Financial techniques Archives

Interest Rates and M&A: How Market Dynamics Shift When Rates Rise or Fall

December 3, 2025 by Emanuele BAROLI

In this article, Emanuele BAROLI (MiF 2025–2027, ESSEC Business School) examines how shifts in interest rates shape the M&A market, outlining how deal structures differ when central banks raise versus cut rates.

Context and objective

The purpose is to explain what interest rates are, how they interact with inflation and liquidity, and how these variables shape merger and acquisition (M&A) activity. The intended outcome is an operational lens you can use to read the current monetary cycle and translate it into cost of capital, valuation, financing structure, and execution windows for deals, distinguishing—when useful—between corporate acquirers and private-equity sponsors.

What are interest rates

Interest rates are the intertemporal price of funds. In economic terms they remunerate the deferral of consumption, insure against expected inflation, and compensate for risk. For real decisions the relevant object is the real rate because it governs the trade-off between investing or consuming today versus tomorrow.

Central banks anchor the very short end through the policy rate and the management of system liquidity (reserve remuneration, market operations, balance-sheet policies). Markets then map those signals into the entire yield curve via expectations about future policy settings and required term premia. When liquidity is ample and cheap, risk-free yields and credit spreads tend to compress; when liquidity becomes scarcer or dearer, yields and spreads widen even without a headline change in the policy rate. This transmission, with its usual lags, is the bridge from monetary conditions to firms’ investment choices.

M&A industry — a definition

The M&A industry comprises mergers and acquisitions undertaken by strategic (corporate) acquirers and by financial sponsors. Activity is the joint outcome of several blocks: the cost and elasticity of capital (both debt and equity), expectations about sectoral cash flows, absolute and relative valuations for public and private assets, regulatory and antitrust constraints, and the degree of managerial confidence. Interest rates sit at the center because they enter the denominator of valuation models—through the discount rate—and they shape bankability constraints through the debt service burden. In other words, rates influence both the price a buyer can rationally pay and the feasibility of financing that price.

Use of leverage

Leverage translates a given cash-flow profile into equity returns. In leveraged acquisitions—especially LBOs—the all-in cost of debt is set by a market benchmark (in practice, Term SOFR at three or six months in the U.S., and Euribor in the euro area) plus a spread reflecting credit risk, liquidity, seniority, and the supply–demand balance across channels such as term loans, high-yield bonds, and private credit. That all-in cost determines sustainable leverage, shapes covenant design, and fixes the headroom on metrics like interest coverage and net leverage. It ultimately caps the bid a sponsor can submit while still meeting target returns. Corporate acquirers usually employ more modest leverage, yet remain rate-sensitive because medium-to-long risk-free yields and investment-grade spreads feed both fixed-rate borrowing costs and the WACC used in DCF and accretion tests, and they influence the value of stock consideration in mixed or stock-for-stock deals.

How interest rates impact the M&A industry

The connection from rates to M&A operates through three main channels. The first is valuation: holding cash flows constant, a higher risk-free rate or higher term premia lifts discount rates, lowers present values, and compresses multiples, thereby narrowing the economic room to pay a control premium. The second is bankability: higher benchmarks and wider spreads raise coupons and interest expense, reduce sustainable leverage, and shrink the set of financeable deals—most visibly for sponsors whose equity returns depend on the spread between debt cost and EBITDA growth. The third is market access: heightened rate volatility and tighter liquidity reduce underwriting depth and risk appetite in loans and bonds, delaying signings or closings; the mirror image under easing—lower rates, stable curves, and tighter spreads—reopens windows, enabling new-money term funding and refinancing of maturities. The net effect is a function of level, slope, and volatility of the curve: lower and calmer curves with steady spreads tend to support volumes; high or unstable curves, even with unchanged spreads, enforce selectivity.

Evidence from 2021–2024 and what the chart shows

M&A deals and interest rates (2021-2024).

Source: Fed.

The global pattern over 2021–2024 is consistent with this mechanism. In 2021, deal counts reached a cyclical peak in an environment of near-zero short-term rates, abundant liquidity, and elevated equity valuations; frictions on the cost of capital were minimal and access to debt markets was easy, so the economic threshold for completing transactions was lower. Between 2022 and 2024, monetary tightening lifted short-term benchmarks rapidly while spreads and uncertainty rose; global deal counts fell materially and the market became more selective, favoring higher-quality assets, resilient sectors, and transactions with stronger industrial logic. Over this period, global deal counts were 58,308 in 2021, 50,763 in 2022, 39,603 in 2023, and 36,067 in 2024, while U.S. short-term rates moved from roughly 0.14% to above 5%; the chart shows an inverse co-movement between the cost of money and activity. Correlation is not causation—antitrust enforcement, energy shocks, equity multiple swings, and the rise of private credit also mattered—but the macro signal aligns with monetary transmission.

What does academic research say

Academic research broadly confirms the mechanism sketched above: when policy rates rise and financing conditions tighten, both the volume and composition of M&A activity change. Using U.S. data, Adra, Barbopoulos, and Saunders (2020) show that increases in the federal funds rate raise expected financing costs, are followed by more negative acquirer announcement returns, and significantly increase the probability that deals are withdrawn, especially when monetary policy uncertainty is high. Fischer and Horn (2023) and Horn (2021) exploit high-frequency monetary-policy shocks and find that a contractionary shock leads to a persistent fall in aggregate deal numbers and values—on the order of 20–30%—with the effect concentrated among financially constrained bidders; at the same time, the average quality of completed deals improves because weaker acquirers are screened out. Work on leveraged buyouts links this to credit conditions: Axelson et al. (2013) document that cheap and abundant credit is associated with higher leverage and higher buyout prices relative to comparable public firms, while theoretical models such as Nicodano (2023) show how optimal LBO leverage and default risk respond systematically to the level of risk-free rates and credit spreads.

Useful resources

Academic articles

Adra, S., Barbopoulos, L., & Saunders, A. (2020). The impact of monetary policy on M&A outcomes. Journal of Corporate Finance, 62, 1-61.

Fischer, J. and Horn, C.-W. (2023), Monetary Policy and Mergers and Acquisitions, Working paper Available at SSRN

Horn, C.-W. (2021) Does Monetary Policy Affect Mergers and Acquisitions? Working paper.

Axelson, U., Jenkinson, T., Strömberg, P., & Weisbach, M. S. (2013) Borrow Cheap, Buy High? The Determinants of Leverage and Pricing in Buyouts, The Journal of Finance, 68(6), 2223-2267.

Financial data

Federal Reserve Bank of New York Effective Federal Funds Rate (EFFR): methodology and data

Federal Reserve Bank of St. Louis Effective Federal Funds Rate (FEDFUNDS)

OECD Data Long-term interest rates

About the author

The article was written in November 2025 by Emanuele BAROLI (ESSEC Business School, Master in Finance (MiF), 2025–2027).

▶ Read all articles by Emanuele BAROLI.

Drafting an Effective Sell-Side Information Memorandum: Insights from a Sell-Side Investment Banking Experience

December 3, 2025 by Emanuele BAROLI

In this article, Emanuele BAROLI (ESSEC Business School, Master in Finance (MiF), 2025–2027) explains how to draft an M&A Information Memorandum, translating sell-side investment-banking practice into a clear, evidence-based guide that buyers can use to progress from interest to a defensible bid.

What is an Info Memo

An information memorandum is a confidential, evidence-based sales document used in M&A processes to enable credible offers while safeguarding the sell-side process. It sets out what is being sold, why it is attractive, and how the deal is framed, and it is structured—consistently and without redundancy—around the following chapters: Executive Summary, Key Investment Highlights, Market Overview, Business Overview, Historical Financial Performance and Current-Year Budget, Business Plan, and Appendix. Each section builds on the previous one so that every claim in the narrative is traceable to data, definitions, and documents referenced in the appendix and the data room.

Executive summary

The executive summary is the gateway to the memorandum and must allow a prospective acquirer to grasp, within a few pages, what is being sold, why the asset is attractive, and how the transaction is framed. It should state the perimeter of the deal, the nature of the stake or assets included, and the essence of the equity story in language that is direct, verifiable, and consistent with the evidence presented later. The narrative should situate the company in its market, outline the recent trajectory of scale, profitability, and cash generation, and articulate—in plain terms—the reasons an informed buyer might assign strategic or financial value. Nothing here should rely on empty superlatives; every claim in the summary must be traceable to supporting material in subsequent sections and to documents made available in the data room. Clarity and internal consistency matter more than flourish: the reader should finish this section knowing what the asset is, why it matters, and what next steps the process anticipates.

Key investment highlights

This section filters the equity story into a small number of decisive arguments, each of which combines a clear assertion, hard evidence, and an explicit investor implication. The prose should explain, not advertise sustainable growth drivers, defensible competitive positioning, quality and predictability of revenue, conversion of earnings into cash, discipline in capital allocation, credible management execution, and identifiable avenues for organic expansion or bolt-on M&A. Each highlight should read as a self-contained reasoning chain—statement, proof, consequence—so that a buyer can connect operational facts to valuation logic.

Market overview

The market overview demonstrates that the asset operates within an addressable space that is sizeable, healthy, and legible. Begin by defining the market perimeter with precision so that later revenue segmentations align with it. Describe the current size and structure of demand, the expected growth over a three-to-five-year horizon, and the drivers that sustain or threaten that growth—technological shifts, regulatory trends, customer procurement cycles, and macro sensitivities. Map the competitive landscape in terms of concentration, barriers to entry, switching costs, and price dynamics across channels. Distinguish between the immediate market in which the company competes and the broader industry environment at national or international level, explaining how each influences pricing power, customer acquisition, and margin stability. All figures and characterizations should be sourced to independent references, allowing the reader to verify both methodology and magnitude.

Business overview

The business overview explains plainly how the company creates value. It should describe what is sold, to whom, and through which operating model, covering products and services, relevant intellectual property or certifications, customer segments and geographies served, and the logic of revenue generation and pricing. The text should make the differentiation intelligible—quality, reliability, speed, functionality, service levels, or total cost of ownership—and then connect that differentiation to commercial traction. Operations deserve a concise, concrete treatment: footprint, capacity and utilization, supply-chain architecture, service levels, and, where material, the technology stack and data security posture. The section should close with the people who actually run the company and are expected to remain post-closing, outlining roles, governance, and incentive alignment. The aim is not to impress with jargon but to let an investor see a coherent engine that turns inputs into outcomes.

Historical financial performance and budget

This chapter turns performance into an intelligible narrative. Present the historical income statement, balance sheet, and cash flow over a three-to-five-year window—preferably audited—and reconcile management accounts with statutory figures so that definitions, policies, and adjustments are transparent. Replace tables-for-tables’ sake with analysis: show where growth and margins come from by decomposing revenue into volume, price, and mix; explain EBITDA dynamics through efficiency, pricing, and non-recurring items; separate maintenance from growth capex; and trace how earnings convert into cash by discussing working-capital movements and seasonality. In a live process, the current-year budget should set out the explicit operating assumptions behind it, the key milestones and risks, and a brief intra-year read so a buyer can compare budget to year-to-date performance. If carve-outs, acquisitions, or other discontinuities exist, present clean pro forma views so the time series remains comparable.

Business plan

The business plan translates the equity story into forward-looking numbers and commitments that can withstand diligence. Build the plan from drivers rather than percentages: revenue as a function of volumes, pricing, mix, and retention; costs split between fixed and variable components with operational leverage and efficiency initiatives laid out; capital needs expressed through capex, working-capital discipline, and any anticipated financing structure. Provide a three-to-five-year view of P&L, cash flow, and balance-sheet implications, making explicit the capacity constraints, hiring requirements, and lead times that link initiatives to outcomes. A sound plan includes a base case and either sensitivities or alternative scenarios, together with risk mitigations that are actually within management control. If bolt-on M&A features in the strategy, describe the screening criteria, integration capability, and the nature of the synergies in a way that distinguishes aspiration from execution.

Appendix

The appendix holds detail without overloading the core narrative and preserves auditability. It should contain the full legal disclaimer and confidentiality terms, a glossary of definitions and KPIs to eliminate ambiguity, detailed financial schedules and reconciliation notes, methodological summaries and citations for market data, concise contractual information for key customers and suppliers where material, operational and ESG indicators that genuinely affect value, and a process note with timeline, bid instructions, Q&A protocols, and site-visit guidance. The organizing principle is traceability: any figure or claim in the memorandum should be traceable to a line item or document referenced here and made available in the data room.

Why should you be interested in this post?

For students interested in corporate finance and M&A, this post shows how to translate sell-side practice into a rigorous structure that investors can actually diligence—an essential skill for internships and analyst roles.

Useful resources

Corporate Finance Institute – (CFI) Confidential Information Memorandum (CIM)

DealRoom How to Write an M&A Information Memorandum

About the author

The article was written in December 2025 by Emanuele BAROLI (ESSEC Business School, Master in Finance (MiF), 2025–2027).

▶ Read all articles by Emanuele BAROLI.

At what point does diversification becomes “Diworsification”?

December 1, 2025November 30, 2025 by Yann TANGUY

In this article, Yann TANGUY (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2023-2027) explains the concept of “diworsification” and shows how to avoid falling into its trap.

The Concept of Diworsification

The word “diworsification” was coined by famous portfolio manager Peter Lynch to denote the habit of supplementing a portfolio with investments which, instead of improving risk-adjusted return, add complexity. It demonstrates a common misconception of one of the fundamental pillars of the Modern Portfolio Theory (MPT): diversification.

Whereas the adage “don’t put all your eggs in one basket” exemplifies the foundation of prudent portfolio building, diworsification occurs when an investor adds too many baskets and thus loses sight of the quality and purpose of each one.

This mistake comes from a fundamental misunderstanding of what diversification actually is. Diversification is not a function of the quantity of assets owned by an investor but of the interconnections of assets. If an investor introduces assets highly correlated with assets owned to a portfolio, the diversification effect of risk is greatly reduced, and a portfolio’s possible return can be diluted.

Practical Example

Let’s assume there are two investors.

An investor who is interested in the tech industry may hold shares in 20 different software and hardware companies. This portfolio appears diversified on the surface. However, since all the companies are in the same industry, they are all subject to the same market forces and risks. In a decline of the tech industry, it is likely many of the stocks will decline at the same time due to their high correlation.

A second investor maintains a portfolio of three low-cost index funds: one dedicated to the total US stock market, another for the total international stock market, and a third focusing on the total bond market. Despite the simplicity of holding just these three positions, this investor enjoys a far more effective level of diversification in their portfolio. The assets, US stocks, international stocks, and bonds, have a low correlation with one another. Consequently, poor performance in one asset class is likely to be counterbalanced by stable or positive returns in another, resulting in a smoother return profile and a reduction in overall portfolio risk.

The portfolio of the first investor is a perfect case of diworsification. Increasing the number of technology stocks did not do any sort of risk diversification, but it introduced complexity and diluted the effect of performing stocks.

The point at which diversification began to operate to its own harm can be identified with several factors. Diversification’s initial goal is to improve the risk-adjusted return, a concept often evaluated using the Sharpe ratio. Diworsification begins when adding a new asset does not contribute to an improvement in the portfolio’s Sharpe ratio.

You can download the Excel below with a numerical example of the impact of correlation in diversification.

Here is a short summary of what is shown in the Excel spreadsheet.

We used two different portfolios, each with 2 assets and both portfolios having a similar expected return and average volatility of assets. The only difference is that the first portfolio has correlated assets, whereas the second portfolio has non-correlated assets.

Correlated portfolio returns over volatility

Non-Correlated portfolio returns over volatility

As you can see in these graphs, the diversification effect is much more potent for the non-correlated portfolio, leading to higher returns for a given volatility.

Target number of assets for a diversified portfolio

One of the most important considerations when assembling a portfolio is determining the optimal number of assets relative to which greater diversification can be realized prior to the onset of diworsification. Studies of equity markets had indicated that a portfolio of 20 to 30 stocks could diversify away unsystematic risk.

However, this number varies according to different asset classes and the complexity of the assets. In the world of alternative investments, a landmark study, “Hedge fund diversification: how much is enough?,” was published by authors François-Serge Lhabitant and Michelle Learned in 2002, for the Journal of Alternative Investments. The authors aimed to dispel the myth that ‘more is better’ in the complex world of hedge funds. They analyzed the effect of the size of the portfolio on risk and return, determining that although adding to the portfolio reduces risk, the marginal benefits of diversification diminished rapidly.

