40 days

CCC (B)=80+50−40=90 Days

Company A has a shorter CCC, indicating quicker cash turnover, while Company B takes longer to convert inventory and receivables into cash, leading to potential liquidity constraints.

Quick Ratio (Acid-Test Ratio)

Purpose: Excludes inventory from current assets to provide a stricter measure of liquidity, as inventory may not be easily converted to cash.

Interpretation: A ratio above 1 typically reflects strong liquidity, especially for companies in industries where inventory turnover is slow.

Example – Consider two (non- financial) companies A & B

Company A:

Current Assets = €200,000

Inventory = €50,000

Current Liabilities = €100,000

Quick Ratio (Company A) = (200,000 – 50,000)/100,000 = 1.5

Company B:

Current Assets = €80,000

Inventory = €30,000

Current Liabilities = €100,000

Quick Ratio (Company B) = (80,000-30,000)/100,000 = 0.5

Here, Company A maintains sufficient liquidity even after excluding inventory, while Company B faces liquidity concerns.

Funding Liquidity

Funding liquidity is the ability of firms, financial institutions, or individuals to meet short-term obligations as they come due, using available cash, liquid assets, or borrowing capacity. It reflects the financial health and cash management practices of an entity.

Key Characteristics:

Access to Cash: Availability of cash or near-cash assets.

Borrowing Capacity: Ability to raise funds through credit lines or issuing debt.

Short-Term Solvency: Ensuring that obligations such as payroll, supplier payments, and loan repayments are met on time.

Examples:

A company maintaining a cash reserve or a revolving credit line for emergencies.

Banks relying on interbank lending markets for overnight funding.

Consequences of Illiquidity

Illiquidity can have a cascading impact on a business’s financial health and operational stability. The impact ranges from borrowing difficulties to bankruptcy. Below are the key consequences of illiquidity:

Missed Payments to Creditors – Companies facing illiquidity may struggle to meet immediate financial obligations such as loan repayments, supplier invoices, or tax liabilities. This can damage relationships with creditors and suppliers, leading to stricter payment terms, higher interest rates, or the refusal of future credit. Missed payments may also result in legal penalties or lawsuits, further exacerbating financial difficulties.

Short-Term Borrowing – To address cash flow gaps, businesses often resort to short-term borrowing, such as credit lines or bridge loans. While this provides immediate relief, repeated reliance on short-term financing can increase interest expenses and leverage, making the company more vulnerable to future liquidity crises. High debt levels may also negatively impact credit ratings, limiting access to affordable financing.

Asset Liquidation – Companies may be forced to sell non-core or underperforming assets to generate quick cash. While this can temporarily alleviate liquidity pressure, it can weaken the firm’s long-term strategic position if valuable or income-generating assets are sold. Additionally, asset liquidation in distress scenarios often leads to unfavourable valuations, further diminishing the firm’s financial standing.

Operational Disruptions – A lack of liquidity can hinder day-to-day operations, such as the inability to purchase raw materials, pay employees, or fund marketing initiatives. These disruptions can result in reduced productivity, loss of market share, and damage to the company’s reputation among customers and stakeholders.

Increased Cost of Capital – Persistent illiquidity may lead to higher borrowing costs as creditors perceive the company as a higher-risk borrower. This increased cost of capital can strain cash flows further and limit the company’s ability to invest in growth opportunities.

Employee Layoffs or Salary Delays – In severe cases, companies may delay salaries or initiate workforce reductions to conserve cash. This can lead to lower employee morale, higher attrition rates, and loss of critical talent, affecting the firm’s long-term capabilities and performance.

Decline in Market Confidence – Illiquidity signals financial distress to investors, customers, and suppliers. A decline in market confidence can lead to reduced stock prices, difficulty in securing contracts, and a potential withdrawal of customer deposits (in the case of financial institutions).

Escalation to Insolvency – If illiquidity persists and the company cannot stabilize cash flows, it may transition into insolvency, where liabilities exceed assets. This often leads to bankruptcy proceedings, such as liquidation (Chapter 7) or reorganisation (Chapter 11).

Regulatory and Legal Penalties – Failure to meet statutory obligations, such as tax payments or compliance filings, can result in regulatory fines or legal action. For financial institutions, illiquidity may lead to intervention by regulators or central banks.

Bankruptcy – If the liquidity crisis persists, the company may be forced to restructure its debts, sell assets at distressed prices, or seek emergency funding. In extreme cases, prolonged illiquidity can result in insolvency, pushing the firm toward bankruptcy proceedings, such as Chapter 7 liquidation (where assets are sold to repay creditors) or Chapter 11 reorganization(where the company restructures to regain financial stability).

Liquidity Management for Companies

Liquidity management is a cornerstone of a company’s financial health, ensuring that it can meet its short-term obligations and operate smoothly without disruptions. Effective liquidity management safeguards against financial distress, supports growth, and enhances the company’s ability to respond to market opportunities or challenges.

Tools for Liquidity Management

1.Cash Flow Forecasting:

Purpose: Predicts cash inflows and outflows over a specific period, allowing companies to anticipate liquidity needs.

Implementation: Regularly updating forecasts based on operational activities, seasonal trends, and external market factors.

Benefits: Helps identify potential shortfalls or surpluses and plan financing or investment activities accordingly.

2.Credit Lines:

Purpose: Pre-approved borrowing arrangements with banks provide immediate access to funds when needed.

Implementation: Companies negotiate revolving credit facilities with financial institutions.

Benefits: Offers flexibility to address liquidity shortfalls without lengthy approval processes.

3.Liquidity Buffers:

Purpose: Reserve cash or easily liquidated assets set aside to manage unforeseen circumstances.

Implementation: Maintaining a percentage of revenue or working capital in liquid form.

Benefits: Acts as an emergency fund to meet unexpected expenses or capitalize on opportunities.

4.Working Capital Optimization:

Purpose: Efficiently managing current assets and liabilities to improve liquidity.

Implementation:
- Reducing inventory levels without compromising production.
- Negotiating longer payment terms with suppliers.
- Accelerating accounts receivable collection.
Benefits: Frees up cash for other uses without requiring additional financing.

5.Treasury Management Systems (TMS):

Purpose: Automates liquidity tracking and management processes.

Implementation: Deploying software to consolidate cash positions, manage risks, and optimize cash usage.

Benefits: Enhances real-time visibility into cash flows and simplifies decision-making.

Building Strong Liquidity Buffers

A well-structured liquidity buffer is essential for corporate firms to withstand financial shocks, economic downturns, and unexpected cash flow disruptions. Establishing and maintaining sufficient liquidity ensures that companies can meet their short-term obligations, maintain investor confidence, and continue operations smoothly during periods of uncertainty. Below are key strategies that firms can adopt to strengthen their liquidity buffers.

Liquidity Reserves:

Liquidity reserves refer to the cash and readily accessible liquid assets that a company maintains to address unforeseen financial needs. These reserves act as a financial safety net, ensuring that a firm can continue operations even during economic downturns, market disruptions, or revenue shortfalls.

Maintain cash reserves or liquid assets to manage unexpected shortfalls. A robust liquidity buffer acts as a financial safety net during crises.

Key Actions:

Allocate a percentage of revenue to a contingency fund.

Invest in low-risk, short-term instruments like treasury bills.

Regularly Review Liquidity Needs

Diversify Cash Holdings Across Financial Institutions

Credit Line Management:

Beyond maintaining cash reserves, companies should have access to credit facilities that provide immediate funding when needed. A well-managed credit line acts as an additional liquidity buffer and prevents financial distress when operational cash flows are temporarily constrained.

Example: A revolving credit line ensures access to immediate funding without lengthy approval processes.

Working Capital Optimization:

Working capital represents a company’s ability to manage its short-term assets and liabilities efficiently. Optimizing accounts receivable, accounts payable, and inventory can significantly enhance liquidity without the need for external borrowing or additional capital injections.

Example: Implementing stricter credit terms for customers and negotiating extended payment terms with suppliers.

Why Should I Be Interested in This Post?

Understanding liquidity and its management is crucial not just for financial professionals but for anyone navigating the modern economic landscape. Whether you are an investor assessing asset portfolios, a corporate leader ensuring operational stability, or a student preparing for a career in finance, liquidity forms the foundation of informed decision-making. This post provides a comprehensive guide to the causes, consequences, and tools of liquidity management, equipping you with the knowledge to evaluate financial health, mitigate risks, and capitalize on opportunities. In a world where liquidity—or the lack thereof—can mean the difference between success and failure, mastering this concept empowers you to make smarter financial decisions, stay resilient during crises, and thrive in dynamic markets.

Useful resources

About the author

The article was written in January 2025 by Snehasish CHINARA (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2025).

Top 10 Cryptocurrencies by Market Capitalisation

May 6, 2025January 14, 2025 by Snehasish CHINARA

In this article, Snehasish CHINARA (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2024) presents the Top 10 Cryptocurrencies by Market Capitalization (August 2024).

Market Capitalization of Cryptocurrencies

Market capitalization, often referred to as market cap, signifies the complete monetary worth of a company’s stock or, in the realm of cryptocurrencies like Bitcoin, the collective value of all mined coins. In the cryptocurrency domain, market cap is computed by multiplying the prevailing price of a single coin by the total quantity of coins mined up to that point. Market cap is important because in the crypto ecosystem (and beyond) it measures the success of the cryptocurrency.

In June 2023, the total market capitalization of all cryptocurrencies worldwide stands at $1.22 Trillion, displaying a 9.11% change compared to a year ago. At this moment, Bitcoin (BTC) holds the highest market cap of $569 Billion, corresponding to a Bitcoin dominance of 46.71%.

Market capitalization facilitates comparing the overall value of different cryptocurrencies, aiding in well-informed investment choices. Cryptocurrencies are usually categorized by market cap into three groups: 1) Large cap, such as Bitcoin and Ethereum, with market caps over $10 billion, perceived as lower risk due to their established growth and liquidity resilience; 2) Mid-cap, between $1 billion and $10 billion, offer potential upside but elevated risk; 3) Small-cap, under $1 billion, are highly vulnerable to market sentiment-driven fluctuations.

Volume Traded of Cryptos

The trading volume holds significant importance in the realm of cryptocurrencies and other financial instruments. It signifies the complete count of tokens or coins traded over a particular timeframe, typically denoted in the base currency (like USD or BTC) or the token (for instance, ETH or XRP) within periods like 24 hours, 7 days, or 30 days.

Cryptocurrency liquidity is often gauged by its trading volume, a measure of market activity. Elevated trading volumes denote heightened buyer and seller presence, facilitating smoother trades without abrupt price shifts. Enhanced liquidity is advantageous, streamlining trade entries or exits. A surge in trade frequency yields more data, refining price determination and aligning cryptocurrency value more accurately. Noteworthy shifts in trading volume might signify sentiment changes – increased volume suggests heightened demand or interest, while reduced volume implies waning enthusiasm or market uncertainty. When coupled with price dynamics, volume aids trend validation; a rising cryptocurrency price coupled with elevated trading volume suggests robust momentum and broader market involvement.

#1 Bitcoin (BTC)

Logo of Bitcoin

Statistics

Market capitalization: $568.57 Billion

Market price : $ 29,230.11

Number of coins in circulation: 18.30 Million

Volume traded (over the past year): $3.36 Trillion

Year founded: 2009

Overview of Bitcoin

Introduced in 2009 by an undisclosed group using the pseudonym Satoshi Nakamoto, Bitcoin marked the debut of accessible cryptocurrencies. Emerging in the aftermath of the global financial crisis, it aimed to provide an alternative to conventional financial systems, granting individuals direct control over their assets and bypassing intermediaries.

Functioning on a blockchain foundation, Bitcoin employs the Proof of Work (PoW) consensus mechanism, where miners compete to validate transactions through solving complex puzzles, ensuring system security and integrity. Initially viewed as a digital currency, Bitcoin’s role has evolved to be similar to “digital gold,” attributed to its capped supply of 21 million coins. This scarcity sets it apart from traditional currencies and positions it as a hedge against inflation and economic uncertainty, making it valuable for remittances and global trade, particularly in regions with limited banking access. Utilizing digital wallets, Bitcoin enables autonomous transactions, offering an alternative to the unbanked population and allowing them to engage in financial activities and preserve value.

Market capitalization

The figure below gives the market capitalization of bitcoin from July 2010 to September 2024. At a given point of time, market capitalization is obtained by multiplying the number of bitcoin in circulation (coin supply) by the price of bitcoin.

Market Capitalization Chart of Bitcoin

Source: Yahoo! Finance. (Computation by Author)

You can download blow the excel file used to build the figure. Historical data for Bitcoin can be downloaded from CoinMarketCap or Yahoo! Finance website.

#2 Ethereum (ETH)

Logo of Ethereum

Statistics

Market capitalization: $223.15 billion

Market price : $1856.8

Number of coins : 120.16M

Year founded: 2013

Overview of Ethereum

In 2013, Ethereum was conceptualized, and it became publicly known in 2015 through the efforts of Vitalik Buterin. Unlike its forerunner, Bitcoin, Ethereum stands as a revolutionary stride in blockchain technology. It goes beyond simple digital currency transfer, incorporating programmable smart contracts into its framework. This innovation has instigated a fresh era of decentralized applications (DApps) spanning various domains. These smart contracts, penned using the Solidity programming language, facilitate self-executing agreements based on predefined conditions, effectively reshaping industries such as finance, supply chain management, and gaming. Ether (ETH), Ethereum’s native cryptocurrency, plays a dual part, serving as a transactional medium within the network while also fueling the execution of these contracts.

At the foundation of Ethereum lies a transition from the energy-intensive Proof of Work (PoW) to a more ecologically conscious Proof of Stake (PoS) consensus protocol. This transformation, embodied in Ethereum 2.0, not only amplifies scalability and efficiency but also guarantees the platform’s endurance and its capacity to meet the growing demand for blockchain-centered solutions. The lasting legacy of Ethereum originates from its conversion of blockchain from a mere digital currency system to a versatile bedrock that stimulates innovation through decentralized applications and smart contracts.

Market capitalization

The figure below gives the market capitalization of ethereum from August 2015 to September 2024. At a given point of time, market capitalization is obtained by multiplying the number of Ethereum in circulation (coin supply) by the price of Ethereum.

Market Capitalisation Chart of Etheream

Source: Yahoo! Finance. (Computation by Author)

The historical data for Ethereum market prices can be downloaded from Yahoo! Finance website: Download the data for Ethereum

You can download blow the excel file used to build the figure. Historical data for Ethereum can be downloaded from CoinMarketCap or Yahoo! Finance website.

#3 Tether (USDT)

Logo of Tether

Statistics

Market capitalization: $83.81 Billion

Market price : $ 0.999

Circulating Supply : $83.53 Billion

Year founded: 2014

Overview of Tether

Released in 2014, Tether entered the cryptocurrency arena amidst the quest for stability within this dynamic market. Conceived as a stablecoin, Tether aimed to counteract the pronounced price volatility synonymous with cryptocurrencies like Bitcoin and Ethereum. Designed as a form of cryptocurrency, stablecoins, like Tether, are meticulously crafted to mirror the value of specific fiat currencies. Each individual Tether coin is intrinsically valued at precisely one unit of the chosen fiat currency, ensuring steadfast equivalence. This is exemplified by the fact that a solitary Tether USDT is perpetually equivalent to one U.S. dollar.

The distinctive hallmark of USDT lies in its pioneering fusion of fiat currency stability with blockchain technology. This characteristic renders it exceedingly practical for preserving or transferring value, as its unchanging price obviates concerns about depreciation. While renowned cryptocurrencies such as Bitcoin and Ethereum undergo price fluctuations based on market dynamics, USDT remains steadfastly pegged to the dollar. Upon entry into the cryptocurrency market, it behaves akin to any other currency, facilitated through blockchain technology. Thus, Tether can be procured or traded via various cryptocurrency exchanges supporting USDT.

Market capitalization

The figure below gives the market capitalization of Tether from February 2015 to September 2024. At a given point of time, market capitalization is obtained by multiplying the number of Tether in circulation (coin supply) by the price of Tether.

Market Capitalisation Chart of Tether

Source: Yahoo! Finance. (Computation by Author)

The historical data for Tether market prices can be downloaded from Yahoo! Finance website: Download the data for Tether

You can download blow the excel file used to build the figure. Historical data for Tether can be downloaded from CoinMarketCap or Yahoo! Finance website.

#4 Binance Coin (BNB)

Logo of BNB

Statistics

Market capitalization: $37.14 Billion

Market price : $241.4

Number of coins : 153.85 Million

Year founded: 2008

Overview of BNB

Binance Coin (BNB) emerged in July 2017 as a significant cryptocurrency, originating from Binance exchange’s forward-looking perspective. Initially designed to provide trading fee incentives to Binance users, the introduction of BNB coincided with the worldwide surge in cryptocurrency interest. It was conceived by tech entrepreneur Changpen Zhao, also known as CZ, and serves as the native utility token for Binance exchange, recognized as the world’s largest cryptocurrency trading platform. BNB operates on the innovative BNB Chain, which incorporates a distinctive amalgamation of Proof of Stake (PoS) and Proof of Authority (PoA) consensus mechanisms. By leveraging both these models, the network achieves comparably reduced fees and swifter transaction processing. The forthcoming BNB Chain roadmap for 2022/23 aims to enhance transaction speed, lower fees, and provide open-source capabilities, catering to developers aiming to construct within the expansive Binance ecosystem.

Market capitalization

The figure below gives the market capitalization of Binance from July 2017 to September 2024. At a given point of time, market capitalization is obtained by multiplying the number of Binance in circulation (coin supply) by the price of Binance.

Market Capitalisation Chart of BNB

Source: Yahoo! Finance. (Computation by Author)

The historical data for BNB market prices can be downloaded from Yahoo! Finance website: Download the data for BNB

You can download blow the excel file used to build the figure. Historical data for Binance can be downloaded from CoinMarketCap or Yahoo! Finance website.

#5 XRP / Ripple (XRP)

Logo of XRP

Statistics

Market capitalization: $36.82 Billion

Market price : $0.698

Number of coins : $ 54.74 Billion

Year founded: 2012

Overview of XRP

XRP is a digital currency that emerged onto the scene in 2012 as a key element of Ripple Labs’ visionary pursuit to transform cross-border financial settlements and payments. The founders of XRP, Chris Larsen and Jed McCaleb, sought to confront the inherent inefficiencies and delays that plagued conventional banking systems. Introducing XRP as a conduit between diverse fiat currencies, the aim was to enable swift and seamless international transactions. Setting it apart from its cryptocurrency peers, XRP wasn’t minted through the typical mining process ; instead, it was pre-mined, featuring a fixed quantity of 100 billion tokens. This strategic move was orchestrated to establish a stable foundation for the currency’s growth.

At the core of Ripple’s groundbreaking xRapid product, XRP plays the role of an intermediary currency, facilitating the exchange of value across various fiat currencies within financial institutions. The intrinsic currency of the XRP Ledger, a cryptographic ledger bolstered by a network of interconnected nodes, XRP empowers these institutions in their pursuit of seamless cross-currency transactions. Notably, Ripple is the architect behind this blockchain-based digital payment settlement system and the extensive crypto exchange network that encompasses it. An embodiment of innovation, Ripple harnesses its native token to facilitate the transition of traditional financial dealings from centralized databases under the jurisdiction of financial authorities to an openly accessible infrastructure.

Market capitalization

The figure below gives the market capitalization of XRP from April 2020 to September 2024. At a given point of time, market capitalization is obtained by multiplying the number of XRP in circulation (coin supply) by the price of XRP.

Market Capitalisation Chart of XRP

Source: Yahoo! Finance. (Computation by Author)

The historical data for XRP market prices can be downloaded from Yahoo! Finance website: Download the data for XRP

You can download blow the excel file used to build the figure. Historical data for XRP can be downloaded from CoinMarketCap or Yahoo! Finance website.

#6 USD Coin (USDC)

Logo of USD Coin

Statistics

Market capitalization: $26.49 billion

Market price : $1.00

Circulating Supply : 26.28 Billion

Year founded: 2018

Overview of USD Coin

The USD Coin (USDC) serves as a stable digital currency, maintaining a steadfast 1:1 value with the US dollar, regardless of the ever-changing tides of the cryptocurrency market. This stability is rooted in its robust foundation – US dollar reserves securely held within regulated US financial institutions. To ensure transparency and reliability, the acclaimed accounting firm Grant Thornton diligently monitors these reserves, releasing detailed monthly attestation reports.

USDC boasts remarkable versatility, seamlessly functioning on various blockchain platforms such as Ethereum, Algorand, Solana, Stellar, and TRON. Key features and use cases of USD Coin are rooted in its stability and ease of use. USDC facilitates seamless, near-instantaneous cross-border transactions and serves as a bridge between traditional finance and the blockchain world, enabling individuals and businesses to transfer value globally without exposure to the volatility inherent in many cryptocurrencies. With its one-to-one peg to the US Dollar, USDC serves as a valuable tool for traders and investors, allowing them to hedge against market fluctuations while remaining within the crypto ecosystem. Moreover, the stablecoin has found application in the decentralized finance (DeFi) sector, where it serves as collateral for loans, liquidity provision, and yield farming, contributing to the vibrant evolution of blockchain-based financial services.

Market capitalization

The figure below gives the market capitalization of USD Coin from October 2018 to September 2024. At a given point of time, market capitalization is obtained by multiplying the number of USD Coin in circulation (coin supply) by the price of USD Coin.

Market Capitalisation Chart of USDC

Source: Yahoo! Finance. (Computation by Author)

The historical data for USD Coin market prices can be downloaded from Yahoo! Finance website: Download the data for USD Coin

You can download blow the excel file used to build the figure. Historical data for USD Coin can be downloaded from CoinMarketCap or Yahoo! Finance website.

#7 Dogecoin (DOGE)

Logo of Doge Coin

Statistics

Market capitalization: $10.88 billion

Market price : $0.077

Circulating Supply: 140.52 Billion

Year founded: 2013

Overview of Doge Coin

Originally conceived as a playful jest aimed at the crypto community, Dogecoin emerged as a whimsical creation inspired by a popular meme. Crafted by the collaborative efforts of software engineers Billy Marcus and Jackson Palmer, the genesis of Dogecoin occurred in the latter part of 2013. Operating on an autonomous blockchain unique to itself, Dogecoin’s digital ledger undergoes ceaseless updates to encompass novel transactions, employing cryptographic techniques to ensure the security of these transactions.

The foundational framework of Dogecoin’s blockchain is rooted in the proof of work consensus mechanism, which necessitates miners to deploy computational prowess in solving intricate mathematical puzzles. This endeavor facilitates the processing and inscription of transactions onto the blockchain. In return for their contributions to upholding the integrity of the blockchain, miners are rewarded with additional Dogecoin holdings, affording them the choice to retain or trade these assets within the open market.

While Dogecoin can feasibly be employed for monetary transactions and acquisitions, its role as a dependable store of value remains compromised. This deficiency primarily arises from the absence of a predetermined upper limit on the quantity of Dogecoins that can be mined, thus inherently imbuing the cryptocurrency with a pronounced inflationary trait.

Market capitalization

The figure below gives the market capitalization of Doge Coinfrom July 2014 to September 2024. At a given point of time, market capitalization is obtained by multiplying the number of Doge Coin in circulation (coin supply) by the price of Doge Coin.

Market Capitalisation Chart of Doge Coin

Source: Yahoo! Finance. (Computation by Author)

The historical data for Doge Coin market prices can be downloaded from Yahoo! Finance website: Download the data for DogeCoin

You can download blow the excel file used to build the figure. Historical data for Doge Coin can be downloaded from CoinMarketCap or Yahoo! Finance website.

#8 Cardano (ADA)

Logo of Cardano

Statistics

Market capitalization: $10.77 Billion

Market price : $0.3076

Circulating Supply : 35.03 Billion

Year founded: 2015

Overview of Cardano

Launched in September 2017 by Charles Hoskinson, one of Ethereum’s co-founders, Cardano strives to manifest as a third-generation blockchain initiative, extending the groundwork laid by Bitcoin (first generation) and Ethereum (second generation). Positioned as an eco-friendly and scalable smart contract platform, Cardano (ADA) operates on a proof-of-stake consensus mechanism known as Ouroboros, effectively validating transactions while minimizing energy consumption. The development landscape of Cardano relies on the utilization of the Haskell programming language, an attribute attributed to facilitating an evidence-centric development approach, subsequently ensuring unmatched security and reliability.

The central ambition of Cardano orbits around the facilitation of smart contracts, empowering developers to craft a diverse array of decentralized financial applications, novel cryptocurrency tokens, interactive games, and more. The architecture of the Cardano blockchain is bifurcated into two distinct tiers: the Cardano Settlement Layer (CSL) and the Cardano Computing Layer (CCL). The former houses the record of accounts and their corresponding balances, additionally serving as the domain where Ouroboros-based consensus mechanisms validate transactions. On the other hand, the latter, namely the CCL, serves as the execution ground for all computations related to blockchain applications, primarily driven by the functionality of smart contracts. This partitioning of the blockchain into dual strata endeavors to empower the Cardano network to effortlessly process a substantial volume of up to a million transactions per second.

Market capitalization

The figure below gives the market capitalization of Cardano from September 2017 to September 2024. At a given point of time, market capitalization is obtained by multiplying the number of Cardano in circulation (coin supply) by the price of Cardano.

Market Capitalisation Chart of Cardano

Source: Yahoo! Finance. (Computation by Author)

The historical data for Cardano market prices can be downloaded from Yahoo! Finance website: Download the data for Cardano

You can download blow the excel file used to build the figure. Historical data for Cardano can be downloaded from CoinMarketCap or Yahoo! Finance website.

#9 Solana (SOL)

Logo of Solana

Statistics

Market capitalization: $9.62 Billion

Market price : $24.45

Circulating Supply : 405.76 Million

Year founded: 2020

Overview of Solana

Solana, an up-and-coming cryptocurrency, has drawn substantial attention in the world of blockchain. Launched in 2020 by Anatoly Yakovenko, a former Qualcomm engineer, Solana’s coin was meticulously crafted to tackle the scalability and performance limitations that earlier blockchain platforms such as Ethereum had encountered. Solana’s developmental journey gained rapid traction as it aspired to bring about a transformation in the realm of decentralized finance (DeFi). This transformation was to be realized through swifter transaction speeds and reduced fees in comparison to its counterparts. This innovative approach captivated both investors and developers, propelling Solana into the spotlight as a highly promising contender in the expansive landscape of blockchain technology.

