Reinventing Wellness: How il Puro Brings Personalization to Nutrition

Emanuele GHIDONI

In this article, Emanuele GHIDONI ESSEC Business School, European Management Track (EMT), 2025-2026) shares his entrepreneurial experience of founding il Puro a startup born from the vision of transforming daily nutrition into a personalized and emotional experience. By combining data-driven personalization with sensory design, il Puro aims to make functional food both effective and enjoyable.

Turning an Idea into a Mission

The idea behind il Puro was born from a simple yet powerful question: how can we help people feel better every day through what they consume? During my studies and professional experiences, I observed a gap between the science of nutrition and the reality of human habits. Many people struggle to meet their daily nutritional needs not because of a lack of awareness, but because healthy choices often feel inconvenient or uninspiring. I wanted to bridge this gap by creating a brand that combined personalization, science, and pleasure, transforming nutrition from a routine task into a daily ritual. That vision became il Puro, a functional food venture built around personalization, emotion, and Italian values.

Logo of il PURO.
Logo of il Puro
Source: The company.

From Concept to Market: Testing and Learning

After shaping the initial concept, the next crucial step was to validate it in the real world. Rather than jumping straight into full production, we focused on building a minimum viable product (MVP) to test the market and understand how potential customers would truly respond. Through this phase, we explored key dimensions such as product–market fit, price sensitivity, and willingness to pay, combining qualitative feedback with quantitative data.

A central part of this process was the use of A/B testing a method where two or more variations of a single element are presented to different groups of users to measure which performs better. For instance, we tested different product formulations, packaging visuals, and website layouts to observe how each influenced user engagement and purchase intent. We also ran price point experiments to identify the threshold at which conversion began to decline, allowing us to estimate optimal pricing and margin trade-offs. Each test generated measurable data, click-through rates, conversion percentages, time-on-page, and cart completion, which we used to make data-driven adjustments.

This structured experimentation reduced uncertainty and transformed creative intuition into quantifiable learning. By systematically measuring what worked and what didn’t, we refined both the product and the brand narrative, ensuring that il Puro evolved through validated consumer insight and real behavioral evidence rather than assumptions alone.

Business concepts related to my project

I present below three financial and business concepts related to my project il Puro, which guided my decision-making during the early development phase of the brand.

.

BE SIMPLE

I build il Puro on simplicity because simplicity compounds financially. A focused hero lineup (2–3 SKUs) keeps COGS tight, inventory turns high, and the cash conversion cycle short. A clean price architecture (starter, core, subscription) reduces choice friction and lifts conversion while protecting margin. I sell where unit economics are strongest DTC via Shopify plus a selective B2B channel with clear MOQs and prepayment terms to de-risk working capital. Operationally, fewer suppliers, standardized Italian actives, and repeatable fulfillment flows mean lower variability, fewer stockouts, and healthier gross margins from day one.

BE SCIENTIFIC

I treat decisions as experiments with a P&L. Personalization isn’t a story; it’s a retention engine that increases LTV: onboarding quizzes → segmentation → tailored formulations → higher reorder rates. I quantify everything and elasticity tests for pricing; split tests on bundles, claims, and creatives; cohort and payback tracking by acquisition channel. My targets are explicit: LTV:CAC ≥ 3:1, first-order contribution margin positive by order #2, subscription retention ≥ 70% at month 3. Clinical substantiation and transparent labeling aren’t just ethical they reduce returns, build trust, and lower CAC over time.

BE DETAIL-ORIENTED

I run il Puro with a unit-economics dashboard, not vibes. COGS broken down to the gram (actives, flavoring, sachet, carton), freight per parcel, pick-pack, payment fees, and support cost per ticket. I design packaging to ship small and light, negotiate lead times to avoid safety-stock bloat, and lock FX/commodity exposure where sensible. My working metrics: DTC gross margin, B2B contribution margin after CAC, paid payback, monthly churn, inventory turns, NPS. Contract manufacturing keeps CAPEX light; disciplined reorders and rolling forecasts keep cash free for growth.

Why should I be interested in this post?

For someone with an entrepreneurial mindset, the journey of il Puro represents the essence of turning vision into execution. Building a startup in the functional food space was not just about creating a product, it was about identifying a real problem, testing assumptions, and translating insights into a viable business model. Every step, from market validation and financial modeling to branding and investor pitching, demanded both strategic thinking and adaptability. It was a hands-on lesson in how innovation happens: through curiosity, experimentation, and resilience. Above all, il Puro reflects a new kind of entrepreneurship, one that merges health, technology, and purpose to create businesses that are not only profitable, but also meaningful in the lives of people.

Related posts on the SimTrade blog

   ▶ All posts about Professional experiences

   ▶ Alexandre VERLET Classic brain teasers from real-life interviews

Useful resources

il Puro – Official website

World Health Organization Healthy Diet Guidelines

Mintel Functional Food and Beverage Trends

NutraIngredients News on Functional Foods and Supplements

McKinsey The Future of Wellness

About the author

The article was written in October 2025 by Emanuele GHIDONI (ESSEC Business School, European Management Track (EMT), 2025-2026).

US Treasury Bonds

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
 Formula for the price of the bond
Source: The author

Relationship between bond price and current bond yield
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.

Download the Excel file to compute the bond price as a function of the current 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.

Related posts on the SimTrade blog

   ▶ Nithisha CHALLADatastream

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

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).
 Formula for the Herfindahl-Hirschman Index (HHI).
Source: the author.

where MSi 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.

Download 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.

Related posts on the SimTrade blog

   ▶ Nithisha CHALLADatastream

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

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
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)
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.

Download 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.

Related posts on the SimTrade blog

   ▶ Nithisha CHALLA Datastream

   ▶ Ziqian ZONG The Yield Curve

   ▶ Youssef LOURAOUI Interest rate term structure and yield curve calibration

   ▶ William ARRATA My experiences as Fixed Income portfolio manager then Asset Liability Manager at Banque de France

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

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.

Related posts on the SimTrade blog

   ▶ Cornelius HEINTZE Two-Stage Valuation Method: Challenges

   ▶ Andrea ALOSCARI Valuation Methods

   ▶ Jorge KARAM DIB Multiples Valuation Method for Stocks

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

Posts

Dawn DENG

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.
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.

Related posts on the SimTrade blog

About credit risk

   ▶ Jayati WALIA Credit risk

   ▶ Jayati WALIA Quantitative risk management

   ▶ Bijal GANDHI Credit Rating

About professional experiences

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

   ▶ Jayati WALIA My experience as a credit analyst at Amundi Asset Management

   ▶ Aamey MEHTA My experience as a credit analyst at Wells Fargo

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 Power of Trust: My Internship Experience in Corporate Restructuring and Charitable Trusts

Dawn DENG

In this article, Dawn DENG (ESSEC Business School, Global Bachelor in Business Administration (GBBA), Smith-ESSEC Double Degree Program, 2024-2026) shares her experience working at a trust company in China, where she contributed to corporate restructuring projects and the design of charitable trusts. She also reflects on how her understanding of the trust system evolved through comparative perspectives between China and Western countries.