Importantly they found that adding too many funds could lead to a convergence toward average market returns, effectively eroding the “alpha” (excess return) that investors seek from active management. Furthermore, even when volatility is reduced, other forms of risks, such as skewness and kurtosis, can get worse. The significance of this research is that it offers empirical evidence for the phenomenon of ‘diworsification’—the idea that, after a certain point, adding assets to a portfolio worsens its efficiency.

Crossover from Diversification to Diworsification

The crossover from diversification to diworsification is normally marked by three main factors.

The first is diluted returns, as the number of assets increases, the performance of the portfolio starts to resemble that of a market index, albeit with elevated costs. The favorable influence of a handful of significant winners is offset by the poor performance of many other investments.

The second is an increase in costs as each asset, and particularly each asset owned through a managed fund, comes with some costs. These can be transaction costs, management fees, or costs of research. The more assets there are, the costs add up and ultimately impose a drag on final performance.

The third is unnecessary complexity as a portfolio with too many holdings becomes hard to keep tabs on, analyze, and rebalance. Which can confuse an investor about his or her asset allocation and expose the portfolio to unnecessary risk.

Causes of Diworsification

The causes for diworsification differ systematically between individual and institutional investors. For individual investors, this fundamental mistake arises from an incorrect understanding of genuine diversification, far too often leading to an emphasis on numbers rather than quality. Behavioral biases, such as familiarity bias, manifested in a preference for investing in well-known names of firms, or fear of missing out, which drives investors toward recently outperforming “hot” stocks, can generate portfolios concentrated in highly correlated securities.

The causes of diworsification for institutional investors are fundamentally different. The asset management business puts on a lot of strain that can lead to diworsification. Fund managers, measured against a comparator index, may prefer to build oversized funds whose portfolios are similar to the index, a process called “closet indexing.” Even if such a strategy reduces the risk of underperforming the comparator and thus losing clients, it also ensures that the fund will not show meaningful outperformance, all the time collecting fees for what is wrongly qualified as active management. In addition, the sale of complex product types like “funds of funds” adds further levels of fees and can mask the fact that the underlying assets are often far from unique.

How to avoid Diworsification

Diworsification doesn’t refer to an abandonment of diversification. Rather, it demands a more intelligent strategy. The emphasis should move from raw number of holdings to the correct asset allocation of the portfolio. The key is to mix asset classes with low or even adverse correlations to each other, for example, stocks, government securities, real estate, and commodities. This method allows for a more solid shelter from price fluctuations than keeping a long list of homogeneous stocks.

A low-cost and efficient means for many investors to achieve this goal is to utilize broad-market index funds and ETFs. These financial products give exposure to thousands of underlying securities representing full asset classes within a single holding, thus eliminating the difficulties and high costs of creating an equivalent portfolio of single assets.

Conclusion

Modern Portfolio Theory provides an intriguing framework for crafting portfolios for investments, and its essential concept of diversification still forms its basis. However, implementing this concept requires thoughtful consideration. Diworsification represents a misinterpretation of the objective, and not an objective to add assets simply in numbers, but to improve the risk-return of the portfolio as a whole.

A successful diversification strategy is built on a foundation of asset allocation to low-correlation assets. By focusing on the quality of diversification rather than the quantity of positions, investors can create portfolios that are closer to what they want, avoiding unnecessary costs and lower returns of a diworsified outcome.

Why should I be interested in this post?

Diworsification is a trap that should be avoided, and is really easy to avoid when you understand the mechanisms at work behind it.

Useful resources

Lhabitant, F.-S., M. Learned (2002) Hedge fund diversification: how much is enough? Journal of Alternative Investments, 5(3):23-49.

Lynch P., J. Rothchild (2000) One up on Wall Street. New York: Simon & Schuster.

Markowitz, H. (1952). Portfolio Selection. The Journal of Finance, 7(1), 77–91.

About the author

This article was written in November 2025 by Yann TANGUY (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2023-2027).

Understanding Snowball Products: Payoff Structure, Risks, and Market Behavior

November 29, 2025 by Tianyi WANG

In this article, Tianyi WANG (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2022-2026) explains the structure, payoff, and risks of Snowball products — one of the most popular and complex structured products in Asian financial markets.

Introduction

Structured products can be positioned along a broad risk–return spectrum.

Snowball Structure Product .

Source: public market data.

As shown in the figure below, Snowball Notes belong to the category of yield-enhancement products, typically offering annualized returns of around 8% to 15%. These products sit between capital-protected structures—which provide lower but more stable returns—and high-risk leveraged instruments such as warrants. This placement highlights a key feature of Snowballs: while they provide attractive coupons under normal market conditions, they come with conditional downside risk once the knock-in barrier is breached. Understanding this relative positioning helps explain why Snowballs are widely marketed during stable or range-bound markets but may expose investors to significant losses when volatility spikes.

Snowball options have become widely traded structured products in Asian equity markets, especially in China, Korea, and Hong Kong. They appeal to investors seeking stable returns in range-bound markets. However, their path-dependent nature and embedded option risks make them highly sensitive to market volatility. During periods of rapid market decline, many Snowball products experience “knock-in” events or even large losses.

To be more specific, a knock-in event occurs when the underlying asset’s price falls below (or rises above, depending on the product design) a predetermined barrier level during the life of the product. Once this barrier is breached, the Snowball option “activates” the embedded option exposure—typically converting what was originally a principal-protected or coupon-paying structure into one that behaves like a short option position. As a result, the investor becomes directly exposed to downside risks of the underlying asset, often leading to significant mark-to-market losses.

This article explains how Snowball products work, their payoff structure, the embedded risks, and how market behavior affects investor outcomes.

Who buys Snowball products?

Snowball products are purchased mainly by:

Retail investors — especially in mainland China and Korea, attracted by high coupons and the perception of stability.
High-net-worth individuals (HNWI) — through private banking channels.
Institutional investors — such as securities firms and structured product funds, often using Snowballs for yield enhancement.

Because Snowballs involve complex embedded options, they are considered unsuitable for inexperienced retail investors. Nevertheless, retail participation has grown significantly in Asian markets.

What is a Snowball product?

A Snowball is a structured product linked to an equity index (e.g., CSI 500, HSCEI) or a single stock. It provides a fixed coupon if the underlying asset stays within certain price barriers. The product contains three key components:

Autocall (Knock-out) — product terminates early at a profit if the underlying rises above a set level.
Knock-in — if the underlying falls below a certain barrier, the investor becomes exposed to downside risk.
Coupon payment — paid periodically as long as knock-in does not occur and knock-out does not trigger.

Snowballs earn steady income in stable markets, but losses can become severe when markets experience sharp declines.

The name “Snowball” comes from the idea of a snowball rolling downhill: it grows larger over time. In structured products, the coupon accumulates (or “rolls”) as long as the product does not knock-in or knock-out. As the months go by, the investor receives a growing stream of accrued coupons — similar to a snowball becoming bigger. However, like a snowball that can suddenly break apart if it hits an obstacle, the product can suffer significant losses once the knock-in barrier is breached.

Market behavior: what does it mean?

In the context of Snowball pricing and risk, “market behavior” refers to two dimensions:

Financial market behavior (price dynamics) — movements of the underlying index or stock, volatility levels, liquidity conditions, and short-term shocks. This includes trends such as rallies, range-bound phases, or sharp sell-offs that affect knock-in and knock-out probabilities.
Investor behavior — how different market participants react: hedging flows from issuers, panic selling during downturns, retail speculation, institutional risk reduction, and shifts in investor sentiment. These behaviors can reinforce price moves and alter Snowball risk.

Together, these elements form “market behavior”: the interaction between market movements and investor actions. For Snowballs, this directly affects whether the product pays coupons, knocks out early, or falls into knock-in and creates losses.

Key barriers in Snowball products

Knock-out (Autocall) barrier

If at any observation date the price exceeds the knock-out barrier (e.g., 103%), the product terminates early and investors receive principal plus accumulated coupons.

Knock-in barrier

If the price falls below the knock-in barrier (e.g., 80%), the product enters a risk state. If at maturity the price remains below the strike, the investor bears the underlying’s loss.

How Snowball payoffs work

The payoff of a Snowball is path-dependent, meaning it depends on the entire trajectory of the underlying index, not just the final price at maturity.

There are three typical outcomes:

Knock-out outcome (early exit)

If the underlying exceeds the knock-out level early, the investor receives:
Principal + accumulated coupons

No knock-in, no knock-out (maturity coupon)

If the underlying never crosses either barrier:
Principal + full coupons

Knock-in triggered (risky outcome)

If knock-in occurs and the final price ends below strike:
The investor bears the underlying loss

Thus, Snowballs deliver strong returns in stable or mildly rising markets but carry significant losses in bear markets.

Why Snowball products are risky

Although marketed as “income products,” Snowballs are essentially short-volatility strategies. The issuer sells downside protection to the investor in exchange for coupons.

Key risks include:

High volatility increases knock-in probability
Sharp declines lead to principal losses
Liquidity risk
Complex payoff makes risks hard to evaluate for retail investors

Case study: Why many Snowballs were hit in 2022–2023

During 2022–2023, Chinese equity markets — especially the CSI 500 and CSI 1000 — experienced large drawdowns due to geopolitical tensions, policy uncertainty, and weak economic recovery. Volatility spiked, and mid-cap indices saw rapid declines.

As a result:

Many Snowballs hit knock-in levels
Investors faced large mark-to-market losses
Issuers reduced new Snowball supply due to elevated volatility

This period highlights how market sentiment and volatility regimes directly impact structured product outcomes.

According to Bloomberg (January 2024), more than $13 billion worth of Chinese Snowball products were approaching knock-in triggers. A rapid decline in the CSI 1000 index pushed many products close to their 80% knock-in barrier.

Some investors experienced immediate 15–25% losses as the embedded short-put exposure was activated.

This real-world case demonstrates how quickly Snowball risk materializes when market volatility rises.

Snowball Take Out.

Source: public market data.

How market behavior affects Snowball performance

Volatility

High volatility increases the likelihood of crossing both barriers.

Trend direction

Upward trends → more knock-outs
Range-bound markets → steady coupon income
Downward trends → knock-in risk and principal loss

Liquidity and investor flows

During sell-offs, Snowball hedging can amplify downward pressure, creating feedback loops.

Snowball knock-in chart.

Source: public market data.

Explanation: The chart illustrates a steep market decline where the underlying index falls below its knock-in barrier. When such drawdowns occur rapidly, Snowball products transition into risk mode, immediately exposing investors to the underlying’s downside. This visualizes how market volatility and negative sentiment can activate the hidden risks in Snowball structures.

Conclusion

Snowball products are appealing due to their attractive coupons, but they involve significant downside risks during volatile markets. Understanding the path-dependent nature of their payoff, barrier mechanics, and market behavior is crucial for investors and product designers.

By analyzing Snowball structures, investors gain deeper insight into how derivative products are created, priced, and risk-managed in real financial markets.

About the author

The article was written in November 2025 by Tianyi WANG (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2022-2026).

Managing Corporate Risk: How Consulting and Export Finance Complement Each Other

December 19, 2025November 25, 2025 by Julien MAUROY

In this article, Julien MAUROY (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2021-2025) shares technical knowledge on risk management in the business world based on his experiences. The concepts of financial risk in business, risk management, and risk analysis will be presented. All of this information is drawn from my experiences and supported by literature on the subject.

Risk as a strategic lever

This topic aims to explore how companies manage risk and transform it into a lever for decision-making and value creation. It ties in with my academic background at ESSEC Business School and my professional experience in two complementary environments: finance and risk consulting at BearingPoint and export financing at Bpifrance. Today, risk-related issues are omnipresent in business. Whether it is competitiveness, investment decisions or international expansion, every strategy involves a degree of uncertainty.

Risk is no longer just a threat, it is anticipated, studied, calculated and has a market price: the cost of seeking advice, the cost of insurance, etc. It therefore becomes a key management factor for companies that can identify, measure and integrate it into their strategic thinking. This is why understanding risk management means understanding how organisations balance growth, stability and performance. It is this dual approach : consulting (risk reduction) and insurance and export financing (risk assessment and pricing), that I would like to share with you.

Reducing and structuring risk with consulting

During my internship at BearingPoint, I discovered how consulting could help companies reduce and structure their strategic, financial and operational risks. Consultants bring an external perspective to a company’s activities. They use an analytical, neutral approach to identify organisational weaknesses and make more informed decisions.

Within the Finance & Risk department, my assignments consisted of improving the financial performance and financial management of the company’s activities. The main topics were data reliability, reporting automation, and optimisation of budgeting and forecasting processes.

By improving the quality of financial information and its analysis, we helped companies become more agile and better able to manage their business. Companies gained visibility and the ability to anticipate future developments. Consulting is therefore the ideal way to transform uncertainty into a structured and effective methodology for addressing the challenges facing these sectors.

It helps companies adopt rigorous governance, allocate resources and budgets more effectively to each activity, and avoid costly strategic errors.

Finally, consulting helps reduce companies’ exposure to risk by providing support at all levels. It makes decision-making more rational, measurable and aligned with long-term strategy in light of competition and industry challenges.

Measuring and pricing risk with export insurance and financing

My experience in Bpifrance’s Export Insurance department gave me a different perspective on risk, this time more quantitative and institutional.

In this organisation, risk is not borne solely by the customer seeking insurance, but also by Bpifrance, which insures French exporters against risk arising from foreign buyers. The risk is therefore shared between the lending bank, the insurer and the French exporter.

In export insurance, risk is not abstract: it is analysed, measured and valued. The accuracy of the analysis is paramount, involving financial, extra-financial and geopolitical analysis. An in-depth study of exporting companies and their international counterparties makes it possible to assess their solidity and their ability to honour their financial commitments.

Each project is subject to a detailed risk assessment: counterparty risk, country risk, sectoral or political risk. These factors have an immediate impact on the premium rate applied to the export guarantee. In other words, the higher the risk of loss, the higher the cost of coverage. This approach, based on collaboration with the French Treasury and the OECD, has enabled me to understand how institutions can price risk on a global scale.

In comparison, consulting helps to anticipate, explore solutions and reduce risk, while insurance seeks to assess and price risk. At that point, risk is not avoidable, but is an integral part of the economic model.

Understanding risk in order to leverage it

These two experiences taught me that risk management is not just about protecting yourself from risk, but understanding it so you can use it as a lever for growth.

In consulting, risk is controlled through better organisation, reliable information and a clear strategy. In finance, risk becomes a measurable parameter, integrated into decision-making models and valued according to its potential impact.

These two approaches are therefore complementary: one aims to make the company more resilient, the other enables it to grow despite uncertainty.

These two perspectives show that risk, far from being a constraint, can become a strategic management tool, a driver of adaptation and a source of sustainable competitiveness.

Conclusion: the strategic value of risk management

Through these experiences, I have understood that risk management is at the heart of finance and strategy.

At BearingPoint, I acquired analytical rigour and the ability to structure my thinking, at Bpifrance I gained a macroeconomic vision and a concrete understanding of the link between risk and financial performance.

This dual perspective on qualitative and quantitative risk convinced me that knowing how to assess, integrate and explain risk is a key skill for the future of business.

In an uncertain world, managing risk means managing the relevance of decisions: this is what distinguishes companies that are able to anticipate the future from those that simply react to it.

Opening the topic with the vision of Frank Knight and Nassim Taleb

The study of risk in business has been the subject of earlier studies and research, notably initiated by Frank Knight in 1921 in Risk, Uncertainty and Profit. Knight distinguishes between two essential realities: risk, which can be quantified and insured against, and uncertainty, which cannot be quantified.

This distinction is further developed by Nassim Taleb in The Black Swan (2007), where he shows that certain extreme disruptions, known as ‘black swans’, cannot be predicted or incorporated into traditional models. Examples include pandemics, political shocks and sectoral collapses. For Taleb, the issue is not only one of prediction, but of building resilient organisations capable of absorbing unexpected shocks.