At its core, Solana operates as a crypto-computing platform with a distinct objective: to achieve remarkable transaction speeds without compromising decentralization. This exceptional speed translates into a notable reduction in congestion and fees. By maintaining these high speeds and low fees, Solana’s ultimate aim is to scale its capabilities to a level where it can rival centralized payment processors like Visa. The primary cryptocurrency associated with Solana is known as SOL. This digital asset serves multiple purposes, including covering transaction fees and facilitating the staking process. Additionally, SOL holders possess the privilege of participating in voting for upcoming upgrades. Notably, SOL is accessible for trading on exchanges such as Coinbase, providing users with an avenue to engage with this evolving crypto phenomenon.

Market capitalization

The figure below gives the market capitalization of Solana from September 2017 to September 2024. At a given point of time, market capitalization is obtained by multiplying the number of Solana in circulation (coin supply) by the price of Solana.

Market Capitalisation Chart of Solana

Source: Yahoo! Finance. (Computation by Author)

The historical data for Solana market prices can be downloaded from Yahoo! Finance website: Download the data for Solana

You can download blow the excel file used to build the figure. Historical data for Solana can be downloaded from CoinMarketCap or Yahoo! Finance website.

#10 Tron (TRX)

Logo of Iron

Statistics

Market capitalization: $7 billion

Market price : $0.078

Circulating Supply : 89.52 Billion

Year founded: 2017

Overview of Tron

In 2017, Tron launched as a blockchain project with a unique approach, foregoing cryptographic advancements in favor of integrating existing elements like dApps, smart contracts, and tokens pioneered by other projects. Its standout strategy was its Asia-focused market penetration, leveraging its founder Justin Sun’s prominence. Fueled by an engaged community, Tron’s futuristic vision yielded success.

Initially an Ethereum-based ERC-20 token, Tron has transitioned into an independent cryptocurrency. At its core, the Tronix network champions decentralization, freeing blockchain data from central control. Content creators earn TRX tokens for their contributions and can even devise custom tokens for the Tron network, enhancing dApp experiences. With plans to introduce decentralized games and a proof-of-stake consensus mechanism manned by super representatives, Tron’s rapid block additions and support for thousands of transactions per second further underscore its distinctive position in the cryptocurrency realm, complemented by its empowering content creator rewards and ownership model.

Market capitalization

The figure below gives the market capitalization of Tron from August 2017 to September 2024. At a given point of time, market capitalization is obtained by multiplying the number of Tron in circulation (coin supply) by the price of Tron.

Market Capitalisation Chart of Tron

Source: Yahoo! Finance. (Computation by Author)

The historical data for Tron market prices can be downloaded from Yahoo! Finance website: Download the data for Tron

You can download blow the excel file used to build the figure. Historical data for Tron can be downloaded from CoinMarketCap or Yahoo! Finance website.

Why should I be interested in this post?

This blog post provides valuable insights into the current landscape of cryptocurrencies. As the digital financial world continues to evolve, learning about the top 10 cryptocurrencies by market capitalization helps us understanding the leading cryptocurrencies and their market standings can provide crucial information for both seasoned investors and newcomers. Whether you’re seeking potential investment opportunities or simply staying informed about the trends shaping the financial future, this article can offer a concise overview of the top-performing cryptocurrencies, making it a must-read for anyone looking to navigate the complex world of digital assets.

Useful resources

Academic research about risk

Longin F. (2000) From VaR to stress testing: the extreme value approach Journal of Banking and Finance, N°24, pp 1097-1130.

Longin F. (2016) Extreme events in finance: a handbook of extreme value theory and its applications Wiley Editions.

Data

Yahoo! Finance Historical data for Litecoin

CoinMarketCap Historical data for Litecoin

About the author

The article was written in October 2024 by Snehasish CHINARA (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2025).

Illiquidity, Solvency & Insolvency : A Link to Bankruptcy Procedures

May 7, 2025January 14, 2025 by Snehasish CHINARA

In this article, Snehasish CHINARA (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2025) delves into the illiquidity, solvency and insolvency, key concepts in that connect financial distress and bankruptcy procedures.

Illiquidity

Illiquidity refers to the inability of a company or individual to convert assets into cash quickly enough to meet short-term financial obligations as they come due. This condition arises from a mismatch in the timing of cash inflows and outflows rather than a fundamental deficiency in overall financial health. For instance, a firm might hold substantial non-liquid assets (e.g., accounts receivable or inventory) that are valuable but not immediately accessible for use in settling debts. Illiquidity is generally viewed as a short-term liquidity risk and is often addressed through measures such as enhanced cash flow management, securing bridge financing, or leveraging credit facilities.

Solvency and Insolvency

Solvency refers to the financial health of an entity, where its assets exceed its liabilities, and it can meet its financial obligations as they fall due (although not in the short term as explained below). A solvent entity demonstrates financial stability and sustainability, which are key factors for stakeholders, such as debt holders (for liquidity reasons at the time debt deadlines) and especially equity holders (for performance reason).

Conversely, insolvency is a financial condition in which an entity’s liabilities exceed its assets, or it is unable to meet its debt obligations as they become due. It represents a state of long-term financial distress, indicating that the entity lacks sufficient resources to satisfy its obligations, even with adequate time to manage cash flows.

Insolvency can manifest in two primary forms:

Balance Sheet Insolvency: Occurs when the total liabilities of a company exceed its total assets. This is typically assessed using the entity’s balance sheet, where negative equity (assets minus liabilities) signals insolvency.

Cash Flow Insolvency: Occurs when an entity cannot pay its debts as they fall due, despite potentially having assets that exceed liabilities. This happens when illiquid assets cannot be quickly converted to cash to meet immediate obligations.

Insolvency is distinct from illiquidity in that it reflects a fundamental imbalance in financial health rather than a short-term cash flow issue. Prolonged insolvency often leads to bankruptcy filings, where legal proceedings determine whether the business should be restructured or liquidated.

Valuation Perspective: Solvency and Insolvency via Net Present Value (NPV)

Formula for Net Present Value (NPV)

The Net Present Value (NPV) is calculated using the following formula:

Figure 1. Net Present Value (NPV) Formula

In this context, cash flows represent the value generated by the firm’s assets, while the discount rate reflects the required return on debt and equity financing. A positive NPV signifies that the firm or project creates value above its cost of capital, while a negative NPV indicates value destruction and financial risk.

From a valuation standpoint, Net Present Value (NPV) is a crucial metric that aligns with the solvency status of an entity. NPV evaluates the difference between the present value of cash inflows and the present value of cash outflows over a given period. It serves as an indicator of the financial viability of a firm or project.

Solvent Firms: NPV > 0

A positive NPV indicates that the firm or project is generating value in excess of the required rate of return.

Such firms are financially sustainable, with the potential to attract investments, repay debts, and grow operations.

Example: A profitable company with strong operational cash flows and prudent capital investments will exhibit a positive NPV.

Insolvent Firms: NPV < 0

A negative NPV signals that the firm or project is destroying value, as cash outflows exceed the discounted cash inflows.

These firms struggle to generate sufficient returns, often resulting in financial distress and eventual insolvency.

Example: A company burdened by declining revenues, rising costs, and high-interest obligations may show a negative NPV.

Bankruptcy Basics

Bankruptcy is a legal framework that helps individuals and businesses unable to meet their financial obligations in the short term. When a company files for bankruptcy, it either seeks to reorganize its debts and operations or liquidate its assets to repay creditors, depending on the type of bankruptcy pursued (Chapter 7 for liquidation or Chapter 11 for reorganization procedures in US bankruptcy law). Reorganisation can offer a pathway to stability, enabling companies to mitigate debt burdens, restructure, and potentially preserve jobs. In this post I explain the link between the two academic concepts of illiquidity and insolvency and the two paths of bankruptcy of liquidation and reorganization.

Liquidation (Chapter 7 Bankruptcy)

Liquidation, often governed by specific bankruptcy codes such as Chapter 7 in the U.S., involves the complete dissolution of a financially distressed entity. Under this process, the firm’s assets are sold off to repay creditors in a legally prioritized manner. Liquidation is typically the final recourse for insolvent entities that lack the ability to restructure or continue operations. It marks the end of the entity’s existence, with any remaining proceeds distributed to stakeholders after settling liabilities.

Figure 2. Number of Chapter 7 Bankruptcy Filings (2013-2022)

Source: computation by the author (data: US Courts Statistics).

Reorganization (Chapter 11 Bankruptcy)

Reorganization, outlined under codes such as Chapter 11 in the U.S., is designed for insolvent entities seeking to restructure their debts and operations while continuing business activities. This process allows the firm to negotiate with creditors to modify repayment terms, reduce debt burdens, or inject fresh capital. Reorganization aims to restore financial stability, preserving the firm’s value and jobs while maximizing recoveries for creditors. It is a more sustainable alternative to liquidation for viable but financially distressed firms.

Figure 3. Number of Chapter 11 Bankruptcy Filings (2013-2022)

Source: computation by the author (data: US Courts Statistics).

Link between Illiquidity, Solvency, and Bankruptcy Outcomes

The determination of whether an illiquid firm should undergo liquidation or reorganization is heavily influenced by its solvency or insolvency status. These financial characteristics provide a structured framework to allocate resources and protect stakeholder interests, ensuring an efficient resolution process that minimizes economic disruption.

Illiquidity and Insolvent Companies: Liquidation

A firm that is both illiquid (unable to meet its short-term obligations) and insolvent (its liabilities exceed its assets) is in a critical financial position. These firms lack the operational capacity to generate sufficient cash flows and the balance sheet strength to cover their obligations. By selling off assets, the firm can repay creditors in an orderly and legally prioritized manner, thereby closing its operations permanently. Liquidation minimizes further losses and provides a clear exit for stakeholders, ensuring that remaining value is distributed equitably.

From a financial perspective:

Asset Realization: Liquidation involves selling the firm’s assets, converting illiquid assets (e.g., inventory, real estate) into cash to settle liabilities.

Creditor Recovery: Creditors are repaid in a hierarchical order—secured creditors (e.g., bondholders) take precedence, followed by unsecured creditors and equity holders.

Economic Efficiency: Liquidation prevents further erosion of value by discontinuing loss-making operations. The proceeds can be redeployed to more productive uses within the economy.

Example: In high-leverage industries such as retail, where asset values may plummet during financial distress, liquidation can be a pragmatic approach to salvaging any remaining value for stakeholders.

Illiquidity and Solvent Companies: Reorganization

Firms that are illiquid (unable to meet its short-term obligations) but remain solvent (its assets exceed its liabilities) present a different scenario. These companies face temporary liquidity constraints but possess the potential for recovery, given their fundamentally sound financial or economic position. By restructuring debts and operations under judicial supervision, reorganization allows the firm to stabilize its finances, regain liquidity, and continue its business activities. This approach helps preserve jobs, maintain operational continuity, and often results in better recovery for creditors compared to liquidation.

Key financial points include:

Debt Restructuring: The firm negotiates with creditors to extend repayment timelines, reduce interest rates, or convert debt into equity, improving short-term liquidity.

Operational Optimization: Reorganization often involves strategic cost-cutting, asset divestitures, or operational restructuring to enhance cash flow generation.

Stakeholder Value Preservation: By avoiding liquidation, reorganization preserves enterprise value, ensuring better recovery for creditors and protecting equity holders’ stakes.

Long-term Viability: Reorganized firms can often leverage their existing assets and market position to regain profitability, benefiting employees, suppliers, and customers.

Example: Airlines facing temporary cash flow issues during economic downturns often turn to reorganization. By negotiating with lessors, restructuring debt, and optimizing operations, they can avoid liquidation and return to profitability.

An Efficient Bankruptcy Procedure

An efficient bankruptcy procedure should distinguish between these two cases (solvent and insolvent firms), leading illiquid and insolvent firms into liquidation and illiquid but solvent firms into reorganization. This tailored approach ensures that:

Insolvent firms with no viable future are dissolved efficiently, maximizing recoveries for creditors.

Solvent but illiquid firms are given a second chance to reorganize and emerge stronger, preserving value for all stakeholders.

Figure 4. Efficient Bankruptcy Procedure

Such a system not only protects creditors and investors but also fosters economic stability by maintaining productive assets and employment where possible, while swiftly resolving entities that no longer contribute to the economy.

This approach not only maximizes financial efficiency but also aligns with broader economic objectives:

Maximizing Creditor Recovery: Insolvent firms should be liquidated to repay creditors as much as possible, avoiding the dilution of recovery through prolonged unviable operations.

Optimizing Economic Resources: Solvent but illiquid firms should undergo reorganization, preserving their workforce, intellectual property, and market position, which might otherwise be lost in liquidation.

Minimizing Systemic Risk: A clear distinction between liquidation and reorganization reduces uncertainty in financial markets, particularly for industries prone to cyclical liquidity crises.

Why Should I Be Interested in This Post?

This post serves as a comprehensive guide to understanding the critical financial concepts of illiquidity, solvency, and insolvency, while connecting them to practical applications in bankruptcy procedures. Whether you’re a finance student, a professional exploring corporate restructuring, or simply curious about the mechanisms behind bankruptcy codes, this article bridges theoretical knowledge with real-world implications.

By explaining the nuanced relationship between illiquidity and solvency/insolvency, and their impact on choosing between liquidation and reorganization, it offers insights into how firms navigate financial distress. Furthermore, it highlights how an ideal bankruptcy procedure aligns with maximizing economic value and minimizing systemic risks.

Useful resources

About the author

The article was written in January 2025 by Snehasish CHINARA (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2025).

Chapter 11 Bankruptcies: A Strategic Insight on Reorganisations

January 14, 2025 by Snehasish CHINARA

In this article, Snehasish CHINARA (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2025) explores the complexities of Chapter 11 bankruptcy laws in the United States, examining how this legal process impacts businesses facing financial distress. With insights into reorganisation (Chapter 11), this post provides a detailed overview of this chapter’s purpose, process, and strategic implications. By examining the purpose, procedures, and strategic implications of Chapter 11, this post sheds light on how firms navigate debt management and financial recovery.

Bankruptcy Basics

Bankruptcy is often perceived as a last resort for struggling businesses, a measure taken when all other avenues for debt resolution have been exhausted. However, for businesses of all sizes, understanding bankruptcy is crucial—not only as a potential safeguard but as a strategic consideration in financial planning and risk management. This knowledge becomes increasingly important in today’s volatile global economy, where the financial resilience of a business can determine its survival and growth.

Bankruptcy is a legal framework that helps individuals and businesses unable to meet their financial obligations. When a company files for bankruptcy, it either seeks to reorganize its debts and operations or liquidate its assets to repay creditors, depending on the type of bankruptcy pursued (Chapter 7 or Chapter 11 procedures in US bankruptcy law). Bankruptcy can offer a pathway to stability, enabling companies to mitigate debt burdens, restructure, and potentially preserve jobs.

Figure 1. Number of Chapter 11 Bankruptcy Filings (2013-2022)

Source: computation by the author (data: US Courts Statistics).

Legal Definition and Purpose of Chapter 11 Bankruptcy

Chapter 11 bankruptcy, commonly known as “reorganisation bankruptcy,” is a legal mechanism under Title 11 of the United States Code that allows financially distressed businesses to restructure their debts and operations while continuing to function. Unlike Chapter 7, which focuses on liquidation, Chapter 11 aims to preserve the business as a going concern, enabling it to regain profitability while protecting creditors’ interests.

Legal Definition: Chapter 11 provides a structured process through which a debtor proposes a reorganisation plan to address its financial obligations. This plan may involve renegotiating debt terms, selling non-core assets, downsizing operations, or finding new investment capital. The reorganisation plan must be approved by the bankruptcy court and often requires agreement from creditors, ensuring fairness and feasibility.

Purpose: The primary objective of Chapter 11 is to balance two critical goals:

Business Rehabilitation: By allowing the debtor to restructure debts rather than liquidate, Chapter 11 ensures that valuable business operations, jobs, and economic contributions are preserved. This is particularly vital for companies with potential long-term viability but temporary financial challenges.

Creditor Protection: The process safeguards creditor interests by ensuring orderly repayment according to a court-approved priority structure. Secured creditors typically recover from collateral-backed assets, while unsecured creditors negotiate for partial repayment through the reorganisation plan.

Chapter 11 is especially beneficial for medium-to-large corporations that need significant operational restructuring or whose debt structure requires complex renegotiation. The process is overseen by a debtor-in-possession (DIP), meaning the company’s existing management continues to operate the business under court supervision while implementing the reorganization plan. The court’s role ensures transparency, equity, and adherence to legal requirements, protecting all stakeholders throughout the process.

Chapter 11 enables businesses to restructure their debt obligations and operations without halting business activities. This process can offer significant advantages, especially for companies with strong core operations but temporary cash flow or liquidity issues. In 2023, approximately 25% of business bankruptcies filed in the U.S. were Chapter 11 cases, showing its popularity among companies aiming to reorganize rather than liquidate.

Key Objectives of Chapter 11

Debt Relief: Restructure and reduce debt obligations to improve cash flow.

Operational Reorganization: Adjust operations to align better with financial health, often through cost-cutting, downsizing, or strategic pivots.

Business Continuity: Unlike Chapter 7, Chapter 11 allows businesses to continue operations, retain jobs, and maintain relationships with customers and suppliers.

Eligibility and Who Can File:

Chapter 11 is primarily available to corporations, partnerships, and sole proprietorships. However, it is most commonly used by medium to large businesses that have a chance to recover.

For individuals, Chapter 11 is an option if they exceed the debt limits set for Chapter 13, though it is rare in personal bankruptcies due to its complexity and cost.

Common Causes of Business Bankruptcy

Chapter 11 bankruptcy is often a lifeline for businesses facing financial distress but with the potential for recovery through reorganizsation. Unlike Chapter 7, which involves liquidation, Chapter 11 allows companies to address their challenges by restructuring debts and operations. Several factors commonly drive businesses into Chapter 11 bankruptcy, reflecting a combination of internal inefficiencies and external pressures.

One major cause is an excessive debt burden, where businesses accumulate unsustainable liabilities relative to their income. This can become unmanageable during revenue declines or rising interest rates. Similarly, declining revenues caused by market shifts, competition, or external shocks often leave businesses unable to meet financial obligations. Economic downturns and external crises like recessions or global pandemics further exacerbate financial distress. In addition to economic pressures, overexpansion is another common issue. Businesses that grow too quickly without sufficient financial planning can overextend resources. Operational challenges such as inefficiencies or failure to innovate are also critical factors. Companies that fail to adapt to changing markets risk becoming obsolete. Additionally, supply chain disruptions, such as delays or rising costs, can disrupt operations, especially for businesses reliant on just-in-time systems. These issues can significantly strain cash flow and increase financial pressure.

Legal challenges often play a role in driving Chapter 11 filings as well. Large settlements, lawsuits, or regulatory fines can create sudden financial burdens that businesses struggle to manage. Cash flow management is another critical issue. Poor working capital planning can leave businesses unable to cover short-term obligations. Retailers with seasonal sales spikes often struggle during off-peak periods, leading to financial distress. Finally, industry disruption caused by technological advancements or shifting consumer preferences can force businesses into bankruptcy.

Businesses typically face bankruptcy due to a mix of internal and external factors. Key factors include:

Excessive Debt Burden
Cause: High levels of debt relative to income can leave businesses unable to service loans, especially during periods of declining revenue or rising interest rates.

Example: Hertz Corporation filed for Chapter 11 in 2020 with over $5 billion in debt. The pandemic-driven collapse in travel demand exacerbated its inability to meet financial obligations.

Declining Revenues
Cause: Sustained revenue declines due to market changes, competition, or external shocks can reduce a company’s ability to cover operating expenses and debt repayments.

Example: American Airlines filed for Chapter 11 in 2011 due to declining revenues from rising fuel costs and competition, using the process to restructure its debt and cut costs.

Economic Downturns and External Crises
Cause: Recessions, global crises, or industry-specific downturns can severely impact revenues and cash flow, driving businesses into insolvency.

Example: The COVID-19 pandemic led to a wave of Chapter 11 filings in 2020, including companies like JC Penney and Neiman Marcus, which faced plummeting consumer demand during lockdowns.

Overexpansion
Cause: Rapid growth without adequate financial controls or market analysis can stretch resources and leave businesses vulnerable to cash flow problems.

Example: Sbarro, a pizza chain, filed for Chapter 11 in 2011 and again in 2014 after overexpanding into underperforming locations, resulting in significant operational inefficiencies.

Operational Inefficiencies
Cause: Ineffective cost management, outdated business models, or failure to innovate can erode profitability, making it difficult to sustain operations.

Example: Kodak filed for Chapter 11 in 2012 due to its inability to adapt to the digital revolution, which rendered its traditional film business obsolete.

Supply Chain Disruptions
Cause: Delays, shortages, or rising costs in supply chains can disrupt production and increase operating expenses, especially for companies dependent on just-in-time systems.

Example: In 2022, many small-to-medium-sized manufacturers in industries like electronics and automotive struggled with supply chain issues, driving some to seek Chapter 11 protection.

Legal Liabilities
Cause: Large settlements, regulatory fines, or lawsuits can create significant financial burdens that businesses cannot manage without restructuring.

Example: Purdue Pharma filed for Chapter 11 in 2019 as part of a settlement to resolve thousands of lawsuits related to the opioid crisis.

Poor Cash Flow Management
Cause: Failure to manage working capital effectively can lead to cash shortages, making it difficult to pay creditors or fund day-to-day operations.

Example: A mid-sized retailer with strong seasonal sales but poor cash flow planning might file for Chapter 11 to restructure its payment obligations during off-peak periods.

High Fixed Costs
Cause: Businesses with significant fixed costs, such as long-term leases or equipment financing, face challenges when revenues fall, as these costs cannot easily be reduced.

Example: JC Penney faced mounting lease expenses and declining store traffic, ultimately filing for Chapter 11 in 2020 to renegotiate terms and restructure operations.

Industry Disruption
Cause: Technological innovation, shifting consumer preferences, or the entrance of disruptive competitors can weaken traditional business models.

Example: Blockbuster filed for Chapter 11 after streaming services like Netflix and Hulu fundamentally disrupted the home entertainment industry.

Key Steps in a Chapter 11 Filing

Filing the Petition and Automatic Stay
- Filing: The Chapter 11 process begins when the debtor files a petition in bankruptcy court. This petition includes comprehensive details about the company’s financial status, such as assets, liabilities, income, expenses, and financial history.
- Automatic Stay: The moment the petition is filed, an automatic stay takes effect, immediately halting all collection actions by creditors. This stay provides the company with breathing room to reorganize without the threat of foreclosure or asset seizure. The automatic stay is crucial for companies in Chapter 11, as it allows operations to continue while management restructures.
Development and Approval of the Reorganization Plan
- Plan Development: The debtor, acting as a “debtor-in-possession” (DIP), typically retains control over business operations. The company is tasked with drafting a reorganization plan, which outlines how it will repay creditors, restructure operations, and achieve profitability.
- Creditor Negotiations: The company works with creditors to gain their support for the reorganization plan. In most cases, the plan must be approved by at least one class of impaired creditors (those not expected to be fully repaid). This approval can involve debt rescheduling, asset sales, or reductions in debt.
- Court Approval: Once creditors approve the plan, the bankruptcy court must confirm it. The court assesses whether the plan is feasible, fair, and in the best interests of creditors. This phase can be complex and costly, as it often requires multiple hearings and potential modifications to satisfy all parties.
Execution and Emergence from Bankruptcy
- Implementation: After court approval, the company begins implementing the reorganization plan, following all terms outlined to repay creditors over time. Changes may include asset sales, layoffs, new debt issuance, or equity restructuring.
- Emergence from Chapter 11: Once the company fulfils the terms of the reorganization plan, it officially exits Chapter 11. This process can take several months to years, depending on the company’s complexity and debt structure. For instance, American Airlines emerged from Chapter 11 after two years of restructuring, merging with US Airways to enhance market competitiveness.

Benefits of Restructuring vs. Liquidation

Preserving Business Value: Reorganization allows the company to maintain operations, preserving its market presence, assets, and workforce. For example, Hertz used Chapter 11 in 2020 to restructure over $5 billion in debt, allowing it to continue operating and ultimately emerge stronger after the pandemic.

Maximizing Creditor Recoveries: Creditors are often more willing to negotiate in Chapter 11, as reorganization usually yields better recoveries than liquidation. According to research, Chapter 11 cases result in creditor recoveries averaging 20-25% higher than Chapter 7 cases due to continued asset generation.

Opportunity for Operational Efficiency: Companies can use Chapter 11 to optimize operations by renegotiating leases, reducing payroll, and streamlining production. These changes help improve financial health and long-term viability.

Risks and Challenges in the Reorganization Process

Cost and Complexity: Chapter 11 can be extremely costly, especially for larger businesses. Legal fees, administrative expenses, and consulting costs can run into millions. A 2019 study revealed that legal and administrative expenses for large Chapter 11 cases average between $1 million and $10 million. For example, Lehman Brothers’ bankruptcy case, the largest in U.S. history, accrued $2.2 billion in fees over its restructuring period.

Extended Time Frame: Chapter 11 cases can be lengthy, taking months or even years to complete. This time commitment may strain cash flow and delay recovery, particularly if the business is in a highly competitive industry. In Hertz’s case, the Chapter 11 process lasted 17 months, and the company only emerged after securing additional financing and renegotiating debt terms.

Uncertainty in Creditor Approval: Creditors must approve the reorganization plan, which can be challenging if there are conflicting interests among different creditor classes. If major creditor groups reject the plan, the court can enforce a “cramdown,” but this is often a contentious and uncertain process.

Risk of Conversion to Chapter 7: If a reorganization plan fails, or the business cannot achieve sustainable operations, the case may be converted to Chapter 7, leading to liquidation. This outcome results in further losses for stakeholders, as assets are sold off, and the business ceases operations.