About the company

The trust company where I completed my internship is one of China’s long-established financial institutions specializing in trust and asset management services. Trust companies in China operate under the supervision of the China Banking and Insurance Regulatory Commission (CBIRC), bridging the gap between banking, investment, and wealth management. They manage funds on behalf of clients for purposes such as industrial investment, real estate development, wealth management, and charity.

During the past decade, the industry has experienced a transformation—from traditional capital pooling products to more specialized trust structures that emphasize risk control, compliance, and innovation. My department focused on special asset management and structured design, handling complex projects that combined legal, financial, and social objectives.

My internship

My three-month internship provided me with a comprehensive introduction to how trusts operate as both financial tools and institutional mechanisms. I worked with a professional team on multiple projects, including corporate restructuring, charitable trust preparation, and policy research on real estate trust registration pilots.

My missions

My main responsibilities included drafting due-diligence reports, designing trust structure diagrams, preparing presentation slides, and taking minutes during client meetings. I also conducted research on relevant legal and policy frameworks. These tasks allowed me to understand how trust projects are structured, negotiated, and implemented in practice.

Required skills and knowledge

The internship required a blend of hard and soft skills. On the technical side, I used financial analysis, document drafting, and data verification skills for due-diligence work. On the interpersonal side, attention to detail, professionalism, and clear communication were essential—especially when assisting senior managers in client discussions or internal reviews. I also learned how legal reasoning, financial modeling, and policy interpretation intersect within trust projects.

What I learned

This internship deepened my understanding of finance beyond traditional banking. I saw how trust companies play a vital role in restructuring distressed enterprises, supporting social causes, and facilitating wealth transmission. More importantly, I realized that financial tools, when governed by institutional trust and transparency, can become powerful instruments for both growth and social good.

Financial concepts related to my internship

I present below three financial concepts related to my internship experience: corporate restructuring, charitable trust, and real estate trust registration.

Corporate restructuring and the role of trust companies

When a listed company in China enters bankruptcy reorganization, two types of investors often emerge: industrial investors and financial investors. Trust companies serve as the latter, contributing capital and structuring expertise. Their advantages include risk isolation—trust assets are independent of the company’s liabilities—and structural flexibility, as they can design debt-to-equity swaps or securitization solutions. This mechanism allows trust companies to participate in corporate recovery while safeguarding investor interests.

Charitable trust

A charitable trust is a legal arrangement where assets are entrusted to a trustee—typically a trust company—for public-interest purposes such as education, poverty alleviation, or healthcare. Its institutional structure involves a settlor, trustee, custodian, supervisor, and beneficiaries. Compared with direct donations, charitable trusts ensure transparency, efficiency, and sustainability: funds are professionally managed, periodically disclosed, and can generate lawful returns for reinvestment into charity. This system transforms goodwill into an enduring and accountable mechanism.

Real estate trust registration

In 2024–2025, several pilot cities in China launched the “real estate into trust” registration policy. For the first time, individuals could legally transfer real estate into trusts, with ownership certificates marked “trust property.” This policy innovation strengthens property-rights protection and facilitates wealth inheritance, family planning, and eldercare models such as “housing-for-pension.” It also marks a milestone in institutionalizing the trust framework within China’s civil law system.

Why should I be interested in this post?

This post offers ESSEC students a window into one of China’s most dynamic financial innovations. Trusts combine finance, law, and governance—they are both capital structures and instruments of social value. For students interested in corporate finance, asset management, or financial regulation, understanding the trust industry provides a unique perspective on how institutions transform abstract trust into tangible impact.

Related posts on the SimTrade blog

   ▶ Samia DARMELLAH Recent Financial Innovations in China in the 2020s

   ▶ Louis DETALLE A quick presentation of the Restructuring job…

Useful resources

What is meant with Restructuring Trust? (MPT Advisory Group)

What is the ownership of trust property in China? (Nature article)

What Is a Charitable Trust & How Does it Work?

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

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) = Σt=1…T ( CFt ) / (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) = CFstable / (r * (1 + r)T)

    • Constant growth in perpetuity:
      PV(Stable phase) = CFT+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: CFstable = 100 ⇒ TV at t=5
    PV(Terminal) = 100 /(0.1*(1.10)5) = 620.92

  • (b) Constant growth g = 2%: CFT+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:

Two-Stage Model Example 1

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.

Two-Stage Model Example Startup

You can download the excel file here:

Download the Excel file for Two-Stage-Model Analysis

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.

Related posts on the SimTrade blog

   ▶ All posts about financial techniques

   ▶ Jorge KARAM DIB Multiples valuation methods

   ▶ Andrea ALOSCARI Valuation methods

   ▶ Samuel BRAL Valuing the Delisting of Best World International Using DCF Modeling

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

My Internship Experience at Alstom as a Market Research Intern

Rishika YADAV

In this article, Rishika YADAV (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2023–2027) shares her professional experience as a Market Research Intern at Alstom in India.

Introduction

As a Global BBA student at ESSEC Business School, I had the opportunity to join Alstom India as a Market Research Intern. This experience allowed me to work at the intersection of strategy, policy, and innovation in the transport sector. My missions ranged from analysing the outlook of the Indian Railways industry to benchmarking global players in the hydrogen-powered engine market and delivering data-driven insights for decision-making.

In this post, I will share my professional journey at Alstom, provide an overview of the industry context in India, and reflect on how market research contributes to shaping strategic positioning in a highly dynamic sector.

About Alstom

Alstom is a global leader in sustainable mobility, designing and manufacturing rolling stock, signaling systems, and railway services. Headquartered in Saint-Ouen, France, Alstom operates in more than 70 countries and employs over 80,000 people worldwide. Its portfolio covers a wide range of solutions, from high-speed trains to metro systems and innovative propulsion technologies, including hydrogen-powered engines.

Logo of Alstom.
Logo of Alstom
Source: the company.

The company plays a central role in the modernization of the Indian Railways, where large-scale infrastructure projects and government initiatives are reshaping mobility. Alstom’s presence in India includes major manufacturing plants, research centers, and long-term partnerships with the Indian government, making it a critical player in the country’s transport ecosystem.

Industry Context: Indian Railways and the Push for Modernization

India’s railway network is the fourth largest in the world, transporting more than 8 billion passengers annually and serving as the backbone of both passenger and freight mobility. With urbanization, growing demand for logistics, and sustainability imperatives, the government has launched ambitious modernization projects.