These two perspectives are directly reflected in corporate risk management. I have observed how consulting helps organisations reduce their exposure to ‘measurable’ risk, and conversely, my experience at Bpifrance immersed me in an approach where risk is quantified and priced. But neither consulting nor finance can eliminate uncertainty in Knight’s sense or Taleb’s ‘black swans’. Their role is to help the company better prepare for them by strengthening strategic robustness and adaptability.

That is why risk is no longer just a threat: it becomes a management tool and a lever for structuring action, in order to build organisations that are resilient in the face of the unexpected.

Useful resources

BearingPoint

Didier Louro (25/09/2024) Le risk management au service de la croissance Bearing Point x Sellia (podcast).

Bpifrance

OECD

Treasury department

Academic articles and books

Cohen E. (1991) Gestion financière de l’entreprise et développement financier, AUF / EDICEF.

Hassid O. (2011) Le management des risques et des crises Dunod.

Knight, F. H. (1921) Risk, Uncertainty and Profit Houghton Mifflin Company.

Mefteh S. (2005) Les déterminants de la gestion des risques financiers des entreprises non financières : une synthèse de la littérature, CEREG Université Paris Dauphine, Cahier de recherche n°2005-03.

Taleb N.N. (2008) The Black Swan Penguin Group.

About the author

The article was written in November 2025 by Julien MAUROY (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2021-2025).

▶ Read all articles by Julien MAUROY.

The role of DCF in valuation

December 7, 2025November 25, 2025 by Roberto RESTELLI

In this article, Roberto RESTELLI (ESSEC Business School, Master in Finance (MiF), 2025–2026) explains the role of discounted cash flow (DCF) within the broader toolkit of company valuation—when to use it, how to build it, and where its limits lie.

Introduction to company valuation

Valuation is the process of determining the value of any asset, whether financial (for example, shares, bonds or options) or real (for example, factories, office buildings or land). It is fundamental in many economic and financial contexts and provides a crucial input for decision-making. In particular, the importance of proper company valuation emerges in the preparation of corporate strategic plans, during restructuring or liquidation phases, and in extraordinary transactions such as mergers and acquisitions (M&As). Company valuations are also useful in regulatory and tax contexts (for example, transfers of ownership stakes or determining value for tax purposes). Entrepreneurs and investors can evaluate the economic attractiveness of strategic options, including selling or acquiring corporate assets.

The need for a company valuation typically arises to answer three questions: Who needs a valuation? When is it necessary? Why is it useful?

Users and uses of company valuation

Different categories rely on valuation. In investment banks, Equity Capital Markets use it for IPO research and coverage (including fairness opinions), while M&A teams analyze transactions and prepare fairness opinions to inform deal decisions. In Private Equity and Venture Capital, valuation supports majority/minority acquisitions, startup assessments, and LBOs. Strategic investors use it for acquisitions or divestitures, stock‑option plans, and financial reporting. Accountants and appraisal experts (CPAs) prepare fairness opinions, tax valuations, technical appraisals in legal disputes, and arbitration advisory.

Beyond these, regulators and supervisory bodies (e.g., the SEC in the U.S., CONSOB in Italy) require precise valuations to ensure market transparency and investor protection. Corporate directors and managers need valuations to define growth strategies, allocate capital, and monitor performance. Courts and arbitrators request valuations in disputes involving contract breaches, expropriations, asset divisions, or shareholder conflicts. Owners of SMEs—backbone of the Italian economy—use valuations to set sale prices, manage generational transfers, or attract investors.

Examples of valuation

Valuations appear in equity research (e.g., a UBS report on Netflix indicating a short‑ to medium‑term target price based on public information), in M&A deal analyses (including subsidiary valuations and group structure changes), and in fairness opinions (e.g., Volkswagen’s acquisition of Scania). They are central in IPOs to set offer prices and expectations. Banks also rely on valuations in lending decisions to assess enterprise value and credit risk, clarifying the allocation of requested capital.

Core competencies in valuation

High‑quality valuation requires business and strategy foundations (industry analysis, competitive context, business‑model strength), theoretical and technical finance (NPV, pricing models, corporate cash‑flow modeling), and economic theory (uncertainty vs. value and limits of standard models). Valuation is not just technique: it balances modeling choices with empirical evidence and fit‑for‑purpose estimates.

A fundamental principle is that a firm’s value is driven by its ability to generate future cash flows, which must be estimated realistically and paired with an appropriate risk assessment. Higher uncertainty in cash‑flow estimates implies a higher discount rate and a lower present value. Discount‑rate choice depends on the model (e.g., CAPM for systematic risk via beta). Sustainability also matters: modern practice increasingly integrates environmental, social, and governance (ESG) factors—climate risk, regulation, and reputation—into valuation.

General approaches and specific methods

Income Approach. Present value of future benefits, risk‑adjusted and long‑term (e.g., discounted cash flows).
Market Approach. Value estimated by comparing to similar, already‑traded assets.
Cost (Asset‑Based) Approach. Value derived by remeasuring assets/liabilities to current condition.

Within these, DCF is among the most studied and used. It can be computed from the asset perspective via free cash flow to the firm (FCFF) or from the equity perspective via free cash flow to equity (FCFE). Under the asset‑based approach, other methods include net asset value and liquidation value. Additional families include economic profit (e.g., EVA, residual income) and market‑based analyses: trading multiples (e.g., P/E, EV/EBITDA), deal multiples, and premium analysis (control premia). Four further techniques often considered are current market value (market capitalisation), real options (valuing flexible investment opportunities), broker/analyst consensus, and LBO analysis (value supported by leveraged acquisition capacity).

Critical aspects and limits of valuation models

Each method has strengths and limits. In DCF, accuracy depends on projection quality; macro cycles can render forecasts unreliable. In market‑multiple analysis, industry/geography differences and poor comparables can distort results. Real options are powerful for uncertainty but require subjective parameters (e.g., volatility), introducing error bands.

Practical applications of company valuation

Firms use valuation to plan growth, allocate capital, and budget projects. In disputes and restructurings, it informs liquidation values and creditor negotiations. It also supports governance and incentives (e.g., option plans) that align managers with shareholders. In short, valuation enables both day‑to‑day management and extraordinary decisions.

Discounted Cash Flow (DCF)

What is a DCF?

The discounted cash flow (DCF) method values a company by forecasting and discounting future cash flows. Originating with John Burr Williams (The Theory of Investment Value), DCF seeks intrinsic value by projecting cash flows and applying the time value of money: one euro today is worth more than one euro tomorrow because it can be invested.

Advantages include accuracy (when inputs are sound) and flexibility (applicable across firms/projects). Risks include reliance on uncertain projections and difficulty estimating both discount rates and cash flows; hence outputs are estimates and should be complemented with other methods.

Uses of DCF

DCF is widely applied to value companies, analyse investments in public firms, and support financial planning. The five fundamental steps are:

Estimate expected future cash flows.
Determine the growth rate of those cash flows.
Calculate the terminal value.
Define the discount rate.
Discount future cash flows and the terminal value to the present.

DCF components.

Source: author.

Discounted cash flow formula (with a perpetuity‑growth terminal value):

DCF = CF₁ / (1 + r)¹ + CF₂ / (1 + r)² + … + CF_T / (1 + r)^T + (CF_T+1 / (r – g)) · 1 / (1 + r)^T

Where CF_t are cash flows in year t, r is the discount rate, and g is the long‑term growth rate.

Building a DCF

Start from operating cash flow (cash‑flow statement) and typically move to free cash flow (FCF) by subtracting capital expenditures. Example: if operating cash flow is €30m and capex is €5m, FCF = €25m. Project future FCF using growth assumptions (e.g., if 2020 FCF was €22.5m and 2021 FCF €25m, growth is ~11.1%). Use near‑term high‑growth and longer‑term fade assumptions to reflect maturation.

Determining the terminal value

The terminal value represents long‑term growth beyond the explicit forecast. A common formula is:

Terminal Value = CF_T+1 / (r – g)

Ensure g is consistent with long‑run economic growth and the firm’s reinvestment needs.

Defining the discount rate

The discount rate reflects risk. Common choices include the risk‑free government yield, the opportunity cost of capital, and the WACC (weighted average cost of capital). In equity‑side models, CAPM is often used to estimate the cost of equity via beta (systematic risk).

Discounting the cash flows

Finally, discount projected cash flows and terminal value at the chosen rate to obtain present value. Sensitivity analysis (varying r, g, margins, capex) and scenario analysis (bull/base/bear) are essential to understand valuation drivers.

Example

You can download below an Excel file with an example of DCF. It deals with Maire Tecnimont, which is an Italian engineering and consulting company specializing in the fields of chemistry and petrochemicals, oil and gas, energy and civil engineering.

Why should I be interested in this post?

If you are an ESSEC student aiming for roles in investment banking, private equity, or equity research, mastering DCF is table‑stakes. This post distills how DCF fits among valuation approaches, the exact steps to build one, and the pitfalls you must stress‑test before using your number in IPOs, M&A, or buy‑side models.

Useful resources

Damodaran online New York University (NYU).

SEC EDGAR company filings

European Central Bank (ECB) statistics

Maire Tecnimont

About the author

The article was written in November 2025 by Roberto RESTELLI (ESSEC Business School, Master in Finance (MiF), 2025–2026).

Book by Slah Boughattas: State of the Art in Structured Products

November 4, 2025 by Slah BOUGHATTAS

In this post, Slah BOUGHATTAS (Ph.D., Associate of the Chartered Institute for Securities & Investment (CISI), London) provides an extract from the book ‘State of the Art in Structured Products: Fundamentals, Designing, Pricing, and Hedging’ (2022).

This post presents pedagogical philosophy, structure, and target audience, including graduate students in finance, university professors, and practitioners in derivatives and structured products.

State of the Art in Structured Products: Fundamentals, Designing, Pricing, and Hedging

Source: the company.

Summary of the book

The book aims to provide both the theoretical background and the practical applications of structured products in modern financial markets. It systematically explores the fundamentals of derivatives, equity and interest rate markets, stochastic calculus, Monte Carlo simulations, Constant Proportion Portfolio Insurance (CPPI), risk management, and the financial engineering processes involved in designing, pricing, and hedging structured products.

Financial concepts related to the book

Structured Products, Derivatives, Options, Swaps, Structured Notes, Bonus certificates, Constant Proportion Portfolio Insurance (CPPI), Monte Carlo Simulation, Fixed Income, Floating Rate-Note (FRN), Reverse FRN, CMS-Linked Notes, Callable Bond, Financial Engineering, Risk Management, Pricing, and Hedging.

Context and Motivation

The financial engineering of structured products remains one of the most sophisticated domains of quantitative finance. While the literature on derivatives pricing is vast, comprehensive references specifically dedicated to the end-to-end process of structured product creation — designing, pricing, and hedging — remain scarce.

State of the Art in Structured Products bridges this gap. The work is structured to serve both as a teaching manual and a professional reference, progressively building from fundamental principles to advanced practical implementations.

Structure of the Book

Derivatives Fundamentals and Market Instruments – recalls the essential mechanics of equity and interest-rate derivatives
Designing Structured Products – shows how term sheets and payoff structures emerge logically from financial objectives
Pricing and Risk Analysis – provides analytical and simulation-based approaches, including Monte Carlo method
Hedging and Risk Management – explores dynamic replication, sensitivities, and practical hedging of structured notes.
Advanced Topics – covers Constant Proportion Portfolio Insurance (CPPI), callable and floating-rate instruments, and swaptions

Why should I be interested in this post?

The book’s main contribution lies in its integrated approach combining conceptual clarity, quantitative rigor, and practical implementation examples. It is intended for professors and instructors of Master’s programs in Finance, graduate students specializing in derivatives or structured products, and professionals such as financial engineers, product controllers, traders, dealing room staff and salespeople, risk managers, quantitative analysts, middle office managers, fund managers, investors, senior managers, research and system developers.

The book is currently referenced in several academic libraries, including ESSEC Business School Paris, Princeton University, London School of Economics, HEC Montreal, Erasmus University Rotterdam, ETH Zurich, IE University, Erasmus University Rotterdam, and NTU Singapore.

Useful resources

Slah Boughattas (2022) State of the Art in Structured Products: Fundamentals, Designing, Pricing, and Hedging Advanced Education in Financial Engineering Editions.

About the author

The article was written in November 2025 by Slah BOUGHATTAS (Ph.D., Associate of the Chartered Institute for Securities & Investment (CISI), London).

The Business Model of Proprietary Trading Firms

January 20, 2026October 25, 2025 by Anis MAAZ

In this article, Anis MAAZ (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2023-2027) explains how prop firms work, from understanding their business model and evaluation processes, to fee structures and risk management rules. The goal is not to promise guaranteed profits, but to provide a transparent, realistic overview of how proprietary trading firms operate and what traders should know before joining one.

Context and objective

Goal: demystify how prop firms make money, how their rules work, and what realistic outcomes look like, even if you are new to prop firms.
Outcome: a technical but accessible guide with a simple numeric example and a due diligence checklist.

What a prop firm is

Proprietary trading firms (prop firms) use their own capital to trade in financial markets, leveraging advanced risk management techniques and state-of-the-art technologies. But how exactly do prop firms make money, and what makes them attractive to aspiring traders? Traders who meet the firm’s rules get access to buying power and share in the profits. Firms protect their capital with strict risk limits (daily loss, max drawdown, product caps). Two operating styles you will encounter: In house/desk model: you trade live firm capital on a desk with a risk manager. Evaluation (“challenge”) model: you pay a fee to prove you can hit a target without breaking rules. If you pass, you receive a “funded” account with payout rules. For example, a classic challenge can be to reach a profit of 6% without losing more than 4% of your initial challenge capital to become funded.

The Proprietary Trading Industry: Origins and Scale

Proprietary trading as a business model emerged in the 1980s-1990s in the US, initially within investment banks’ trading desks before regulatory changes (notably the Volcker Rule in 2010) pushed prop trading into independent firms. The modern “retail prop firm” model, offering funded accounts to individual traders via evaluation challenges, gained momentum in the 2010s, particularly after 2015 with firms like FTMO (Czech Republic, 2014) and TopstepTrader (US, 2012).

Today, the industry includes an estimated 200+ prop firms globally, concentrated in the US, UK, and UAE (Dubai has become a hub due to favorable regulations). Major players include FTMO, TopstepTrader, Apex Trader Funding, Alphafutures, and MyForexFunds. Most are privately owned by founders or small investor groups and some (like Topstep) have received venture capital. The market size is difficult to quantify precisely, but industry reports estimate the global prop trading sector handles billions in trading capital, with the retail-focused segment growing 40-50% annually from 2020-2024.

Core Characteristics of prop firms

Capital Allocation: Prop firms provide traders with access to firm capital, enabling them to trade larger positions than they could on their own.
Profit Sharing: A trader’s earnings are typically a percentage of the profits generated. This incentivizes high-caliber performance.
Training Programs: Many prop firms invest in the development of new traders via structured training programs, equipping them with proven strategies and technologies.
Diverse Markets: Prop traders operate across various asset classes, such as stocks, forex, options, cryptocurrencies, and commodities.

How the business model works

The money comes from evaluation fees and resets: a major revenue line for challenge-style firms because most applicants do not pass the challenges. Once funded, a trader keeps the majority of the profits generated (often 70–90%) and the firm keeps the rest. Some firms charge for platform, data or advanced tools such as a complete order book, and pay exchange/clearing fees on futures.

In some cases, firms may charge onboarding or monthly platform fees to cover operational costs, such as trading infrastructure, data services, and proprietary software. However, top firms often waive such fees for consistently profitable traders.

For example, a firm charging $150 for a $50,000 evaluation challenge that attracts 10,000 applicants per month generates $1.5M in fee revenue. If 8% pass (800 traders) and receive funded accounts, and only 20% of those (160) reach a payout, the firm pays out perhaps $500,000-$800,000 in profit splits while retaining the rest as margin. Add-on services (resets at $100 each, platform fees) further boost revenue.

Who Are the Traders?

Prop firm traders come from diverse backgrounds: retail traders seeking leverage, former bank traders, students, and career-changers. No formal degree is required. The average trader age ranges from 25-40, though firms accept anyone 18+. Most traders operate as “independent contractors”, not employees, they receive profit splits, bearing their own tax obligations.