Debtor-in-Possession (DIP) Financing: Definition, Purpose, and Relevance in Chapter 11 Filings

Debtor-in-Possession (DIP) financing is a specialized form of funding that allows businesses undergoing Chapter 11 bankruptcy to secure the liquidity needed to continue operations during the reorganization process. The term “debtor-in-possession” refers to the debtor retaining control of its assets and operations while the bankruptcy case is under court supervision. Unlike standard loans, DIP financing is uniquely designed for companies in financial distress and requires court approval to ensure fairness and transparency.

Definition and Features

DIP financing is a post-petition loan that takes precedence over most existing debts, including secured loans, under U.S. bankruptcy law. This super-priority status ensures that DIP lenders are repaid before pre-petition creditors, making the financing attractive even for lenders dealing with financially distressed companies. The terms of DIP financing often include strict covenants, requiring the debtor to adhere to the reorganization plan and meet operational milestones.

Purpose of DIP Financing.

The primary purpose of DIP financing is to provide businesses with the liquidity needed to continue essential operations during the reorganization process. This includes paying employees, suppliers, and other operating expenses. Without this funding, many companies would face operational paralysis, undermining the feasibility of reorganization.

Maintain Operations: Fund day-to-day activities such as payroll, supplier payments, and utility bills to prevent operational shutdown.

Stabilize the Business: Provide working capital to preserve the company’s going-concern value, ensuring it can generate revenue during the restructuring process.

Support Creditor Confidence: By maintaining operations, DIP financing reassures creditors that the debtor is working toward recovery and maximising the value of their claims.

Relevance in Chapter 11 Bankruptcies

DIP financing plays a pivotal role in Chapter 11 filings, bridging the gap between insolvency and reorganization. A business in financial distress often lacks the liquidity to continue operations, which is critical to preserving asset value and employee morale during bankruptcy. Without DIP financing, many companies would be forced to liquidate under Chapter 7, leading to the loss of jobs, assets, and creditor recoveries.

For creditors, DIP financing ensures that the company retains its going-concern value, which typically leads to higher recoveries than a piecemeal liquidation. The court-approved nature of DIP financing also provides a transparent framework for ensuring that new and existing creditors are treated fairly.

Case Study: Hertz Global Holdings – A Successful Chapter 11 Reorganization

Background

Hertz Global Holdings, one of the largest car rental companies in the world, filed for Chapter 11 bankruptcy on May 22, 2020, during the height of the COVID-19 pandemic. Founded in 1918, Hertz operated a fleet of over 700,000 vehicles across 12,000 locations worldwide. Despite its strong market presence, the company faced mounting financial pressures exacerbated by the collapse of global travel during the pandemic.

Causes of Financial Distress

Revenue Collapse: The COVID-19 pandemic caused a dramatic decline in travel demand, with global car rental revenues dropping by nearly 50% in 2020. Hertz’s core business was severely affected, leading to unsustainable losses.

Excessive Debt: Hertz entered the pandemic carrying over $19 billion in total debt, including vehicle leasing obligations. The revenue shortfall made it impossible for the company to service its debt.

Operational Challenges: Hertz struggled with a bloated fleet and high fixed costs. The sudden drop in demand left thousands of vehicles idle, further straining the company’s cash flow.

The Chapter 11 Filing

Hertz filed for Chapter 11 protection to restructure its debts and operations while continuing to operate. The reorganization aimed to address several key issues:

Debt Restructuring: Hertz sought to renegotiate terms with creditors to reduce its debt load and extend repayment periods.

Fleet Optimization: The company planned to sell off a portion of its vehicle inventory to generate cash and align fleet size with demand.

Securing Financing: Hertz needed additional liquidity to sustain operations during the reorganization process.

Key Steps in the Reorganization Process

Debtor-in-Possession (DIP) Financing: Hertz secured $1.65 billion in DIP financing to fund its operations during bankruptcy. This financing provided the necessary cash flow to continue serving customers and paying employees while restructuring.

Asset Sales: Hertz sold off approximately 200,000 vehicles from its fleet, generating liquidity and reducing carrying costs. This move also allowed the company to focus on optimizing its remaining assets.

Debt Negotiations: Hertz renegotiated with creditors to eliminate nearly $5 billion in debt. Creditors received equity and cash payments in exchange, ensuring some recovery while allowing the company to stabilize.

Strategic Investment: In May 2021, Hertz exited bankruptcy after receiving a $5.9 billion equity injection from a group of institutional investors, including Knighthead Capital and Certares Management. This recapitalization provided a strong financial foundation for the company’s post-bankruptcy growth.

Outcome

Hertz emerged from Chapter 11 on June 30, 2021, as a leaner and more competitive company. The reorganization allowed the company to:

Reduce Debt: Hertz significantly reduced its debt obligations, creating a more sustainable financial structure.

Optimize Operations: The sale of excess vehicles and strategic investments in fleet technology enhanced efficiency.

Leverage New Opportunities: Post-reorganization, Hertz announced plans to invest in electric vehicles (EVs), including a major purchase of 100,000 Teslas in 2021, positioning itself as a leader in the EV rental market.

Impact on Stakeholders

Creditors: Creditors recovered a portion of their investments through equity stakes and cash payments, avoiding the complete loss often associated with liquidation.

Employees: The reorganization preserved thousands of jobs, allowing Hertz to retain its workforce while stabilizing operations.

Customers: Hertz continued serving customers without major disruptions, ensuring the brand’s market presence remained intact.

Investors: The post-bankruptcy equity investment attracted new institutional investors, reflecting confidence in Hertz’s growth potential.

Lessons for Students

Importance of DIP Financing: Securing DIP financing is critical for maintaining operations during reorganization. Hertz’s ability to secure $1.65 billion ensured stability during a turbulent period.

Strategic Asset Management: Selling non-core assets, such as excess fleet vehicles, is a practical way to generate liquidity and reduce costs in Chapter 11 cases.

Investor Confidence: Attracting strategic investors during reorganization can provide not only financial resources but also market credibility.

Adaptability and Innovation: Post-bankruptcy, Hertz’s pivot toward electric vehicles demonstrates the importance of aligning business strategies with future market trends.

Why Should I Be Interested in This Post?

Understanding Chapter 11 bankruptcy is essential for anyone aspiring to excel in business, finance, law, or management. It is not merely a legal process but a strategic tool capable of reshaping businesses, preserving jobs, and driving economic recovery. This post provides an in-depth exploration of its mechanics, real-world applications, and strategic insights, offering immense value to students and professionals alike. By studying Chapter 11, you can gain a deep understanding of corporate reorganization frameworks, enhancing your ability to evaluate restructuring strategies and navigate complex financial scenarios. Expertise in this area is highly sought after in fields such as corporate finance, restructuring consulting, investment banking, and insolvency law, with knowledge of concepts like DIP financing, creditor negotiations, and reorganization plans opening doors to careers in distressed asset investing, turnaround consulting, and credit risk management. Moreover, learning about Chapter 11 develops critical skills in assessing financial health, managing liabilities, and evaluating risk—skills that are vital for credit analysis, equity research, and financial strategy roles. Additionally, with the globalization of business, understanding Chapter 11 principles provides transferable insights into international insolvency frameworks, laying a strong foundation for analyzing and adapting reorganization strategies across jurisdictions.

Useful resources

About the author

The article was written in January 2025 by Snehasish CHINARA (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2025).

Chapter 7 Bankruptcies: A Strategic Insight on Liquidations

December 24, 2024December 18, 2024 by Snehasish CHINARA

In this article, Snehasish CHINARA (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2025) delves into the intricacies of bankruptcy laws, focusing on the pivotal role of Chapter 7 in the United States. This legal framework governs the liquidation process, providing a structured approach for businesses facing severe financial distress. By examining the purpose, procedures, and strategic implications of Chapter 7, this post sheds light on how firms navigate debt management and financial recovery.

Bankruptcy Basics

Legal Definition and Purpose of Chapter 7 Bankruptcy

Chapter 7 bankruptcy, often referred to as “liquidation bankruptcy,” is a legal process under the U.S. Bankruptcy Code that allows individuals and businesses to discharge most of their unsecured debts by liquidating non-exempt assets.

The purpose of Chapter 7 – Liquidation is two fold:

To provide a “fresh start” for debtors who are unable to repay their debts by eliminating the legal obligation for most outstanding liabilities.

To maximize recovery for creditors by selling the debtor’s assets and distributing the proceeds according to a court-approved priority system.

Chapter 7 bankruptcy is widely used when a business is unable to operate profitably or lacks the means to restructure effectively. Chapter 7 typically results in the complete closure of a business, with its assets sold off to repay creditors, as opposed to reorganization under Chapter 11. Below is a detailed breakdown of the Chapter 7 process, implications, and a real-world case study to provide further insight.

Eligibility Criteria: To file under Chapter 7, a business or individual must demonstrate insolvency, where liabilities exceed assets. However, certain entities, such as governmental units and banks, are ineligible and must pursue other legal avenues if insolvent.

This type of bankruptcy is typically used by businesses that are no longer viable or individuals with limited income and substantial debts. Unlike Chapter 11 or Chapter 13 bankruptcies, Chapter 7 does not involve a repayment plan, and businesses filing under Chapter 7 usually cease operations. The process is overseen by a court-appointed trustee, who is responsible for liquidating the debtor’s non-exempt assets, paying creditors, and ensuring compliance with bankruptcy laws.

Figure 1. Number of Chapter 7 Bankruptcy Filings (2013-2022)

Source: computation by the author (data: US Courts Statistics).

Common Causes of Business Bankruptcy

Chapter 7 bankruptcy, or liquidation bankruptcy, is often the final step for businesses unable to overcome financial distress. One major cause is excessive debt, where high liabilities outpace a company’s ability to generate income, as seen with Circuit City. Similarly, declining revenues and changing market demand, like in the case of Toys “R” Us, can leave businesses unable to cover costs.

Poor financial management and high fixed costs, such as rent and payroll, exacerbate financial strain, especially during economic downturns or external shocks like the COVID-19 pandemic. Legal liabilities, such as lawsuits or fines, can also overwhelm a business, forcing liquidation.

Companies failing to adapt to technological disruption, like Blockbuster, or those affected by supply chain issues, risk bankruptcy as they lose competitive footing. Additionally, overexpansion without proper financial controls often depletes resources, leading to insolvency.

Chapter 7 highlights the importance of managing debt, adapting to market changes, and planning for risks to avoid liquidation and ensure business longevity.

Excessive Debt and Overleveraging: Businesses with high levels of debt relative to income often struggle to meet financial obligations, especially if revenue declines. Excessive borrowing, particularly during growth phases, can leave companies vulnerable during economic downturns.

Declining Revenues and Market Demand: A sustained drop in sales or market demand, often due to changing consumer preferences, technological disruption, or increased competition, can cripple a business. With insufficient revenue, businesses cannot cover fixed costs like rent, utilities, and payroll.

Poor Financial Management: Mismanagement of finances, such as inadequate cash flow planning, overinvestment in non-essential assets, or failing to monitor costs, can lead to insolvency. Companies that lack strong financial controls often find themselves unable to weather financial challenges.

Economic Downturns and External Shocks: Recessions, pandemics, or unexpected global events can sharply reduce demand, disrupt supply chains, or increase operational costs. Businesses with thin margins or limited reserves are particularly vulnerable.

Legal Liabilities and Litigation: Lawsuits, regulatory fines, or liability claims can create sudden and overwhelming financial burdens for businesses. Legal judgments can lead to asset seizures, leaving businesses unable to continue operations.

High Fixed Costs and Low Profit Margins: Businesses with high fixed costs (e.g., rent, utilities, long-term leases) and narrow profit margins are especially vulnerable to revenue fluctuations. Even small declines in income can create large deficits, leading to insolvency.

Technological Disruption: Companies that fail to adapt to technological advancements or changing industry practices often lose competitiveness, leading to financial decline. Industries undergoing rapid innovation can quickly make certain business models obsolete.

Lack of Access to Financing: Businesses that cannot secure financing or additional credit to address cash flow issues often resort to Chapter 7. Inability to refinance debt or raise capital can leave businesses unable to meet obligations.

Supply Chain Issues: Disruptions in the supply chain, such as rising costs, delays, or shortages, can increase expenses or reduce product availability, causing financial distress. This is particularly true for businesses reliant on just-in-time inventory systems.

Overexpansion: Rapid expansion without sufficient market analysis or operational capacity often leads to cash flow issues and increased debt. Overestimating demand or investing heavily in new locations can stretch resources thin.

Key Steps in a Chapter 7 Filing

Filing the Petition – The bankruptcy process begins with the debtor filing a Chapter 7 petition in federal bankruptcy court. This petition includes a comprehensive list of all assets, liabilities, income, expenses, and a statement of financial affairs. By filing, the business immediately gains protection from creditors under an automatic stay, preventing further collection actions.

Appointment of a Trustee – After the petition, the court appoints a bankruptcy trustee to oversee the liquidation. The trustee’s role includes managing the debtor’s estate, reviewing asset and liability documentation, and identifying assets eligible for liquidation. The trustee is also responsible for maximizing asset recovery to distribute funds to creditors fairly.

Asset Liquidation and Debt Discharge – The trustee liquidates the non-exempt assets of the business, such as inventory, equipment, and property. Assets are prioritized based on secured and unsecured creditor claims, following a hierarchy established by bankruptcy law. Generally, secured creditors are paid first, followed by priority and unsecured creditors. In most cases, unsecured creditors recover only a fraction of their claims—often below 10%—due to limited available assets. Once assets are distributed, the business’s unsecured debts are discharged, meaning the company is no longer obligated to repay them. This final step formally closes the business, and the entity is typically dissolved.

Implications for Businesses and Creditors

The following are the implications for the businesses and other stakeholders as a result of Chapter 7 bankruptcies –

Pros:

Debt Relief: Business owners are released from most unsecured debts, allowing them to move forward without remaining financial burdens from the insolvent entity.

Simplified Process: Chapter 7 is relatively fast and straightforward compared to Chapter 11, typically concluding within 3-6 months. This timeframe provides a more immediate resolution for both owners and creditors.

Lower Costs: With less need for ongoing legal and operational expenses, Chapter 7 is more cost-effective.

Cons:

Loss of Control: Business owners lose all control of the entity and its assets once the trustee is appointed, limiting their role in decision-making and asset management.

No Future Operations: Chapter 7 results in the closure of the business, removing the opportunity for restructuring or reorganization.

Negative Credit Impact: Owners may face challenges in securing future financing due to the adverse impact on their credit.

Circuit City – A Lesson in Chapter 7 Bankruptcy

Background

Circuit City, founded in 1949, was once a leading electronics retailer in the United States, with over 700 stores and 34,000 employees at its peak. The company was renowned for its innovative approach to retail and customer service, being among the first to adopt superstore formats for consumer electronics. However, by the late 2000s, Circuit City found itself struggling in an increasingly competitive market.

Causes of Financial Collapse

Circuit City’s road to Chapter 7 bankruptcy was marked by several critical missteps and external pressures:

Strategic Mismanagement:

The company attempted to cut costs by eliminating 3,400 of its most experienced sales associates in 2007. This move alienated customers, as less knowledgeable staff were unable to provide the high-quality service that was a hallmark of Circuit City’s brand.

Circuit City also failed to embrace e-commerce aggressively, losing significant market share to competitors like Amazon and Best Buy.

Economic Pressures: The 2008 financial crisis led to a sharp decline in consumer spending, particularly on non-essential items like electronics. Circuit City, already facing financial strain, was hit hard by reduced foot traffic and declining revenues.

Overexpansion and High Fixed Costs: The company had expanded aggressively, opening numerous stores that failed to generate sufficient revenue. This left Circuit City burdened with high lease costs and operational expenses.

Poor Inventory Management: Circuit City struggled with inventory issues, frequently stocking items that were outdated or not in demand. This created significant inefficiencies in cash flow and customer satisfaction.

Filing for Bankruptcy

On November 10, 2008, Circuit City filed for Chapter 11 bankruptcy, intending to restructure its debts and remain operational. However, the reorganization efforts failed for several reasons:

The company was unable to secure adequate financing to support operations during the bankruptcy process.

Suppliers became wary of Circuit City’s ability to pay and began restricting credit terms, creating inventory shortages during the crucial holiday shopping season.

Attempts to find a buyer or merger partner were unsuccessful.

By January 16, 2009, Circuit City announced it would close all its remaining stores and transition to Chapter 7 bankruptcy. The decision marked the end of Circuit City’s 60-year legacy.

The Liquidation Process

Under Chapter 7, a court-appointed trustee oversaw the liquidation of Circuit City’s assets. Key steps included:

Selling Inventory: The company conducted massive clearance sales, liquidating its electronics stock at deep discounts.

Auctioning Real Estate: Store leases and properties were auctioned to recover funds for creditors.

Distributing Proceeds: Proceeds from the liquidation were distributed to creditors based on bankruptcy priority rules:
- Secured Creditors: Received most of the proceeds, as their claims were backed by collateral (e.g., store leases, equipment).
- Unsecured Creditors: Received only a small fraction of their claims, reflecting the risks of unsecured lending.
- Shareholders: As is typical in Chapter 7 cases, shareholders received nothing.

Impact on Stakeholders

Employees: Over 34,000 employees lost their jobs, highlighting the human cost of liquidation bankruptcies. Many workers did not receive severance pay, sparking debates about labour protections in bankruptcy law.

Suppliers: Circuit City’s failure left many suppliers with unpaid invoices, creating ripple effects throughout the electronics supply chain.

Competitors: Circuit City’s exit from the market allowed competitors like Best Buy to capture a larger share of the consumer electronics market, reinforcing the importance of strategic agility in competitive industries.

Lessons Learned

The Circuit City case offers valuable lessons for students and professionals analysing Chapter 7 bankruptcies:

Customer Experience Matters: Cost-cutting measures that compromise customer satisfaction can have long-term consequences, especially in competitive industries.

Adaptation is Crucial: Failure to embrace e-commerce and innovate in response to changing consumer preferences sealed Circuit City’s fate.

Cash Flow is King: Poor inventory management and inability to secure financing during bankruptcy underscored the importance of liquidity for survival.

Chapter 7 as a Last Resort: Circuit City’s transition from Chapter 11 to Chapter 7 illustrates the challenges of restructuring without a strong operational and financial foundation.

Why Should I Be Interested in This Post?

Understanding Chapter 7 bankruptcy is crucial for anyone pursuing a career in finance, business strategy, or law. This post explores the mechanics of liquidation bankruptcy, shedding light on how businesses resolve insolvency and its impact on creditors, employees, and the economy. It provides insights into the strategic decisions driving liquidation under Chapter 7, equipping readers to analyze distressed scenarios and develop a critical perspective on financial risk and recovery strategies.

Moreover, expertise in bankruptcy law opens doors to specialized fields such as turnaround consulting, distressed asset investing, and insolvency law. As global markets increasingly adopt frameworks similar to Chapter 7, this knowledge is highly transferable, offering opportunities to navigate insolvency cases across international markets. Whether you aim to excel in credit analysis, investment banking, or corporate restructuring, this post offers valuable lessons to enhance your strategic and financial acumen.

Useful resources

About the author

The article was written in August 2023 by Snehasish CHINARA (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2025).

Chapter 7 vs Chapter 11 Bankruptcies: Insights on the Distinction between Liquidations & Reorganizations

December 24, 2024December 18, 2024 by Snehasish CHINARA

In this article, Snehasish CHINARA (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2025) explores the complexities of Chapter 7 and Chapter 11 bankruptcy laws in the United States, examining how these legal processes impact businesses facing financial distress. With insights into liquidation (Chapter 7) and reorganization (Chapter 11), this post provides a detailed overview of each chapter’s purpose, process, and strategic implications, offering valuable lessons in managing debt and financial recovery.

Bankruptcy Basics

Bankruptcy is a legal framework that helps individuals and businesses unable to meet their financial obligations. When a company files for bankruptcy, it either seeks to reorganize its debts and operations or liquidate its assets to repay creditors, depending on the type of bankruptcy pursued (Chapter 7 or Chapter 11 procedures in US bankruptcy law). Over 30,000 businesses filed for bankruptcy in the US in 2023, demonstrating the critical role this process plays in managing corporate distress. Bankruptcy can offer a pathway to stability, enabling companies to mitigate debt burdens, restructure, and potentially preserve jobs.

1. General Motors (2009) – Corporate Example

Background: General Motors (GM), one of the largest automobile manufacturers, faced a severe financial crisis in 2009. With declining sales, a massive debt load, and high operational costs, GM was unable to meet its financial obligations.

Bankruptcy Filing: GM filed for Chapter 11 bankruptcy to reorganize its debts. Through the bankruptcy framework, GM was able to reduce its liabilities, renegotiate labour contracts, and streamline operations, ultimately emerging as a more financially sustainable company.

Outcome: The bankruptcy framework allowed GM to reorganize its operations and avoid liquidation, protecting jobs and enabling it to continue as a key player in the automotive industry.

2. Lehman Brothers (2008) – Corporate Example

Background: Lehman Brothers, a global financial services firm, was heavily leveraged and exposed to subprime mortgage debt. When the real estate market collapsed, Lehman was unable to meet its debt obligations.

Bankruptcy Filing: Lehman filed for Chapter 11 bankruptcy, marking one of the largest corporate bankruptcies in history. The legal framework allowed Lehman to begin asset liquidation and distribute proceeds to creditors under court supervision.

Outcome: Though Lehman did not emerge as a reorganized company, the bankruptcy framework facilitated an orderly process for winding down the firm and managing creditor claims, preventing a more chaotic collapse.

3. Curtis James Jackson III (50 Cent) – Personal Bankruptcy Example

Background: The rapper and entrepreneur Curtis Jackson (known as 50 Cent) filed for Chapter 11 bankruptcy in 2015 after facing lawsuits and substantial debts he could not pay.

Bankruptcy Filing: Chapter 11 allowed Jackson to reorganize his debts without liquidating his assets entirely. He was able to negotiate repayment terms with creditors while continuing his business ventures.

Outcome: Through the bankruptcy framework, Jackson completed a reorganization plan, ultimately repaying creditors over time and successfully emerging from bankruptcy while preserving his business interests.

Figure 1. Number of Chapter 7 (Liquidation) & Chapter 11 (Reorganisation) 2013 – 2022

Source: US Courts Data (Computation by Author).

Legal Definition and Purpose

The U.S. Bankruptcy Code is a comprehensive set of federal laws enacted to provide a legal framework for bankruptcy filings. It is codified in Title 11 of the United States Code and governs all bankruptcy cases in the country, with different chapters addressing various types of financial distress.

The Code’s objectives include ensuring a fair distribution of the debtor’s assets among creditors, offering a fresh start to debtors, and establishing a structured process for both liquidation and reorganization. The Bankruptcy Code covers everything from the types of bankruptcy filings available to the specific steps and criteria needed for each process. All bankruptcy cases are overseen by federal bankruptcy courts, with judges responsible for ensuring compliance with the Code and adjudicating disputes between debtors and creditors.

Legally, bankruptcy is a federal judicial process governed by the U.S. Bankruptcy Code, which provides the framework to address insolvent companies’ financial liabilities. The primary purposes of bankruptcy law are:

Fair and Equitable Treatment of Creditors: Bankruptcy law ensures that creditors are repaid as fairly as possible based on their claims and priorities.

Relief and Protection for the Debtor: Filing for bankruptcy gives businesses temporary relief from creditor actions, such as lawsuits and collections, allowing them to reorganize or liquidate assets without external pressure.

Rehabilitation or Exit from Market: Depending on the situation, bankruptcy provides businesses with the opportunity to restructure and regain stability or exit the market responsibly.

In practice, bankruptcy serves as both a shield and a tool, giving companies the time and resources to evaluate and act on their financial situation in a structured manner.

Common Causes of Business Bankruptcy

Businesses typically face bankruptcy due to a mix of internal and external factors. Key factors include:

Poor Financial Management: Mismanagement of finances, including high debt levels and inadequate cash flow, is a primary cause. About 50% of small businesses fail within the first five years, often due to financial missteps.

Economic Downturns: Recessions and economic instability can severely impact sales and profit margins, leaving companies unable to meet their financial obligations. The COVID-19 pandemic saw a 20% increase in business bankruptcies in specific sectors, especially retail and hospitality.

High Debt Obligations: When companies rely too heavily on borrowed capital, downturns can leave them unable to service their debt, resulting in financial distress.

Industry Disruptions: Changes in technology and consumer preferences can render a business model obsolete, pushing companies toward bankruptcy. For example, retailers like Sears and J.C. Penney filed for bankruptcy as online shopping trends transformed the retail landscape.

Legal and Regulatory Challenges: Companies in highly regulated industries, such as healthcare and finance, may face significant legal and compliance costs, which can lead to bankruptcy if they are not adequately prepared.

Differences between Chapter 7 & Chapter 11 Bankruptcies

The table below presents the differences between Chapter 7 (liquidation) and Chapter 11 (reorganization) procedures in the US bankruptcy law:

Table 1. Chapter 7 (liquidation) and Chapter 11 (reorganization) procedures in the US bankruptcy law

Source: US Courts

When to opt for Liquidation (Chapter 7) vs. Reorganization (Chapter 11)

Choosing between Chapter 7 liquidation and Chapter 11 reorganization is a pivotal decision for distressed businesses. This choice hinges on various strategic, financial, and operational factors that impact not only the business’s future but also creditors, employees, and shareholders.

Liquidation (Chapter 7)

Ideal Situations for Liquidation:

No Path to Profitability: If a business has no viable path to profitability due to declining industry demand, obsolete products, or irreparable operational inefficiencies, Chapter 7 might be the optimal choice. For example, Circuit City, a major electronics retailer, filed for Chapter 7 in 2009 after failing to adapt to e-commerce trends. With revenue losses of nearly 20% year-over-year and no viable turnaround options, liquidation was chosen to maximize asset value.

Severe Debt Burden: When a business’s debt load is unsustainable and far exceeds its asset value, liquidation might offer the highest recovery rate for creditors. Companies in this position often have debts that are difficult to renegotiate, and without sufficient cash flow to cover interest and principal payments, they are left with no restructuring options.

Asset-Heavy Businesses: Companies with valuable physical assets may benefit more from Chapter 7, where assets like real estate, equipment, and inventory can be sold to partially satisfy creditors. For instance, Toys “R” Us converted to Chapter 7 in 2018, liquidating $1 billion in inventory and assets to repay secured creditors when reorganization proved unfeasible.