To structure my analysis, I applied a PESTEL framework (Political, Economic, Social, Technological, Environmental, Legal), which helped me capture the multifaceted drivers shaping the industry:

  • Political: Strong government commitment to railway electrification by 2030 and the development of high-speed rail projects such as the Mumbai–Ahmedabad bullet train.
  • Economic: Massive infrastructure spending, growing freight demand, and India’s ambition to become a global logistics hub.
  • Social: Rapid urbanization and rising middle-class demand for safe, reliable, and sustainable transport.
  • Technological: Deployment of digital signaling, automation in metro systems, and investments in green technologies such as hydrogen propulsion.
  • Environmental: Climate change policies driving the shift away from diesel and the adoption of zero-emission mobility solutions.
  • Legal: “Make in India” requirements for domestic production and procurement rules encouraging partnerships between multinational firms and local manufacturers.

For companies like Alstom, this environment presents both opportunities and challenges. Success depends on aligning with government priorities, anticipating regulatory frameworks, and delivering sustainable solutions that address the mobility needs of a rapidly urbanizing population.

Market Research and Strategic Outlook

Building on the PESTEL framework, my primary task was to translate macro-level industry dynamics into strategic insights for Alstom’s marketing team. I applied elements of Porter’s Five Forces to evaluate competitive pressures, particularly the bargaining power of government procurement agencies, the threat of substitute technologies, and the intensity of rivalry among global players.

For instance, the Indian government’s procurement model places strong emphasis on cost-effectiveness and local value creation. This heightened the importance of analyzing procurement cycles and budget allocations, as these factors directly determine entry opportunities. Similarly, the rise of indigenous technology developers suggested a potential medium-term substitution risk for foreign OEMs (Original Equipment Manufacturers).

My contribution was to synthesize these complex dynamics into actionable recommendations for Alstom’s leadership. By mapping government initiatives (such as 100% electrification by 2030) against Alstom’s innovation pipeline, I helped highlight priority areas for investment and partnership. This showed how market research acts as a bridge between public policy directions and private strategic decisions.

Competitive Analysis in the Hydrogen-Powered Engine Market

A key part of my internship involved conducting a competitive benchmarking study on the hydrogen-powered engine market, an emerging field in sustainable transport. My analysis compared Alstom’s positioning with that of leading competitors, including Siemens Mobility (Germany), CRRC (China), and Stadler Rail (Switzerland). The benchmarking exercise focused on three dimensions:

  1. Technological efficiency – energy conversion rates, operational range, and adaptability to existing rail infrastructure.
  2. Regulatory compliance – alignment with safety standards, certification requirements, and government adoption incentives.
  3. Innovation roadmaps – timelines for pilot projects, R&D collaborations, and commercial deployments.

As part of the study, I also examined India’s first hydrogen train initiative, announced under the “Hydrogen Mission” in 2021 and piloted on the Jind–Sonipat route in Haryana. This project provided a reference point for assessing how domestic adoption could influence demand for hydrogen solutions and how foreign players like Alstom might participate in future collaborations.

The outcome of this competitive analysis was a set of strategic benchmarks that highlighted Alstom’s strengths (global experience, proven prototypes in Europe) and areas where adaptation to the Indian context would be critical (local supply chain integration, cost competitiveness).

Conclusion

My internship at Alstom was more than an introduction to the transport sector — it was a formative experience that sharpened my analytical, strategic, and collaborative skills. Through market research, I learned how to transform complex and unstructured data into clear insights that directly supported executive decision-making. By benchmarking global competitors and tracking procurement patterns, I discovered the importance of combining rigorous analysis with an understanding of policy and technology trends.

Equally important, I developed strong stakeholder management skills by working with senior leadership, and I learned to deliver results under tight deadlines in a fast-moving industry. These experiences deepened my interest in strategy and finance, particularly in industries undergoing technological and regulatory transformation. Looking ahead, I aspire to build a career where I can contribute to shaping sustainable and innovative solutions at the crossroads of business strategy, financial decision-making, and global infrastructure development.

Business concepts related to my internship

I present below three concepts related to my internship and explain how they connect to my missions at Alstom: Total Cost of Ownership (TCO), Public Procurement Economics, and Benchmarking & Competitive Advantage.

Total Cost of Ownership (TCO)

Total Cost of Ownership refers to the overall cost of an asset across its life cycle, including purchase, operation, maintenance, and disposal. In railway procurement, decision-makers often evaluate not only the initial price of rolling stock or propulsion systems but also long-term operating costs such as energy consumption and maintenance. During my internship, I integrated TCO considerations into market analyses by comparing the long-run economics of hydrogen-powered versus diesel and electric trains. This helped demonstrate how Alstom could position its products as cost-efficient over their lifetime, even if initial capital expenditure was higher.

Public Procurement Economics

Public procurement represents a large share of railway investment in India. It is shaped by budget cycles, fiscal priorities, and policy objectives such as “Make in India.” Understanding procurement economics was central to my internship, since I analysed over 500 data points on tenders, contracts, and project timelines. By linking procurement patterns with budget allocations, I helped Alstom anticipate periods of high demand (for example, after fiscal budget announcements) and adapt bid strategies accordingly. This ensured better alignment of Alstom’s proposals with the financial and institutional realities of government buyers.

Benchmarking & Competitive Advantage

Benchmarking involves comparing a company’s performance, costs, and capabilities against competitors to identify strengths and gaps. In my competitive analysis of the hydrogen-powered engine market, I benchmarked Alstom’s offerings against Siemens Mobility, CRRC, and Stadler Rail. This comparison focused on efficiency ratios, regulatory readiness, and innovation timelines. By identifying areas where Alstom’s European experience was a strength, and where local cost competitiveness needed improvement, the benchmarking exercise informed strategic positioning in India. It demonstrated how analytical tools can translate into competitive advantage in bidding and partnerships.

Why Should You Be Interested in This Post?

This post offers a first-hand view of how market research bridges the gap between public policy and private strategy in one of the most dynamic transport markets in the world. If you are curious about:

  • How global companies adapt to government-driven reforms,
  • How benchmarking and data analysis inform business positioning,
  • Or how sustainability goals like hydrogen-powered mobility are transforming traditional industries,

…then this post provides a concrete example from inside Alstom’s operations in India. Beyond an internship story, it illustrates how analytical tools and strategic thinking can shape the future of mobility.

Related posts on the SimTrade blog

   ▶ All posts about Professional experiences

   ▶ Max ODEN Leveraged Finance: My Experience as an Analyst Intern at Haitong Bank

   ▶ Anouk GHERCHANOC My Internship Experience as a Corporate Finance Analyst in the 2IF Department of Inter Invest Group

   ▶ Lara HADDAD My Internship Experience as a Market Analyst at L’Oréal

   ▶ Samia DARMELLAH My Experience as a Credit Risk Portfolio Analyst at Société Générale Private Banking

   ▶ Alexandre VERLET Classic brain teasers from real-life interviews

Useful resources

Alstom — official website

Indian Railways Official portal

Press Information Bureau of India Government announcements and policy updates

NITI Aayog (Indian government think tank) Reports on hydrogen policy and sustainable transport

International Energy Agency (IEA) The Future of Rail Report

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.