Retention is actually very low: industry data suggests 60-70% of funded traders lose their accounts within 3 months due to rule violations or drawdowns. Only 10-15% maintain funded status beyond 6 months. The model is inherently high-churn: firms continuously recruit through affiliates and ads, knowing most will fail but a small percentage will generate consistent trading activity and profit-share revenue.

What successful traders share :

The ability to manage risk and follow rules.
Analytical skills and a deep understanding of market behavior.
Psychological toughness to handle the highs and lows of trading.

It’s not an easy industry at all, and it’s better to have a real job, because only a small fraction of traders pass and an even smaller fraction reach payouts after succeeding in a challenge. Fee income arrives upfront, payouts happen later and only for those who succeed and manage to be disciplined through time.

For new traders, it’s not easy to pass a challenge when the rules are strict, because trading with someone else’s capital often amplifies fear and greed. Success is judged not only by profitability but also by consistency and adherence to firm guidelines, and any new traders struggle to maintain profitability and burn out within months.

EU regulators have long reported that most retail accounts lose money on leveraged products like CFDs: typically 74–89%, which helps explain why challenge pass rates are low without strong process and discipline.

Success rates: what is typical and why most traders fail

“Pass rate” (applicants who complete the challenge) is commonly cited around 5–10%. “Payout rate among funded traders” is often ~20%. End to end, only ~1–2% of all applicants reach a payout. All of these statistics vary by firm, product, and rules. Most people fail due to rule breaches under pressure (daily loss, news locks), overtrading, and inconsistent execution. Psychological factors like revenge trading, FOMO (Fear of missing out), are the usual culprits.

Trading Strategies, Markets, and Tools

Which Markets?

Most prop firms focus on futures (E-mini S&P, Nasdaq, crude oil), forex (EUR/USD, GBP/USD), and increasingly cryptocurrencies (Bitcoin, Ethereum). Some firms also offer equities (US stocks). The choice depends on the firm’s clearing relationships and risk appetite. Futures dominate because of high leverage, deep liquidity, and high trading windows.

Common Strategies

Prop traders typically employ “intraday strategies”:

Scalping (holding positions seconds to minutes)
Momentum trading (riding short-term trends), and mean reversion (fading extremes)
Swing trading (multi-day holds) is less common due to overnight risk rules
High-frequency strategies are rare in retail prop firms, and most traders use setups based on technical indicators (moving averages, RSI, volume profiles).

Tools and Platforms

Firms provide access to professional platforms like NinjaTrader, TradingView, MetaTrader 4/5,). Traders receive Level 2 data (order book), news feeds (Bloomberg, Reuters), and sometimes proprietary risk dashboards. Some firms offer replay tools to practice historical data.

The key performance idea

Positive expectancy = you make more on your average winning trade than you lose on your average losing trade, often enough to overcome costs. Here is a simple way to check:

Step 1: Out of 10 trades, how many are winners? Example: 5 winners, 5 losers (50% win rate). Step 2: What’s your average win and average loss? Example: average win €120; average loss €80. Step 3: Expected profit per trade ≈ (wins × avg win − losses × avg loss) ÷ number of trades. Here: (5 × 120 − 5 × 80) ÷ 10 = (€600 − €400) ÷ 10 = €20 per trade. If costs/slippage are below €20 per trade, you likely have an edge worth scaling, subject to the firm’s risk limits.

The firm wants you to stay inside limits, your average loss is controlled (stops respected), and your results are repeatable across days. They avoid the “luck factor” by putting rules like 2 minimum winning days to pass a challenge and impossible to make more than half of the challenge target in one day.

There are many ways to pass a challenge, depending on your trading strategy: If you aim for trades where your win is 5 times higher than what you risk, you do not need a winrate of 50% or 80% to pass the challenges and be profitable.

Payout mechanics: example with Topstep (to clarify the “50%” point)

Profit split: you keep 100% of the first $ 10,000 you withdraw; after that, the split is 90% to you / 10% to Topstep (per trader, across accounts).

Per request cap: Express Funded Account: request up to the lesser of $ 5,000 or 50% of your account balance per payout, after 5 winning days. Live Funded Account: up to 50% of the balance per request (no $ 5,000 cap). After 30 non consecutive “winning days” in Live, you can unlock daily payouts up to 100% of balance.

Note: “50%” here is a cap on how much you may withdraw per request—not the profit split. Other firms differ (some advertise 80–90% splits, 7–30 day payout cycles, or higher first withdrawal shares), so always read the current Terms.

Why traders choose prop firms (psychology and practical reasons)

Traders are attracted to prop firms for both psychological and practical reasons. The appeal starts with small upfront risk: instead of depositing a large personal account, you pay a fixed evaluation fee. If you perform well within the rules, you gain access to greater buying power, which lets you scale faster than you could with a small personal account.

But this method is indeed a psychological trap, because most of the traders will fail their first account, buy another one because it’s “cheap” and it will become an addiction when they will start burning accounts every day because it “doesn’t feel real” for them. The trade offs are real, evaluation fees and resets can add up, rules may feel restrictive, and pressure tends to spike near limits or payout thresholds. All these factors contribute to why many candidates ultimately fail.

However, for experimented traders who can manage psychology, the built in structure, risk limits, reviews, and a community adds accountability and often improves discipline. Payouts can also serve as a capital building path, gradually seeding your own account over time.

Regulation: A Gray Zone

Proprietary trading firms operate in a largely unregulated space, especially the evaluation-based model. In the US, prop firms are not broker-dealers; they typically collaborate with registered FCMs (Futures Commission Merchants) or brokers who handle execution and clearing, but the firm itself is often a private LLC with minimal oversight. The CFTC (Commodity Futures Trading Commission) regulates futures markets but not prop firms’ internal challenge mechanisms.

In France, the AMF has issued warnings about unregulated prop firms and emphasized that if a firm collects fees from French residents, it may fall under consumer protection law. Some firms have pulled out of France or adjusted terms. The UK FCA has similarly warned consumers. The UAE (DIFC, DMCC) offers more permissive environments, attracting many firms to Dubai.

Conclusion

Prop trading firms offer a compelling proposition: controlled access to institutional sized buying power, standardized risk limits, and a structured pathway for transforming skill into capital without large personal deposits. In this model, firms protect capital through rules and fees, while profitable traders create a scalable environment for strategy development and execution.

At the same time, the evaluation-and-payout cycle can amplify cognitive and emotional traps. Fee resets, drawdown thresholds, and profit targets concentrate attention on short-term outcomes, which can foster overtrading, sensation seeking, and schedule-driven risk-taking. The same leverage that accelerates account growth also magnifies behavioral errors and variance, making intermittent reinforcement (occasional big wins amid frequent setbacks) psychologically sticky and potentially addictive.

In the end, prop firms are neither shortcut nor scam, but a high-constraint laboratory. They reward, stable execution, rule adherence, and penalize improvisation and impulse. As a venue, they are well suited to disciplined traders with repeatable processes, robust risk controls, and patience for incremental scale. Without those traits, the structure that protects the firm can become a treadmill for the trader.

At the end of the day, the prop firm model is designed for the firm to profit from fees, not trader success. With 1-2% end-to-end success rates, it’s closer to a paid training lottery than a career path.

If your goal is to learn trading, SimTrade, paper trading, or small personal accounts teach discipline without predatory fee structures. Joining a bank’s graduate program gives you access to senior traders, research, and real market-making or flow trading experience.

If you’ve already traded profitably for 1-2 years, have a proven strategy, need leverage, and fully understand the fee economics, then a top-tier firm (FTMO, Topstep) could provide capital to scale. But as a first step out of ESSEC, I would prioritize banking or buy-side roles that offer mentorship, stability, and credentials.

Why should I be interested in this post?

Prop firms reveal how trading businesses monetize edge while enforcing strict risk management and incentive design. Grasping evaluation rules, fee structures, and payout mechanics sharpens your ability to assess unit economics and governance. This knowledge is directly applicable to careers in trading, risk, and fintech—helping you make informed choices before joining a program.

Useful Resources

Topstep payout policy and FAQs (current rules and examples)

The Funded Trader statistics on pass/payout rates

How prop firms make money (evaluation fees vs profit share): neutral primers and industry explainers

General overviews of prop trading mechanics and risk controls

About the author

The article was written in October 2025 by Anis MAAZ (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2023-2027).

▶ Read all posts written by Anis MAAZ

Modern Portfolio Theory: What is it and what are its limitations?

October 24, 2025 by Yann TANGUY

In this article, Yann TANGUY (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2023-2027) explains the Modern Portfolio Theory and how Post-Modern Portfolio Theory solves some of its limitations.

Creation of Modern Portfolio Theory (MPT)

Developed in 1952 by Nobel laureate Harry Markowitz, MPT revolutionized the way investors think about portfolios. Before Markowitz, investment decisions were mostly based on the relative nature of each investment. MPT changed the way to think about investing by showing that an investment cannot be thought of in isolation but as part of contribution to portfolio risk and return.

At the center of MPT is the diversification theory. The adage “don’t put all your eggs in one basket” is the base of this theory. By diversifying a portfolio with assets having different risk and return profiles and a low correlation, an investor can build a portfolio that has a lower risk than any of its components.

A Practical Example

Let’s assume that we have just two assets: stocks and bonds. Stocks have given higher returns over a long period of time compared to bonds but are riskier. On the other hand, bonds are less risky but return less.

An investor who puts all their money in stocks will have huge returns in a bull market but will suffer huge losses in a bear market. A conservative investor who puts money in bonds alone will have a smooth portfolio but will be denied the chance of better growth.

MPT believes that the combination of different investments in a portfolio can have a better risk-reward ratio than single investments. The key is the correlation of the assets. If the correlation is less than 1, the portfolio’s risk will be less than the weighted average of each individual asset’s risk. In this simplified example, stocks are performing poorly when bonds are performing well and vice versa, so they have a negative correlation, hedging out the overall returns of the portfolio.

Mathematical explanation

To estimate the risk of a portfolio, MPT uses statistical measures like variance and standard deviation. Variance is calculated to then obtain the standard deviation, which we use to assess the risk of an asset as it indicates how much said asset’s price fluctuates.

On the other hand, correlation and covariance quantify how two assets move compared to each other. Covariance and correlation give an indication of change in value, i.e. Do the assets move in the same way. Correlation is between -1 and 1, a correlation of 1 means that the asset moves in the exact same way and -1 means that they move in opposite ways.

The portfolio variance is calculated as follows for a portfolio of asset A and asset B:

Where:

R = return
w = weight of the asset
Var = variance
Cov = covariance

The variance of a portfolio is then not equal to the weighted average risk of its components because we factor in the covariance of said components.

The aim of MPT is to find the optimal portfolio mix that minimizes the portfolio standard deviation for a given level of expected return or that maximizes the portfolio expected return for a given level of standard deviation. This can be graphically represented as the efficient frontier, a line representing the set of optimal portfolios.

This Efficient Frontier represents different allocations of assets in a portfolio. All portfolios on this frontier are called efficient portfolios, meaning that they have the best risk adjusted returns possible with this combination of assets. This means that when choosing the allocation for a portfolio one should pick a portfolio located on the frontier based on their risk tolerance and return objective.

The figure below represents the efficient frontier when investors can invest in risky assets only.

Efficient Portfolio Frontier.
Portfolio Efficient Frontier
Source: Computation by the Author.

Quantifying performance

To quantify the performance of a portfolio, MPT utilizes Sharpe ratio. The Sharpe ratio measures the excess return of the portfolio (the return over the risk-free rate) for the risk of the portfolio (defined by portfolio standard deviation). The formula is as follows:

Where:

E(R_P) = expected return of portfolio P
R_f = risk-free rate
σP = standard deviation of returns of portfolio P

A higher Sharpe ratio indicates a better risk-adjusted return.

Limitations of MPT

Even though MPT has been around in finance for decades now, it is not universally accepted. The biggest criticism against it is that it employs standard deviation to measure price movement, but the problem is that no difference is made between positive and negative volatility. They are both seen as risky.

However, many investors would be happy with a portfolio that performs 20% or 40% returns every year, but this portfolio could be considered risky by MPT, even if it always performs better than the return needed as there is a lot of variation, however this variation does not matter to you if your return objective is always met. This means that investors care more about downside risks, the risk of performing worse than your return objective.

Emergence of Post-Modern Portfolio Theory (PMPT)

PMPT, introduced in 1991 by software designers Brian M. Rom and Kathleen Ferguson, is a refinement of MPT to overcome its main shortcoming. The key difference lies in the fact that PMPT focuses on downside deviation as a measure of risk, rather than the normal standard deviation that takes every form of deviation into account.

The origins of PMPT can be linked to the work of A. D. Roy with his “Safety First” principle in his 1952 paper, “Safety First and the Holding of Assets”. In his paper, Roy argued that investors are primarily motivated by the desire to avoid disaster rather than to maximize their gains. As he put it, “Decisions taken in practice are less concerned with whether a little more of this or of that will yield the largest net increase in satisfaction than with avoiding known rocks of uncertain position or with deploying forces so that, if there is an ambush round the next corner, total disaster is avoided.” Roy proposed that investors should seek to minimize the probability that their portfolio’s return will fall below a certain minimum acceptable level, or “disaster” level which is now known as MAR for “Minimum Acceptable Return”.

PMPT introduces the concept of the Minimum Acceptable Return (MAR), i.e., the lowest return that the investor wishes to receive. Instead of looking at the overall volatility of a portfolio, PMPT looks only at the returns below the MAR.

Calculating Downside Deviation

To compute downside deviation, we carry out the following:

Define the Minimum Acceptable Return (MAR).
Calculate the difference between the portfolio return and the MAR for each period.
Square the negative differences.
Sum the squared negative differences.
Divide by the number of periods.
Take the square root of the result to obtain the downside deviation.

You can download the Excel file below which illustrates the difference between MPT and PMPT with two examples of market conditions (correlation).

In this file we find 2 combinations of assets: Example 1 and Example 2. The first combination has a positive correlation (0.72) and the second combination a negative one (-0.75) all the while having very similar standard deviation and returns for each asset.

First, using MPT, we demonstrate how high correlation leads to a worsened diversification effect, and a lower increase in portfolio efficiency (Sharpe Ratio) compared to a very similar portfolio with a low correlation.

Diversification effect on Sharpe Ratio (High correlation)

Diversification effect on Sharpe Ratio (Low correlation)

Afterwards, we use PMPT to show how correlation also impacts the diversification effect through the lens of downside deviation, meaning how much does the portfolio moves below the MAR, keeping in mind that these portfolios have only around a 0.1% difference in average return and originally have almost the same volatility.

Diversification effect on Downside Deviation (High correlation)

Diversification effect on Downside Deviation (Low correlation)

Focusing on downside risk is made even more important when you consider that financial returns are rarely normally distributed, as is often assumed in MPT. In their 2004 paper, “Portfolio Diversification Effects of Downside Risk,” Namwon Hyung and Casper G. de Vries show that returns often show signs of what they call “fat tails,” meaning that extreme negative events are more common than a normal distribution would predict.

They find that in this environment; diversification is even more powerful in reducing downside risk. They state: “The VaR-diversification-speed is higher for the class of (finite variance) fat tailed distributions in comparison to the normal distribution”. Meaning that for investors concerned about downside risk, diversification is a more potent tool than they might realize as diversification becomes even more efficient when taking into account the real distribution of returns.

Conclusion

Modern Portfolio Theory has been the main theory used by investors for more than half a century. Its basic premise of diversification and asset allocation is as valid as it ever was. But the usage of Standard Deviation of returns only gives a side of picture, a picture fully captured by PMPT.

Post-Modern Portfolio Theory is more advanced way of managing risk. With its focus on downside deviation, it provides investors with an accurate sense of what they are risking and allows them to build portfolios better aligned with their goals and risk tolerance. MPT was the first iteration, but PMPT has built a more practical framework to effectively diversify a portfolio.

An effective diversification strategy is built on a solid foundation of asset allocation among low-correlation asset classes. By focusing on the quality of diversification rather than only the quantity of holdings, investors can build portfolios that are better aligned with their goals, avoiding the unnecessary costs and diluted returns that come with a diworsified approach.

Why should I be interested in this post?

MPT is a theory widely used in Asset management, the understanding of its principles and limitations is primordial in nowadays financial landscape.