Advantages of Chapter 7:

Speed of Resolution: Chapter 7 cases typically conclude within 3 to 6 months, allowing for a quicker closure and reducing prolonged financial strain.

Lower Costs: Compared to Chapter 11, Chapter 7 has lower administrative and legal fees, with an estimated cost between $20,000 and $50,000 for small to medium-sized businesses, whereas Chapter 11 often involves significant legal expenses.

Reorganization (Chapter 11)

Ideal Situations for Reorganisation:

Operational Viability: If a business has strong core operations but is experiencing temporary financial setbacks, Chapter 11 reorganization allows for restructuring while continuing operations. American Airlines, which filed for Chapter 11 in 2011 with over $40 billion in liabilities, was able to reduce labour costs, restructure debt, and emerge stronger through a merger with US Airways.

Need for Asset Preservation: Businesses with valuable intangible assets, such as patents, brands, or customer relationships, can benefit from Chapter 11 to maintain their brand value and market share. Hertz, the global rental car giant, used Chapter 11 in 2020 to retain its market position and shed $5 billion in debt while reorganizing, eventually re-emerging with a stronger balance sheet.

Possibility of Financing and Restructuring: Companies that can attract post-petition financing and renegotiate debts stand a better chance in Chapter 11. Lenders are often more willing to provide financing if the company has a solid plan and ongoing revenue streams. For instance, General Motors secured $30 billion in federal aid during its 2009 Chapter 11 process, allowing it to restructure and continue operations.

Advantages of Chapter 11:

Long-Term Viability: Chapter 11 provides companies with the time and flexibility to reorganise their debts and adjust operations, potentially leading to sustainable profitability.

Creditor Negotiation: Chapter 11 allows debtors to negotiate with creditors for more favourable repayment terms, often resulting in higher recovery rates for unsecured creditors compared to Chapter 7.

Case Study: Toys “R” Us

In 2018, the iconic toy retailer Toys “R” Us filed for Chapter 7 bankruptcy, transitioning from an initial Chapter 11 reorganization filing. The bankruptcy marked one of the most significant retail closures in recent history, affecting 33,000 employees and closing over 700 stores in the U.S. alone.

Background and Context

Company Overview:

Founded: 1948

Industry: Retail (Specialty Toy and Baby Products)

Global Reach: Operated over 1,600 stores worldwide at its peak, including over 700 stores in the U.S.

Legacy: Toys “R” Us was one of the largest toy retailers globally and an iconic brand for several generations.

Financial Background:

Debt Load: Carried approximately $5 billion in debt, primarily from a leveraged buyout (LBO) in 2005 by private equity firms. This debt created a significant financial burden, consuming profits and limiting the company’s ability to reinvest in modernization efforts.

Revenue Pressures: Struggled to compete with e-commerce giants like Amazon and low-cost retailers like Walmart, which offered competitive pricing and convenience.

Initial Bankruptcy Filing (Chapter 11):

Date: September 2017

Objective: The initial Chapter 11 filing was intended to restructure debts and revive the company’s operations. Toys “R” Us aimed to reduce its debt load and improve liquidity to invest in a digital presence and update store experiences.

Challenges: Despite the plan, Toys “R” Us could not generate sufficient revenue to cover operational and restructuring costs due to stiff online competition, seasonal sales dependency, and lack of investor confidence.

Transition to Chapter 7 (Liquidation)

Reasons for Conversion:

Failed Restructuring: By early 2018, the restructuring under Chapter 11 was unsuccessful. The company faced critical cash flow issues and was unable to secure the financing needed to support the reorganization.

Market Challenges: The rise of e-commerce, coupled with consumer preferences shifting away from physical stores, reduced Toys “R” Us’s competitive advantage and viability.

Debt Burden: Servicing a high debt load further strained finances, with Toys “R” Us spending millions annually in interest payments, limiting funds available for reinvestment.

Decision:

Date: March 2018

Outcome: Toys “R” Us officially filed for Chapter 7, leading to the closure and liquidation of its U.S. stores and operations. The transition marked the end of its efforts to survive as a going concern.

The Liquidation Process

Role of the Trustee:

A trustee was appointed to oversee the liquidation of Toys “R” Us’s assets. The trustee’s duties included identifying and valuing assets, conducting sales, and distributing proceeds to creditors based on a priority system.

Assets Liquidated:

Inventory and Merchandise: All remaining toy inventory and other merchandise were liquidated through clearance sales.

Real Estate: Store leases and property rights were sold, with some locations acquired by competitors or other businesses.

Intellectual Property: The “Toys “R” Us” brand, Geoffrey the Giraffe mascot, and other trademarks were sold to generate additional revenue.

Outcome:

Total Proceeds: The liquidation generated approximately $1 billion, but this amount was insufficient to cover the $5 billion debt load fully.
Creditors’ Recovery: Due to the liquidation hierarchy:

Secured Creditors: Received a higher percentage of their claims, as their loans were backed by collateral.

Unsecured Creditors: Recovered less than 5% of their initial investments, reflecting the typical outcome for unsecured claims in Chapter 7 cases.

Impact on Stakeholders

Employees: Approximately 33,000 employees lost their jobs, sparking national debates on the treatment of workers in corporate bankruptcies. Many workers did not receive severance pay, leading to calls for legislative reform in corporate bankruptcy processes.

Suppliers and Partners: Suppliers faced unpaid invoices and significant losses due to the liquidation. The bankruptcy also created ripple effects in the toy industry, impacting toy manufacturers reliant on Toys “R” Us as a major retailer.

Community and Local Economy: The closure of over 700 stores in the U.S. led to economic downturns in local communities, where Toys “R” Us had served as a major employer and contributor to commercial activity.

Key Lessons and Takeaways

1. High Leverage Risks:

The leveraged buyout in 2005 saddled Toys “R” Us with an unsustainable debt load, diverting critical funds toward interest payments instead of innovation and digital transformation.

Insight: Businesses in highly competitive industries should maintain manageable debt levels, particularly when rapid market shifts (like e-commerce growth) threaten traditional business models/

2. Market Adaptation and Innovation:

Toys “R” Us struggled to adapt to changing consumer behaviour, as shoppers increasingly turned to online platforms. The failure to invest in e-commerce further weakened the company’s market position.
Insight: Businesses must continuously invest in technology and customer experience to remain relevant, particularly in the retail sector where consumer preferences shift rapidly.

3. Stakeholder Impact in Chapter 7:

The liquidation resulted in minimal recoveries for unsecured creditors and severe job losses, highlighting the often-painful impact of Chapter 7 on stakeholders.
Insight: Chapter 7 filings may serve as a stark reminder for stakeholders about the importance of financial due diligence and credit protections when engaging with highly leveraged companies.

4. Corporate Governance and Accountability:

The Toys “R” Us case spurred debates on corporate governance, particularly regarding the responsibilities of private equity owners in highly leveraged companies. Questions were raised about whether the company could have been saved with better financial management.
Insight: Effective corporate governance, with a focus on sustainable financing and operational resilience, is essential for long-term business health.

Why Should I Be Interested in This Post?

Understanding bankruptcy is essential for students pursuing careers in finance, consulting, corporate strategy, or law. It provides valuable insights into risk management, financial restructuring, and strategic decision-making, equipping you to navigate complex financial scenarios.

This post enhances your strategic awareness by explaining the frameworks behind liquidation versus reorganization decisions, sharpens your financial acumen to assess distress and recovery strategies, and highlights career opportunities in fields like restructuring and distressed asset investing. With a global perspective, it also offers knowledge transferable across interconnected markets, preparing you for specialized roles in today’s dynamic economy.

Useful resources

About the author

The article was written in August 2023 by Snehasish CHINARA (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2025).

My Apprenticeship Experience as Customer Finance & Credit Risk Analyst at Airbus

December 24, 2024December 11, 2024 by Snehasish CHINARA

Snehasish CHINARA Customer Finance & Credit Risk Analyst

In this article, Snehasish CHINARA (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2025) shares his experience as Customer Finance & Credit Risk Analyst at Airbus, which is a leader in the commercial aviation industry as an original equipment manufacturer (OEM).

About Airbus SAS

Airbus SAS, founded in 1970, is a leading European multinational aerospace corporation with a global presence. Specializing in the design, manufacturing, and delivery of aerospace products, services, and solutions, Airbus has established itself as a cornerstone of innovation and excellence in the aviation industry.

From commercial aircraft to defence and space systems, Airbus covers a wide array of sectors, each driven by cutting-edge technology and a commitment to sustainability. Their iconic product line, including the A320 and A350 families, represents the forefront of efficiency, safety, and performance in aviation.

Beyond manufacturing, Airbus is also deeply engaged in digital transformation, pushing boundaries with initiatives in autonomous flying, AI-driven processes, and greener aviation solutions. As an industry leader, Airbus is committed to decarbonizing the aerospace industry, having set ambitious goals to reduce its environmental footprint through innovations such as sustainable aviation fuels and hydrogen-powered aircraft.

With a global workforce of over 130,000 employees and operations in more than 170 locations worldwide, Airbus continues to be at the heart of the aerospace revolution, shaping the future of flight.

Logo of Airbus.

Source: the company.

My Experience as a Customer Finance & Credit Risk Analyst at Airbus

During my time as a Customer Finance & Credit Risk Analyst at Airbus, which was part of my Master in Management degree at ESSEC Business School, I had the opportunity to play a pivotal role in leading financial analyses and supporting high-stakes deal campaigns in the aviation sector. This experience was instrumental in sharpening my analytical and credit risk assessment capabilities, as I worked on transactions exceeding €200M, where each decision carried significant financial implications.

In this role, I focused on developing advanced financial models and internal customer credit rating models, applying methodologies from major credit rating agencies like Moody’s, S&P, and Fitch. These models, built using tools such as Excel, R, and Python, allowed my team to improve the accuracy of risk predictions for over 200 global client companies (mostly airline companies) air. By conducting industry-wide credit risk analyses, I ensured that each deal was supported by a thorough understanding of financial and credit health, helping Airbus mitigate risks and seize opportunities in a highly competitive market.

A key highlight of my work involved analysing the impact of M&A and restructuring activities within the aviation industry. This hands-on experience further honed my ability to deliver comprehensive financial forecasts and credit risk analyses.

One of the most rewarding aspects of my role was the opportunity to present these financial insights directly to senior executives. Communicating complex financial data effectively is crucial when high-value transactions are involved, and this responsibility significantly enhanced my presentation and communication skills. My experience in presenting to top executives helped me not only translate data into actionable strategies but also contributed to the decision-making process at the highest level.

Overall, my role as a Customer Finance & Credit Risk Analyst at Airbus was a formative experience that deepened my expertise in financial modelling, credit risk analysis, and strategic financial communication. It was an invaluable opportunity to contribute to significant aviation industry deals and refine my skills in evaluating financial performance and credit health at a global scale.

My missions

The objective my project was to achieve the following:

Led the migration of Airbus’ internal credit rating model from a manual Excel-based system to an automated and scalable R-based system, improving data processing accuracy and decision-making.

Educated internal teams on industry-specific financial metrics and KPIs to help them understand the financial health of Airbus’ customers.

Conducted comprehensive financial health analyses and credit rating evaluations for over 200 global companies, using tools such as Excel, R, and Python.

Supported marketing and sales campaigns by providing financial insights, risk evaluations, and industry trends to improve Airbus’ position in the aviation sector.

Required Skills and Knowledge

As a Customer Finance & Credit Risk Analyst at Airbus, several key skills and knowledge areas were essential to fulfilling my responsibilities effectively:

Financial Analysis and Modelling: Proficiency in developing financial models and credit rating models was crucial. These models helped me assess the financial health of clients and predict risks. Additionally, I frequently used tools like Excel, R, and Python to develop robust financial models that supported decision-making processes.

Credit Risk Assessment: Applying methodologies from Moody’s, S&P, and Fitch allowed me to conduct comprehensive credit risk assessments. Understanding credit rating criteria and financial ratios helped me evaluate over 200 global companies in the aviation sector, ensuring accurate risk predictions.

Industry Knowledge: Understanding the aerospace industry inside and out was essential. I became familiar with the dynamics between OEMs, lessors, airlines, and financial institutions. This helped me make better-informed decisions when assessing the creditworthiness of our clients and providing insights that contributed to Airbus’ overall financial strategies.

Data Analysis and Reporting: I worked with large datasets to analyse financial performance and assess risk factors. Creating financial reports, dashboards, and presentations helped me convey complex data in a way that was clear and actionable, especially when presenting to senior executives.

Automation and Process Improvement: One of my major projects involved transitioning our internal credit rating system from Excel to a more efficient R-based platform. This required me to develop a scalable solution that not only improved accuracy but also streamlined the data processing workflow, making everything faster and more reliable.

Collaboration and Stakeholder Management: Working closely with various teams within Airbus and external partners taught me the importance of effective communication and teamwork. Presenting my financial insights to senior executives also sharpened my ability to convey complex information in a clear, understandable way, ensuring everyone was aligned with our financial strategies.

This diverse set of skills allowed me to support high-value transactions, improve credit risk assessment processes, and contribute to strategic initiatives at Airbus.

What I learned

Key Learning Outcomes of this project :

Applying Financial Models to Real-World Scenarios: I gained hands-on experience using advanced financial models such as DCF, LBO, and credit rating models. This helped me make informed, evidence-based conclusions to assess credit risk and guide strategic decision-making.

Enhanced Risk Assessment Skills: I learned how to apply credit rating methodologies from major agencies like Moody’s, S&P, and Fitch. This allowed me to develop a deeper understanding of risk factors affecting both the aviation sector and individual companies, enhancing my ability to forecast risks with greater accuracy.

Collaboration and Stakeholder Engagement: Collaborating with cross-functional teams within Airbus, I developed strong communication skills, particularly in presenting complex financial insights to senior executives and aligning my work with broader corporate objectives.

Data-Driven Decision Making: I honed my ability to analyse large datasets, extract meaningful financial insights, and turn them into actionable recommendations. This process strengthened my strategic thinking and ability to contribute to critical business decisions.

Process Automation and Efficiency Improvement: Leading the automation of the internal credit rating system taught me how to streamline workflows and improve efficiency, significantly reducing the time spent on manual processes while enhancing data accuracy.

Concepts related my Apprenticeship

I explain below three business concepts related my apprenticeship: value chain, credit risk analysis, and financial ratios.

Value Chain

The commercial aviation sector comprises multiple interconnected players, each contributing to different stages of the value chain. The value chain begins with aircraft Original Equipment Manufacturers (OEMs) like Airbus and Boeing, which design and manufacture aircraft. These OEMs negotiate deal terms with airlines and lessors for the sale or lease of aircraft. The deals can range from firm orders, where aircraft are purchased outright, to leasing agreements, where airlines lease aircraft for operational flexibility.

In this value chain, airlines are the primary end users, operating the aircraft to transport passengers (commercial airplane) and freight (cargo airplane). Lessors act as intermediaries, purchasing aircraft from OEMs and leasing them to airlines, offering flexibility in fleet management. Additionally, Maintenance, Repair, and Overhaul (MRO) providers play a critical role in ensuring the safety and performance of aircraft throughout their lifecycle. Financial institutions and credit rating agencies are also integral players, assessing the creditworthiness of the companies involved and financing large-scale aircraft transactions.

The deal-signing process with OEMs often involves complex negotiations on pricing, delivery schedules, and terms of financing. Types of deals include sale agreements, wet or dry leases, and purchase options. The financial arrangements and credit risk evaluations play a pivotal role in securing these deals, ensuring that all parties can fulfil their obligations over the aircraft’s operational life.

Credit Risk Analysis

Credit risk analysis is the process of evaluating the likelihood that a borrower or counterparty will default on their financial obligations. In the context of my work at Airbus, credit risk analysis was crucial for understanding the financial health of customers—whether they were airlines, lessors, or MRO service providers. By analysing financial statements, liquidity ratios, and external market factors, we could gauge the risk of default and the overall creditworthiness of these companies.

Credit ratings, provided by the three major credit rating agencies—Moody’s, S&P, and Fitch, are a standardized way to assess a company’s financial health and default risk. These agencies evaluate the financial statements of companies, industry trends, and macroeconomic conditions to assign ratings that range from AAA (lowest risk) to D (in default). Credit ratings are essential for investors and lenders in determining the risk profile of potential investments and for companies like Airbus when structuring deals.

Airbus, like many large corporations, uses internal customer credit rating models alongside external credit ratings to gain deeper insights into the financial stability of its clients. These models allow Airbus to account for industry-specific factors and customer performance metrics that external agencies might overlook. Internal models are particularly valuable in predicting potential risks and making informed decisions about financing, delivery schedules, and long-term contracts, ensuring that Airbus minimizes exposure to credit risk.

Financial Ratios

Financial ratios (key performance indicators (KPIs) for the financial health of a firm) are vital in assessing the financial health of companies in the aviation sector. During my time at Airbus, I focused on analysing these KPIs to evaluate the financial stability and creditworthiness of our customers:

Liquidity Ratios: Indicators like the current ratio and quick ratio show a company’s ability to meet its short-term obligations. A higher ratio suggests stronger liquidity and a lower risk of financial distress.

Debt-to-Equity Ratio: This KPI measures the proportion of debt financing relative to equity. A lower debt-to-equity ratio typically indicates a more financially stable company, with less risk of default in turbulent market conditions.

Profitability Margins: Metrics like net profit margin and EBITDA margin give insights into how efficiently a company is operating. Higher profitability suggests a company can generate sufficient revenue to cover its expenses, even in challenging times.

Gearing Ratio: A company’s gearing ratio measures its financial leverage and how reliant it is on debt to finance its operations. A higher gearing ratio may indicate increased financial risk.

Altman Z-Score: This is a composite score used to predict bankruptcy risk, combining profitability, leverage, liquidity, solvency, and activity ratios. It’s particularly useful for assessing companies under financial stress, a key concern in the aviation sector post-COVID-19.

Cash Flow from Operations: A company’s ability to generate consistent cash flow from its core operations is a strong indicator of financial health. In the aviation sector, where cash flow can be cyclical, maintaining positive cash flow is critical for long-term sustainability.

The following table provides some of the important financial ratios used to estimate the risk of a company. High financial risk is implied by high or low measure according to the ratio.

Table 1. Financial ratios

Financial ratios

Source: The author.

Ratios are most useful when compared between companies in similar sectors and over time. Multiple measurements may be necessary for each given firm to fully comprehend the financial risk.

Why Should I Be Interested in This Post?

If you are passionate about the aviation sector, finance, and risk management, this role as a Customer Finance & Credit Risk Analyst at Airbus offers an exceptional opportunity to develop a deep understanding of the global aviation market while working on high-impact financial transactions. You’ll be at the forefront of evaluating the creditworthiness of major airlines, lessors, and other key players in the industry, gaining valuable insights into how financial health and risk factors influence large-scale deals.

This position also allows you to hone your skills in advanced financial modeling, risk assessment, and credit rating, using real-world data to drive decision-making on transactions worth millions of euros. The chance to work closely with cross-functional teams, present findings to senior executives, and contribute directly to Airbus’ business strategy ensures that you will grow both technically and professionally.

Additionally, the aviation industry is dynamic, with constant innovations in technology, sustainability initiatives, and global market trends. By working in this role, you’ll be part of a sector that plays a pivotal role in global transportation and trade, offering immense potential for career growth and advancement.

Useful resources

Airbus

Allianz Trade Financial Risk

Deloitte Financial Risk

About the author

The article was written in October 2024 by Snehasish CHINARA (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2025).

Litecoin: Analysis of the Pioneering Cryptocurrency’s Impact on Digital Finance

January 5, 2025July 31, 2024 by Snehasish CHINARA

In this article, Snehasish CHINARA (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2024) explains cryptocurrency Litecoin, shedding light on its impact and value propositions for digital finance.

Historical context and background

Litecoin, often referred to as the “silver to Bitcoin’s gold,” emerged in 2011 as one of the earliest altcoins, or alternative cryptocurrencies, following the launch of Bitcoin in 2009. It was created by Charlie Lee, a former Google engineer and Coinbase employee. Lee designed Litecoin with the intention of addressing some of the perceived limitations of Bitcoin, such as transaction speed and scalability.

One of the key innovations of Litecoin was its adoption of the Scrypt hashing algorithm instead of Bitcoin’s SHA-256 algorithm. This choice made Litecoin more accessible to individual miners using consumer-grade hardware, as it reduced the advantage of specialized mining equipment known as ASICs (Application-Specific Integrated Circuits). As a result, Litecoin initially gained popularity among miners and enthusiasts who sought a more democratic and decentralized mining ecosystem.

Litecoin also introduced a faster block generation time compared to Bitcoin, with new blocks being created approximately every 2.5 minutes instead of every 10 minutes. This faster block time enabled quicker transaction confirmations, making Litecoin more suitable for everyday transactions.

Over the years, Litecoin has established itself as one of the most prominent cryptocurrencies in the market, often considered a reliable and stable digital asset. Its longevity and consistent development have contributed to its reputation as a credible alternative to Bitcoin.

Litecoin’s journey has been marked by various milestones, including network upgrades, partnerships, and integrations into payment systems and exchanges. Despite facing competition from other cryptocurrencies and undergoing market fluctuations, Litecoin has maintained a strong community of supporters and continues to be actively traded and utilized for various purposes, including payments, remittances, and investment.

Litecoin Logo

Source: Litecoin

Figure 1. Key Dates in Litecoin History

Source: Yahoo! Finance.

Key features

Scrypt Algorithm

Unlike Bitcoin’s SHA-256 algorithm, Litecoin utilizes the Scrypt hashing algorithm for its proof-of-work consensus mechanism. This algorithm was chosen to enable faster block generation and to promote more decentralized mining by reducing the advantage of specialized mining hardware (Application-Specific Integrated Circuits (ASIC) versus consumer-grade hardware like standard PCs).

Faster Block Time

Litecoin has a target block time of approximately 2.5 minutes, compared to Bitcoin’s 10 minutes. This faster block time allows for quicker transaction confirmations, making Litecoin more suitable for everyday transactions.

Higher Maximum Coin Supply

Litecoin has a maximum coin supply limit of 84 million coins, four times the maximum supply of Bitcoin. This larger supply aims to facilitate more widespread adoption and usage while still maintaining scarcity.

Segregated Witness (SegWit) Activation

Litecoin was one of the first major cryptocurrencies to activate Segregated Witness (SegWit), a protocol upgrade aimed at improving transaction throughput and scalability. SegWit also paved the way for the implementation of the Lightning Network on Litecoin, enabling off-chain transactions for faster and cheaper payments.

Atomic Swaps

Litecoin has been at the forefront of implementing Atomic Swaps, a technology that allows for the trustless exchange of cryptocurrencies across different blockchains without the need for intermediaries like crypto platforms. This feature enhances interoperability and decentralization within the cryptocurrency ecosystem.

Litecoin Improvement Proposals (LIPs)

Similar to Bitcoin Improvement Proposals (BIPs), Litecoin has its own proposal system called Litecoin Improvement Proposals (LIPs). These proposals allow community members to suggest changes or improvements to the Litecoin protocol, fostering a transparent and collaborative development process.

Use cases

Peer-to-Peer Payments

Litecoin’s fast transaction confirmations and low fees make it suitable for peer-to-peer transactions. Users can quickly send and receive funds across the globe without relying on traditional banking systems, making Litecoin an efficient option for remittances and international payments.

Online Purchases

As of 2023, over 2,000 online merchants and service providers accept Litecoin as a form of payment globally. Litecoin’s transaction volume has steadily increased, with an average of over 100,000 transactions per day. In 2022, Litecoin processed over 35 million transactions, highlighting its growing use for everyday payments. Many online merchants and service providers accept Litecoin as a form of payment. There are over 5,000 cryptocurrency ATMs worldwide that support Litecoin, allowing users to buy and sell LTC with cash. Users can use Litecoin to purchase a wide range of goods and services, including electronics, clothing, digital products, and more.

Micropayments

Litecoin’s low transaction fees and fast processing times make it well-suited for micropayments, enabling users to make small transactions economically. This use case is particularly relevant for content creators, online tipping, and pay-per-view services.

Cross-Border Transactions

Litecoin’s borderless nature makes it an attractive option for cross-border transactions, as users can avoid the high fees and long processing times associated with traditional remittance services and bank transfers.

Privacy Transactions

While not as focused on privacy as some other cryptocurrencies like Monero or Zcash, Litecoin offers a degree of privacy through features like confidential transactions and the option to use privacy-enhancing wallets. This makes Litecoin appealing for users who prioritize privacy in their transactions.

Technology and underlying blockchain

Litecoin operates on a blockchain-based decentralized network, sharing many similarities with Bitcoin while incorporating several key technical differences. At its core, Litecoin’s blockchain serves as a distributed ledger that records all transactions made with its native cryptocurrency, LTC. One of the distinguishing features of Litecoin is its utilization of the Scrypt proof-of-work algorithm, which differs from Bitcoin’s SHA-256 algorithm. This algorithm was chosen to promote a more equitable mining process, allowing individuals to mine LTC using consumer-grade hardware and reducing the dominance of specialized mining equipment.

The Litecoin blockchain maintains a target block time of approximately 2.5 minutes, significantly faster than Bitcoin’s 10-minute block time. This faster block generation rate enables quicker transaction confirmations, making Litecoin well-suited for use in everyday transactions and enhancing its scalability. Additionally, Litecoin implemented Segregated Witness (SegWit) in 2017, a protocol upgrade aimed at improving transaction throughput and reducing network congestion by separating transaction signatures from transaction data.

Furthermore, Litecoin has experimented with technologies like the Lightning Network, a layer-2 scaling solution designed to facilitate instant and low-cost transactions by leveraging payment channels. This technology enables off-chain transactions that can be settled on the Litecoin blockchain, further enhancing its transaction speed and efficiency.

Supply of coins

Figure Figure 2. Litecoin Supply

Source: Yahoo! Finance.

Litecoin’s coin supply is governed by its protocol, which dictates the issuance rate and maximum supply limit. Unlike traditional fiat currencies that are subject to centralized control by governments and central banks, Litecoin operates on a decentralized network secured by blockchain technology. The issuance of new Litecoin coins occurs through a process called mining, where miners use computational power to validate transactions and add new blocks to the blockchain.