My Internship Experience with the Economic and Market Intelligence Team at Daimler Truck

Vincent WALGENBACH

In this article, Vincent WALGENBACH (ESSEC Business School, Grande Ecole Program – Master in Management, Exchange Student) compares the role of an economic analyst within the financial industry to that in the corporate sector and highlights the associated career trade-offs.

Daimler Truck AG

Daimler Truck is the world’s largest manufacturer of commercial vehicles, selling over 460,000 trucks globally in 2024 and generating €54.1 billion in revenue. Headquartered near Stuttgart, Germany, the company designs, produces, and sells trucks and buses. As of Q2 2025, it employs more than 110,000 people worldwide and operates dozens of production sites. Daimler Truck was established in 2021 as a spin-off from Mercedes-Benz and builds on a long-standing tradition in the industry.

Logo of the company.
Logo of Daimler Truck
Source: Daimler Truck.

A Comparison of an Analyst Position in the Financial and Corporate Sectors

During an internship in the Economic and Market Intelligence Team, the author gained insight into how economists work within a multinational corporation. While economists are often associated with banks, insurance companies, or financial service providers, many large industrial firms also maintain economics departments. Unlike in banks, where teams are highly specialized, corporate economics departments are smaller and require team members to cover a wide range of expertise.

Economists in the Financial Industry: Specialists in a Complex Organization

In banks and other financial institutions, economics teams are typically large and highly specialized. Each economist focuses on a narrow field – such as monetary policy in a specific country, credit risk, or commodity markets. This high degree of specialization reflects the complexity of modern financial markets and the importance of precise analysis.

For example, one analyst might dedicate their entire career to analyzing the U.S. Federal Reserve’s policy decisions and their impact on bond yields, others may focus on niche areas such as energy markets, foreign exchange dynamics, or the effects of fiscal policy on sovereign debt.

Another important aspect of their work is risk assessment. Analysts in banks are tasked with stress-testing portfolios against different macroeconomic scenarios – such as a sudden spike in inflation, a geopolitical crisis, or a global recession.

In summary, these are some core features of the economist’s role within financial institutions:

  • Focus areas: Interest rates, inflation forecasts, financial market dynamics
  • Work style: Highly quantitative, model-driven, and often tied to investment decisions
  • Career tradeoff: Economists gain deep expertise in a niche area but may have limited exposure to broader economic questions

Economists in the Corporate Sector: Generalists with a Broad View

By contrast, economics departments in multinational corporations are usually smaller. At Daimler Truck, the Economic and Market Intelligence Team had only five employees covering a wide range of topics, from global macroeconomic trends to industry-specific market forecasts, and energy markets.

Because the team was small, each member had to be flexible and work across multiple domains. This required not only strong analytical skills but also the ability to communicate insights to non-economists, such as managers in strategy, sales, or procurement. The main responsibility of the team was to provide both quantitative and qualitative insights into the global truck market as well as the macroeconomic outlook of key regions for decision-makers. To this end, the team prepared a weekly briefing for the board and a more extensive report for the CFO, in addition to delivering analyses for the strategy department. Beyond top management, we also supported other departments, for example, providing inflation analyses to procurement or HR to assist them in their ongoing negotiations. In addition to supporting the team with day-to-day requests and briefings, I was also assigned independent projects. These included analyzing potential growth markets and assessing the economic impact of carbon neutrality policies.

The three most important concerns for the team were economic growth, inflation, and energy economics.

Economic Growth

Economic growth, measured primarily by Gross domestic product (GDP), was a key focus due to its strong correlation with truck sales. The company operates on a B2B model, and growth in the broader economy typically encourages firms to invest and expand their vehicle fleets—especially under favorable economic conditions. To assess this, the team relied on data from various economic research institutes and providers such as S&P Global. These datasets were then adjusted and evaluated according to internal standards, with models like Aggregate Supply and Aggregate Demand (AS/AD) serving as analytical frameworks.

Inflation

Inflation – both consumer and producer price inflation – was another critical factor. On one hand, the company runs a large procurement division responsible for sourcing truck components, and inflation plays a central role in supplier negotiations. On the other hand, inflation directly affects the financial department, especially in areas like leasing and financing, where trucks are often acquired through loans or lease agreements. Moreover, inflation influences monetary policy, and interest rate decisions by the ECB and the Fed are highly relevant for investment planning, leasing conditions, and overall demand.

Energy Economics

At the time, Europe was facing significant energy supply challenges and sharp price increases. As a result, energy economics, typically not a core focus for the team, became critically important. This was due both to the fact that trucks primarily run on fuel, which affects customer investment decisions, and because the company’s own operations and production processes consume large amounts of electricity and gas. In fact, the firm operates its own power plants. To navigate this, the team applied classical supply-and-demand analysis and closely monitored geopolitical developments and energy market news.

In summary, these are some core features of the economist’s role within the corporate sector:

  • Focus areas: macroeconomics, Industry trends, global trade flows
  • Work style: Broader scope, combining quantitative analysis with qualitative judgment
  • Career tradeoff: Economists develop versatility but may not reach the same level of technical specialization as in finance

Key Takeaways from My Internship – Career Implications

For those considering an Analyst position, the choice between the financial industry and the corporate sector involves a tradeoff between specialization and versatility.

  • If you enjoy mastering a narrow field and working with advanced models, the financial industry may be the right fit.
  • If you prefer applying economics to a wide range of real-world business challenges, a corporate economics department is super interesting.

Economists in financial institutions often occupy a central role at the very heart of the organization. Their analyses directly influence investment strategies, risk management, and overall business performance. By contrast, within multinational corporations, economists tend to hold a more specialized and somewhat “exotic” position. Their insights are primarily directed toward senior management and the board, supporting strategic decision-making rather than day-to-day operations.

This distinction has important career implications. In the corporate world, economists may find it more challenging to climb the organizational ladder, as their role is less integrated with the core functions of the firm. Unlike finance, marketing, or operations, economics is not always seen as a natural pathway to executive leadership. As a result, corporate economists often remain valuable advisors rather than becoming decision-makers themselves.

My internship provided a comprehensive introduction to the wide range of fields an economic analyst can pursue. This broad exposure is particularly valuable for those considering future specialization, as it offers a clear overview of the different domains and helps in identifying which areas may be most rewarding to pursue in greater depth.

Why should I be interested in this post?

This post compares the role of an analyst in an economics team within the financial industry to that in the corporate sector, highlighting key differences in specialization and the career trade-offs involved.

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Professional experiences

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   ▶ Anouk GHERCHANOC My Internship Experience as a Corporate Finance Analyst in the 2IF Department of Inter Invest Group

   ▶ Lara HADDAD My Internship Experience as a Market Analyst at L’Oréal

   ▶ Samia DARMELLAH My Experience as a Credit Risk Portfolio Analyst at Société Générale Private Banking

   ▶ Nithisha CHALLA Job description – Financial analysts

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Economics and data

   ▶ Bijal GANDHI Inflation Rate

   ▶ Nithisha CHALLA Bloomberg

   ▶ Nithisha CHALLA S&P Global Market Intelligence

Useful resources

Daimler Truck AG

European Central Bank (ECB)

Federal Reserve (Fed)

Eurostat Data

Federal Reserve Economic Data

International Energy Agency (IEA)

About the author

The article was written in September 2025 by Vincent WALGENBACH (ESSEC Business School, Grande Ecole Program – Master in Management, Exchange Student).