Useful resources

Ferguson, K. (1994) Post-Modern Portfolio Theory Comes of Age, The Journal of Investing, 1:349-364

Geambasu, C., Sova, R., Jianu, I., and Geambasu, L., (2013) Risk measurement in post-modern portfolio theory: Differences from modern portfolio theory, Economic Computation and Economic Cybernetics Studies and Research, 47:113-132.

Markowitz, H. (1952) Portfolio Selection, The Journal of Finance, 7(1):77–91.

Roy, A.D. (1952) Safety First and the Holding of Assets, Econometrica, 20, 431-449.

Hyung, N., & de Vries, C. G. (2004) Portfolio Diversification Effects of Downside Risk, Working paper.

Sharpe, W.F. (1966) Mutual Fund Performance, Journal of Business, 39(1), 119–138.

Sharpe, W.F. (1994) The Sharpe Ratio, Journal of Portfolio Management, 21(1), 49–58.

About the author

This article was written in October 2025 by Yann TANGUY (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2023-2027).

Understanding Risk-Adjusted Return: Sharpe Ratio & Beyond

October 22, 2025 by Rishika YADAV

In this article, Rishika YADAV (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2023–2027) explains the concept of risk-adjusted return, with a focus on the Sharpe ratio and complementary performance measures used in portfolio management.

Risk-adjusted return

Risk-adjusted return measures how much return an investment generates relative to the level of risk taken. This allows meaningful comparisons across portfolios and funds. For example, two portfolios may both generate a 12% return, but the one with lower volatility is superior because most investors are risk-averse — they prefer stable and predictable returns. A portfolio that achieves the same return with less risk provides higher utility to a risk-averse investor. In other words, it offers better compensation for the risk taken, which is precisely what risk-adjusted measures like the Sharpe Ratio capture.

The Sharpe Ratio

The Sharpe Ratio is the most widely used risk-adjusted performance measure. It standardizes excess return (return minus the risk-free rate) by total volatility and answers the question: how much additional return does an investor earn per unit of risk?

Sharpe Ratio = (E[R_P] − R_f) / σ_P

where R_p = portfolio return, R_f = risk-free rate (e.g., T-bill yield), and σ_p = standard deviation of portfolio returns (volatility).

Interpretation

The Sharpe Ratio was developed by Nobel Laureate William F. Sharpe (1966) as a way to measure the excess return of an investment relative to its risk. A higher Sharpe ratio indicates better risk-adjusted performance.

< 1 = sub-optimal
1–2 = acceptable to good
2–3 = very good
> 3 = excellent (rarely achieved consistently)

In real financial markets, sustained Sharpe Ratios above 1.0 are uncommon. Over the past four decades, broad equity indices like the S&P 500 have averaged between 0.4 and 0.7, while balanced multi-asset portfolios often fall in the 0.6–0.9 range. Only a handful of hedge funds or quantitative strategies have achieved Sharpe ratios consistently above 1.0, and values exceeding 1.5 are exceptionally rare. Thus, while the Sharpe ratio is a useful comparative tool, the theoretical thresholds (e.g., >3 as “excellent”) are not typically observed in real markets.

Capital Allocation Line (CAL) and Capital Market Line (CML)

The Capital Allocation Line (CAL) represents the set of portfolios obtainable by combining a risk-free asset with a chosen risky portfolio P. It is a straight line in the (risk, expected return) plane: investors choose a point on the CAL according to their risk preference.

The equation of the CAL is:

E[R_Q] = R_f + ((E[R_P] − R_f) / σ_P) × σ_Q

where:

E[R_p] = expected return of the combined portfolio
R_f = risk-free rate
E[R_P] = expected return of risky portfolio P
σ_P = standard deviation of P
σ_Q = resulting standard deviation of the combined portfolio (proportional to weight in P)

The slope of the CAL equals the Sharpe ratio of portfolio P:

Slope(CAL) = (E[R_P] − R_f) / σ_P = Sharpe(P)

The Capital Market Line (CML) is the CAL when the risky portfolio Q is the market portfolio (M). Under CAPM/Markowitz assumptions the market portfolio is the tangent (highest Sharpe) point on the efficient frontier and the CML is tangent to the efficient frontier at M.

The equation of the CML is:

E[R_Q] = R_f + ((E[R_M] − R_f) / σ_M) × σ_Q

where M denotes the market portfolio.

The slope of the CML, (E[R_M] − R_f) / σ_M, is the Sharpe ratio of the market portfolio.

The link between the CAL, CML and Sharpe ratio is illustrated in the figure below.

Figure 1. Capital Allocation Line (CAL), Capital Market Line (CML) and the Sharpe ratio.

Source: computation by author.

Strengths of the Sharpe Ratio

Simple and intuitive — easy to compute and interpret.
Versatile — applicable across asset classes, funds, and portfolios.
Balances reward and risk — combines excess return and volatility into a single metric.

Limitations of the Sharpe Ratio

Assumes returns are approximately normally distributed — real returns often show skewness and fat tails.
Penalizes upside and downside volatility equally — it does not distinguish harmful downside movements from beneficial upside.
Sensitive to the chosen risk-free rate and the return measurement horizon (daily/monthly/annual).

Beyond Sharpe: Alternative measures

Treynor Ratio — uses systematic risk (β) instead of total volatility: Treynor = (R_p − R_f) / β_p. Best for well-diversified portfolios.
Sortino Ratio — focuses only on downside deviation, so it penalizes harmful volatility (losses) but not upside variability.
Jensen’s Alpha — α = R_p − [R_f + β_p(R_m − R_f)]; measures manager skill relative to CAPM expectations.
Information Ratio — active return (vs benchmark) divided by tracking error; useful for evaluating active managers.

Applications in portfolio management

Risk-adjusted metrics are used by asset managers to screen and rank funds, by institutional investors for capital allocation, and by analysts to determine whether outperformance is due to skill or increased risk exposure. When two funds have similar absolute returns, the one with the higher Sharpe Ratio is typically preferred.

Why should I be interested in this post?

Understanding the Sharpe Ratio and complementary risk-adjusted measures is essential for students interested in careers in asset management, equity research, or investment analysis. These tools help you evaluate performance meaningfully and make better investment decisions.

Useful resources

Jensen, M. (1968) The Performance of Mutual Funds in the Period 1945–1964, Journal of Finance, 23(2), 389–416.

Sharpe, W.F. (1966) Mutual Fund Performance, Journal of Business, 39(1), 119–138.

Sharpe, W.F. (1994) The Sharpe Ratio, Journal of Portfolio Management, 21(1), 49–58.

Sortino, F. and Price, L. (1994) Performance Measurement in a Downside Risk Framework, Journal of Investing, 3(3), 59–64.

About the author

This article was written in October 2025 by Rishika YADAV (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2023–2027). Her academic interests lie in strategy, finance, and global industries, with a focus on the intersection of policy, innovation, and sustainable development.

US Treasury Bonds

December 2, 2025October 19, 2025 by Nithisha CHALLA

In this article, Nithisha CHALLA (ESSEC Business School, Grande Ecole Program – Master in Management (MiM, 2021-2024) gives a comprehensive overview of U.S. Treasury bonds, covering their features, benefits, risks, and how to invest in them.

Introduction

Treasury bonds, often referred to as T-bonds, are long-term debt securities issued by the U.S. Department of the Treasury. They are regarded as one of the safest investments globally, offering a fixed interest rate and full backing by the U.S. government. This article aims to provide an in-depth understanding of Treasury bonds, from their basics to advanced concepts, making it an essential read for finance students and professionals.

What Are Treasury Bonds?

Treasury bonds are government debt instruments with maturities ranging from 10 to 30 years. Investors receive semi-annual interest payments and are repaid the principal amount upon maturity. Due to their low credit risk, Treasury bonds are a popular choice for conservative investors and serve as a benchmark for other interest-bearing securities.

Types of Treasury Securities

Treasury bonds are part of a broader category of U.S. Treasury securities, which include:

Treasury Bills (T-bills): Short-term securities with maturities of one year or less, sold at a discount and matured at face value.
Treasury Notes (T-notes): Medium-term securities with maturities between 2 and 10 years, offering fixed interest payments.
Treasury Inflation-Protected Securities (TIPS): Securities adjusted for inflation to protect investors’ purchasing power.
Treasury Bonds (T-bonds): Long-term securities with maturities of up to 30 years, ideal for investors seeking stable, long-term income.

Historical Performance of Treasury Bonds

Historically, Treasury bonds have been a cornerstone of risk-averse portfolios. During periods of economic uncertainty, they act as a haven, preserving capital and providing reliable income. For instance, during the 2008 financial crisis and the COVID-19 pandemic, Treasury bond yields dropped significantly as investors flocked to their safety.

Despite their stability, T-bonds are sensitive to interest rate fluctuations. When interest rates rise, bond prices typically fall, and vice versa. Over the long term, they have delivered modest returns compared to equities but excel in capital preservation.

Investing in Treasury Bonds

Investing in Treasury bonds can be done through various channels like Direct Purchase, Brokerage Accounts, Mutual Funds and ETFs, and Retirement Accounts:

Direct Purchase: Investors can buy T-bonds directly from the U.S. Treasury via the TreasuryDirect website.
Brokerage Accounts: Treasury bonds are also available on secondary markets through brokers.
Mutual Funds and ETFs: Investors can gain exposure to Treasury bonds through funds that focus on government securities.
Retirement Accounts: T-bonds are often included in 401(k) plans and IRAs for diversification.

Factors Affecting Treasury Bond Prices

Several factors influence the prices and yields of Treasury bonds such as Interest Rates, Inflation Expectations, Federal Reserve Policy, and Economic Conditions:

Interest Rates: An inverse relationship exists between bond prices and interest rates.
Inflation Expectations: Higher inflation erodes the real return on bonds, causing prices to drop.
Federal Reserve Policy: The Federal Reserve’s actions, such as changing the federal funds rate or engaging in quantitative easing, directly impact Treasury yields.
Economic Conditions: In times of economic turmoil, demand for Treasury bonds increases, driving up prices and lowering yields.

Relationship between bond price and current bond yield

Let us consider a US Treasury bond with nominal value M, coupon C, maturity T, and interests paid twice a year every semester. The coupon (or interest paid every period) is computed with the coupon rate. The nominal value is reimbursed at maturity. The current yield is the market rate, which may be lower or greater than the rate at the time of issuance of the bond (the coupon rate used to compute the dollar value of the coupon). The formula below gives the formula for the price of the bond (we consider a date just after the issuance date and different yield rates.

Formula for the price of the bond

Source: The author

Relationship between bond price and current bond yield

Source: The author

You can download below the Excel file for the data used to build the figure for the relationship between bond price and current bond yield.

Risks and Considerations

While Treasury bonds are low-risk investments, they are not entirely risk-free, there are several factors to consider, such as Interest Rate Risk (Rising interest rates can lead to capital losses for bondholders), Inflation Risk (Fixed payments lose purchasing power during high inflation periods), Opportunity Cost (Low returns on T-bonds may be less attractive compared to higher-yielding investments like stocks).

Treasury Bond Futures

Treasury bond futures are standardized contracts that allow investors to speculate on or hedge against future changes in bond prices. These derivatives are traded on exchanges like the Chicago Mercantile Exchange (CME) and are essential tools for managing interest rate risk in sophisticated portfolios.

Treasury Bonds in the Global Market

The U.S. Treasury market is the largest and most liquid government bond market worldwide. It plays a pivotal role in the global financial system:

Reserve Currency: Many central banks hold U.S. Treasury bonds as a key component of their foreign exchange reserves.
Benchmark for Other Securities: Treasury yields serve as a reference point for pricing other debt instruments.
Foreign Investment: Countries like China and Japan are significant holders of U.S. Treasury bonds, underscoring their global importance.

Conclusion

Treasury bonds are fundamental to the financial landscape, offering safety, stability, and insights into broader economic dynamics. Whether you are a finance student building foundational knowledge or a professional refining investment strategies, understanding Treasury bonds is indispensable. As of 2023, the U.S. Treasury market exceeds $24 trillion in outstanding debt, reflecting its vast scale and importance. By mastering the nuances of Treasury bonds, you gain a competitive edge in navigating the complexities of global finance.

Why should I be interested in this post?

Understanding Treasury bonds is crucial for anyone pursuing a career in finance. These instruments provide insights into Monetary Policy, Fixed-Income Analysis, Portfolio Management, and Macroeconomic Indicators.

Useful resources

Treasury Direct Treasury Bonds

Fiscal data U.S. Treasury Monthly Statement of the Public Debt (MSPD)

Treasury Direct Understanding Pricing and Interest Rates

About the author

The article was written in October 2025 by Nithisha CHALLA (ESSEC Business School, Grande Ecole Program – Master in Management (MiM), 2021-2024).

Herfindahl-Hirschmann Index

October 19, 2025October 19, 2025 by Nithisha CHALLA

In this article, Nithisha CHALLA (ESSEC Business School, Grande Ecole Program – Master in Management (MiM), 2021-2024) delves into the Herfindahl-Hirschmann Index(HHI).

History of the Herfindahl-Hirschmann Index(HHI)

The Herfindahl–Hirschman Index (HHI) originated in the mid-20th century as a measure of market concentration. Its roots trace back to Albert O. Hirschman, who in 1945 introduced a squaring-based method to assess trade concentration in his book “National Power and the Structure of Foreign Trade.” A few years later, Orris C. Herfindahl independently applied a similar concept in his 1950 doctoral dissertation on the U.S. steel industry, formalizing the formula that sums the squares of firms’ market shares to capture dominance. Over time, economists combined their contributions, naming it the Herfindahl–Hirschman Index.

During the 1970s and 1980s, the measure gained prominence in industrial organization and competition economics. In 1982, the U.S. Department of Justice and the Federal Trade Commission officially adopted the HHI in their Merger Guidelines to evaluate market concentration and the impact of mergers, establishing it as a global standard. Since then, competition authorities worldwide, including the European Commission and the OECD, have incorporated HHI into their antitrust frameworks, and it remains widely used today to assess competition across various industries, such as banking, telecommunications, and energy.

The Herfindahl-Hirschman Index (HHI) is a widely used measure of market concentration and competition in various industries. The HHI has become a crucial tool for finance professionals, policymakers, and regulatory bodies to assess the level of competition in a market. In this article, we will delve into the basics of the HHI, its calculation, interpretation, and advanced applications, including recent statistics and news.

What is the Herfindahl-Hirschman Index (HHI)?

The HHI is a numerical measure that calculates the market concentration of a particular industry by considering the market share of each firm. The index ranges from 0 to 10,000, where higher values indicate greater market concentration and reduced competition. For example, a market comprising four firms with market shares of 30%, 30%, 20%, and 20% would have an HHI of 2,600 (30² + 30² + 20² + 20² = 2,600).

Calculation of the HHI

The HHI is calculated by summing the squares of the market shares of each firm in the industry. The market share is typically expressed as a percentage of the total market size. The formula for calculating the HHI is:

Formula for the Herfindahl-Hirschman Index (HHI).

Source: the author.

where MS_i is the market share of firm i, and N the number of firms.

The HHI ranges from 0 (perfect competition) to 10,000 (monopoly).

Interpretation of the HHI

According to the HHI, the concentration of sectors can be categorized as low, moderate and high:

Low concentration (HHI < 1,500): Indicates a highly competitive market with many firms.
Moderate concentration (1,500 ≤ HHI < 2,500): Suggests a moderately competitive market with some dominant firms.
High concentration (HHI ≥ 2,500): Indicates a highly concentrated market with limited competition.

I built an Excel file to illustrate the three cases: low, moderate, and high concentration.

Low concentration: HHI < 1,500
Low concentration (according to the HHI)
Source: the author.

Moderate concentration: 1,500 < HHI < 2,500
Moderate concentration (according to the HHI)
Source: the author.

High concentration: HHI > 2,500
High concentration (according to the HHI)
Source: the author.

You can download below the Excel file for the data used to build the figure for the HH index.