Litecoin employs a deflationary monetary policy, with a fixed issuance schedule that halves the block reward approximately every four years. Initially, the block reward was set at 50 Litecoins per block, but it reduces by half every 840,000 blocks. This process, known as “halving,” aims to curb inflation over time and maintain scarcity, similar to Bitcoin’s issuance schedule. As of now, the block reward stands at 12.5 Litecoins per block, and this rate will continue to halve periodically until the maximum supply of 84 million Litecoins is reached.

The predictable issuance schedule and maximum supply cap of Litecoin contribute to its scarcity and value proposition, aligning with principles of sound money and monetary decentralization. This transparent and algorithmic approach to coin issuance fosters confidence among users and investors, as it prevents arbitrary inflation and ensures the integrity of Litecoin’s monetary policy over the long term.

Historical data for Litecoin

How to get the data?

The Litecoin is popular cryptocurrency on the market, and historical data for the Litecoin such as prices and volume traded can be easily downloaded from the internet sources such as Yahoo! Finance, Blockchain.com & CoinMarketCap. For example, you can download data for Litecoin on Yahoo! Finance (the Yahoo! code for Litecoin is LTC-USD).

Figure Figure 3. Litecoin data

Source: Yahoo! Finance.

Historical data for the Litecoin market prices

Since its inception in 2011, Litecoin has undergone multiple bull and bear cycles, with its price witnessing remarkable volatility. In its early years, Litecoin’s market price remained relatively low, often trading at a fraction of Bitcoin’s value. However, as the cryptocurrency market gained traction and Litecoin’s utility as a fast and affordable payment method became recognized, its price began to appreciate steadily. The price of Litecoin experienced its first major surge in late 2013, reaching an all-time high above $50 USD. This rally was fueled by increased adoption, media attention, and speculation within the cryptocurrency community.

Following the 2013 peak, Litecoin underwent a prolonged bear market, with its price declining significantly over the subsequent years. However, Litecoin’s resilience and active development continued to attract interest, leading to periodic price rallies and subsequent corrections. The cryptocurrency market’s overall volatility, regulatory uncertainty, and competition from other digital assets also influenced Litecoin’s price movements during this period.

One of the most significant price rallies in Litecoin’s history occurred during the cryptocurrency bull market of 2017-2018. During this period, Litecoin’s price surged to unprecedented levels, reaching an all-time high of over $300 USD in December 2017. This rally was fueled by factors such as increased mainstream adoption, the integration of Segregated Witness (SegWit) and the Lightning Network, and speculative buying spurred by the broader cryptocurrency market rally.

Since the 2017-2018 bull market, Litecoin has experienced periods of both consolidation and volatility. While its price has not reached the same highs as during the peak of the bull market, Litecoin has maintained a relatively stable position within the cryptocurrency market, often regarded as one of the top digital assets by market capitalization. The ongoing development of Litecoin’s protocol, partnerships, and adoption efforts continue to shape its market prices, as investors and enthusiasts closely monitor its evolution in the broader cryptocurrency landscape.

Figure 4 below represents the evolution of the price of Litecoin in US dollar over the period September 2014 – May 2024. The price corresponds to the “closing” price (observed at 10:00 PM CET at the end of the month).

Figure 4. Evolution of Litecoin price

Source: Yahoo! Finance.

R program

The R program below written by Shengyu ZHENG allows you to download the data from Yahoo! Finance website and to compute summary statistics and risk measures about the Litecoin.

Data file

The R program that you can download above allows you to download the data for the Litecoin from the Yahoo! Finance website. The database starts on September 2014.

Table 1 below represents the top of the data file for the Litecoin downloaded from the Yahoo! Finance website with the R program.

Table 1. Top of the data file for the Litecoin

Source: computation by the author (data: Yahoo! Finance website).

Python code

You can download the Python code used to download the data from Yahoo! Finance.

Python script to download Litecoin historical data and save it to an Excel sheet:

import yfinance as yf

import pandas as pd

# Define the ticker symbol for Cardano “ADA-USD”

Litecoin_ticker = “LTC-USD”

# Define the date range for historical data

start_date = “2014-09-01”

end_date = “2024-04-30”

# Download historical data using yfinance

CLitecoin_data = yf.download(Litecoin_ticker, start=start_date, end=end_date)

# Create a Pandas DataFrame from the downloaded data

Litecoin_df = pd.DataFrame(Litecoin_data)

# Define the Excel file path

excel_file_path = ” Litecoin_historical_data.xlsx”

# Save the data to an Excel sheet

Litecoin_df.to_excel(excel_file_path, sheet_name=”Litecoin Historical Data”)

print(f”Data saved to {excel_file_path}”)

# Make sure you have the required libraries installed and adjust the “start_date” and “end_date” variables to the desired date range for the historical data you want to download.

Evolution of the Litecoin

Figure 5 below gives the evolution of the Litecoin on a daily basis.

Figure 5. Evolution of the Litecoin

Source: computation by the author (data: Yahoo! Finance website).

Figure 6 below gives the evolution of the Litecoin returns from September 2014 to May 2024 on a daily basis.

Figure 6. Evolution of the Litecoin returns.

Source: computation by the author (data: Yahoo! Finance website).

The R program that you can download above also allows you to compute summary statistics about the returns of the Litecoin.

Table 2 below presents the following summary statistics estimated for the Litecoin:

The mean

The standard deviation (the squared root of the variance)

The skewness

The kurtosis.

The mean, the standard deviation / variance, the skewness, and the kurtosis refer to the first, second, third and fourth moments of statistical distribution of returns respectively.

Table 2. Summary statistics for Litecoin

Source: computation by the author (data: Yahoo! Finance website).

Statistical distribution of the Litecoin returns

Historical distribution

Figure 7 represents the historical distribution of the Litecoin daily returns for the period from September 2014 to May 2024.

Figure 7. Historical distribution of Litecoin returns.

Source: computation by the author (data: Yahoo! Finance website).

Gaussian distribution

Figure 9 below represents the Gaussian distribution of the Litecoin daily returns with parameters estimated over the period from September 2014 to May 2024.

Figure 8. Gaussian distribution of the Litecoin returns.

Source: computation by the author (data: Yahoo! Finance website).

Risk measures of the Litecoin returns

The R program that you can download above also allows you to compute risk measures about the returns of the Litecoin.

Table 3 below presents the following risk measures estimated for the Litecoin:

The long-term volatility (the unconditional standard deviation estimated over the entire period)

The short-term volatility (the standard deviation estimated over the last three months)

The Value at Risk (VaR) for the left tail (the 5% quantile of the historical distribution)

The Value at Risk (VaR) for the right tail (the 95% quantile of the historical distribution)

The Expected Shortfall (ES) for the left tail (the average loss over the 5% quantile of the historical distribution)

The Expected Shortfall (ES) for the right tail (the average loss over the 95% quantile of the historical distribution)

The Stress Value (SV) for the left tail (the 1% quantile of the tail distribution estimated with a Generalized Pareto distribution)

The Stress Value (SV) for the right tail (the 99% quantile of the tail distribution estimated with a Generalized Pareto distribution)

Table 3. Risk measures for the Litecoin

Source: computation by the author (data: Yahoo! Finance website).

The volatility is a global measure of risk as it considers all the returns. The Value at Risk (VaR), Expected Shortfall (ES) and Stress Value (SV) are local measures of risk as they focus on the tails of the distribution. The study of the left tail is relevant for an investor holding a long position in the Litecoin while the study of the right tail is relevant for an investor holding a short position in the Litecoin.

Why should I be interested in this post?

This blog offers an intriguing journey into the world of Litecoin, tailored for both newcomers and seasoned cryptocurrency enthusiasts alike. It unveils the innovative features and unique characteristics that set Litecoin apart in the digital currency landscape. From its inception to its current standing, we explore Litecoin’s historical journey, shedding light on its pivotal moments and market dynamics. Whether you’re intrigued by its faster block time or its active development community, this post provides a comprehensive understanding of Litecoin’s significance and potential. Whether you’re a curious observer or an investor seeking new opportunities, join us as we delve into the fascinating world of Litecoin and uncover its role in shaping the future of decentralized finance.

Useful resources

Academic research about risk

Longin F. (2000) From VaR to stress testing: the extreme value approach Journal of Banking and Finance, N°24, pp 1097-1130.

Longin F. (2016) Extreme events in finance: a handbook of extreme value theory and its applications Wiley Editions.

Data

Yahoo! Finance Historical data for Litecoin

CoinMarketCap Historical data for Litecoin

About the author

The article was written in July 2024 by Snehasish CHINARA (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2024).

Tron: Unveiling the Future of Decentralized Applications

May 29, 2024 by Snehasish CHINARA

In this article, Snehasish CHINARA (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2024) explains the innovative world of Tron cryptocurrency, shedding light on its role in the evolution of decentralized applications and the broader blockchain ecosystem.

Historical context and background

Tron (TRX) was founded by Justin Sun in 2017, aiming to revolutionize the entertainment industry and decentralize the internet. The project’s initial coin offering (ICO) raised approximately $70 million, showcasing significant investor interest. Tron was designed as a decentralized platform to support smart contracts and high-throughput applications, providing an infrastructure that could handle a large volume of transactions efficiently.

Initially, Tron was launched as an ERC-20 token on the Ethereum blockchain. This allowed Tron to leverage Ethereum’s robust development community and infrastructure while developing its own blockchain. On June 25, 2018, Tron completed its mainnet launch, migrating from the Ethereum network to its proprietary blockchain, known as Odyssey. This transition marked Tron’s independence and its capability to support decentralized applications (dApps) natively.

The Tron ecosystem has expanded rapidly, integrating various blockchain-based projects and applications. In July 2018, Tron acquired BitTorrent, a popular peer-to-peer file-sharing protocol, further expanding its reach and application base. This acquisition was significant as it integrated BitTorrent’s extensive user base with Tron’s blockchain technology, exemplifying Tron’s vision of a decentralized internet.

Moreover, Tron’s commitment to fostering a decentralized web extends to its support for decentralized finance (DeFi). By providing a robust platform for creating and deploying decentralized applications, Tron has positioned itself as a key player in the DeFi space, enabling applications such as JustSwap, a decentralized exchange, and various other lending and borrowing platforms.

Tron Logo

Source: Tron.

Figure 1. Key Dates in Tron History

Source: Yahoo! Finance.

Key features

Delegated Proof of Stake (DPoS) Consensus Mechanism

Tron’s blockchain utilizes the DPoS consensus algorithm, which enhances scalability and transaction speed. In this system, TRX holders vote for super representatives (SRs) who are responsible for validating transactions and securing the network. This method is more energy-efficient and faster compared to traditional Proof of Work (PoW) systems.

High Throughput and Scalability

Tron’s network is capable of processing up to 2,000 transactions per second (TPS), significantly higher than Bitcoin and Ethereum. This high throughput makes Tron suitable for a wide range of decentralized applications (dApps) that require rapid and efficient transaction processing.

Low Transaction Fees

Transaction fees on the Tron network are minimal, making it an attractive platform for developers and users alike. This cost-efficiency is crucial for microtransactions and frequent trading activities, which are common in decentralized finance (DeFi) applications.

Smart Contracts and dApps

Tron supports the development and deployment of smart contracts and decentralized applications. The Tron Virtual Machine (TVM) is fully compatible with the Ethereum Virtual Machine (EVM), enabling developers to easily port their Ethereum-based dApps to Tron. This compatibility fosters a rich ecosystem of decentralized applications across various sectors.

TRC-20 and TRC-721 Tokens

Tron offers its own standards for tokens: TRC-20 for fungible tokens and TRC-721 for non-fungible tokens (NFTs). These standards are similar to Ethereum’s ERC-20 and ERC-721, respectively, allowing developers to create and manage a wide range of digital assets on the Tron network.

Decentralized Storage

Tron aims to decentralize the internet, and part of this vision includes decentralized storage solutions. By integrating BitTorrent technology, Tron provides a decentralized file sharing and storage system, enhancing data privacy and security.

Rich Developer Resources

Tron offers extensive resources for developers, including comprehensive documentation, development tools, and support programs. This robust support infrastructure encourages innovation and development within the Tron ecosystem.

Use cases

Decentralized Apps (dApps)

Tron is designed to support dApps across multiple sectors. These applications benefit from Tron’s high throughput and low transaction fees, making them more efficient and cost-effective. Popular dApps on Tron include games, social media platforms, and financial services.

Decentralized Finance (DeFi)

Tron has become a significant player in the DeFi space, providing infrastructure for decentralized exchanges (DEXs), lending platforms, and stablecoins. JustSwap, Tron’s decentralized exchange, allows users to trade TRC-20 tokens without intermediaries. Additionally, platforms like JUST provide decentralized lending and borrowing services.

Digital Content and Entertainment

Tron’s primary mission is to decentralize the internet, particularly the digital content and entertainment industry. Platforms like TRON TV and BitTorrent integrate with Tron to offer decentralized content distribution, allowing content creators to share their work directly with consumers without intermediaries, ensuring fairer revenue distribution.

Supply Chain Management

Tron can be used to enhance transparency and traceability in supply chain management. By recording transactions and product movements on the blockchain, companies can ensure authenticity and reduce fraud. This use case is particularly relevant for industries like pharmaceuticals, food, and luxury goods.

Gaming

Tron’s blockchain supports various blockchain-based games that leverage the benefits of decentralized infrastructure. These games offer players true ownership of in-game assets, transparent reward systems, and the ability to trade items outside the game environment. Titles like WINk and others on the Tron platform showcase the potential for blockchain in gaming.

Voting and Governance

Tron’s DPoS consensus mechanism is an example of blockchain-based voting and governance in action. The Tron community votes for super representatives who validate transactions and make governance decisions. This model can be applied to other voting systems, including corporate governance, municipal voting, and organizational decision-making processes.

Data Storage and Sharing

Integrating BitTorrent with Tron provides a decentralized solution for data storage and sharing. This system allows users to store and share files securely without relying on centralized servers, enhancing privacy and reducing the risk of data breaches.

Technology and underlying blockchain

The Tron blockchain is a high-performance, decentralized platform designed to support a vast array of decentralized applications (dApps) and smart contracts. At its core, Tron utilizes a Delegated Proof of Stake (DPoS) consensus mechanism, which enhances transaction throughput and network scalability. DPoS involves TRX holders voting for super representatives (SRs) who are responsible for validating transactions and maintaining the blockchain, ensuring efficient and democratic governance. Tron’s architecture includes the Tron Virtual Machine (TVM), which is fully compatible with the Ethereum Virtual Machine (EVM). This compatibility enables seamless migration of dApps from Ethereum to Tron, fostering cross-chain interoperability. Tron’s blockchain can process up to 2,000 transactions per second (TPS), significantly outpacing many other blockchain networks, making it suitable for applications requiring high transaction volumes. Additionally, Tron supports the creation of custom tokens through its TRC-20 and TRC-721 standards, analogous to Ethereum’s ERC-20 and ERC-721, facilitating the development of fungible tokens and non-fungible tokens (NFTs). This robust infrastructure, combined with low transaction fees, makes Tron an attractive platform for developers and enterprises looking to build decentralized solutions.

Supply of coins

Tron (TRX) operates with a total maximum supply of 100 billion tokens. At its inception, Tron’s token allocation included 40% for initial token sale, 15% for the Tron Foundation, 35% for private placements, and 10% for the team. Over time, Tron’s supply distribution has undergone changes due to various factors such as token burns, airdrops, and network upgrades. Notably, Tron has implemented periodic token burns to reduce the circulating supply and increase scarcity, thereby potentially driving up the value of TRX. Additionally, the Tron Foundation periodically releases tokens from its allocated supply for ecosystem development, partnerships, and other strategic initiatives aimed at fostering the growth of the Tron network. Overall, Tron’s coin supply dynamics are managed with a focus on maintaining balance between liquidity, scarcity, and ecosystem development to support the long-term sustainability and adoption of the Tron blockchain.

Historical data for TRX (Tron)

How to get the data?

Tron is a popular cryptocurrency on the market, and historical data for the Tron such as prices and volume traded can be easily downloaded from the internet sources such as Yahoo! Finance, Blockchain.com & CoinMarketCap. For example, you can download data for Tron on Yahoo! Finance (the Yahoo! code for Tron is TRX-USD).How to get the data?

Figure 2. Tron data

Source: Yahoo! Finance.

Historical data for the TRX (Tron) market prices

TRX (Tron) has experienced a dynamic evolution in its market prices since its inception in 2017. Initially introduced at a fraction of a cent, TRX quickly gained attention within the cryptocurrency community due to its ambitious vision and promising technology. The early months saw considerable volatility, with TRX experiencing rapid price fluctuations amid speculation and market sentiment. As Tron’s ecosystem matured and adoption grew, its market prices reflected both the broader trends in the cryptocurrency market and Tron-specific developments. Notable milestones, such as mainnet launches, strategic partnerships, and ecosystem expansions, often correlated with significant price movements. Despite periods of volatility, Tron’s market prices have shown resilience and a tendency to recover from downturns, reflecting investor confidence in its long-term potential. Over time, TRX has established itself as one of the prominent cryptocurrencies, with a growing user base and a diverse range of use cases. As the cryptocurrency market continues to evolve, Tron’s market prices are likely to reflect both internal and external factors, shaping its trajectory within the broader blockchain landscape.

Figure 3 below represents the evolution of the price of Tron (TRX) in US dollar over the period November 2017 – May 2024 . The price corresponds to the “closing” price (observed at 10:00 PM CET at the end of the month).

Figure 3. Evolution of Tron price

Source: Yahoo! Finance.

R program

The R program below written by Shengyu ZHENG allows you to download the data from Yahoo! Finance website and to compute summary statistics and risk measures about the Tron (TRX).

Data file

The R program that you can download above allows you to download the data for Tron from the Yahoo! Finance website. The database starts on November 2017.

Table 1 below represents the top of the data file for the Tron (TRX) downloaded from the Yahoo! Finance website with the R program.

Table 1. Top of the data file for Tron (TRX)

Source: computation by the author (data: Yahoo! Finance website).

Python code

You can download the Python code used to download the data from Yahoo! Finance.

Python script to download Tron (TRX) historical data and save it to an Excel sheet:

import yfinance as yf

import pandas as pd

# Define the ticker symbol for Iron

Tron_ticker = “TRX -USD”

# Define the date range for historical data

start_date = “2017-05-30”

end_date = “2024-04-30”

# Download historical data using yfinance

Tron_data = yf.download(Tron_ticker, start=start_date, end=end_date)

# Create a Pandas DataFrame from the downloaded data

Tron_df = pd.DataFrame(Tron_data)

# Define the Excel file path

excel_file_path = “Tron_historical_data.xlsx”

# Save the data to an Excel sheet

Tron_df.to_excel(excel_file_path, sheet_name=”Tron Historical Data”)

print(f”Data saved to {excel_file_path}”)

# Make sure you have the required libraries installed and adjust the “start_date” and “end_date” variables to the desired date range for the historical data you want to download.

Evolution of the TRX (Tron)

Figure 4 below gives the evolution of the TRX (Tron) on a daily basis.

Figure 4. Evolution of the TRX (Tron)

Source: computation by the author (data: Yahoo! Finance website).

Figure 5 below gives the evolution of the TRX (Tron) returns from May 2017 to May 2024 on a daily basis.

Figure 5. Evolution of the TRX (Tron) returns.

Source: computation by the author (data: Yahoo! Finance website).

Summary statistics for the TRX (Tron)

The R program that you can download above also allows you to compute summary statistics about the returns of the TRX (Tron).

Table 2 below presents the following summary statistics estimated for TRX (Tron):

The mean

The standard deviation (the squared root of the variance)

The skewness

The kurtosis.

The mean, the standard deviation / variance, the skewness, and the kurtosis refer to the first, second, third and fourth moments of statistical distribution of returns respectively.

Table 2. Summary statistics for TRX (Tron)

Source: computation by the author (data: Yahoo! Finance website).

Statistical distribution of the TRX (Tron) returns

Historical distribution

Figure 6 represents the historical distribution of the TRX (Tron) daily returns for the period from May 2017 to May 2024.

Figure 6. Historical distribution of TRX (Tron) returns.

Source: computation by the author (data: Yahoo! Finance website).

Gaussian distribution

Figure 7 below represents the Gaussian distribution of the TRX (Tron) daily returns with parameters estimated over the period from May 2017 to May 2024.

Figure 7. Gaussian distribution of the TRX (Tron) returns.

Source: computation by the author (data: Yahoo! Finance website).

Risk measures of the TRX (Tron) returns

The R program that you can download above also allows you to compute risk measures about the returns of the TRX (Tron).

Table 3 below presents the following risk measures estimated for the TRX (Tron):

The long-term volatility (the unconditional standard deviation estimated over the entire period)

The short-term volatility (the standard deviation estimated over the last three months)

The Value at Risk (VaR) for the left tail (the 5% quantile of the historical distribution)

The Value at Risk (VaR) for the right tail (the 95% quantile of the historical distribution)

The Expected Shortfall (ES) for the left tail (the average loss over the 5% quantile of the historical distribution)

The Expected Shortfall (ES) for the right tail (the average loss over the 95% quantile of the historical distribution)

The Stress Value (SV) for the left tail (the 1% quantile of the tail distribution estimated with a Generalized Pareto distribution)

The Stress Value (SV) for the right tail (the 99% quantile of the tail distribution estimated with a Generalized Pareto distribution)

Table 3. Risk measures for the TRX (Tron)

Source: computation by the author (data: Yahoo! Finance website).

The volatility is a global measure of risk as it considers all the returns. The Value at Risk (VaR), Expected Shortfall (ES) and Stress Value (SV) are local measures of risk as they focus on the tails of the distribution. The study of the left tail is relevant for an investor holding a long position in the TRX (Tron) while the study of the right tail is relevant for an investor holding a short position in the TRX (Tron).

Why should I be interested in this post?

This post provides an engaging exploration of Tron cryptocurrencies, tailored to both newcomers and seasoned crypto enthusiasts alike. It delves into Tron’s innovative blockchain technology and its transformative impact across diverse industries, from entertainment to decentralized finance. By delving into Tron’s underlying technology, consensus mechanism, and ecosystem development, readers can grasp the potential of Tron to revolutionize decentralized applications, scalability, and cross-chain interoperability. Furthermore, the post examines Tron’s historical price trends, market dynamics, and community governance structure, offering valuable insights for investors, traders, and ecosystem participants. Whether you’re curious about cutting-edge blockchain solutions or searching for investment opportunities in the crypto market, this post provides comprehensive insights into the significance and potential of Tron in shaping the future of decentralized technologies.

Useful resources

Academic research about risk

Longin F. (2000) From VaR to stress testing: the extreme value approach Journal of Banking and Finance, N°24, pp 1097-1130.

Longin F. (2016) Extreme events in finance: a handbook of extreme value theory and its applications Wiley Editions.

Data

Yahoo! Finance Historical data for Tron

CoinMarketCap Historical data for Tron

About the author

The article was written in May 2024 by Snehasish CHINARA (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2024).

BNB’s Journey through the Digital Economy’s Cryptocurrency Landscape

May 15, 2024May 15, 2024 by Snehasish CHINARA

In this article, Snehasish CHINARA (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2024) explains the intricate workings and transformative potential of Binance Coin (BNB), exploring its origins, utility, market dynamics, and future prospects of this cornerstone of the digital economy.

Historical context and background

Binance Coin (BNB) is one of the most prominent cryptocurrencies in the market, known for its unique utility and its connection to the world’s largest cryptocurrency exchange, Binance. Binance, founded by Changpeng Zhao (CZ) in 2017, quickly rose to prominence as one of the leading cryptocurrency exchanges globally. Its rapid growth can be attributed to several factors, including its user-friendly interface, wide range of supported cryptocurrencies, and a robust security system. Binance gained significant traction during the bull run of 2017, becoming the go-to platform for traders and investors seeking to capitalize on the burgeoning cryptocurrency market.

In July 2017, Binance conducted an Initial Coin Offering (ICO) to raise funds for the development of its platform and ecosystem. During the ICO, the exchange issued its native cryptocurrency, Binance Coin (BNB), which operates on the Ethereum blockchain as an ERC-20 token. The ICO was a massive success, raising $15 million within just a few weeks.

BNB was initially designed as a utility token to facilitate transactions on the Binance platform, offering users various benefits such as discounted trading fees and participation in token sales hosted on the exchange. Binance committed to using a portion of its profits to buy back and burn BNB tokens periodically, reducing the overall supply over time and potentially increasing its value.

As Binance continued to expand its services and offerings, the utility of BNB expanded beyond just a means of reducing trading fees. BNB became an integral part of the Binance ecosystem, serving as the native currency for various applications and services, including Binance Launchpad, Binance Smart Chain, Binance DEX (decentralized exchange), and Binance NFT marketplace, among others.

The introduction of Binance Smart Chain (BSC) in 2020 further elevated the importance of BNB in the cryptocurrency space. BSC, a blockchain platform compatible with Ethereum Virtual Machine (EVM), was developed to provide an alternative to Ethereum for decentralized finance (DeFi) applications and decentralized applications (DApps). BNB serves as the native currency of BSC, powering transactions and fueling the ecosystem’s growth.

BNB Logo

Source: Binance.

Figure 1. Key Dates in BNB History

Source: Yahoo! Finance.

Key features

Utility Token

BNB was initially designed as a utility token to facilitate transactions on the Binance platform. Users can utilize BNB to pay for trading fees, exchange fees, and various other services offered by Binance.

Discounted Fees

One of the primary benefits of holding BNB on the Binance exchange is the opportunity to receive discounts on trading fees. Binance offers users who pay their trading fees with BNB a significant discount, incentivizing its use within the platform.

Token Burn

Binance commits to using a portion of its profits to buy back and burn BNB tokens regularly. This token burning mechanism reduces the total supply of BNB over time, potentially increasing its scarcity and value.

Multiple Use Cases

BNB has expanded beyond its original utility and is now used for various purposes within the Binance ecosystem. It serves as the native currency for token sales on Binance Launchpad, transaction fees on Binance DEX, and participation in Binance NFT marketplace, among other applications.

Binance Smart Chain (BSC)

BNB is the native cryptocurrency of Binance Smart Chain, a blockchain platform compatible with Ethereum Virtual Machine (EVM). BSC offers low transaction fees and high throughput, making it an attractive option for decentralized applications (DApps) and decentralized finance (DeFi) projects.