Valuing the Delisting of Best World International Using DCF Modeling

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.

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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).

Forecasting Airline Route Profitability with Monte Carlo Simulation

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.

Download the Excel file for Monte Carlo simulation

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.

Related posts on the SimTrade blog

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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).

My internship experience as a Financial Controller at Talan

Samuel BRAL

In this article, Samuel BRAL (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2022-2026) shares his professional experience as Assistant Financial Controller at Talan.

About the company

Talan is a French consulting and IT services firm that supports large organizations in their digital transformation. Founded in 2002, the group now operates in over 15 countries with more than 5,000 employees. Its activities cover business consulting, data & AI, transformation management, and IT systems integration.

The company has experienced rapid growth in recent years, reaching €600 million in revenue in 2023. Talan’s value proposition lies in combining business understanding with technical expertise to create tailored, high-impact solutions.

Logo of Talan.
Logo of Talan
Source: the company.

I worked within the Group FP&A (Financial Planning & Analysis) department at the Paris headquarters. This central team oversees the performance monitoring and financial reporting for all business units (BU), directly supporting the CFO and COMEX.

My internship

My missions

During my internship at Talan, my missions focused on supporting financial reporting, tool optimization, and performance monitoring across Talan’s international business units. My first responsibility was to assist in producing monthly management reports and P&L statements for each business unit. To do so, I extracted and reconciled financial data from systems such as Kimble, Jedox, and SuccessFactors. I created detailed revenue and margin reports used by the CFO and COMEX during monthly performance reviews. In one instance, I was tasked with explaining a sudden drop in margin for the Iberia BU, which led me to identify under-reported subcontractor costs and propose adjustments that improved margin accuracy by 15%.

In parallel, I was assigned to enhance and maintain our internal reporting tools. I updated Power BI dashboards to reflect changes in budget KPIs, created dynamic filters to allow managers to track performance by project or team, and integrated new reporting metrics requested by HR. A concrete example includes building a resource utilization dashboard that tracked billable vs. non-billable hours across 20+ consultants. This became a key element in weekly performance meetings.

I also contributed to the improvement of the Jedox budgeting model by testing input logic and spotting misalignments between operational forecasts and financial planning. My test case simulation revealed a recurring mismatch between headcount forecasts in SuccessFactors and budgeted salaries in Jedox, this insight helped improve the accuracy of HR cost planning. Lastly, I supported daily project performance follow-up. I maintained Excel trackers for monitoring project delivery rates, billing status, and work-in-progress (WIP). In one project, I flagged €1.2 million in delayed invoices at our UK subsidiary and proposed a process with the project manager and billing team to correct invoice triggers and reduce WIP exposure the following month.

Required skills and knowledge

This internship demanded both technical and soft skills. Technically, I had to master Excel (pivot tables, advanced formulas), Power BI, and become comfortable with integrated tools like Jedox, Kimble, and SuccessFactors. A solid understanding of accounting principles and management control basics was essential to analyze P&Ls and challenge budget assumptions.

But beyond tools and numbers, what really made a difference was my ability to adapt quickly, communicate clearly, and collaborate with different teams: from business unit managers to the finance department. I learned how to handle pressure during closing periods and gained confidence in presenting insights to senior stakeholders.

What I learned

This experience allowed me to apply classroom knowledge to real-world challenges. I saw how data, when properly structured and analyzed, can support strategic decision-making. I also learned the importance of data reliability, reconciling figures between systems and ensuring consistency across dashboards was a daily concern. Finally, I came out of the internship with a clearer picture of what FP&A means in practice: it’s not just about reporting, but about driving performance.

Financial concepts related to my internship

I present below three financial concepts related to my internship: variance analysis, working capital, and margin optimization.

Variance Analysis

Variance analysis was at the heart of my role. Each month, we compared actual figures with the budget and previous year (N-1) to explain key deviations in revenue, costs, and margins. This involved discussions with business unit heads to understand operational reasons behind the numbers: new project delays, staffing issues, or cost overruns. It’s a fundamental tool for financial control and performance steering.

Working Capital

Although I didn’t manage working capital directly, I learned how crucial it is in project-based firms like Talan. Delays in project billing or collection can quickly impact cash flow. Some of our dashboards tracked project completion status vs. invoicing, helping identify WIP (Work in Progress) accumulation. It gave me a concrete view of how accounting flows translate into liquidity risks.

Margin Optimization

One of our KPIs was project margin, calculated using resource allocation, billing rates (TJM), and direct costs. I worked on visualizing these margins in Power BI and exploring scenarios with the team. For example, we modelled the impact of raising the average billing rate or optimizing staffing on low-yield projects. This showed me how financial insight directly supports business decisions.

Why should I be interested in this post?

If you’re an ESSEC student interested in corporate finance, FP&A is a great field to explore. This internship gave me exposure to reporting, performance analysis, budgeting, and tools like Power BI and Jedox. It’s also a great entry point to understand how strategy and operations connect through numbers.

Related posts on the SimTrade blog

   ▶ All posts about Professional experiences

Useful resources

Talan website

Microsoft Power BI

Jedox EPM platform

About the author

The article was written in September 2025 by Samuel BRAL (ESSEC Business School, Global Bachelor in Business Administration, 2022–2026).

My experience at Schneider Electric Singapore as a Finance Intern

William LONGIN

In this article, William LONGIN (Sorbonne School of Economics, Master in Money Banking Finance Insurance, 2024-2026) discusses his experience as a Finance intern at Schneider Electric Singapore.

About Schneider Electric

Schneider Electric is a French multinational company (MNC) that is specialized in energy management and automation solutions. It was founded in 1836 during the Industrial Revolution in Europe. Its headquarters are in Rueil-Malmaison (France). Schneider Electric develops technologies that help businesses and households optimize energy use. The relevance of the activities of Schneider Electric has increased with the increasing demand for electrical power with the surge in data center and electrical power demand. The company operates in more than 100 countries and is one of the global leaders in electrical equipment and industrial solutions.

Schneider Electric Singapore

After a year of study at Nanyang Business School in Singapore, I joined Schneider Electric’s Singapore office as an intern. As a French citizen, I obtained a Work Holiday Pass (WHP), which allowed me to remain in Singapore for six months. The building itself showcases Schneider Electric’s expertise in energy management and automation, being partly powered by renewable energy and retrofitted with energy-efficient systems.

Schneider Electric Singapore Kallang offices also called “Kallang Pulse”
Schneider Electric Singapore Kallang office
Source: Schneider Electric Singapore.