Advanced Applications of the HHI

The HHI has several advanced applications in finance, economics, and regulatory frameworks. Some of these applications include:

Merger analysis: Regulatory bodies, such as the US Federal Trade Commission (FTC), use the HHI to assess the potential impact of mergers and acquisitions on market competition.
Industry analysis: Finance professionals use the HHI to analyze the competitive landscape of an industry and identify potential investment opportunities.
Antitrust policy: The HHI is used to inform antitrust policy and enforcement, helping to prevent anti-competitive practices and promote competition.
Market structure analysis: The HHI is used to analyze the market structure of an industry, including the number of firms, market shares, and barriers to entry.

Criticisms and Limitations of the HHI

While the HHI is a widely used and useful measure of market concentration, it has several criticisms and limitations. Some of these include:

Simplistic assumption: The HHI assumes that market shares are a good proxy for market power, which may not always be the case.
Ignorance of other factors: The HHI ignores other factors that can affect market competition, such as barriers to entry, product differentiation, and firm conduct.
Sensitive to market definition: The HHI is sensitive to the definition of the market, which can affect the calculation of market shares and the resulting HHI value.

Real-World Examples

US Airline Industry: The HHI for the US airline industry has increased significantly over the past two decades, indicating growing market concentration. According to a 2020 report by the US Government Accountability Office, the HHI for the US airline industry increased from 1,041 in 2000 to 2,041 in 2020.

US Technology Industry: The HHI for the US technology industry has also increased significantly over the past decade, indicating growing market concentration. According to a 2022 report by the US FTC, the HHI for the US technology industry increased from 1,500 in 2010 to 3,000 in 2020.

Recent Statistics and News

A 2021 FTC staff report on acquisitions by major technology firms highlighted a “systemic nature of their acquisition strategies,” indicating a clear trend toward market concentration as they frequently acquired startups and potential competitors.
A 2020 article by the American Enterprise Institute noted that while the HHI for the US airline industry had increased by 41% since the early 2000s, inflation-adjusted ticket prices had actually fallen.
In its 2019 antitrust lawsuit to block the T-Mobile and Sprint merger, the US Department of Justice argued the deal was “presumptively anticompetitive,” citing HHI calculations that showed the merger would substantially increase concentration in the mobile wireless market.
Recent studies have utilized the HHI to analyze hospital market concentrations. For example, research on New Jersey’s hospital markets revealed increasing consolidation, with several regions classified as “highly concentrated” based on HHI scores. This information is crucial for understanding the implications of market concentration on healthcare accessibility and pricing

Regulatory Framework

The HHI is widely used by regulatory bodies around the world to assess market competition and concentration. In the US, the FTC and the Department of Justice use the HHI to evaluate mergers and acquisitions and to enforce antitrust laws. Similarly, in the European Union, the European Commission uses the HHI to assess market competition and concentration in various industries.

Conclusion

The Herfindahl-Hirschman Index remains a fundamental instrument for assessing market concentration and competition. Its applications have evolved across various sectors, providing valuable insights into market structures. However, practitioners should be mindful of its limitations and consider complementing the HHI with other analytical tools for a comprehensive market assessment.

Why should I be interested in this post?

The Herfindahl-Hirschman Index is a powerful tool for analyzing market structure and assessing competitive dynamics. As markets continue to evolve, the HHI will remain an essential tool for navigating the complexities of competition in the modern economy. So as business and finance students, it is necessary to know such an important index to keep up with the evolving world around us.

Useful resources

United states Department of Justice Herfindahl–Hirschman index

Eurostat Glossary:Herfindahl Hirschman Index (HHI)

United States Census Bureau Herfindahl–Hirschman index

Academic articles

Bach, G. D. (2020, March 18). Strong Competition Among US Airlines Before COVID-19 Pandemic. American Enterprise Institute.

Federal Trade Commission. (2021, September). FTC Staff Presents Report on Nearly a Decade of Unreported Acquisitions by the Biggest Technology Companies. Federal Trade Commission

United States Department of Justice. (2019, June 11). Complaint, United States of America et al. v. Deutsche Telekom AG et al. (Case 1:19-cv-01713). United States District Court for the District of Columbia

About the author

The article was written in October 2025 by Nithisha CHALLA (ESSEC Business School, Grande Ecole Program – Master in Management (MiM), 2021-2024).

Overview of US Treasuries

October 19, 2025 by Nithisha CHALLA

In this article, Nithisha CHALLA (ESSEC Business School, Grande Ecole Program – Master in Management (MiM), 2021-2024) gives an overview of US Treasuries, their types, characteristics, and advanced applications.

Introduction

US Treasuries are a cornerstone of global financial markets, serving as a benchmark for risk-free investments and a safe-haven asset during times of economic uncertainty. As a finance professional, understanding the basics and intricacies of US Treasuries is essential for making informed investment decisions and navigating the complexities of global finance. In this article, we will provide a comprehensive overview of US Treasuries, covering the basics, types, characteristics, market structure, and advanced applications.

What are US Treasuries?

US Treasuries are debt securities issued by the US Department of the Treasury to finance government spending and pay off maturing debt. They are considered one of the safest investments globally, backed by the full faith and credit of the US government.

Types of US Treasuries

There are four main types of US Treasuries:

Treasury Bills (T-bills)

Short-term securities with maturities ranging from a few weeks to 52 weeks
Sold at a discount to face value, with the difference representing the interest earned.
Low risk, low return investment (low duration fixed-income securities)

Treasury Notes (T-Notes)

Medium-term securities with maturities ranging from 2 to 10 years
Sold at face value, with interest paid semi-annually
Moderate risk, moderate return investment (medium duration fixed-income securities)

Treasury Bonds (T-Bonds)

Long-term securities with maturities ranging from 10 to 30 years
Sold at face value, with interest paid semi-annually
Higher risk, higher return investment (high duration fixed-income securities)

Treasury Inflation-Protected Securities (TIPS)

Securities with principal and interest rates adjusted to reflect inflation
Designed to provide a hedge against inflation
Low risk, low return investment

Figure 1 below gives the Evolution of the Structure of U.S. Federal Debt by Security Type from 2005 to 2024.

Evolution of the Structure of U.S. Federal Debt by Security Type from 2005 to 2024

Source: U.S. Department of Treasury

Figure 2 below gives the U.S. Federal Debt by Security Type on August 31, 2025.

U.S. Federal Debt by Security Type on August 31, 2025
US Federal Debt by Security Type on August 31, 2025
Source: U.S. Department of Treasury

Characteristics of US Treasuries

US Treasuries have several key characteristics such as Risk-free status, Liquidity, Taxation, and Return characteristics

Risk-free status: US Treasuries are considered one of the safest investments globally, backed by the full faith and credit of the US government.

Liquidity: US Treasuries are highly liquid, with a large and active market.

Taxation: Interest earned on US Treasuries is exempt from state and local taxes.

Return characteristics: US Treasuries offer a relatively low return compared to other investments, but provide a high degree of safety and liquidity.

Market Structure

The US Treasury market is one of the largest and most liquid markets globally, with a wide range of participants, including:

Primary dealers: Authorized dealers that participate in US Treasury auctions.
Investment banks: Firms that provide underwriting, trading, and advisory services.
Asset managers: Firms that manage investment portfolios on behalf of clients.
Central banks: Institutions that manage a country’s monetary policy and foreign exchange reserves.

Advanced Applications of US Treasuries

US Treasuries have several advanced applications, including:

Yield curve analysis: US Treasuries are used to construct the yield curve, which is a graphical representation of interest rates across different maturities.
Hedging strategies: US Treasuries are used to hedge against interest rate risk, inflation risk, and credit risk.

Figure 3 below gives the yield curve for the Treasuries in the United States on June 28, 2024.

Yield curve for US Treasuries (31/12/2024)

Source: U.S. Department of Treasury

You can download below the Excel file for the data used to build the figure for the yield curve for US Treasuries.

Conclusion

US Treasuries are a fundamental component of global financial markets, offering a safe-haven asset and a benchmark for risk-free investments. By understanding the basics and intricacies of US Treasuries, finance professionals can make informed investment decisions and navigate the complexities of global finance.

Why should I be interested in this post?

Understanding US Treasuries is crucial for anyone pursuing a career in finance. These instruments provide insights into Monetary Policy, Fixed-Income Analysis, Portfolio Management, and Macroeconomic Indicators.

Useful resources

Treasury Direct Treasury Bonds

US Treasury Yield curve data

About the author

The article was written in October 2025 by Nithisha CHALLA (ESSEC Business School, Grande Ecole Program – Master in Management (MiM), 2021-2024).

The Art of a Stock Pitch: From Understanding a Company to Building a Coherent Logics

October 16, 2025 by Dawn DENG

In this article, Dawn DENG (ESSEC Business School, Global Bachelor in Business Administration (GBBA), Smith-ESSEC Double Degree Program, 2024-2026) offers a practical introduction to building a beginner-friendly stock pitch—from selecting a company you truly understand, to structuring the investment thesis, and translating logic into valuation. The goal is not to produce “perfect numbers,” but to make your reasoning coherent, transparent, and testable.

Why learn to do a stock pitch?

Learning to pitch a stock is learning to tell a story in financial language. Whether you are aiming at investment banking, asset management, or equity research roles—or competing in a student investment fund—the stock pitch is a core exercise that reveals both how you think and how you communicate. Within ten minutes, you must answer three questions: Who is this company? Why is it worth investing in? And how much is it worth? A strong pitch convinces not by breadth of information, but by reasoning that is consistent, evidence-based, and verifiable.

Choosing a company: balance understanding and interest

For beginners, picking the right company matters more than picking the right industry. Do not start by hunting the next “multibagger.” Start with a business you can truly explain: how it makes money, who its customers are, and what drives its costs. Familiar products and clear business models are your best teachers. I first learned how to build a stock pitch during my Investment Banking Preparatory Program at my home university, Queen’s Smith School of Business. The program was designed to train first- and second-year students in the fundamentals of financial modeling, valuation, and investment reasoning. In my first pitch with the audience from school investment clubs and the professor, I chose L3Harris Technologies (NYSE: LHX)—working across defense communications and space systems. Its complexity pushed me to locate it precisely in the value chain: not a weapons maker, but a critical node in command-and-control. No valuation model can substitute that kind of business understanding.

Industry analysis: space, structure, and cycle

The defense sector operates under multi-year budget cycles, long procurement timelines, and high barriers to entry. The market is dominated by five major U.S. contractors—Lockheed Martin, Northrop Grumman, General Dynamics, Raytheon, and L3Harris. While peers tend to focus on platform manufacturing, L3Harris differentiates itself through integrated communication and command systems, giving it recurring revenue and a lighter asset base. This focus positions the company at the intersection of AI-driven defense innovation and space-based data systems—a niche expected to grow rapidly as military operations become more network-centric.

Investment thesis: three key arguments

(1) Strategic Layer – “Why now”

The defense industry is entering a new digitalization cycle. L3Harris’s acquisition of Aerojet Rocketdyne expands its vertical integration into propulsion and guidance, while its strong exposure to secure communication networks aligns with rising defense budgets for AI and satellite modernization.

(2) Competitive Layer – “Why this company”

Compared to peers, L3Harris demonstrates strong operational efficiency and disciplined capital allocation. Its EBITDA margin of ~20% and R&D intensity near 4% of revenue outperform sector averages. Management has proven its ability to sustain synergy realization post-merger, reducing leverage faster than expected.

(3) Financial Layer – “Why it matters”

The company’s robust cash generation supports consistent dividend growth and share repurchases, signaling confidence and financial flexibility. Our base-case target price was USD 287, implying ~12% upside, supported by improving free cash flow yield and moderate multiple expansion.

Valuation: turn logic into numbers

Valuation quantifies your logic. At the beginner level, focus on two complementary methods: Relative Valuation and Absolute Valuation (DCF). The first tells you how markets price similar assets; the second estimates intrinsic value under your assumptions. Use them to cross-check each other.

Relative Valuation

We benchmarked L3Harris Technologies against major U.S. defense peers including Lockheed Martin, Northrop Grumman, and Raytheon Technologies, using EV/EBITDA and P/E multiples as our key comparative metrics. Peers traded at around 14–16× EV/EBITDA, consistent with the industry’s steady cash-flow profile. However, given L3Harris’s stronger growth visibility, improving free cash flow, and synergies expected from the Aerojet Rocketdyne acquisition, we assigned a justified multiple of 17× EV/EBITDA—positioning it slightly above the sector average. This premium reflects not only its operational efficiency but also its role in the ongoing digital transformation of defense communications and space systems.

Absolute Valuation (Discounted Cash Flow)

DCF values the business as the present value of future free cash flows. Build operational drivers in business terms (volume/price, mix, scale effects), then translate into FCF:
FCF = EBIT × (1 – tax rate) + D&A – CapEx – ΔWorking Capital. Choose a WACC consistent with long-term capital structure (equity via CAPM; debt via yield or recent financing, after tax). For terminal value, use a perpetual growth rate aligned with nominal GDP and industry logic, or an exit multiple consistent with your relative valuation. Present a range via sensitivity (WACC, terminal growth, margins, CapEx) rather than a single precise point. Where DCF and multiples converge, your target price gains credibility; where they diverge, explain the source—cycle position, peer distortions, or different long-term assumptions.

Risks and catalysts: define uncertainty

Every pitch must face uncertainty head-on. Map the fragile links in your logic—macro and policy (rates, budgets, regulation), competition and disruption (new entrants, technology shifts), execution and governance (integration, capacity ramp-up, incentives). Then specify catalysts and timing windows: earnings and guidance, major contracts, launches or pricing moves, structural margin inflections, M&A progress, or regulatory milestones. Make it explicit what would validate your thesis and when you would reassess.

Useful resources

Mergers & Inquisitions How to Write a Stock Pitch

Training You Stock Pitch en Finance de Marché : définition et méthode

Harvard Business School Understanding the Discounted Cash Flow (DCF) Method

Corporate Finance Institute Types of Valuation Multiples and How to Use Them

About the author

The article was written in October 2025 by Dawn DENG (ESSEC Business School, Global Bachelor in Business Administration (GBBA), Smith-ESSEC Double Degree Program, 2024-2026).

Assessing a Company’s Creditworthiness: Understanding the 5C Framework and Its Practical Applications

October 16, 2025October 14, 2025 by Dawn DENG

Posts

In this article, Dawn DENG (ESSEC Business School, Global Bachelor in Business Administration (GBBA), Smith-ESSEC Double Degree Program, 2024-2026) presents a practical framework for assessing a company’s creditworthiness. The analysis integrates both financial and non-financial dimensions of trust, using the classic 5C framework widely adopted in banking and corporate finance.

Why assess creditworthiness

In corporate finance, assessing a company’s creditworthiness lies at the heart of lending, underwriting, and risk management. For banks, it is not only a “yes/no” lending decision (and also the level of interest rate propose to the client); it is a structured way to understand repayment capacity, operating quality, and long-term sustainability. The goal is not to label a company as “good” or “bad,” but to answer three questions: Can it repay? Will it repay? If not, how much can be recovered?

The five pillars of credit analysis: the 5C framework

The 5C framework, an industry standard that crystallized over decades of banking practice and supervisory guidance, assesses five core dimensions: Character, Capacity, Capital, Collateral, and Conditions. Rather than originating from a single author or institution, it emerged progressively across lenders’ credit manuals, central-bank training, and regulator handbooks, and is now embedded in banks’ risk-rating and loan-pricing models. These components are interdependent: strength in one area can mitigate weaknesses in another, while vulnerabilities may compound when several Cs deteriorate at the same time.

The five pillars of credit analysis: the 5C framework.

Source: the author.

Character: reputation and track record

Character covers the firm’s reputation and willingness to honor obligations. Analysts review borrowing history, repayment behavior, disclosure practices, management integrity, and banking relationships. A consistent record of timely payments and transparent reporting typically earns a stronger credit score.

For example, a mid-sized manufacturer that consistently meets payment deadlines and maintains transparent reporting will typically be viewed as a low-risk borrower, even if its margins are moderate.

Capacity: ability to repay

Capacity assesses whether operating cash flow can service debt on time. Core indicators include: Interest Coverage (EBIT/Interest), DSCR, and Liquidity ratios (Current/Quick/Cash). As a rule of thumb, an interest coverage below 2× or DSCR below 1.0× often signals liquidity pressure.

For example, in 2023, several property developers in China exhibited DSCR levels below 1.0 amid declining sales, illustrating how even profitable firms can face repayment stress when cash inflows weaken.