Governance

BNB holders have the opportunity to participate in the governance of the Binance ecosystem. They can vote on proposals and decisions related to the development and direction of Binance, providing a degree of decentralization and community involvement.

Staking and Yield Farming

BNB holders can participate in staking and yield farming programs, allowing them to earn rewards or yield by locking up their BNB tokens and contributing to the security and operation of the network.

Use cases

Participation in Token Sales

BNB holders often have exclusive access to token sales hosted on the Binance Launchpad platform. These sales allow users to invest in promising blockchain projects at an early stage, with BNB serving as the primary currency for participation.

Payment Method

BNB can be used as a payment method for various goods and services, both online and offline. Several merchants and businesses accept BNB as a form of payment, enabling users to utilize their cryptocurrency holdings for everyday transactions.

Binance Ecosystem Services

BNB is utilized within various services and applications offered by the Binance ecosystem. This includes decentralized finance (DeFi) protocols, decentralized exchanges (DEXs), non-fungible token (NFT) marketplaces, and more. BNB serves as the native currency for transactions, governance, and incentives within these platforms.

Cross-Chain Compatibility

With the introduction of Binance Smart Chain (BSC), BNB gained cross-chain compatibility, enabling it to be used in decentralized applications (DApps) and DeFi protocols across different blockchain networks. This expansion of utility enhances the interoperability and flexibility of BNB as a cryptocurrency.

Technology and underlying blockchain

Binance Coin (BNB) operates on two primary blockchain networks: the Ethereum blockchain and the Binance Smart Chain (BSC). Initially launched as an ERC-20 token on the Ethereum blockchain, BNB has since undergone a significant expansion with the introduction of the Binance Smart Chain. The Ethereum-based BNB tokens are utilized for various functions within the Binance ecosystem, including trading fee discounts, participation in token sales, and payments. However, to address scalability and transaction cost concerns, Binance developed the Binance Smart Chain, a parallel blockchain compatible with Ethereum Virtual Machine (EVM). BSC combines high performance with low transaction fees, offering a viable alternative for decentralized applications (DApps) and decentralized finance (DeFi) protocols. BNB serves as the native currency of BSC, facilitating transactions, powering smart contracts, and providing liquidity across the Binance Smart Chain ecosystem. BSC employs a proof-of-stake (PoS) consensus mechanism, where validators stake BNB to secure the network and validate transactions. This hybrid approach leverages the benefits of both decentralization and efficiency, making BNB and the Binance Smart Chain integral components of the rapidly evolving blockchain landscape.

Supply of coins

The total supply of Binance Coin (BNB) is capped at 200 million tokens. However, BNB’s initial distribution was structured to release a portion of this supply gradually over time. During its initial coin offering (ICO) in July 2017, Binance allocated 50% of the total token supply to the public sale, representing 100 million BNB tokens. The remaining 50% was split among the Binance team, early investors, and strategic partners, with a significant portion earmarked for ecosystem development and marketing efforts. Notably, Binance committed to periodic token burns, where a portion of BNB tokens used to pay for trading fees on the Binance exchange is systematically removed from circulation and permanently destroyed. These token burns serve to reduce the overall supply of BNB over time, potentially increasing its scarcity and value. As of [current date], multiple token burns have occurred, steadily decreasing the circulating supply of BNB and contributing to its deflationary nature. Additionally, BNB’s migration from the Ethereum blockchain to its own blockchain, Binance Chain, introduced a new mechanism for token issuance and governance, further shaping the supply dynamics of BNB within the Binance ecosystem.

Historical data for Binance Coin (BNB)

How to get the data?

The Binance Coin (BNB) is popular cryptocurrency on the market, and historical data for the Binance Coin (BNB) such as prices and volume traded can be easily downloaded from the internet sources such as Yahoo! Finance, Blockchain.com & CoinMarketCap. For example, you can download data for Binance Coin (BNB) on Yahoo! Finance (the Yahoo! code for Binance Coin is BNB-USD).

Figure 2. Binance Coin (BNB) data

Source: Yahoo! Finance.

Historical data for Binance Coin (BNB) market prices

Binance Coin (BNB) has experienced significant volatility and price fluctuations since its inception in 2017. Initially launched at a price of around $0.10 during its ICO, BNB quickly gained traction and surged to an all-time high of over $40 in January 2018, fueled by the widespread adoption of the Binance exchange and the overall bullish sentiment in the cryptocurrency market at the time. However, like many other cryptocurrencies, BNB subsequently underwent a prolonged bear market, with its price plummeting to single-digit levels by the end of 2018. The following years saw periods of both growth and consolidation for BNB, with its price closely tied to developments within the Binance ecosystem, regulatory developments, and broader market trends. Notable milestones, such as the launch of Binance Smart Chain (BSC) and the subsequent rise of decentralized finance (DeFi) applications, have often coincided with bullish movements in BNB’s price. As of the latest data, BNB has demonstrated resilience and continued to maintain its position among the top cryptocurrencies by market capitalization, reflecting its enduring relevance and utility within the digital asset landscape.

Figure 5 below represents the evolution of the price of Binance Coin (BNB) in US dollar over the period November 2017 – May 2024. The price corresponds to the “closing” price (observed at 10:00 PM CET at the end of the month).

Figure 3. Evolution of Binance Coin (BNB) price.

Source: Yahoo! Finance.

R program

The R program below written by Shengyu ZHENG allows you to download the data from Yahoo! Finance website and to compute summary statistics and risk measures about the Binance Coin (BNB).

Data file

The R program that you can download above allows you to download the data for the Binance Coin (BNB) from the Yahoo! Finance website. The database starts on November, 2017.

Table 1 below represents the top of the data file for the Binance Coin (BNB) downloaded from the Yahoo! Finance website with the R program.

Table 1. Top of the data file for the Binance Coin (BNB)

Source: computation by the author (data: Yahoo! Finance website).

Python code

You can download the Python code used to download the data from Yahoo! Finance.

Python script to download Binance Coin (BNB) historical data and save it to an Excel sheet::

import yfinance as yf

import pandas as pd

# Define the ticker symbol for Binance Coin (BNB)

Binance_ticker = “BNB-USD”

# Define the date range for historical data

start_date = “2020-01-01”

end_date = “2022-01-01”

# Download historical data using yfinance

Binance_data = yf.download(Binance_ticker, start=start_date, end=end_date)

# Create a Pandas DataFrame from the downloaded data

Binance_df = pd.DataFrame(Binance_data)

# Define the Excel file path

excel_file_path = “Cardano _historical_data.xlsx”

# Save the data to an Excel sheet

Binance_df.to_excel(excel_file_path, sheet_name=” Binance Historical Data”)

print(f”Data saved to {excel_file_path}”)

# Make sure you have the required libraries installed and adjust the “start_date” and “end_date” variables to the desired date range for the historical data you want to download.

Evolution of the Binance Coin (BNB)

Figure 4 below gives the evolution of the Binance Coin (BNB) on a daily basis.

Figure 4. Evolution of the Binance Coin (BNB)

Source: computation by the author (data: Yahoo! Finance website).

Figure 5 below gives the evolution of the Binance Coin (BNB)returns from November, 2017 to May, 2024 on a daily basis.

Figure 5. Evolution of the BNB returns.

Source: computation by the author (data: Yahoo! Finance website).

Summary statistics for the Binance Coin (BNB)

The R program that you can download above also allows you to compute summary statistics about the returns of the Binance Coin (BNB).

Table 2 below presents the following summary statistics estimated for Binance Coin (BNB):

The mean

The standard deviation (the squared root of the variance)

The skewness

The kurtosis.

The mean, the standard deviation / variance, the skewness, and the kurtosis refer to the first, second, third and fourth moments of statistical distribution of returns respectively.

Table 2. Summary statistics for Binance Coin (BNB).

Source: computation by the author (data: Yahoo! Finance website).

Statistical distribution of the Binance Coin (BNB) returns

Historical distribution

Figure 6 represents the historical distribution of the Binance Coin (BNB) daily returns for the period from November, 2017 to May, 2024.

Figure 6. Historical BNB distribution of the returns.

Source: computation by the author (data: Yahoo! Finance website).

Gaussian distribution

Figure 7 below represents the Gaussian distribution of the Binance Coin (BNB)daily returns with parameters estimated over the period from November 2017 to May 2024.

Figure 7. Gaussian distribution of Binance Coin (BNB) returns.

Source: computation by the author (data: Yahoo! Finance website).

Risk measures of the Binance Coin (BNB)returns

The R program that you can download above also allows you to compute risk measures about the returns of the Binance Coin (BNB).

The long-term volatility (the unconditional standard deviation estimated over the entire period)

The short-term volatility (the standard deviation estimated over the last three months)

The Value at Risk (VaR) for the left tail (the 5% quantile of the historical distribution)

The Value at Risk (VaR) for the right tail (the 95% quantile of the historical distribution)

The Expected Shortfall (ES) for the left tail (the average loss over the 5% quantile of the historical distribution)

The Expected Shortfall (ES) for the right tail (the average loss over the 95% quantile of the historical distribution)

The Stress Value (SV) for the left tail (the 1% quantile of the tail distribution estimated with a Generalized Pareto distribution)

The Stress Value (SV) for the right tail (the 99% quantile of the tail distribution estimated with a Generalized Pareto distribution)

Table 3. Risk measures for Binance Coin (BNB).

Source: computation by the author (data: Yahoo! Finance website).

The volatility is a global measure of risk as it considers all the returns. The Value at Risk (VaR), Expected Shortfall (ES) and Stress Value (SV) are local measures of risk as they focus on the tails of the distribution. The study of the left tail is relevant for an investor holding a long position in the Binance Coin (BNB)while the study of the right tail is relevant for an investor holding a short position in the Binance Coin (BNB).

Why should I be interested in this post?

This post offers a captivating exploration of Binance Coin (BNB), tailored for both newcomers to the cryptocurrency realm and seasoned enthusiasts alike. It delves into the innovative utility and multifaceted ecosystem surrounding BNB, shedding light on its pivotal role within the Binance platform and beyond. By dissecting BNB’s diverse applications, including trading fee discounts, participation in token sales, and ecosystem services like decentralized finance (DeFi) and non-fungible tokens (NFTs), readers can grasp the breadth of opportunities presented by this digital asset. Additionally, the post delves into the historical price data and supply dynamics of BNB, providing valuable insights for investors, traders, and stakeholders navigating the volatile cryptocurrency market. Whether you’re intrigued by the potential of utility tokens or seeking to harness the power of the Binance ecosystem, this post offers an insightful journey into the significance and possibilities of Binance Coin in shaping the future of decentralized finance and digital economies.

Useful resources

Academic research about risk

Longin F. (2000) From VaR to stress testing: the extreme value approach Journal of Banking and Finance, N°24, pp 1097-1130.

Longin F. (2016) Extreme events in finance: a handbook of extreme value theory and its applications Wiley Editions.

Data

Yahoo! Finance Historical data for Binance Coin (BNB)

CoinMarketCap Historical data for Binance Coin (BNB)

About the author

The article was written in May 2024 by Snehasish CHINARA (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2024).

Cardano: Exploring the Future of Blockchain Technology

May 29, 2025May 6, 2024 by Snehasish CHINARA

In this article, Snehasish CHINARA (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2024) explains the cryptocurrency Cardano.

Historical context and background

Cardano is a blockchain platform that was founded in 2017 by Charles Hoskinson, one of the co-founders of Ethereum. The project was initiated by Input Output Hong Kong (IOHK), a technology company focused on blockchain and cryptocurrency. Cardano’s development was guided by a scientific philosophy and peer-reviewed research, aiming to create a more scalable, sustainable, and interoperable blockchain platform. Cardano is named after Gerolamo Cardano, the Italian mathematician, whereas the cryptocurrency associated with the platform is named after Ada Lovelace, the English mathematician.

Cardano distinguishes itself through its layered architecture, which separates the platform’s settlement layer (Cardano Settlement Layer, CSL) from its computation layer (Cardano Computation Layer, CCL). This separation allows for greater flexibility and scalability, as well as easier implementation of updates and improvements.

Another notable feature of Cardano is its consensus mechanism, called Ouroboros, which is based on a proof-of-stake algorithm. Ouroboros aims to achieve both security and scalability by allowing users to participate in the consensus process based on the amount of cryptocurrency they hold, rather than requiring expensive computational resources like Bitcoin’s proof-of-work mechanism.

Cardano’s development has been divided into phases, each focusing on different aspects of the platform’s functionality and features. These phases include Byron (foundation), Shelley (decentralization), Goguen (smart contracts), Basho (scaling), and Voltaire (governance). As of the time of writing, Cardano has successfully completed the Byron and Shelley phases, with ongoing work on the Goguen phase, which will enable the implementation of smart contracts and decentralized applications (dApps) on the platform.

Cardano Logo

Source: Cardano.

Figure 1. Key Dates in Cardano History

Source: Yahoo! Finance.

Key features

Layered Architecture

Cardano’s architecture is divided into two layers – the Cardano Settlement Layer (CSL) and the Cardano Computation Layer (CCL). This separation allows for greater flexibility, scalability, and easier implementation of updates and improvements.

Ouroboros Consensus Protocol:

Cardano uses the Ouroboros proof-of-stake consensus algorithm, which is designed to be secure, scalable, and energy-efficient. It allows users to participate in the consensus process based on the amount of cryptocurrency they hold, rather than requiring expensive computational resources.

Scalability

Cardano is designed to be highly scalable, capable of handling a large number of transactions per second. Through its layered architecture and consensus mechanism, Cardano aims to achieve scalability without sacrificing security or decentralization.

Interoperability

Cardano aims to enable interoperability between different blockchain networks and protocols. This will allow for seamless transfer of assets and data between different platforms, facilitating greater connectivity and usability of decentralized applications (dApps).

Smart Contracts

Cardano is developing support for smart contracts, which are self-executing contracts with the terms of the agreement directly written into code. Smart contracts will enable the creation of decentralized applications (dApps) on the Cardano platform, opening up a wide range of possibilities for developers and users.

Governance

Cardano has a built-in governance mechanism that allows stakeholders to participate in the decision-making process for the future development of the platform. This includes voting on proposals for protocol upgrades, funding development projects, and other governance-related decisions.

Formal Verification

Cardano emphasizes formal methods and peer-reviewed research in its development process. This includes using formal verification techniques to ensure the correctness and security of its protocols and smart contracts, reducing the risk of bugs and vulnerabilities.

Use cases

Decentralized Finance (DeFi)

Cardano’s smart contract capabilities can facilitate various DeFi applications, including decentralized exchanges (DEXs), lending platforms, stablecoins, and automated market makers (AMMs). Smart contracts on Cardano can enable programmable financial transactions without the need for intermediaries, providing users with greater control over their assets and reducing counterparty risk.

Supply Chain Management

Cardano’s blockchain can be utilized to track and authenticate products throughout the supply chain, ensuring transparency and accountability. By recording every step of a product’s journey on the blockchain, stakeholders can verify its origin, quality, and authenticity, thereby reducing fraud, counterfeiting, and logistical inefficiencies.

Identity Management

Cardano’s blockchain can serve as a secure and decentralized platform for identity management, enabling individuals to control and manage their digital identities without relying on centralized authorities. By leveraging cryptographic techniques, users can securely authenticate themselves and access various services, such as voting, healthcare, and financial transactions, while maintaining privacy and security.

Voting and Governance

Cardano’s blockchain can support transparent and tamper-resistant voting and governance systems, enabling communities to make collective decisions and govern decentralized organizations (DAOs). By using blockchain technology, voting processes can be made more secure, efficient, and auditable, ensuring fair and democratic outcomes.

Tokenization of Assets

Cardano’s blockchain can tokenize various real-world assets, such as real estate, stocks, and commodities, making them easily tradable and transferable on a global scale. By representing assets as digital tokens on the blockchain, ownership rights can be easily verified, fractional ownership can be enabled, and liquidity can be increased, unlocking new opportunities for investment and asset management.

Technology and Underlying Blockchain

Cardano is built on a multi-layered architecture designed to provide scalability, interoperability, and sustainability. At its core, Cardano utilizes a proof-of-stake (PoS) consensus mechanism called Ouroboros, which offers a more energy-efficient and secure alternative to traditional proof-of-work (PoW) systems. Ouroboros divides time into epochs and slots, with slot leaders responsible for creating new blocks and validating transactions within each slot. This approach ensures that the blockchain remains secure and decentralized while enabling high transaction throughput and low latency.

Cardano’s blockchain consists of two main layers: the Cardano Settlement Layer (CSL) and the Cardano Computation Layer (CCL). The CSL serves as the foundation for the platform’s native cryptocurrency, ADA, and facilitates secure and efficient peer-to-peer transactions. It employs a UTXO (Unspent Transaction Output) model similar to Bitcoin, where transactions are represented as inputs and outputs, ensuring transparency and immutability.

On top of the CSL, the CCL enables the execution of smart contracts and decentralized applications (dApps) using Plutus, Cardano’s purpose-built programming language. Plutus is based on Haskell, a functional programming language known for its safety and reliability, and allows developers to write smart contracts with formal verification capabilities, ensuring correctness and security. Additionally, Cardano supports interoperability with other blockchains through sidechains and cross-chain communication protocols, enabling seamless integration with existing infrastructure and networks.

Cardano’s development is guided by a rigorous scientific approach, with ongoing research and peer-reviewed papers driving innovation and advancement. The platform’s roadmap is divided into distinct phases, including Byron (foundation), Shelley (decentralization), Goguen (smart contracts), Basho (scaling), and Voltaire (governance), each focusing on specific features and functionalities. This modular approach allows for continuous improvement and evolution, ensuring that Cardano remains at the forefront of blockchain technology.

Supply of Coins

The supply of coins for Cardano (ADA) is governed by a predetermined protocol established during its initial launch. The total maximum supply of ADA is capped at 45 billion coins. Unlike some cryptocurrencies that have fixed supplies, Cardano’s distribution occurs gradually through a process called “minting.” During the initial phase, ADA tokens were distributed through a public sale and allocated to early supporters, development, and the Cardano treasury. Ongoing minting of ADA occurs through the process of staking, where ADA holders can delegate their coins to stake pools to participate in the network’s consensus and earn rewards. This incentivizes stakeholders to actively participate in the security and governance of the network while also distributing newly minted coins in a decentralized manner. As a result, the circulating supply of ADA gradually increases over time, with new coins being minted and distributed to participants in the Cardano ecosystem.

Historical data for Cardano

How to get the data?

The Cordano is popular cryptocurrency on the market, and historical data for the Cordano such as prices and volume traded can be easily downloaded from the internet sources such as Yahoo! Finance, Blockchain.com & CoinMarketCap. For example, you can download data for Cardano on Yahoo! Finance (the Yahoo! code for Cardano is ADA-USD).

Figure 2. Cordano data

Source: Yahoo! Finance.

Historical data for the Cardano (ADA) market prices

Cardano (ADA) has experienced notable fluctuations in its market price since its inception, reflecting both broader trends in the cryptocurrency market and developments specific to the Cardano project. Following its launch in 2017, ADA initially saw rapid growth, fueled by enthusiasm for its innovative technology and ambitious roadmap. However, like many cryptocurrencies, ADA’s price has been subject to volatility, with periods of sharp appreciation followed by corrections and consolidation. Historical data for Cardano’s market prices reveals a series of peaks and troughs, influenced by factors such as market sentiment, regulatory developments, technological milestones, and macroeconomic trends. Despite this volatility, Cardano has maintained its position as one of the top cryptocurrencies by market capitalization, attracting a dedicated community of supporters and investors. As the project continues to evolve and achieve key milestones, its market price remains closely watched by traders, investors, and stakeholders in the cryptocurrency ecosystem.

Figure 3 below represents the evolution of the price of Cardano (ADA) in US dollar over the period November 2017 – May 2024. The price corresponds to the “closing” price (observed at 10:00 PM CET at the end of the month).

Figure 3. Evolution of Cardano price

Source: Yahoo! Finance.

R program

The R program below written by Shengyu ZHENG allows you to download the data from Yahoo! Finance website and to compute summary statistics and risk measures about the Cardano (ADA).

Data file

The R program that you can download above allows you to download the data for the Cardano (ADA) from the Yahoo! Finance website. The database starts on November, 2017.

Table 1 below represents the top of the data file for the Cardano (ADA) downloaded from the Yahoo! Finance website with the R program.

Table 1. Top of the data file for the Cardano

Source: computation by the author (data: Yahoo! Finance website).

Python code

You can download the Python code used to download the data from Yahoo! Finance.

Python script to download Cardano (ADA) historical data and save it to an Excel sheet::

import yfinance as yf

import pandas as pd

# Define the ticker symbol for Cardano “ADA-USD”

Cardano_ticker = “ADA-USD”

# Define the date range for historical data

start_date = “2020-01-01”

end_date = “2022-01-01”

# Download historical data using yfinance

Cardano_data = yf.download(Cardano_ticker, start=start_date, end=end_date)

# Create a Pandas DataFrame from the downloaded data

Cardano_df = pd.DataFrame(Cardano_data)

# Define the Excel file path

excel_file_path = “Cardano _historical_data.xlsx”

# Save the data to an Excel sheet

Cardano_df.to_excel(excel_file_path, sheet_name=”Cardano Historical Data”)

print(f”Data saved to {excel_file_path}”)

# Make sure you have the required libraries installed and adjust the “start_date” and “end_date” variables to the desired date range for the historical data you want to download.

Evolution of the Cardano (ADA)

Figure 4 below gives the evolution of the Cardano (ADA) on a daily basis.

Figure 4. Evolution of the Cardano (ADA)

Source: computation by the author (data: Yahoo! Finance website).

Figure 5 below gives the evolution of the Cardano (ADA) returns from November, 2017 to May, 2024 on a daily basis.

Figure 5. Evolution of the Cardano returns

Source: computation by the author (data: Yahoo! Finance website).

Summary statistics for the Cardano (ADA)

The R program that you can download above also allows you to compute summary statistics about the returns of the Cardano (ADA).

Table 2 below presents the following summary statistics estimated for the Cardano (ADA):

The mean

The standard deviation (the squared root of the variance)

The skewness

The kurtosis.

The mean, the standard deviation / variance, the skewness, and the kurtosis refer to the first, second, third and fourth moments of statistical distribution of returns respectively.

Table 2. Summary statistics for Cardano.

Source: computation by the author (data: Yahoo! Finance website).

Statistical distribution of the Cardano (ADA) returns

Historical distribution

Figure 6 represents the historical distribution of the Cardano (ADA) daily returns for the period from November, 2017 to May, 2024.

Figure 6. Historical Cardano distribution of the returns.

Source: computation by the author (data: Yahoo! Finance website).

Gaussian distribution

Figure 7 below represents the Gaussian distribution of the Ethereum daily returns with parameters estimated over the period from November, 2017 to May, 2024.

Figure 7. Gaussian distribution of the Cardano returns.

Source: computation by the author (data: Yahoo! Finance website).

Risk measures of the Cardano (ADA) returns

The R program that you can download above also allows you to compute risk measures about the returns of the Cardano (ADA).

Table 3 below presents the following risk measures estimated for the Cardano (ADA):

The long-term volatility (the unconditional standard deviation estimated over the entire period)

The short-term volatility (the standard deviation estimated over the last three months)

The Value at Risk (VaR) for the left tail (the 5% quantile of the historical distribution)

The Value at Risk (VaR) for the right tail (the 95% quantile of the historical distribution)

The Expected Shortfall (ES) for the left tail (the average loss over the 5% quantile of the historical distribution)

The Expected Shortfall (ES) for the right tail (the average loss over the 95% quantile of the historical distribution)

The Stress Value (SV) for the left tail (the 1% quantile of the tail distribution estimated with a Generalized Pareto distribution)

The Stress Value (SV) for the right tail (the 99% quantile of the tail distribution estimated with a Generalized Pareto distribution)

Table 3. Risk measures for the Cardano.

Source: computation by the author (data: Yahoo! Finance website).

The volatility is a global measure of risk as it considers all the returns. The Value at Risk (VaR), Expected Shortfall (ES) and Stress Value (SV) are local measures of risk as they focus on the tails of the distribution. The study of the left tail is relevant for an investor holding a long position in the Cardano(ADA) while the study of the right tail is relevant for an investor holding a short position in the Cardano(ADA).

Why should I be interested in this post?

This post provides a compelling exploration of Cardano, catering to both novices and seasoned cryptocurrency enthusiasts alike. It delves into Cardano’s innovative blockchain technology and its role in revolutionizing various sectors, including finance, governance, and social impact initiatives. By understanding Cardano’s layered architecture, consensus mechanism, and ongoing development phases, readers can gain valuable insights into its potential to address scalability, interoperability, and sustainability challenges in the blockchain space. Moreover, the post examines Cardano’s historical performance, market dynamics, and community-driven governance model, offering invaluable perspectives for investors, traders, and stakeholders. Whether you’re intrigued by cutting-edge blockchain solutions or seeking investment opportunities in the cryptocurrency market, this post provides comprehensive insights into the significance and potential of Cardano in shaping the future of decentralized technologies.

Useful resources

Academic research about risk

Longin F. (2000) From VaR to stress testing: the extreme value approach Journal of Banking and Finance, N°24, pp 1097-1130.

Longin F. (2016) Extreme events in finance: a handbook of extreme value theory and its applications Wiley Editions.

Data

Yahoo! Finance Historical data for Cardano

CoinMarketCap Historical data for Cardano

About the author

The article was written in March 2024 by Snehasish CHINARA (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2024).

Solana: Ascendancy of the High-Speed Blockchain

May 29, 2025April 17, 2024 by Snehasish CHINARA

In this article, Snehasish CHINARA (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2024) explains the evolution of high performance blockchain powered cryptocurrency, Solana.

Historical context and background

Solana, a relatively new entrant in the cryptocurrency arena, emerged against the backdrop of an increasingly crowded and competitive landscape in the digital currency space. Founded in 2020 by Anatoly Yakovenko, a former engineer at Qualcomm, Solana sought to address some of the inherent scalability and speed limitations plaguing earlier blockchain platforms like Ethereum. The platform is named after the Solana Beach in California, where Yakovenko often surfed, symbolizing the project’s ambition to ride the waves of innovation and technological advancement.