Global Supply Chain Finance Manufacturing

Quarterly reporting

During my 6 months internship I integrated the Finance team of the Global Supply Chain (GSC) division of Schneider Electric Singapore. The Finance division plays a central role in the creation of financial forecasts and accurate financial reporting for both internal and external purposes. The primary goal in quarterly reporting is to provide reliable financial data that reflects the performance of operations across regions and business units. The quarterly results are the fruit of the cooperation between the Finance Business Partners (FBPs) and accounting teams of Schneider Electric in several East Asian countries (Thailand, Vietnam, Indonesia, etc.). Finance Business Partners (FBPs) play a coordination role in the process of quarterly reporting and ensure the accuracy of financial statements with regard to manufacturing realities.

Standardization of financial reporting across East Asia

As an intern at Schneider Electric Singapore I contributed to the standardization of financial reporting across East Asia (EA). The process of standardization of financial reporting is key to make comparable metrics. The harmonization of cost centers reduces errors and improves efficiency and was done through direct communication with Finance Business Partners and accountants. Over the course of my internship cost centers were standardized meaning that entities would report similar type costs under the same line item (code number). Overall the work of the Finance team contributed to support more accurate decision-making.

In factory missions

The Schneider Electric Singapore GSC Finance team works closely with the factories/plants of the region. During my internship I had the opportunity to visit plants in Singapore and Indonesia. The proximity with employees on site allowed for more accurate tracking of material flows and stock levels, reducing discrepancies between financial records and actual usage. By monitoring inventories closely, the team maintained a balance between cost efficiency and operational continuity. The finance team places strong value on visits and human contact as part of its role within the Global Supply Chain.

Excel methods

The Finance team relied on advanced Excel techniques. During my internship, I used Power Query to build dynamic spreadsheets that cleaned and transformed large datasets and presented information for easy comparisons. Schneider Electric leverages SAP databases, so I also extracted internal data using Data Format Layout (DFL) to support analysis.

Transversal role

In addition to my core finance responsibilities, I had the chance to explore other parts of the global supply chain, particularly Procurement. In Procurement, the team validates supplier cost structures and reconciles material prices against assumptions. One of my missions was to perform some data analysis on a very large data set. My analysis gave the tools to Procurement to negotiate more effectively with suppliers.

Nomenclature

Purchase Orders (POs): Formal documents issued by a buyer to a supplier to authorize a purchase, specifying items, quantities, and agreed prices. The Global Supply Chain (GSC) finance team is sometimes brought to analyse samples of them.

Consolidated Standard Costing (CSC): A unified costing method that standardizes cost structures across plants or regions, enabling consistent financial comparisons.

Base vs. Variable Costs:

  • Base costs (fixed costs) remain stable regardless of production volume (e.g., rent, salaries, depreciation).
  • Variable costs fluctuate with activity or output (e.g., raw materials, utilities, logistics).

Steps of the Purchase Process: Typically include requisition, approval, purchase order issuance, supplier confirmation, delivery, and invoice/payment processing.

Lean manufacturing

Beyond financial forecasting and reporting, the GSC Finance team in Singapore has adopted a systematic philosophy when working on projects. My mentor was an advocate of Six Sigma Lean Manufacturing principles. These principles include reducing variability and defects, relying on data-driven analysis and structured steps such as DMAIC (Define, Measure, Analyze, Improve, Control). The application of lean manufacturing principles increased the efficiency of processes. During my internship I passed some internal training and obtained my green belt of six sigma. An example of six sigma lean manufacturing was a project to create an app that allows to track inventories and makes auditing more reliable and efficient.

Conclusion

My internship at Schneider Electric Singapore was more than a professional experience — it was a learning journey. I discovered how finance is not only about producing figures but also about supporting operations and connecting people across cultures.

Why should you be interested in this post?

If you are curious about how finance operates at the crossroads of global supply chains, this post offers a concrete view from inside Schneider Electric’s East Asia hub. Beyond numbers, it shows how financial teams play a transversal role in harmonizing reporting across countries.

About the author

The article was written in September 2025 by William LONGIN (Sorbonne School of Economics, Master in Money Banking Finance Insurance, 2024-2026)

Excel Dashboards in HR and Finance: Visualizing Data for Smarter Decision-Making

 Snehasish CHINARA

In this article, Alisa-Arifa AGALI ABDOU TOURÉ (ESSEC Business School, Global Bachelor of Business Administration – Exchange student from Germany, 2024-2025) describes the benefits of using Excel dashboards in human resources (HR) and financial management.

Also, how Excel dashboards help to accurately evaluate, clearly display and efficiently analyze important key figures such as fluctuation, absences, turnover or cash flow.

What is an Excel dashboard (for example in the HR or finance department)?

Excel dashboard in HR management or finance department is a visual analysis tool that provides a clear overview of important key figures in a company. These include, for example: key personnel figures, employee absences, fluctuation and more. It helps the HR department in the company to quickly evaluate data and ensure that everything is accurate, helping them to make the right decisions. With the help of tables or diagrams and the right formatting, important information can be captured, and anomalies are quickly visible. Dashboards save time, increase transparency and support data-based personnel management.

Advantage

How can Excel dashboards be an advantage in finance?

Excel dashboards offer a number of advantages in finance, for example, the ability to filter and update data at any time, providing a clear and transparent overview of the various financial developments. The use of Excel dashboards in a company promotes transparency in areas such as income, expenses, budgets and forecasts, which is very important for the controlling and HR departments. Another important reason why Excel dashboards are beneficial in a company is that dashboards can play a major role in important decisions, as they clearly show important key figures such as cash flow, profit margins or ROI. Dashboards also save a company an enormous amount of time when processing data.

Key components and application areas of financial dashboards in Excel

A financial dashboard shows all relevant key figures to present the financial situation of a company as simply and comprehensibly as possible. Among other things, a financial dashboard shows the development of turnover over various periods of time and also provides a detailed cost analysis, breaking down into fixed and variable costs. Other important components that a financial dashboard shows are the profit and loss statement, cash flow overviews and key financial figures such as ROI or liquidity ratios. Excel dashboards are used in numerous areas such as controlling, financial planning, capital budgeting and reporting. They support employees in the respective departments in their analyses and strategic decisions through the acquired data they find there.

Efficient data processing and analysis with Excel tools and functions

Excel provides a variety of tools and functions that simplify the processing of data and provide a clear overview. The useful tools and functions include, for example, tables and charts, which enable flexible and dynamic data analysis in companies, slicers and timeline filters facilitate control. Formulas and functions such as “IF” or “INDEX” can be used to perform calculations and automatically adapt to changes. Another important point regarding the efficient use of Excel dashboards is the help of Power Query and Power Pivot, which allows large amounts of data to be easily and efficiently evaluated, adjusted, imported or modalized.