Capital: structure and leverage

Capital reflects how the company balances debt and equity. Key metrics are Debt-to-Equity, Debt-to-Assets, and Net Debt/EBITDA. Higher leverage raises financial risk, but acceptable ranges are industry-specific: capital-intensive sectors may tolerate 2–3× EBITDA, while asset-light tech/retail often sit closer to 0.5–1.5×.

A practical example: L3Harris Technologies, a U.S. defense contractor, maintains moderate leverage with strong cash conversion, reinforcing its credit profile despite large-scale acquisitions.

Collateral: security and guarantees

Collateral is the lender’s safety net. Recoveries depend on the value and liquidity of pledged assets (property, receivables, equipment). Asset-light firms lack hard collateral and thus rely more on cash-flow quality and relationship history to mitigate risk.

Asset-light companies (e.g., software, consulting) rely more on cash flow and relationship capital rather than tangible assets, making consistent performance crucial to maintaining credit access.

Conditions: macro and industry context

Conditions cover both external factors (interest rates, regulations, economic cycles) and loan-specific purposes.

During tightening monetary cycles, higher financing costs can compress margins, while in recessionary or trade-sensitive sectors, declining demand directly raises default risk. For example, during 2022’s rate hikes, small exporters with floating-rate debt experienced significant declines in credit ratings due to rising interest expenses.

Financial perspective: reading credit signals in the statements

Effective credit analysis connects the three statements: the income statement (profitability), balance sheet (capital structure and asset quality), and cash flow statement (true repayment capacity).

Income statement: focus on revenue stability, margin trends, and the weight of non-recurring items. Persistent declines in gross or operating margins may indicate weakening competitiveness.

Balance sheet: examine asset quality and liability mix. High receivables or inventory build-ups can flag liquidity strain; heavy short-term debt raises refinancing risk.

Cash flow statement: the practical health check. Sustainable, positive operating cash flow that covers interest and capex signals solvency; strong accounting profits with chronically negative cash flow suggest poor earnings quality.

Useful cross-checks include Operating Cash Flow/Total Debt (coverage of principal from operations) and the persistence of negative free cash flow funded by external capital (a sign of structural vulnerability).

Beyond numbers: governance, transparency, and relationship capital

Creditworthiness extends beyond ratios. Governance quality, reporting transparency, competitive barriers, and banking relationships shape real-world risk. Policy-sensitive sectors (e.g., energy, real estate) exhibit higher cyclicality; tech and retail hinge on stable cash generation and customer retention. Stable leadership, prudent accounting, and timely disclosures build lender confidence. Long-standing cooperation and on-time performance often translate into better terms, a compounding of “relationship capital.”

At its core, credit is a form of deferred trust: banks lend to future behaviors and cash flows. Whether a firm deserves that trust depends on how it balances transparency, responsibility, and disciplined execution.

Conclusion

Credit analysis is not merely about numbers, it is about understanding how financial structure, behavioral consistency, and institutional trust interact. The 5C framework provides a structured map, yet effective analysts also recognize the fluid connections among its components: good character supports capital access, strong capacity reinforces collateral confidence, and favorable conditions amplify all others. Assessing creditworthiness is thus the art of finding order amid uncertainty, of determining whether a company can remain stable when markets turn turbulent.

Useful resources

Allianz Trade Determining Customer Creditworthiness

Emagia blog Assessing a Company’s Creditworthiness

About the author

The article was written in October 2025 by Dawn DENG (ESSEC Business School, Global Bachelor in Business Administration (GBBA), Smith-ESSEC Double Degree Program, 2024-2026).

The Two-Stage Valuation Method and its challenges

October 4, 2025 by Cornelius HEINTZE

In this article, Cornelius HEINTZE (ESSEC Business School, Global Bachelor in Business Administration (GBBA) – Exchange Student, 2025) explains how the two-stage valuation model and the segmentation in growth stage and stable phase impact the valuation of companies and which problems tend to arise with the use of this model.

Why this is important

The valuation of companies is always present in the world of finance. We see it in Mergers and Acquisitions (M&A), initial public offerings (IPOs) and daily stock market pricing where firms are valued within seconds based on new information. For markets to function properly, valuations need to represent the underlying company as precisely as possible. Otherwise, information asymmetries increase, leading to inefficient or even dysfunctional markets.

The Two-Stage Model

The Two-Stage Model is the traditional model that is used by finance experts across the world. What makes it stand out is the segmentation of the valuation in two steps:

Growth phase (explicit forecast period): In this phase, the company’s future cash flows are projected in detail for each year t = 1 … T. These cash flows are then discounted back to the valuation date using the discount rate r:

PV(Growth phase) = Σ_t=1…T ( CF_t ) / (1 + r)^t

Stable phase (terminal value): After the explicit forecast horizon, the company is assumed to enter a stable stage. There are two assumptions needed to fulfill this stage and its equations. First it is assumed that the company can realize the cashflows over an indefinite timespan. Second, it is assumed that the perpetual growth rate g does not exceed the growth rate of the whole economy. The two common resulting equations are:
- No growth (steady state):
  PV(Stable phase) = CF_stable / (r * (1 + r)^T)
- Constant growth in perpetuity:
  PV(Stable phase) = CF_T+1 / ((r − g) * (1 + r)^T)

Total firm value is then the sum of both parts:

Value = PV(Growth phase) + PV(Stable phase)

Problems with the Two-Stage Model

If we look closer at the equations for the stable phase you will realize that they show a perpetuity. Looking at the assumptions given, this is also the only possible outcome. But given this circumstance we encounter the first big problem of the Two-Stage Model: the stable phase often makes up over 50% of the firm value. This is a problem as the assumptions for the stable phase are often very subjective and not very realistic. The problem evolves even more when it is assumed that there is a constant growth rate. Let’s look at this through an example:

Assumptions: discount rate r = 10%, explicit forecast over T = 5 years with free cash flows (in €m): 80, 90, 95, 98, 100. After year 5, we consider two terminal cases.

Phase 1 – Present value of explicit cash flows

Year 1: 80 / (1.10)¹ = 72.73
Year 2: 90 / (1.10)² = 74.38
Year 3: 95 / (1.10)³ = 71.37
Year 4: 98 / (1.10)⁴ = 66.94
Year 5: 100 / (1.10)⁵ = 62.09

PV(Phase 1) ≈ 347.51 (€m)

Phase 2 – Stable phase

(a) No growth: CF_stable = 100 ⇒ TV at t=5
PV(Terminal) = 100 /(0.1*(1.10)⁵) = 620.92

(b) Constant growth g = 2%: CF_T+1 = 100 ⇒ TV at t=5
PV(Terminal) = 100/((0.10-0.08) * (1.10)⁵) = 776.15

Total value and weights

No growth: Total = 347.51 + 620.92 = 968.43 ⇒ Stable Phase share ≈ 64.1%, Phase-1 share ≈ 35.9%
g = 2%: Total = 347.51 + 776.15 = 1,123.66 ⇒ Stable Phase share ≈ 69.1%, Phase-1 share ≈ 30.9%
Impact of growth: Increase in the firm value of 155.23 or ≈ 16%

Takeaway: A modest increase in the perpetual growth rate from 0% to 2% raises the terminal present value by ~155 (€m) and lifts its weight from ~64% to ~69% of total value. This illustrates the strong sensitivity of the two-stage model to terminal assumptions.

If you want to try out for yourself and learn more about the sensitivity of the growth rate in relation to the firm value you can do so in the excel-file I have created in order for this example as shown below:

Another very interesting fact gets visible, while trying out the model, which is commonly seen in early tech startups or general startups, that have very high early investment costs (for example software development). They will have a negative firm value in the growth phase but in the long run it is assumed that these companies will have a constant growth rate and positive cashflows, therefore evening out the negative growth phase. This again shows how much of an impact the stable and the growth phase has on the firm value.

You can download the excel file here:

Implications for practical use and solutions

As seen in the example, the impact of the stable phase and therefore the assumptions about the cashflows and the circumstances of the company as to whether it is appropriate to use a growth rate plays a big role in on the valuation of the firm. Deciding these assumptions lies at the feet of the firms that valuate the company or at the company valuating itself. Therefore, they are highly subjective and must be transparent at all times to ensure an appropriate valuation of the firm. If this is not the case firms can be valued at a much higher value than it is appropriate and therefore convey false information.

To fight this it is recommended to incorporate various valuation methods to verify that the value is not too high or too low but rather on a bandwidth of values which are plausible. This is often times part of a fairness opinion which is issued by an independent company. You can see an example here when Morgan Stanley drafted a fairness opinion for Monsanto for the merger with Bayer:

Full SEC Statement for the merger

To sum up…

The Two-Stage Valuation Model remains a cornerstone in corporate finance because of its simplicity and structured approach. However, as the example shows, the stable phase dominates the overall result and makes valuation highly sensitive to small changes in assumptions. In practice, analysts and other users of the information provided by the valuing company should therefore apply the model with caution, test alternative scenarios, and complement it with other methods. Looking ahead, the combination of traditional models with advanced techniques such as multi-stage models, sensitivity analyses, or even simulation approaches can provide a more balanced and reliable picture of a company’s value.

Why should I be interested in this post?

Whether you are a student of finance, an investor, or simply curious about how firms are valued, understanding the Two-Stage Valuation Model is essential. It is one of the most widely used approaches in practice and often determines the prices we see in the markets, from IPOs to M&A. By being aware of both its strengths and its limitations, you can better interpret valuation results and make more informed financial decisions.

Useful resources

Paul Pignataro (2022) “Financial modeling and valuation: a practical guide to investment banking and private equity” Wiley, Second edition.

Tim Koller, Marc Goedhart, David Wessels (2010) “Valuation: Measuring and Managing the Value of Companies”, McKinsey and Company.

Fairness Opinion Example

About the author

The article was written in October 2025 by Cornelius HEINTZE (ESSEC Business School, Global Bachelor in Business Administration (GBBA) – Exchange Student, 2025

Valuing the Delisting of Best World International Using DCF Modeling

January 20, 2026September 25, 2025 by Samuel BRAL

In this article, Samuel BRAL (ESSEC Business School, Global BBA – Exchange at NUS, 2025) shares how he conducted a valuation of Best World International using a Discounted Cash Flow model in Excel. This modeling exercise was part of a corporate finance case during his exchange at the National University of Singapore.

Context of the project

During my exchange at NUS, I was asked to evaluate the fair price at which Best World International, a Singaporean skincare and wellness company, could be taken private. The company had announced its intention to delist from the Singapore Exchange (SGX). My role was to determine the intrinsic value per share using a discounted cash flow approach that distinguishes between a high-growth projection period and a long-term steady-state phase. The goal was to assess whether the proposed buyout price was fair to minority shareholders.

Understanding the DCF method

The Discounted Cash Flow method estimates the value of a company by forecasting its future free cash flows and discounting them back to their present value using the firm’s Weighted Average Cost of Capital. This method is widely used by investment banks, private equity firms, and corporate finance teams for valuing companies, especially in the context of M&A and privatizations.

Well-known examples of its application include the valuation of Twitter during its acquisition by Elon Musk in 2022 and the fairness opinions issued by investment banks in LBO transactions such as the Bain Capital acquisition of Kioxia.

Step-by-step technical implementation

The Excel model followed a two-stage DCF approach: an explicit forecast period from 2024 to 2028 and a terminal value from 2029 onward. Below is a breakdown of the modeling process:

1. Revenue Forecasting

I projected revenue growth using a blended approach. I considered:

The average historical CAGR of BWI’s revenues between 2021 and 2023.
The expected CAGR for the ASEAN cosmetics and wellness industry (7–9%) based on Statista and Euromonitor data.

Revenue = Previous Year Revenue × (1 + Growth Rate)

2. EBIT Estimation

I calculated EBIT by projecting the cost structure of the business:

I took historical averages of cost items such as COGS and SG&A as a percentage of revenue.
Assumed that operating leverage would allow fixed costs to grow slower than revenue, improving margins over time.

EBIT = Revenue – Operating Costs

3. Tax Adjustment and NOPAT

I applied a normalized effective tax rate based on BWI’s historical tax filings and Singapore’s corporate tax regime (17%).

NOPAT = EBIT × (1 – Tax Rate)

4. Depreciation and CAPEX

I assumed CAPEX as a stable % of revenue, using 2023 data as the benchmark. Depreciation was projected using the historical ratio of D&A to CAPEX.

Free Cash Flow = NOPAT + Depreciation – CAPEX – ΔWorking Capital

5. Net Working Capital (NWC)

NWC = Current Assets – Current Liabilities. I used the average NWC-to-revenue ratio from past years to forecast changes in NWC.

6. Terminal Value and Discounting

The Terminal Value, which captures the value of a business beyond the explicit forecast period in a DCF analysis – often 5 or 10 years into the future. was calculated using the Gordon Growth formula:

TV = FCF_2028 × (1 + g) / (WACC – g)

Where g was estimated at 2.5%, reflecting long-term GDP and sector growth rates in the ASEAN region.

Both FCFs and Terminal Value were discounted using WACC (5.55%). The present values were then summed to calculate Enterprise Value.

7. Equity Value per Share

Enterprise Value – Net Debt + Cash = Equity Value

Equity Value / Number of Shares = Value per Share

WACC and Beta calculation

WACC reflects the average cost of capital from both equity and debt, weighted by their proportions in the firm’s capital structure, it serves as the discount rate for projecting future cash flows. For companies like BWI, which operate in niche, consumer-focused markets, WACC provides a benchmark for evaluating whether future growth justifies current valuations

Cost of equity was derived using the Capital Asset Pricing Model (CAPM):

Cost of Equity = Risk-Free Rate + Beta × Market Risk Premium

Beta was computed by unlevering and relevering betas of comparable firms in China, Taiwan, and Malaysia. This accounts for business and financial risk.
Cost of debt was based on comparable bond yields and company-specific risks.
Capital structure weights were based on BWI’s most recent financial statements.

The photos below are showing how I proceeded

WACC Computation

Beta Computation

Key results and analysis

The model output was:

Enterprise Value = SGD 4.8 billion
Equity Value = SGD 4.18 billion
Intrinsic Value per Share = SGD 9.72 (vs. proposed delisting price of SGD 7.00)

This suggests that the buyout offer undervalued the company by more than 30%. This raised questions of fairness for minority shareholders, echoing similar cases in Asia such as the privatization of Wing Tai Holdings or the delisting of Global Logistic Properties.

Download the Excel file

If you want to access a part of my work on the projections and DCF, click the link below:

Download the Excel file for WACC and Beta analysis

Why should I be interested in this post?

This modeling project not only strengthened my technical finance skills but also helped me think critically about shareholder rights, valuation fairness, and the role of financial modeling in defending minority interests. Mastering the DCF approach is essential for anyone pursuing investment banking, private equity, or corporate strategy roles.

Useful resources

SimTrade Platform

Monetary Authority of Singapore

About the author

This article was written in September 2025 by Samuel BRAL (ESSEC Business School, Global Bachelor in Business Administration – Exchange at NUS).

▶ Read all posts written by Samuel BRAL

Forecasting Airline Route Profitability with Monte Carlo Simulation

January 20, 2026September 25, 2025 by Samuel BRAL

In this article, Samuel BRAL (ESSEC Business School, Global BBA – Exchange at NUS, 2025) explains how he applied Monte Carlo simulations to support Emirates Airlines in evaluating the profitability of launching a new long-haul route under conditions of uncertainty.

Context of the project

This project was part of the course “Decision Analytics using Spreadsheets” at the National University of Singapore (NUS). I was asked to provide a quantitative recommendation to Emirates Airlines on selecting a new international route from Dubai. The available destination options included Buenos Aires, Tokyo, Cape Town, and Cairo.

Due to the complexity of airline operations and the uncertainty surrounding factors such as demand, ticket prices, no-show rates, and operating costs, a traditional static financial model would not be sufficient. Instead, I built a Monte Carlo simulation model to capture the dynamic range of possible outcomes and assess the risk-return profile of each destination.

What is a Monte Carlo simulation?

A Monte Carlo simulation is a mathematical technique used to estimate the probability distribution of outcomes when there is uncertainty in the input variables. By running thousands of simulations using random values generated from defined probability distributions, the method provides insights into the range, likelihood, and volatility of potential results.