Unlike many other cryptocurrencies that primarily rely on Proof of Work (PoW) or Proof of Stake (PoS) consensus mechanisms, Solana introduced a novel consensus mechanism known as Proof of History (PoH). This mechanism aims to optimize transaction processing speed by organizing transactions into a series of chronological events, enabling parallel transaction processing, and significantly enhancing scalability. Solana’s emphasis on scalability and throughput has positioned it as a promising platform for decentralized applications (dApps) and decentralized finance (DeFi) projects seeking high-performance blockchain infrastructure. Its innovative approach has garnered attention and support from investors and developers alike, propelling Solana into the spotlight as one of the leading contenders in the cryptocurrency space.

Solana Logo

Source: Google.

Figure 1. Key Dates in Solana History.

Source: Yahoo! Finance.

Key features

Scalability

Solana is designed to be highly scalable, capable of processing thousands of transactions per second. Its unique consensus mechanism, Proof of History (PoH), combined with a network of nodes running parallel processing, enables Solana to handle a high throughput of transactions efficiently.

Fast Transaction Speeds

With its focus on scalability, Solana boasts incredibly fast transaction speeds. Transactions can be confirmed in milliseconds, making it suitable for applications requiring rapid transaction processing, such as decentralized finance (DeFi) platforms and high-frequency trading.

Low Transaction Costs

Solana aims to keep transaction costs low, even during periods of high network activity. Its efficient use of resources and high throughput allow for cost-effective transactions, making it accessible to users and developers alike.

Proof of History (PoH)

Solana’s unique consensus mechanism, PoH, serves as a historical record for the ordering and time-stamping of transactions. By leveraging PoH, Solana achieves high throughput without sacrificing decentralization or security.

Support for Smart Contracts

Solana is compatible with smart contracts, allowing developers to build decentralized applications (dApps) and execute programmable transactions on the blockchain. It supports smart contract languages like Rust and Solidity, enabling a wide range of developers to build on the platform.

Ecosystem and Development Tools

Solana boasts a growing ecosystem of projects and development tools to support developers in building decentralized applications. Its developer-friendly environment includes tools such as Solana Studio, a web-based IDE for building and deploying smart contracts, and libraries for interacting with the Solana blockchain.

Interoperability

Solana is designed to be interoperable with other blockchains and protocols, facilitating seamless communication and asset transfer between different networks. This interoperability opens up possibilities for cross-chain decentralized applications and enhances the overall utility of the Solana ecosystem.

Use cases

Non-Fungible Tokens (NFTs)

Solana provides an efficient infrastructure for minting, trading, and storing NFTs. Artists, creators, and collectors are utilizing Solana-based marketplaces like Solanart to buy and sell digital collectibles, artwork, and virtual assets. Solana’s high throughput enables seamless NFT transactions, while its low fees make it appealing for creators seeking an alternative to Ethereum’s congested network.

Gaming and Virtual Worlds

Solana’s high-performance blockchain is well-suited for gaming applications and virtual worlds that require fast transaction processing and scalability. Game developers are leveraging Solana’s infrastructure to create blockchain-based games, in-game assets, and decentralized gaming platforms. Projects like Star Atlas, a space-themed massively multiplayer online game (MMO) built on Solana, demonstrate the platform’s potential to disrupt the gaming industry.

Decentralized Autonomous Organizations (DAOs)

Solana provides a robust framework for building decentralized autonomous organizations (DAOs) that enable community governance and decision-making. DAOs on Solana leverage smart contracts to automate voting mechanisms, distribute governance tokens, and execute proposals transparently and efficiently. These DAOs empower communities to collectively manage and govern decentralized protocols, platforms, and resources.

Tokenization of Real-World Assets

Solana facilitates the tokenization of real-world assets such as real estate, stocks, and commodities, enabling fractional ownership and increased liquidity. Projects are exploring Solana’s blockchain to tokenize and trade various asset classes, unlocking new investment opportunities and reducing barriers to entry for traditional markets.

Technology and underlying blockchain

At the core of Solana’s architecture is the Proof of History (PoH) consensus mechanism, which orders transactions before they are processed into blocks. This deterministic sequencing allows for parallel transaction processing and enhances overall network efficiency. Additionally, Solana utilizes a Byzantine Fault Tolerance (BFT) consensus algorithm called Tower BFT, which further ensures network security and integrity.

Solana’s blockchain implements a novel data structure known as the “Solana Architecture,” which includes a combination of a single global state, a high-speed networking stack, and a high-performance virtual machine (VM). This architecture enables Solana to achieve impressive transaction throughput, with the capability to process thousands of transactions per second (TPS) and sub-second transaction finality. Furthermore, Solana leverages a unique mechanism called “Turbine” to optimize block propagation and reduce network latency, enhancing the overall scalability and performance of the platform.

The Solana ecosystem also features a built-in decentralized exchange (DEX), supporting seamless token swaps and liquidity provision directly on-chain. Smart contracts on Solana are executed using a high-performance VM called Sealevel, which is designed to efficiently process complex computations while maintaining low transaction costs. Overall, Solana’s technology stack, comprising innovative consensus mechanisms, advanced data structures, and optimized networking protocols, positions it as a leading blockchain platform capable of supporting a wide range of decentralized applications (dApps) and use cases at scale.

Supply of coins

Solana (SOL) operates on a fixed supply model, with a maximum supply of 489,026,837 SOL tokens. Unlike traditional fiat currencies, Solana’s tokenomics are governed by the principles of cryptocurrency protocols. The initial distribution of SOL tokens occurred through a combination of token sales, strategic partnerships, ecosystem incentives, and network validators’ rewards. Notably, Solana employs a deflationary economic model, wherein a portion of transaction fees is burned, reducing the overall token supply over time. This deflationary mechanism is designed to counterbalance any potential inflationary pressures as the network expands, ensuring the long-term sustainability and scarcity of SOL tokens. Additionally, SOL tokens are used to facilitate various functions within the Solana ecosystem, including transaction fees, staking rewards, governance participation, and decentralized application interactions. As Solana continues to grow and gain adoption, the controlled and predictable token supply dynamics play a crucial role in maintaining the network’s integrity and value proposition.

Historical data for Solana

How to get the data?

The Solana (SOL) is a popular cryptocurrency on the market, and historical data for the Solana such as prices and volume traded can be easily downloaded from the internet sources such as Yahoo! Finance, Blockchain.com & CoinMarketCap. For example, you can download data for Solana on Yahoo! Finance (the Yahoo! code for Solana is SOL-USD).

Figure 2. Solana data

Source: Yahoo! Finance.

Historical data for the Solana market prices

The historical data for Solana market prices demonstrates a dynamic evolution marked by significant fluctuations and notable trends since its inception. Initially, SOL experienced modest trading activity and price levels, but as Solana gained recognition for its innovative blockchain architecture and scalability features, its market value began to ascend. Early adopters and investors drove demand for SOL tokens, leading to periods of rapid appreciation interspersed with corrections and consolidation phases. Milestones such as protocol upgrades, partnerships, and successful dApp launches often coincided with significant price movements. Moreover, broader market trends and sentiment towards cryptocurrencies influenced SOL’s price dynamics, contributing to both bullish and bearish cycles over time. Overall, SOL’s price trajectory reflects Solana’s journey from its early stages to becoming a prominent player in the blockchain space, highlighting its potential to revolutionize decentralized applications and digital finance despite the inherent volatility of the cryptocurrency market.

Figure 3 below represents the evolution of the price of Solana (SOL) in US dollar over the period April 2020 – December 2023. The price corresponds to the “closing” price (observed at 10:00 PM CET at the end of the month).

Figure 3. Evolution of the Solana (SOL) price

Source: Yahoo! Finance.

R program

The R program below written by Shengyu ZHENG allows you to download the data from Yahoo! Finance website and to compute summary statistics and risk measures about the Solana (SOL).

The R program that you can download above allows you to download the data for the Solana (SOL) from the Yahoo! Finance website. The database starts on April, 2020.

Table 1 below represents the top of the data file for the Solana (SOL) downloaded from the Yahoo! Finance website with the R program.

Table 1. Top of the data file for the Solana (SOL)

Source: computation by the author (data: Yahoo! Finance website).

Python code

You can download the Python code used to download the data from Yahoo! Finance.

Python script to download Solana (SOL) historical data and save it to an Excel sheet::

import yfinance as yf

import pandas as pd

# Define the ticker symbol for Solana Coin

SOL_ticker = “SOL-USD”

# Define the date range for historical data

start_date = “2020-01-01”

end_date = “2022-01-01”

# Download historical data using yfinance

SOL_data = yf.download(SOL_ticker, start=start_date, end=end_date)

# Create a Pandas DataFrame from the downloaded data

doge_df = pd.DataFrame(SOL_data)

# Define the Excel file path

excel_file_path = “SOL_historical_data.xlsx”

# Save the data to an Excel sheet

SOL_df.to_excel(excel_file_path, sheet_name=”SOL_historical_data”)

print(f”Data saved to {excel_file_path}”)

# Make sure you have the required libraries installed and adjust the “start_date” and “end_date” variables to the desired date range for the historical data you want to download.

Evolution of the Solana (SOL)

Figure 4 below gives the evolution of the Solana (SOL) returns from April, 2020 to December 31, 2023 on a daily basis.

Figure 4. Evolution of the Solana (SOL) returns.

Source: computation by the author (data: Yahoo! Finance website).

Summary statistics for the Solana (SOL)

The R program that you can download above also allows you to compute summary statistics about the returns of the Solana (SOL). Table 2 below presents the following summary statistics estimated for the Solana (SOL):

The mean

The standard deviation (the squared root of the variance)

The skewness

The kurtosis.

The mean, the standard deviation / variance, the skewness, and the kurtosis refer to the first, second, third and fourth moments of statistical distribution of returns respectively.

Table 2. Summary statistics for Solana (SOL).

Source: computation by the author (data: Yahoo! Finance website).

Statistical distribution of the Solana (SOL) returns

Historical distribution

Figure 5 represents the historical distribution of the Solana (SOL) daily returns for the period from April, 2020 to December 31, 2023.

Figure 5. Historical Solana (SOL) distribution of the returns.

Source: computation by the author (data: Yahoo! Finance website).

Gaussian distribution

Figure 6 below represents the Gaussian distribution of the Solana (SOL) daily returns with parameters estimated over the period from April, 2020 to December, 2023.

Figure 6. Gaussian distribution of the Solana (SOL) returns.

Source: computation by the author (data: Yahoo! Finance website).

Risk measures of the Solana (SOL) returns

The R program that you can download above also allows you to compute risk measures about the returns of the Solana (SOL).

Table 3 below presents the following risk measures estimated for the Solana (SOL):

The long-term volatility (the unconditional standard deviation estimated over the entire period)

The short-term volatility (the standard deviation estimated over the last three months)

The Value at Risk (VaR) for the left tail (the 5% quantile of the historical distribution)

The Value at Risk (VaR) for the right tail (the 95% quantile of the historical distribution)

The Expected Shortfall (ES) for the left tail (the average loss over the 5% quantile of the historical distribution)

The Expected Shortfall (ES) for the right tail (the average loss over the 95% quantile of the historical distribution)

The Stress Value (SV) for the left tail (the 1% quantile of the tail distribution estimated with a Generalized Pareto distribution)

The Stress Value (SV) for the right tail (the 99% quantile of the tail distribution estimated with a Generalized Pareto distribution)

Table 3. Risk measures for the Solana (SOL).

Source: computation by the author (data: Yahoo! Finance website).

The volatility is a global measure of risk as it considers all the returns. The Value at Risk (VaR), Expected Shortfall (ES) and Stress Value (SV) are local measures of risk as they focus on the tails of the distribution. The study of the left tail is relevant for an investor holding a long position in the Solana (SOL) while the study of the right tail is relevant for an investor holding a short position in the Solana (SOL).

Why should I be interested in this post?

This blog offers an engaging exploration into a cryptocurrency that transcends traditional finance, appealing to a wide audience due to its cultural relevance, investment potential, and vibrant community. Solana’s reputation for high performance in blockchain technology, boasting the capability to process thousands of transactions per second, makes it an appealing option for developers and users seeking efficient transaction processing. Moreover, staying updated on Solana can offer insights into the growth of its ecosystem, including the development of decentralized applications (dApps) and strategic partnerships. For investors, Solana’s increasing popularity and ecosystem growth may signal investment opportunities, making it worthwhile to track news and discussions surrounding the platform. Additionally, Solana’s innovative technical advancements in scalability, consensus mechanisms, and developer tools are of interest to those intrigued by blockchain technology. Engaging with the Solana community provides opportunities for networking and gaining valuable insights into this rapidly expanding ecosystem.

Useful resources

Academic research about risk

Longin F. (2000) From VaR to stress testing: the extreme value approach Journal of Banking and Finance, N°24, pp 1097-1130.

Longin F. (2016) Extreme events in finance: a handbook of extreme value theory and its applications Wiley Editions.

Data

Yahoo! Finance Historical data for Solana

CoinMarketCap Historical data for Solana

About the author

The article was written in April 2024 by Snehasish CHINARA (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2024).

Doge Coin: Unraveling the Phenomenon of the Internet’s Favourite Cryptocurrency

May 2, 2024April 5, 2024 by Snehasish CHINARA

In this article, Snehasish CHINARA (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2024) explains the evolution of the most popular memecoin, Dogecoin.

Historical context and background

Dogecoin, a cryptocurrency that started as a joke, quickly evolved into a significant player in the world of digital assets. Created in December 2013 by software engineers Billy Markus and Jackson Palmer, Dogecoin was initially intended to satirize the hype surrounding cryptocurrencies at the time. The coin’s logo features the Shiba Inu dog from the “Doge” meme, which was immensely popular on the internet during that period. Despite its humorous origins, Dogecoin gained traction due to its welcoming and inclusive community, as well as its low transaction fees and fast confirmation times.

The early days of Dogecoin saw rapid adoption and a vibrant online community rallying around the coin. It gained attention for its philanthropic efforts, including fundraising campaigns to sponsor charity events and support causes such as building water wells in developing countries and sponsoring Olympic athletes. These initiatives helped distinguish Dogecoin from other cryptocurrencies and fostered a sense of community among its users.

Over time, Dogecoin’s popularity continued to grow, fueled in part by endorsements from notable figures such as Elon Musk, who frequently tweeted about the coin, further amplifying its visibility. Despite facing occasional security issues and challenges, Dogecoin has persevered, becoming one of the most recognized and traded cryptocurrencies in the market. Its unique blend of humor, community spirit, and accessibility has endeared it to a wide range of users, making it a significant player in the ever-expanding crypto landscape.

Doge Coin Logo

Source: Yahoo! Finance.

Figure 1. Key Dates in Doge Coin History

Source: Yahoo! Finance .

Key features

Decentralization

Like most cryptocurrencies, Dogecoin operates on a decentralized network, meaning it is not controlled by any single entity or organization. Transactions are recorded on a public ledger known as the blockchain, which is maintained by a network of nodes.

Scrypt Algorithm

Dogecoin uses the Scrypt algorithm for its proof-of-work consensus mechanism, which is less energy-intensive compared to Bitcoin’s SHA-256 algorithm. This allows for greater accessibility to mining for individuals with standard computer hardware.

Fast Transactions

Dogecoin boasts relatively fast transaction times, with blocks being mined approximately every minute. This makes it suitable for quick and efficient transfers of value.

Low Transaction Fees

Transaction fees on the Dogecoin network are typically minimal, making it cost-effective for transferring even small amounts of value.

Meme Culture

Dogecoin’s branding and marketing heavily leverage internet meme culture, particularly the “Doge” meme featuring the Shiba Inu dog. This playful and approachable branding sets Dogecoin apart from other cryptocurrencies and contributes to its widespread appeal.

Use cases

Tipping

Dogecoin gained popularity early on for its use as a tipping currency on social media platforms like Reddit and Twitter. Users can easily send small amounts of Dogecoin to content creators or other users as a form of appreciation.

Charitable Donations

The Dogecoin community has a history of supporting charitable causes and disaster relief efforts. Dogecoin has been used to raise funds for various initiatives, including sponsoring athletes, funding clean water projects, and aiding during natural disasters.

E-commerce

Some online merchants and retailers accept Dogecoin as a form of payment for goods and services. This includes businesses ranging from small independent shops to larger e-commerce platforms.

Micropayments

Dogecoin’s low transaction fees and fast confirmation times make it suitable for micropayments, allowing users to easily transfer small amounts of value online.

Community Engagement

Dogecoin continues to serve as a vehicle for community engagement and participation. Its lighthearted and inclusive nature fosters a sense of camaraderie among its users, who often come together for events, fundraisers, and online discussions.

Experimental Projects

Developers and enthusiasts sometimes use Dogecoin for experimental projects or to explore new applications of blockchain technology. These projects can range from art and gaming to decentralized finance (DeFi) experiments.

Technology and underlying blockchain

Dogecoin operates on a blockchain-based technology similar to Bitcoin and many other cryptocurrencies. It employs a decentralized peer-to-peer network that relies on nodes spread across the globe to validate and record transactions. Dogecoin’s blockchain uses the Scrypt hashing algorithm, which was initially designed to facilitate quicker confirmation times compared to Bitcoin’s SHA-256 algorithm. This choice of algorithm allows for a more accessible mining process, enabling individuals with standard computer hardware to participate in securing the network and earning rewards. Transactions on the Dogecoin network are grouped into blocks, which are then added to the blockchain through a process known as mining. Miners compete to solve complex mathematical puzzles, and the first miner to solve a puzzle validates the transactions in a block and adds it to the blockchain. Dogecoin’s block time is approximately one minute, resulting in faster transaction confirmations compared to Bitcoin’s ten-minute block time. Additionally, Dogecoin originally had a limitless supply, with a fixed reward of 10,000 DOGE per block; however, this changed in 2014 to an inflationary model, where a fixed number of coins are added to the supply each year. This combination of technology and economic design contributes to Dogecoin’s unique characteristics and its appeal within the cryptocurrency ecosystem.

Supply of coins

Dogecoin’s supply dynamics are distinctive within the cryptocurrency landscape. Initially launched with no hard cap on its total supply, Dogecoin features an inflationary issuance model designed to maintain a steady influx of coins into the market. Unlike Bitcoin’s fixed supply of 21 million coins, Dogecoin’s issuance rate started at 5 billion coins per year and gradually decreases over time. This inflationary nature ensures a continuous supply of Dogecoin, theoretically allowing for ongoing miner rewards and a sustained incentive for network participation. However, it’s worth noting that while the supply of Dogecoin is technically infinite, the rate of new coin creation diminishes over time, resulting in a decreasing inflation rate and a more stable supply trajectory. This unique supply mechanism distinguishes Dogecoin from many other cryptocurrencies and can influence its long-term economic dynamics and utility as a medium of exchange or store of value.

Historical data for Doge Coin

How to get the data?

The Doge Coin is popular cryptocurrency on the market, and historical data for the Doge Coin such as prices and volume traded can be easily downloaded from the internet sources such as Yahoo! Finance, Blockchain.com & CoinMarketCap. For example, you can download data for Doge Coin on Yahoo! Finance (the Yahoo! code for Doge Coin is Doge-USD).

Figure 2. Doge Coin data

Source: Yahoo! Finance.

Historical data for the Doge Coin market prices

Since its inception in 2013, Dogecoin has experienced notable price fluctuations, driven by a combination of factors including market speculation, community engagement, and broader trends in the digital asset space. Initially launched as a joke currency, Dogecoin’s price remained relatively stable for several years, trading at fractions of a cent. However, its price surged dramatically in early 2021, fueled by social media hype and celebrity endorsements, reaching all-time highs of over 70 cents per coin. This unprecedented rally brought Dogecoin into the spotlight, attracting widespread attention from investors and media outlets. Despite subsequent price corrections, Dogecoin has maintained a prominent position in the cryptocurrency market, with its price influenced by various factors including Elon Musk’s tweets, meme culture, and broader market sentiment. Overall, the historical price evolution of Dogecoin exemplifies the volatile and dynamic nature of the cryptocurrency market, highlighting the interplay between community enthusiasm, market speculation, and broader industry trends.

Figure 3 below represents the evolution of the price of Doge Coin in US dollar over the period November 2018 – December 2022. The price corresponds to the “closing” price (observed at 10:00 PM CET at the end of the month).

Figure 3. Evolution of the Doge Coin price

Source: Yahoo! Finance.

R program

The R program below written by Shengyu ZHENG allows you to download the data from Yahoo! Finance website and to compute summary statistics and risk measures about the Doge Coin.

Data file

The R program that you can download above allows you to download the data for the Doge Coin from the Yahoo! Finance website. The database starts on October, 2018.

Table 1 below represents the top of the data file for the Doge Coin downloaded from the Yahoo! Finance website with the R program.

Table 1. Top of the data file for the DOGE Coin

Source: computation by the author (data: Yahoo! Finance website).

Python code

You can download the Python code used to download the data from Yahoo! Finance.

Python script to download Doge historical data and save it to an Excel sheet::

import yfinance as yf

import pandas as pd

# Define the ticker symbol for Doge Coin

doge_ticker = “DOGE-USD”

# Define the date range for historical data

start_date = “2020-01-01”

end_date = “2022-01-01”

# Download historical data using yfinance

doge_data = yf.download(doge_ticker, start=start_date, end=end_date)

# Create a Pandas DataFrame from the downloaded data

doge_df = pd.DataFrame(doge_data)

# Define the Excel file path

excel_file_path = “DOGE_historical_data.xlsx”

# Save the data to an Excel sheet

doge_df.to_excel(excel_file_path, sheet_name=”DOGE_historical_data”)

print(f”Data saved to {excel_file_path}”)

# Make sure you have the required libraries installed and adjust the “start_date” and “end_date” variables to the desired date range for the historical data you want to download

Evolution of the Doge Coin

Figure 4 below gives the evolution of the Doge Coin on a daily basis.

Figure 4. Evolution of the DOGE Coin Figure 5 below gives the evolution of the Doge Coin returns from November, 2018 to December 31, 2022 on a daily basis.

Figure 5. Evolution of the Doge Coin returns.

Source: computation by the author (data: Yahoo! Finance website).

Summary statistics for the Doge Coin

The R program that you can download above also allows you to compute summary statistics about the returns of the Doge Coin. Table 2 below presents the following summary statistics estimated for the Doge Coin:

The mean

The standard deviation (the squared root of the variance)

The skewness

The kurtosis.

The mean, the standard deviation / variance, the skewness, and the kurtosis refer to the first, second, third and fourth moments of statistical distribution of returns respectively.

Table 2. Summary statistics for Doge Coin.

Source: computation by the author (data: Yahoo! Finance website).

Statistical distribution of the Doge Coin returns

Figure 6 represents the historical distribution of the Doge Coin daily returns for the period from November, 2018 to December 31, 2022.

Figure 6. Historical Doge Coin distribution of the returns.

Source: computation by the author (data: Yahoo! Finance website).

Gaussian distribution

The Gaussian distribution (also called the normal distribution) is a parametric distribution with two parameters: the mean and the standard deviation of returns. We estimated these two parameters over the period from October, 2018 to December 31, 2022. Figure 7 below represents the Gaussian distribution of the Ethereum daily returns with parameters estimated over the period from October, 2018 to December, 2022.

Figure 9. Gaussian distribution of the Doge Coin returns.

Source: computation by the author (data: Yahoo! Finance website).

Risk measures of the Doge Coin returns

The R program that you can download above also allows you to compute risk measures about the returns of the Doge Coin.

Table 3 below presents the following risk measures estimated for the Doge Coin:

The long-term volatility (the unconditional standard deviation estimated over the entire period)

The short-term volatility (the standard deviation estimated over the last three months)

The Value at Risk (VaR) for the left tail (the 5% quantile of the historical distribution)

The Value at Risk (VaR) for the right tail (the 95% quantile of the historical distribution)

The Expected Shortfall (ES) for the left tail (the average loss over the 5% quantile of the historical distribution)

The Expected Shortfall (ES) for the right tail (the average loss over the 95% quantile of the historical distribution)

The Stress Value (SV) for the left tail (the 1% quantile of the tail distribution estimated with a Generalized Pareto distribution)

The Stress Value (SV) for the right tail (the 99% quantile of the tail distribution estimated with a Generalized Pareto distribution)

Table 3. Risk measures for the Doge Coin.

Source: computation by the author (data: Yahoo! Finance website).

The volatility is a global measure of risk as it considers all the returns. The Value at Risk (VaR), Expected Shortfall (ES) and Stress Value (SV) are local measures of risk as they focus on the tails of the distribution. The study of the left tail is relevant for an investor holding a long position in the XRP while the study of the right tail is relevant for an investor holding a short position in the Doge Coin.

Why should I be interested in this post?

This blog offers an engaging exploration into a cryptocurrency that transcends traditional finance, appealing to a wide audience due to its cultural relevance, investment potential, and vibrant community. From its origins as a meme coin to its remarkable price movements, understanding Dogecoin’s dynamics provides valuable insights into both the cryptocurrency market and internet culture. Delving into its technological underpinnings, community engagement, and market trends offers a concise yet comprehensive overview of Dogecoin’s significance in the evolving landscape of digital currencies.

Useful resources

Academic research about risk

Longin F. (2000) From VaR to stress testing: the extreme value approach Journal of Banking and Finance, N°24, pp 1097-1130.

Longin F. (2016) Extreme events in finance: a handbook of extreme value theory and its applications Wiley Editions.

Data

Yahoo! Finance Historical data for Doge Coin

CoinMarketCap Historical data for Doge Coin

About the author

The article was written in March 2024 by Snehasish CHINARA (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2024).

USD Coin: Deep Dive into the Role of Stablecoins in Modern Finance

January 15, 2025April 2, 2024 by Snehasish CHINARA

In this article, Snehasish CHINARA (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2024) explains the stable coin USD Coin.

Historical context and background

USD Coin (USDC) is a type of cryptocurrency known as a stablecoin, designed to maintain a stable value relative to the US dollar (USD). It was launched in September 2018 by Centre Consortium, a collaboration between cryptocurrency exchange Coinbase and blockchain technology company Circle. The primary goal of USDC is to provide a digital asset that can be easily transferred between users and used for transactions, while minimizing the volatility typically associated with other cryptocurrencies like Bitcoin or Ethereum.