To summarize

It is a great advantage for companies to introduce Excel dashboards into their departments, especially in departments such as HR and financial management. They help to work more efficiently and simplify processes, create a clear and transparent overview of data processing and visualize important key figures such as employee turnover, absenteeism, sales or cash flow.

Thanks to the elements and functions such as filters, charts, pivot tables, Power Query and Power Pivot offered by Excel Dashboards, the large and unclear amount of data can be filtered, analyzed and updated in a targeted manner so that the data is as up to date as possible.

Excel dashboards also promote strategic planning and save valuable time when creating reports. They support data-based management, are flexible and cost-effective for the company and save time.

Why should I be interested in this post?

As an ESSEC student, this article may be of interest because it shows how Excel dashboards in HR and finance can contribute to data-based decision-making and the important role they play in a company. Tools such as Excel dashboards are an important component in today’s world and a skill that is in great demand both in studies and in the professional world, as Excel dashboards help to analyze processes efficiently and present important key figures in an understandable way.

Related posts on the SimTrade blog

   ▶ Alisa-Arifa AGALI ABDOU TOURÉ My Experience at DHL- Bremen in the HR department

About the author

The article was written in August 2025 by Alisa-Arifa AGALI ABDOU TOURÉ (ESSEC Business School, Global Bachelor of Business Administration – Exchange student from Germany, 2024-2025).

My Experience at DHL- Bremen in the HR department

 Snehasish CHINARA

In this article, Alisa-Arifa AGALI ABDOU TOURÉ (ESSEC Business School, Global Bachelor of Business Administration – Exchange student from Germany, 2024-2025) shares her professional experience as an intern at DHL.

About the company

DHL was founded in San Francisco in 1969 by Adrian Dalsey, Larry Hillblom and Robert Lynn. The company’s global headquarters are located in Bonn, Germany. In 1998, Deutsche Post AG began the takeover and fully integrated DHL into the Group in 2002, which today operates under the name DHL Group.

HR DHL
Logo of DHL
Source: the company.

My internship

During my time in the HR department at DHL Bremen, I was able to gain valuable insights, for example, I was able to see and understand the HR processes. The department is responsible for several aspects such as recruiting & onboarding, employee support, payroll accounting and supporting personnel development. In a company as large as DHL, the HR team ensures smooth communication between management and employees within the company.

My Mission

My internship involved a range of responsibilities, including employer branding & engagement, administrative support for employees, and the recruiting process.

  • Employer Branding & Engagement: Participation in employee events, surveys and employee retention initiatives
  • Administrative support for employees: planning, maintaining sick notes and absences, master data management
  • Recruiting process: This includes determining requirements, creating job advertisements and organizing structured onboarding for the successful integration of new employees.

What have I learned

During my time at DHL in Bremen, I was able to get to know important processes that I had already learned theoretically during my studies, but was able to apply practically in my job in the HR department. I was able to get to know processes such as how recruiting, onboarding and personnel administration are developed. I learned how important it is to plan vacation and absence management as accurately as possible and how important it is for the company that everything happens as smoothly as possible. I was also able to expand my knowledge in the areas of personnel budget planning, fluctuation rates and remuneration models. The close cooperation with different departments and employees was particularly valuable for me. I was able to learn a lot of new things and apply and expand my existing skills and knowledge.

Required skills

The position requires communication and organizational skills. To be able to plan and organize employee events as accurately as possible, this requires precise analysis, coordination of surveys to meet the expectations and wishes of the employees. It also requires knowledge in the administration of personnel planning, such as vacation planning, sick leave and absence control. Another important skill for this position is working together as a team to determine requirements, create job descriptions and organize a structured onboarding process. Teamwork and empathy are also very important.

Business concepts related to my internship

Personnel budget planning

An important financial concept in HR activities is personnel budget planning, HR key figures, salary structures and remuneration models. Personnel budget planning is an important component of strategic HR work. Personnel budget planning shows exactly what budget is available to the company in relation to personnel costs. Costs such as salaries, social security contributions, recruiting costs and others are planned and allocated for the year. It is important to plan this as accurately as possible in order to avoid staff shortages and act as efficiently as possible. The HR department works very closely with the Controlling department, the planning of the budget is an important point for an efficient and sustainable personnel strategy.

HR key figures

HR KPIs include the analysis of key figures such as turnover rate. The turnover rate describes the number of employees who voluntarily or involuntarily leave the company in a year. It is also an important aspect, as it reflects the stability and satisfaction of employees. In addition, the turnover rate must always be kept in mind, as a high fluctuation rate can indicate structural problems in the company. If you have this well under control, you can avoid additional costs in the company.

Salary structures and remuneration models

Remuneration must be planned as accurately and appropriately as possible. Salary structures and remuneration models lead to fair remuneration management in the company. The salary structures and remuneration models determine how the employees’ salaries are composed, including aspects such as the employee’s position and function, experience and qualifications. The Salary structures and remuneration model also incorporates active plus points such as awards and bonuses. This serves to increase the motivation of existing employees and to attract new employees for various positions.

Why should I be interested in this post?

As an ESSEC Business School student, the position at DHL can be very interesting, as the company shows you how theoretical knowledge from the areas of HR, controlling and organization is applied in practice in the company. In the company, you gain valuable insights into various areas such as recruiting, personnel budget planning and employer branding, giving you valuable insights into strategic HR work in an international group.

Related posts on the SimTrade blog

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Useful resources

careers.dhl.com/global/en/job/

About the author

The article was written in August 2025 by Alisa-Arifa AGALI ABDOU TOURÉ (ESSEC Business School, Global Bachelor of Business Administration – Exchange student from Germany, 2024-2025).

My internship at the law firm Maître Abouba Aly Maiga et Associés

 Snehasish CHINARA

In this article, Alisa-Arifa AGALI ABDOU TOURÉ (ESSEC Business School, Global Bachelor of Business Administration – Exchange student from Germany, 2024-2025) shares her professional experience as an intern at the law firm Maître Abouba Aly Maiga et Associés in Mali.

About the company

The law firm was founded in 1990 by Mr. Maître Abouba Aly Maiga. Since then, the firm has provided services in a wide range of legal fields. The areas of law offered by Maître Abouba Aly Maiga and Associes are commercial law, business law, national and international criminal law, administrative law, matrimonial law, labor law and international law. One of the reasons for the firm’s success is that it offers its clients services in various areas of law.

Logo of the law firm Abouba Aly Maiga et Associés.
Logo of Maître Abouba Aly Maiga et Associés
Source: the company.

The firm represents not only civilians but also, for example, ministers, soldiers and large companies and deals with international relations and litigation. Maître Abouba Aly Maiga began his career in Bamako, Mali. In addition to the firm, he is the first Vice President and President Africa of the International Bar Association and Vice President of the A.E.A. (European Bar Association), he is also a former member of the Council of the Order.