This approach is commonly used in financial modeling, risk analysis, and engineering. For example, investment banks use Monte Carlo models to simulate portfolio returns and Value at Risk (VaR), while oil and gas companies apply them to forecast drilling success and production volumes.

Simulation approach and methodology

I built a simulation model in Excel that executed 2,000 trials per route. Each trial simulated a potential outcome based on randomly generated values for key variables. The profit was calculated using the following formula:

Profit = (Tickets Sold × Ticket Price) – Operating Costs – Compensation Costs

Here is how each component was modeled:

Passenger demand: Modeled as a normal distribution using historical demand averages and standard deviations for each route. For example, Tokyo exhibited more stable demand, while Buenos Aires showed higher variance due to geopolitical and economic volatility in Argentina.
Ticket price: Ticket prices were generated using NORM.INV(RAND(), mean, stdev) to account for fluctuations caused by competitive pricing, seasonal variation, and macroeconomic factors like fuel costs and currency movements.
No-show rate: Modeled with a uniform distribution between 5% and 10%, based on IATA statistics and academic studies on airline overbooking behavior (source: IATA Global Passenger Survey, 2023).
Aircraft assignment: Simulated using a discrete probability distribution based on the actual Emirates fleet composition (e.g., A380, Boeing 777). Larger aircraft allowed more passengers but incurred higher operating costs.
Compensation cost: Incurred when demand exceeded seat capacity, reflecting the cost of rebooking, refunds, and customer service. These costs were calibrated using Emirates’ historical compensation data for overbooking cases (source: Emirates Annual Report 2023).

To execute the simulations, I used Excel’s Data Table function to loop through trials and capture the output profit distribution for each destination. From this distribution, I calculated:

Expected profit (mean)
Standard deviation of profit (volatility)
Probability of a loss (profit < 0)
Probability of a significant loss (loss > SGD 100,000)

Key results and insights

The simulation identified Buenos Aires as the most profitable option with an expected profit of SGD 292,247 and a 99.65% chance of profitability. However, the route also exhibited a small 0.1% risk of incurring losses above SGD 100,000 due to volatile demand and long travel distance.

Cape Town, while less profitable, offered near-zero downside risk. Tokyo had moderate returns and relatively low variance. This reflects a classic risk-return tradeoff that airlines often face: should the company pursue high-reward but volatile destinations, or opt for stable but lower-margin routes?

Additionally, I tested various overbooking strategies. An overbooking rate of 9.3% was found to optimize expected profits while keeping the cost of passenger compensation within an acceptable range. This mirrors real-world practices, where carriers like Delta and Lufthansa use algorithmic overbooking based on historical no-show patterns to maximize seat utilization (source: MIT Airline Data Project). If you want to have access to the work, here is the Excel file on the overview of all routes as well as the work for Buenos Aires.

Why should I be interested in this post?

This project demonstrates how Monte Carlo simulations transform business decision-making under uncertainty. Instead of relying on single-point forecasts, the model enabled me to quantify risk, test strategic decisions (like overbooking), and provide data-driven recommendations.

For students and professionals in finance, consulting, or operations, Monte Carlo simulation is a core technique for scenario planning and risk assessment. It enhances decision quality in fields as diverse as project finance, asset management, supply chain optimization, and policy modeling.

Useful resources

SimTrade Platform

IATA Global Passenger Survey 2023

Emirates Annual Report and Press Releases

MIT Airline Data Project

About the author

This post was written in September 2025 by Samuel BRAL (ESSEC Business School, Global Bachelor in Business Administration – Exchange at NUS).

▶ Read all posts written by Samuel BRAL

Understanding organizations’ role in bargaining tariffs

January 20, 2026July 12, 2025 by Annie YEUNG

In this article, Annie YEUNG (ESSEC Business School, Global Bachelor in Business Administration (GBBA) – Exchange Student, 2025) explains about understanding tariff bargaining that involves international organizations, governments, and industries..

The World Trade Organization (WTO)

The WTO was established in 1995 and is the most influential international organization in managing and negotiating for global trade. The WTO includes 164 member countries, and its main goal includes facilitating trade negations and monitor trade policies, especially tariffs. There has been statistics showing that average global tariffs has decreased since the establishment of WTO. For example, average bound tariffs for decreased by nearly 3 percent from 1995 to 2023.

The WTO is also plays an important role in bargaining tariffs. For example, the Doha Development Round, launched in 2001 under the WTO has focused on improving trading for developing nations. This negotiation, initiated by the WTO, has aimed to make global trade equitable. Its missions include reducing agricultural subsidies, lowering tariffs, which all have effect in order to help developing nations improve access to trading in the global market. There has been negotiations created amongst developed and undeveloped countries, with these negotiations focusing on farm subsidies and market access, as well as lowering industrial tariffs to allow developed nations to better participate in global trading activities . however it is important to note that despite ongoing talks, this round of negotiation has not been successful in delivering an agreement. This failure could have been attributed to the challenges in different trading activity interests amongst different countries. For example, there has been different perspectives in trading such as agricultural subsidies, and tariffs on services, resulting in unresolved conflicts despite negotiation.

WTO negotiations reflect deeper global power imbalances, which are manifested in trading activities and tariffs imposed. The more influential and wealthier nations often hold more bargaining power and power in the global trading market landscapes. For example, these influential countries, particularly those in the G20, often set agenda in the trade negotiations and possess more negotiating capacity. As a result these countries often dominate in negotiations, setting a dynamic that advocate for their trade interests during the process of bargaining of tariffs.

The African Group, the Association of Southeast Asian Nations (ASEAM) are organizations that have become more active, which these countries have formed organizations in order to bargain for equity in the global trading landscape. These organizations that are forming alliances in order to bargain for equitable trading systems that are recognizing asymmetries between economies to push for stabilization for less developed countries. While developed nations are pushing for lower tariffs across all sectors. However, the countries with large economies often contend that decreased tariffs will destabilize economies. As a result, the WTO plays a crucial role to address this imbalance. For example, the “Special Differential Treatment” proposed by the WTO framework promotes more support for developing countries in implementing trade agreements and reducing tariffs. This could reduce unfair trade advantages and reconcile global trade liberalization amongst developed and undeveloped countries..

Trade Negotiation Teams – Representatives

Trade negotiations are often led by high-ranking officials representing their nations. For example, the U.S. Trade Representative (USTR) is a team of experts that are specializing in multiple fields in industries, including agriculture, technology, labor, environment etc. For example, the negotiation team participating in the 2024 Trade Policy Agenda which the USTR emphasized on commitments for their countries interests, negotiating for high-standard commitments in sustainable trade practice to bolster supply chain resilience. The USTR also participates in the World Trade Organization to unify positions with other organizations such as the African Group and the ASEAN to implement trade agreements with other developing countries.

For example, the United States imposes an average agricultural tariff of 5.1%. India has an average agriculture tariff of 38% to protect domestic producers. The European Union imposes 11% in agricultural tariffs. These disparities often lead complex negotiations, especially with agriculture which is crucial for food security .Hence, negotiation objectives often focus on reciprocity. This means to maximize benefits, which trading partners much seek for equivalent concessions, and negotiate on agreements to match others. For example, if one agrees to lower tariffs, the trading partner shall agree to provide benefits on a similar traded product. This ensures mutual benefit in policy making and reaching political goals. However, reciprocity could sometimes be challenging when two countries are under asymmetrical power dynamics, such as negotiating between developed and developing countries, Furthermore, during negotiation, while trade liberation is a long-term mission in tariff negotiations, each country approaches tariff negotiation seeking to protect strategic sectors, preserve jobs, and ensuring their own country’s interests. In negotiating for tariff cuts, some countries may insist on tariffs in protection for their own domestic producers, or in goal to safeguard their own strategic sectors.

Examples of governments’ Tariffs – a case study

Trump – the U.S. – China Trade War during 2018 to 2020

The U.S. China trade war during the 2018-2020 led to higher prices for American consumers. China responded with retaliatory tariffs on U.S. goods, and global trade dynamics were disrupted. The trade wars between U.S. and China also casted an effect on the economies globally, as the two countries have such large economies.

As a result from the trade war, both the U.S. and China have experienced profound effects in multiple perspectives of their economies. The U.S> economy were affected in their GDP and employment. According to a report from Bloomberg Economics, the trade war in 2019 has costed the US economy at $316 billion. The trade wars has also resulted U.S. in stock market losses, with research from the federal reserve Bank of New York finding U.S. firms losing at least $1.7 trillion in market value as a result from the tariffs imposed on imports from China. China has also experienced economic challenges as a result from the trade wars, with export decline and experiencing economic pressures. China has experienced an export growth slow-down, indicating deepened economic challenges.

Furthermore, the trade wars has casted a great effect to the global economic markets. With the two countries being two of the world’s largest economies, their trade tensions has led to significant shifts in market dynamics and economic growth on a global scale. According to data from Banque de France, 10 percent increase in tariffs could reduce global GDP by 3%. This decline as a result from the trade wars and increased tariffs is a result of increased prices that leads to decreased productivity, higher financing costs and reduced investment demand. According to data from The World Economic Forum, it is documented that the trade war has resulted a decrease of global GDP growth to 2.8% in 2019.

Useful resources

A Quick Review of 250 Years of Economic Theory About Tariffs

Tariff Negotiations and Renegotiations under the GATT and the WTO — Procedures and Practices

A quantitative analysis of multi-party tariff negotiations

About the author

This article was written in June 2025 by Annie YEUNG (ESSEC Business School, Global Bachelor in Business Administration (GBBA) – Exchange Student, 2025).

▶ Read all posts written by Annie YEUNG

Understanding the Evolution of Tariffs

January 20, 2026July 12, 2025 by Annie YEUNG

In this article, Annie YEUNG discusses the historical development of tariffs and the evolution of tariffs over economic landscapes overtime.

Brief explanation of history of global tariffs

In the 19th century, tariffs were the main source of government revenue in aim for protectionism. Tariffs were widely used to protect domestic industries that were in their beginning stages. Many countries such as the United States and Europe has imposed high tariffs in order to ensure the development of their industrializing economies. For example, the U.S. implemented tariffs, such as the Tariff of Abominations in 1828 to protect its manufacturing industry. During the post World War II era, the General Agreement on Tariffs and Trade was established in 1947. In response to the post war devastation, countries lowered their tariffs in order to promote economic growth. Reciprocal lower tariffs were also implemented amongst countries and trading partners. Starting in near the beginning of the 21st center, the launch of the World Trade Organization in 1995 marked a significant evolution in global trade, where governance of trade tariffs were established through the launch of the WTO. The WTO emphasized on the trading of goods and introduced a governance structure for development considerations, granting special support for developing and less developed countries. The WTO also introduced an institutional structure for the dispute settlement procedures on global trading. Today, tariff reductions have continued due to negotiations and regional trade agreements, which deepened the harmonization of the global markets, facilitating increased global trade volumes. However, during the last decade, there has been an introduction of resurgence of tariffs amongst increased instability in the current geopolitical grounds. Tariffs has served as a political tool. For example, the U.S.- China trade war has seen tariffs as an economic tool under rising geopolitical tensions where billions of dollars of goods subject to tariffs. As two of biggest economies globally, this trade war has disrupted global supply chains. This has posed challenges as other countries have also employed tariffs for protectionism goals.

The Protectionism Approach

The United States has been maintaining high tariffs to nurture for its developing domestic industries during the 19th centuries. The United States has seen an increase in the average tariff rate over a century of time. During the beginning of the 19th century, the U.S.Average Tariff Rate was 35%, whereas by 1913, the U.S. Average Tariff Rate has increased to 40%. This historical evolution could be attributed to the need for domestic protection; early industrialization period during the early 19th century required protection, whereas in the beginning of the 20th century, tariffs needed to support the growing industry. European countries such as Germany and France also utilized tariffs to protect industrial growth during this period of time. However, developing countries struggled developing tariffs as threir internal markets were still in developing stages. However, beginning from the early 1900s, there has been a further rise in nationalism. Some tariffs rates exceeded 60%, and as a result, global trade decreased by 66% between 1929 to 1934. This was also during the period of the Great Depression, in which these tariff hikes and set-back in economy was a result of reduced international trade.

Trade Liberalization

After the establishment of the General Agreement on Tariffs and Trade in 1947 to reduce tariffs, there have been successful negotiation amongst nations to cut average tariffs worldwide in order to reduce protectionism and open up markets to global trading activity. This is seen from the data presented from the World Bank World Development Indicators, which there has been a gradual deduction in average global tariff rate from 15% to 6% from from the year 1950 to 2000. Since the beginning of tariff reductions and the start of post-war rehabilitation and rebuilding of the economy in 1950, continued multilateral negotiations has resulted in a historic low of tariffs in the year 2000. As a result, trade volumes increased and there was a global economic growth.

Complex Socio-economical landscapes

Despite the decreasing of average global tariff rate, trade policies in the 21st century, especially in the recent years, has grown to become more complex. For example, there has been targeted tariffs and trade conflicts, leading. To increased uncertainty in global trading markets. For example, U.S. has increased its tariffs from the year 2017 to 2020, with the average U.S. Tariff Rate of 1.6% in 2017 plummeting to 3.1% in the year 2020. This could have been attributed to the increased policies on tariffs on steel, aluminum, and the trade wars with China, resulting to much higher tariff rates compared to the beginning of the 2000s. For example, targeted tariffs has become a strategic target tariff, a tool with political goals. For example, the steel and aluminum tariff was to protective domestic industries, which the Trump administrated imposed a 25% tariff on steel and 10% on aluminum in March 2018. These tariffs were to uphold national security to maintain the U.S. domestic metals industry. However, this tariff led to price increases and resulted in retaliation of tariff policies from its trading partners.

< p> Increased tariffs from one country often results in retaliatory tariffs from its trading partners. Not just China, but there were also other countries that have responded to U.S.’s tariff hikes, including Canada, the European Union, and India. As a result, the increase in tariff has resulted in increased uncertainty to the global trade environment, affecting stock markets, companies, local businesses etc. Hence, the tariff can cast direct effects to each producer and consumer domestically, as investors raise concern over costs, and supply chains are rendered volatile, slowing businesses.

A Case Study: The European Union’s tariff on Chinese Electric Vehicles

In the year 2024, the European Union imposed tariffs of 38.1% on Chinese imported electrical vehicles. This is an example of the shifting grounds of global trading market environments. Today, tariffs have increased globally, and this tariff is an example that has marked a shift in the European Union trade policy and has great implications for the global automotive industry as well as the international trading landscapes. For example, the EU has imposed different tariffs targeting on specific different companies. The SAIC Motor has an 38.1% tariff, whereas Geely experiences a 20% tariff, while BYD has a 17.4% tariff for all goods imported into the European Union from China. These tariffs were imposed by the EU due to the low prices that Chinese manufacturers deliver to the European market, which may potentially undermine local producers through competition. In response to the tariff, China has filed a complaint with the World Trade Organization to contend for EU’s actions, appealing that the EU may have constituted to protectionism under fair competition. The tariff casts a large impact on the European EV market, which European consumers are facing higher prices. Market share also shifts, as the tariffs has changed the competitive landscape; Chinese manufactured EVs are facing higher costs, which may benefit domestic European manufacturers. Hence, the EU’s recent tariffs on Chinese manufactured electric vehicles marks today’s international trade policies amongst the historical evolution of global trade and tariffs, and has sparked debate and challenges.

Evolution of tariffs

Source: ACEA.

Why should I be interested in this post?

Evolution of tariffs is crucial as it reveals how economic policies shape international trade dynamics, and this affects to domestic industries, producers, consumers, and also has a wider effect to the global market. Studying the changes of tariffs throughout time can allow us to gain insights into historical trends, and stay informed upon future policy decisions.

Useful resources

A history of free trade — and the deep irony of ‘liberation day’

The Evolution of Tariffs

History of U.S. tariffs and why it matters today

The Problem of the Tariff in American Economic History, 1787–1934

Financial Times Transcript: Tariffs past, present and future. With Doug Irwin

About the author

The article was written in June 2025 by Annie YEUNG (ESSEC Business School, Global Bachelor in Business Administration (GBBA) – Exchange Student, 2025).

▶ Read all posts written by Annie YEUNG

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