The need for stablecoins like USDC arose due to the inherent volatility of many cryptocurrencies. While Bitcoin and other digital assets have gained significant attention and adoption, their prices can fluctuate dramatically over short periods, which can make them less practical for everyday transactions and financial contracts. Stablecoins like USDC offer a solution to this problem by pegging their value to a stable asset, such as the US dollar, thereby providing stability and predictability for users.

USDC operates on the Ethereum blockchain as an ERC-20 token, making it compatible with a wide range of decentralized applications (dApps) and enabling seamless integration with the broader cryptocurrency ecosystem. This infrastructure allows users to easily send and receive USDC tokens across various platforms and services, including exchanges, wallets, and payment processors.

Since its launch, USDC has seen significant growth in adoption and usage. It has become one of the most widely used stablecoins in the cryptocurrency market, with billions of dollars worth of USDC tokens in circulation. Its stability and liquidity have made it a popular choice for traders, investors, and businesses looking to transact in digital assets without exposure to the volatility of other cryptocurrencies.

USD Coin Logo

Source: Yahoo! Finance.

Figure 1. Key Dates in USDC History

Source: Yahoo! Finance.

Key features

Stability

USD Coin is a stablecoin, meaning it is pegged to the value of the US dollar on a 1:1 basis. This stability is maintained through regular audits and backing by reserves of US dollars held in custody by regulated financial institutions.

Transparency

USDC operates on blockchain technology, providing transparency and immutability of transactions. Every USDC token is backed by an equivalent number of US dollars held in reserve, which is regularly audited and transparently reported to ensure trust among users.

Speed and Efficiency

Transactions involving USDC can be executed quickly and efficiently on blockchain networks, enabling near-instantaneous settlement compared to traditional banking systems, which may take days for cross-border transactions.

Global Accessibility

USDC enables borderless transactions, allowing users to send and receive payments globally without the need for intermediaries such as banks. This accessibility empowers individuals and businesses, particularly in regions with limited access to traditional financial services.

Interoperability

USDC is compatible with various blockchain platforms and protocols, including Ethereum, Algorand, and Solana, among others. This interoperability facilitates its integration into a wide range of decentralized applications (DApps) and decentralized finance (DeFi) ecosystems.

Use cases

Remittances and Cross-Border Payments:

USDC provides a cost-effective and efficient solution for remittance payments and cross-border transactions, enabling individuals and businesses to transfer value across borders quickly and securely without the need for traditional banking intermediaries.

Stable Value Storage

Due to its stable value pegged to the US dollar, USDC serves as a reliable store of value and a hedge against volatility in the cryptocurrency market. Users can hold USDC as a stable asset to preserve purchasing power and mitigate the risks associated with price fluctuations in other cryptocurrencies.

Decentralized Finance (DeFi) Applications

USDC is widely used as a liquidity provider and collateral asset in various DeFi protocols and applications such as decentralized exchanges (DEXs), lending platforms, yield farming, and liquidity pools. Users can leverage USDC to earn interest, borrow assets, or participate in yield farming strategies within the DeFi ecosystem.

Commerce and Payments

Merchants and businesses can accept USDC as a form of payment for goods and services, leveraging its fast transaction settlement times and low transaction fees compared to traditional payment methods. Integrating USDC payments can streamline cross-border commerce and reduce friction associated with fiat currency conversions.

Financial Inclusion

USDC plays a crucial role in expanding financial inclusion by providing access to digital financial services for individuals and communities underserved by traditional banking infrastructure. By utilizing blockchain technology and stablecoins like USDC, individuals without access to traditional banking services can participate in the global economy and access a wide range of financial products and services.

Technology and underlying blockchain

USD Coin (USDC) operates on a blockchain-based infrastructure, primarily leveraging the Ethereum blockchain as its foundation. Utilizing Ethereum’s smart contract functionality, USDC tokens are issued, transferred, and redeemed in a transparent and trustless manner. The ERC-20 standard, a set of rules and protocols defining interactions between tokens on the Ethereum network, governs the behavior of USDC tokens, ensuring compatibility with a wide range of wallets, exchanges, and decentralized applications (DApps). Moreover, USDC employs a consortium model for governance and operation, with regulated financial institutions serving as members responsible for the issuance, custody, and redemption of USDC tokens. These institutions adhere to strict regulatory compliance measures and conduct regular audits to verify that each USDC token is fully backed by an equivalent reserve of US dollars held in custody. This combination of blockchain technology, smart contracts, and regulatory oversight ensures the integrity, transparency, and stability of USD Coin, making it a trusted and widely adopted stablecoin within the cryptocurrency ecosystem.

ERC-20 Standard of Ethereum for USD Coin

The ERC-20 standard, short for Ethereum Request for Comment 20, is a widely adopted technical specification governing the creation and implementation of fungible tokens on the Ethereum blockchain. Introduced by Fabian Vogelsteller and Vitalik Buterin in 2015, ERC-20 defines a set of rules and functions that enable seamless interoperability between different tokens, ensuring compatibility with various decentralized applications (DApps) and wallets. Tokens adhering to the ERC-20 standard are characterized by a consistent set of methods, including transfer, balance inquiry, and approval mechanisms, facilitating easy integration and widespread adoption across the Ethereum ecosystem. This standardization has played a pivotal role in the proliferation of tokenization, empowering developers to create diverse tokenized assets, conduct crowdfunding campaigns through Initial Coin Offerings (ICOs), and establish decentralized exchanges (DEXs) where ERC-20 tokens are traded autonomously. Additionally, ERC-20 compliance enhances security and interoperability, fostering trust and usability within the Ethereum network.

Supply of coins

The supply dynamics of USD Coin (USDC) are governed by its underlying smart contract protocol and the management of its issuer, Centre Consortium, a collaboration between Circle and Coinbase. USDC operates on a principle of full backing, where each USDC token issued is backed by an equivalent number of US dollars held in reserve. This backing ensures a 1:1 peg to the US dollar, maintaining its stability. The issuance and redemption of USDC are facilitated through regulated financial institutions that hold the corresponding fiat reserves. Moreover, USDC’s supply is transparently audited on a regular basis, with attestations provided by reputable auditing firms to verify the adequacy of reserves. Through these mechanisms, the supply of USDC remains elastic, expanding or contracting based on market demand while preserving its stability and trustworthiness as a stablecoin in the digital asset ecosystem.

Historical data for USDC

How to get the data?

The USDC is popular cryptocurrency on the market, and historical data for the USDC such as prices and volume traded can be easily downloaded from the internet sources such as Yahoo! Finance, Blockchain.com & CoinMarketCap. For example, you can download data for USDC on Yahoo! Finance (the Yahoo! code for USD Coin is USDC-USD).

Figure 2. USD Coin data

Source: Yahoo! Finance.

Historical data for the USD Coin market prices

The historical market price of USD Coin (USDC) has remained relatively stable, as its primary function is to maintain a value pegged to the US dollar at a 1:1 ratio. Since its inception, USDC has consistently traded around the $1 mark, with minor fluctuations typically attributed to market dynamics and liquidity conditions. Investors and traders often utilize USDC as a safe haven asset or a means of temporarily exiting volatile cryptocurrency markets, contributing to its stability. This stability has made USDC a preferred choice for individuals and institutions seeking to hedge against cryptocurrency volatility or facilitate seamless transitions between digital and fiat currencies. Additionally, the transparent backing of USDC by reserves of US dollars held in custody by regulated financial institutions further enhances market confidence and contributes to its stable market price over time.The historical market price of USD Coin (USDC) has remained relatively stable, as its primary function is to maintain a value pegged to the US dollar at a 1:1 ratio. Since its inception, USDC has consistently traded around the $1 mark, with minor fluctuations typically attributed to market dynamics and liquidity conditions. Investors and traders often utilize USDC as a safe haven asset or a means of temporarily exiting volatile cryptocurrency markets, contributing to its stability. This stability has made USDC a preferred choice for individuals and institutions seeking to hedge against cryptocurrency volatility or facilitate seamless transitions between digital and fiat currencies. Additionally, the transparent backing of USDC by reserves of US dollars held in custody by regulated financial institutions further enhances market confidence and contributes to its stable market price over time.

Figure 3 below represents the evolution of the price of USD Coin in US dollar over the period Oct 2018 – Dec 2022. The price corresponds to the “closing” price (observed at 10:00 PM CET at the end of the month).

Figure 3. Evolution of the USD Coin price

Source: Yahoo! Finance.

R program

The R program below written by Shengyu ZHENG allows you to download the data from Yahoo! Finance website and to compute summary statistics and risk measures about the USD Coin.

Data file

The R program that you can download above allows you to download the data for the USD Coin from the Yahoo! Finance website. The database starts on Oct, 2018. Table 1 below represents the top of the data file for the USD Coin downloaded from the Yahoo! Finance website with the R program.

Table 1. Top of the data file for the USD Coin

Source: computation by the author (data: Yahoo! Finance website).

Python code

You can download the Python code used to download the data from Yahoo! Finance.

Python script to download USD Coin historical data and save it to an Excel sheet::

import yfinance as yf

import pandas as pd

# Define the ticker symbol for USD Coin

usdc_ticker = “USDC-USD”

# Define the date range for historical data

start_date = “2020-01-01”

end_date = “2022-01-01”

# Download historical data using yfinance

usdc_data = yf.download(usdc_ticker, start=start_date, end=end_date)

# Create a Pandas DataFrame from the downloaded data

usdc_df = pd.DataFrame(usdc_data)

# Define the Excel file path

excel_file_path = “USDC_historical_data.xlsx”

# Save the data to an Excel sheet

usdc_df.to_excel(excel_file_path, sheet_name=”USDC Historical Data”)

print(f”Data saved to {excel_file_path}”)

# Make sure you have the required libraries installed and adjust the “start_date” and “end_date” variables to the desired date range for the historical data you want to download.

Evolution of the USD Coin

Figure 4 below gives the evolution of the USDC on a daily basis.

Figure 4. Evolution of the USD Coin.

Source: computation by the author (data: Yahoo! Finance website).

Figure 5 below gives the evolution of the USD Coin returns from Oct, 2018 to December 31, 2022 on a daily basis.

Figure 5. Evolution of the USD Coin returns

Source: computation by the author (data: Yahoo! Finance website).

Summary statistics for the USD Coin

The R program that you can download above also allows you to compute summary statistics about the returns of the USD Coin. Table 2 below presents the following summary statistics estimated for the USD Coin:

The mean

The standard deviation (the squared root of the variance)

The skewness

The kurtosis.

The mean, the standard deviation / variance, the skewness, and the kurtosis refer to the first, second, third and fourth moments of statistical distribution of returns respectively.

Table 2. Summary statistics for USDC.

Source: computation by the author (data: Yahoo! Finance website).

Statistical distribution of the USD Coin returns

Historical distribution

Figure 6 represents the historical distribution of the USD Coin daily returns for the period from Oct, 2018 to December 31, 2022.

Figure 6. Historical USDC distribution of the returns.

Source: computation by the author (data: Yahoo! Finance website).

Gaussian distribution

Figure 7 below represents the Gaussian distribution of the USD Coin daily returns with parameters estimated over the period from October, 2018 to December, 2022.

Figure 7. Gaussian distribution of the USDC returns.

Source: computation by the author (data: Yahoo! Finance website).

Risk measures of the USD Coin returns

The R program that you can download above also allows you to compute risk measures about the returns of the USD Coin.

Table 3 below presents the following risk measures estimated for the USD Coin:

The long-term volatility (the unconditional standard deviation estimated over the entire period)

The short-term volatility (the standard deviation estimated over the last three months)

The Value at Risk (VaR) for the left tail (the 5% quantile of the historical distribution)

The Value at Risk (VaR) for the right tail (the 95% quantile of the historical distribution)

The Expected Shortfall (ES) for the left tail (the average loss over the 5% quantile of the historical distribution)

The Expected Shortfall (ES) for the right tail (the average loss over the 95% quantile of the historical distribution)

The Stress Value (SV) for the left tail (the 1% quantile of the tail distribution estimated with a Generalized Pareto distribution)

The Stress Value (SV) for the right tail (the 99% quantile of the tail distribution estimated with a Generalized Pareto distribution)

Table 3. Risk measures for the USDC.

Source: computation by the author (data: Yahoo! Finance website).

The volatility is a global measure of risk as it considers all the returns. The Value at Risk (VaR), Expected Shortfall (ES) and Stress Value (SV) are local measures of risk as they focus on the tails of the distribution. The study of the left tail is relevant for an investor holding a long position in the XRP while the study of the right tail is relevant for an investor holding a short position in the XRP.

Why should I be interested in this post?

The post offers an opportunity for both newcomers and seasoned cryptocurrency enthusiasts to delve into the concept of stablecoins, gaining insights into how digital assets maintain stability amidst market volatility. Furthermore, the post highlights USDC’s role in fostering financial inclusion by enabling borderless transactions, appealing to readers passionate about democratizing finance. Additionally, exploring USDC’s significance in the burgeoning realm of decentralized finance (DeFi) could intrigue those interested in innovative financial technologies and investment opportunities. Examining USDC’s historical performance and market dynamics can offer valuable insights for investors and traders, while shedding light on its compliance measures and regulatory landscape can address concerns regarding legal risks, contributing to readers’ understanding and confidence in this digital asset.

Useful resources

Academic research about risk

Longin F. (2000) From VaR to stress testing: the extreme value approach Journal of Banking and Finance, N°24, pp 1097-1130.

Longin F. (2016) Extreme events in finance: a handbook of extreme value theory and its applications Wiley Editions.

Data

Yahoo! Finance Historical data for USDC

CoinMarketCap Historical data for USDC

About the author

The article was written in March 2024 by Snehasish CHINARA (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2024).

Tether: Unraveling the Impact of the Stablecoin on Modern Finance

May 20, 2024March 25, 2024 by Snehasish CHINARA

In this article, Snehasish CHINARA (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2024) explains the cryptocurrency Tether.

Historical context and background

Tether (USDT) emerged in July 2014 as a groundbreaking cryptocurrency designed to bridge the gap between traditional fiat currencies and the digital realm. Conceived by Brock Pierce, Craig Sellars, and Reeve Collins, Tether aimed to offer a stable alternative to the price volatility commonly associated with other cryptocurrencies like Bitcoin. Functioning as a stablecoin, Tether is pegged to the value of a fiat currency, primarily the US Dollar, maintaining a 1:1 ratio. This pegging mechanism provides a degree of price stability, making Tether an attractive option for traders and investors seeking to mitigate the risks inherent in the crypto market. Since its inception, Tether has grown to become one of the most widely used cryptocurrencies, playing a pivotal role in facilitating liquidity and serving as a gateway for funds within the broader digital asset ecosystem. Despite its popularity, Tether has faced scrutiny regarding its transparency and reserve backing, adding layers of complexity to its intriguing history within the crypto landscape.

Tether Logo

Source: Yahoo! Finance

Figure 1. Key Dates in Tether History

Source: Yahoo! Finance

Key features

Stability: Tether is designed to maintain a 1:1 peg with a fiat currency, typically the US Dollar. This pegging mechanism provides stability in value, reducing the volatility commonly associated with other cryptocurrencies.

Widespread Adoption: Tether is widely adopted across various cryptocurrency exchanges and platforms. Its broad acceptance makes it a popular choice for traders and investors.

Liquidity Provider: Tether serves as a crucial source of liquidity within the cryptocurrency market. Traders often use USDT as a safe haven during times of market turbulence.

Fast Transactions: Tether transactions are conducted on blockchain networks like Ethereum and Tron, enabling fast and efficient cross-border transactions compared to traditional banking systems.

Global Accessibility: Tether provides a borderless financial solution, allowing users to send and receive funds globally without the need for traditional banking intermediaries.

Smart Contract Compatibility: Tether is compatible with blockchain-based smart contracts, enabling developers to integrate it into decentralized applications (DApps) for various financial services.

Use cases

Trading Pairs: Tether is extensively used as a trading pair on cryptocurrency exchanges. It allows traders to quickly move in and out of positions while avoiding the market volatility associated with other digital assets.

Hedging: Investors often use Tether as a hedging tool to safeguard their portfolios against market fluctuations. By converting their holdings to USDT during uncertain times, investors can protect their capital.

Remittances: Tether facilitates cross-border remittances, offering a faster and potentially more cost-effective alternative to traditional money transfer services.

Stable Value Storage: Tether serves as a stable store of value, allowing users to preserve their wealth in a cryptocurrency that is less prone to price fluctuations compared to other volatile digital assets.

Decentralized Finance (DeFi): Tether is a common stablecoin used in decentralized finance protocols for lending, borrowing, and yield farming activities within the growing DeFi ecosystem.

Tokenized Assets: Tether is utilized to represent traditional assets in tokenized form on blockchain platforms, enabling fractional ownership and increased liquidity for real-world assets.

Controversy

While Tether has gained popularity for its stability and utility, it has also faced criticism and regulatory scrutiny, particularly regarding its reserve backing and transparency. The paper “Is Bitcoin Really Untethered?”, analyses Tether’s controversies revealing that the significant growth of Tether, the largest pegged cryptocurrency, may not solely be driven by organic investor demand but also by a scheme to artificially inflate cryptocurrency prices. By analyzing the blockchains of Bitcoin and Tether, the researchers found evidence suggesting that a major player on Bitfinex utilizes Tether to purchase large amounts of Bitcoin during price downturns, effectively supporting Bitcoin prices. This intervention is particularly pronounced near round-number price thresholds, indicating a strategic approach to price support. The data also suggests that Tether issuance correlates with a month-end need for dollar reserves, implying partial reserve backing. Overall, these findings lend support to the hypothesis that Tether’s growth is supply-driven and potentially manipulative.

Furthermore, the paper highlights the broader implications of these findings, suggesting that price manipulation in cryptocurrencies can have significant distortive effects beyond standard supply-and-demand dynamics. It highlights the need for external surveillance, monitoring, and regulatory frameworks in the cryptocurrency space, challenging the notion that innovative technologies alone can bypass the need for traditional oversight. The conclusion suggests that while cryptocurrencies offer new possibilities for financial innovation, they are not immune to the risks associated with dubious activities and speculative bubbles, which could ultimately lead to further price distortions and negative impacts on the market.

Technology and underlying blockchain

Tether (USDT), a the stablecoin, utilizes blockchain technology across various networks to provide a digital asset pegged to traditional fiat currencies. The Ethereum-based ERC-20 version is the most prevalent, benefiting from Ethereum’s widespread adoption and smart contract capabilities, making it a cornerstone for decentralized exchanges and DeFi applications. Tether also operates on the Tron blockchain (TRC-20), offering faster transactions and reduced fees, particularly appealing for high-frequency trading. Acknowledging the demand for scalability and cost-effectiveness, Tether has expanded to alternative blockchains like Binance Smart Chain and Solana. These adaptations highlight Tether’s commitment to meeting diverse market needs. Managed by Tether Limited, the stablecoin asserts stability through regular audits and claims of holding equivalent fiat reserves, although controversies have arisen regarding transparency. The dynamic interplay of Tether across various blockchains underscores its pivotal role in the evolving landscape of digital finance.

Supply of coins

The market supply of Tether (USDT), a leading stablecoin, plays a crucial role in the broader cryptocurrency ecosystem. Tether’s supply dynamics are intrinsically linked to its unique issuance mechanism. Tether Limited, the company behind USDT, mints new tokens by receiving equivalent amounts of fiat currency, primarily the US Dollar, in its reserves. This issuance process is supposed to ensure a 1:1 peg between USDT and the underlying fiat currency, promoting price stability.

The market supply of Tether expands as demand for the stablecoin increases. Traders and investors often turn to USDT as a safe haven during times of high volatility, effectively increasing the circulating supply. This heightened demand results in additional Tether being issued to maintain the peg, thus influencing the overall supply in the market.

Monitoring Tether’s supply is of particular interest due to its significant impact on liquidity within the cryptocurrency space. An influx of USDT into the market provides traders with a reliable means to navigate price fluctuations without fully exiting the crypto market, enhancing liquidity and potentially mitigating market volatility.

Historical data for Tether

How to get the data?

The Tether is popular cryptocurrency on the market, and historical data for the Tether such as prices and volume traded can be easily downloaded from the internet sources such as Yahoo! Finance, Blockchain.com & CoinMarketCap. For example, you can download data for Tether on Yahoo! Finance (the Yahoo! code for Tether is USDT-USD).

Figure 1. Tether data

Source: Yahoo! Finance.

Historical data for the Tether market prices

Exploring the historical market price of Tether (USDT) unveils a wealth of insights into the cryptocurrency’s role and its impact on the broader market. The stability of Tether, designed to maintain a 1:1 peg with traditional fiat currencies, is reflected in its price history. Observing deviations from this peg over time provides a gauge of Tether’s effectiveness as a stablecoin. Moreover, fluctuations in Tether’s historical price serve as a barometer for market sentiment, indicating periods of increased demand for stability during cryptocurrency market volatility. Tether’s price movements also offer a lens into liquidity trends, showcasing its role in various financial activities within the crypto ecosystem. Crucially, analyzing Tether’s historical price can illuminate market responses to controversies, regulatory developments, and its correlation with other major cryptocurrencies. Understanding these dynamics is essential for investors and traders seeking to navigate the intricate landscape of digital assets and stablecoins.

Figure 2 below represents the evolution of the price of Tether in US dollar over the period Nov 2017 – Dec 2023. The price corresponds to the “closing” price (observed at 10:00 PM CET at the end of the month).

Figure 2. Evolution of the Tether price

Source: Yahoo! Finance.

R program

The R program below written by Shengyu ZHENG allows you to download the data from Yahoo! Finance website and to compute summary statistics and risk measures about the Ethereum.

Data file

The R program that you can download above allows you to download the data for the Ethereum from the Yahoo! Finance website. The database starts on December, 2017.

Table 1 below represents the top of the data file for the Ethereum downloaded from the Yahoo! Finance website with the R program.

Table 1. Top of the data file for the Tether

Source: computation by the author (data: Yahoo! Finance website).

Python code

You can download the Python code used to download the data from Yahoo! Finance.

Python script to download Ethereum historical data and save it to an Excel sheet::

import yfinance as yf

import pandas as pd

# Define the ticker symbol for Tether

eth_ticker = “USDT-USD”

# Define the date range for historical data

start_date = “2020-01-01”

end_date = “2022-01-01”

# Download historical data using yfinance

usdt_data = yf.download(eth_ticker, start=start_date, end=end_date)

# Create a Pandas DataFrame from the downloaded data

usdt_df = pd.DataFrame(usdt_data)

# Define the Excel file path

excel_file_path = “tether_historical_data.xlsx”

# Save the data to an Excel sheet

usdt_df.to_excel(excel_file_path, sheet_name=”USDT Historical Data”)

print(f”Data saved to {excel_file_path}”)

# Make sure you have the required libraries installed and adjust the “start_date” and “end_date” variables to the desired date range for the historical data you want to download.

Evolution of the Tether

Figure 3 below gives the evolution of the Tether on a daily basis.

Figure 3. Evolution of the Tether.

Source: computation by the author (data: Yahoo! Finance website).

Figure 4 below gives the evolution of the Tether returns from November 09, 2017 to December 31, 2022 on a daily basis.

Figure 4. Evolution of the Tether returns.

Source: computation by the author (data: Yahoo! Finance website).

Summary statistics for the Ethereum

The R program that you can download above also allows you to compute summary statistics about the returns of the Tether.

Table 4 below presents the following summary statistics estimated for the Ethereum:

The mean

The standard deviation (the squared root of the variance)

The skewness

The kurtosis.

The mean, the standard deviation / variance, the skewness, and the kurtosis refer to the first, second, third and fourth moments of statistical distribution of returns respectively.

Table 2. Summary statistics for Tether.

Source: computation by the author (data: Yahoo! Finance website).

Statistical distribution of the Tether returns

Historical distribution

Figure 5 represents the historical distribution of the Tether daily returns for the period from November 09, 2017 to December 31, 2022.

Figure 5. Historical Tether distribution of the returns.

Source: computation by the author (data: Yahoo! Finance website).

Gaussian distribution

Figure 6 below represents the Gaussian distribution of the Tether daily returns with parameters estimated over the period from November 09, 2017 to December 31, 2022.

Figure 6. Gaussian distribution of the Tether returns.

Source: computation by the author (data: Yahoo! Finance website).

Risk measures of the Tether returns

The R program that you can download above also allows you to compute risk measures about the returns of the tether.

Table 3 below presents the following risk measures estimated for the Ethereum:

The long-term volatility (the unconditional standard deviation estimated over the entire period)

The short-term volatility (the standard deviation estimated over the last three months)

The Value at Risk (VaR) for the left tail (the 5% quantile of the historical distribution)

The Value at Risk (VaR) for the right tail (the 95% quantile of the historical distribution)

The Expected Shortfall (ES) for the left tail (the average loss over the 5% quantile of the historical distribution)

The Expected Shortfall (ES) for the right tail (the average loss over the 95% quantile of the historical distribution)

The Stress Value (SV) for the left tail (the 1% quantile of the tail distribution estimated with a Generalized Pareto distribution)

The Stress Value (SV) for the right tail (the 99% quantile of the tail distribution estimated with a Generalized Pareto distribution)

Table 5. Risk measures for the Tether.

Source: computation by the author (data: Yahoo! Finance website).

The volatility is a global measure of risk as it considers all the returns. The Value at Risk (VaR), Expected Shortfall (ES) and Stress Value (SV) are local measures of risk as they focus on the tails of the distribution. The study of the left tail is relevant for an investor holding a long position in the XRP while the study of the right tail is relevant for an investor holding a short position in the XRP.

Why should I be interested in this post?

Tether is a compelling subject for students, especially those in finance, economics, business, and technology. Ripple’s focus on revolutionizing cross-border transactions and its unique blockchain technology offer insights into innovations in financial technology. Exploring Tether provides a deeper understanding of blockchain, cryptocurrency markets, stable coins, regulatory challenges, and the practical applications of decentralized systems. Students can gain valuable knowledge about market dynamics, investment opportunities, and the intersection of technology and finance by delving into the complexities of Tether and its impact on the financial industry.

Useful resources

Academic research about risk

Longin F. (2000) From VaR to stress testing: the extreme value approach Journal of Banking and Finance, N°24, pp 1097-1130.

Longin F. (2016) Extreme events in finance: a handbook of extreme value theory and its applications Wiley Editions.

Data