My internship

My 6 months of my internship abroad at the law firm was an extremely valuable experience for me and gave me a deep insight into a law firm. During my time there, I was able to take part in many exciting and enriching tasks that not only helped me professionally, but also personally. In particular, the work in the field of corporate law and international law as well as the participation in court proceedings and hearings have shaped me a lot and broadened my view of legal practice.

My missions

During my internship at the law firm, I had a variety of tasks that gave me a deep insight into the day-to-day work of a law firm and showed me what the process in a law firm is like. My tasks included, for example, taking part in client meetings and helping lawyers to develop individual legal solutions. Another important insight was to see how professional client communication works.

Another task I was allowed to take on during my internship at the law firm was to read case files and follow legal processes, especially in the areas of corporate and international law. I regularly attended court hearings, where I was able to experience the lawyers’ argumentation strategies first-hand. Furthermore, I was able to learn the practical handling of complex cases. In addition, I prepared the team meetings and created presentations, took minutes and thus contributed to the internal communication of the law firm.

Required skills and knowledge

Certain skills and knowledge were particularly important for my internship at the Law firm Maître Abouba Aly Maiga. These included basic knowledge of international and corporate law, and a good understanding of legal structures and procedures. Another important point is strong communication skills and intercultural competence to work successfully with colleagues and clients from different backgrounds.

Language skills, especially in French, were also very helpful in order to be able to actively participate in discussions and court hearings. In addition, strategic thinking, initiative, confidentiality in dealing with sensitive information and confident use of digital tools were also important skills.

What I learned

During my internship, I was able to gain valuable insights, such as the internal processes of an international law firm, and I was able to apply my theoretical knowledge from my studies in practice. Especially the work in the area of international and corporate law helped me to better understand the processes. I was also able to improve my French language skills through daily exchanges with colleagues and clients. Overall, I learned to act more confidently in a new environment and to adapt flexibly to different professional and cultural situations.

Financial concepts related to my internship

I present below three financial concepts related to my internship: invoicing and cost management, SWOT analysis for strategic planning, and client retention through advertising and pricing.

Invoicing and Cost Management

During my internship, I was made aware of the importance of structured invoicing for a law firm’s cash flow. Accurate documentation of services rendered, and timely invoicing are crucial to ensure regular income for the law firm. Efficient cost management was a key issue at the law firm, as the firm constantly strives to control and document its expenditure, for example on personnel, technology and office infrastructure. A good cost structure has a direct impact on the profit margin and competitiveness in the market.

Strategic Planning using SWOT Analysis

Another important point is the SWOT analysis. Through the SWOT analysis, I quickly understood how financial opportunities and risks are identified and integrated into strategic planning. Aspects such as investing in technology or recognizing threats from cheaper online law firms play an important role.

Client Retention through Advertising and Pricing

Targeted advertising campaigns and appropriate prices are effective ways of retaining customers in the long term and ensuring financial stability. It was particularly important to adapt the price structure to the target groups’ willingness to pay in order to ensure financial stability.

Why should I be interested in this post?

As an exchange student ESSEC Business School student, I am interested in the position at Maître Abouba Aly Maiga et Associés because it offers a combination of international business law and strategic thinking. The internship at the law firm offers a very deep insight into the areas of legal work and business aspects such as SWOT analyses or cost management. It is particularly interesting to work on real cases, attend court hearings and carry out financial and legal analyses relevant to the firm and make decisions. The internship is ideal for students who are interested in business law or who want to work at the interface of law and business.

Related posts on the SimTrade blog

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Useful resources

Cabinet Abouba Aly Maiga et Associés

About the author

The article was written in August 2025 by Alisa-Arifa AGALI ABDOU TOURÉ (ESSEC Business School, Global Bachelor of Business Administration – Exchange student from Germany, 2024-2025).

My professional experience as Head of Data Modelling

Rohit SALUNKE

In this article, Rohit SALUNKE (ESSEC Business School, Grande Ecole Program – Master in Management, 2018-2021) shares his professional experience as Head of Data Modelling, leading advanced analytics, AI, and reporting solutions for a global investment organisation.

About the company

Tikehau Capital is a global alternative asset management group headquartered in Paris, France. Founded in 2004, it has become a multi-billion-euro investment house with expertise across private debt, real assets, private equity, and capital markets strategies. The company manages assets for institutional and private investors worldwide, relying on a long-term investment philosophy and strong entrepreneurial culture.

Tikehau Capital is listed on Euronext Paris and operates in over a dozen countries. Its diversified investment strategies and robust risk management have contributed to consistent growth and resilience across market cycles.

Logo of Tikehau Capital.
Logo of Tikehau Capital
Source: Tikehau Capital.

About the role

As Head of Data Modelling, I was responsible for the strategic design, architecture, and delivery of the organisation’s enterprise-wide analytics infrastructure. My role bridged technology, quantitative modelling, and business strategy, ensuring that investment, risk, and operational teams had access to powerful, automated, and reliable data-driven tools. I oversaw the entire lifecycle of data and analytics projects—from ideation and design to deployment and continuous improvement—while directly coordinating with C-level executives and department heads to align technology solutions with organisational goals.

My Experience

My core responsibility was managing the architecture of our Databricks and PowerBI/Python reporting ecosystem, making it the central platform for the organisation’s portfolio analytics and operational reporting. I led the data strategy for our IT-Quant Cell, which served as a specialised unit delivering high-value analytics to investment and risk teams across multiple asset classes.

One of my most impactful projects was the full-stack development of an AI assistant for answering investor due diligence questionnaires (DDQs). This system combined Databricks Genie with open-webui, enabling internal teams to query complex datasets interactively. Additionally, I built NLP-based solutions to parse and extract information from unstructured documents—such as contracts, company filings, and financial statements—streamlining internal research and reporting workflows.

On the quantitative modelling front, I developed a bond valuation engine capable of pricing both individual securities and portfolios, as well as default probability models for issuers and securities. These tools allowed risk managers to proactively identify watchlist names, foresee covenant breaches, and anticipate coupon defaults. I also delivered full-stack stress testing and credit spread models for private debt and distressed debt portfolios, enabling portfolio managers to assess market scenarios and security-level risks with precision.

For cash flow management, I designed a forecasting engine tailored to private debt portfolios, integrating it with operational and client service functions to automate forecast reporting for investors. I also led the development of large-scale automated reporting solutions capable of generating PDF, PPTX, Word, and Excel outputs, meeting the regulatory and investor requirements of multiple jurisdictions.

Collaboration and Leadership

My role demanded close coordination with the CTO, COO, CFO, department heads, and technical leads to define priorities, allocate resources, and ensure delivery. I managed multi-departmental projects spanning Risk, Investment, Operations, Sales, and Finance, as well as asset class–specific initiatives in Private Debt and Equity, Fixed Income, CLOs, and Real Estate. This cross-functional exposure ensured our solutions were both technically sound and operationally relevant.

Beyond technical delivery, I implemented interactive dashboards for risk monitoring, fundraising, investor onboarding, and portfolio analytics—empowering top management, risk managers, and portfolio managers with actionable insights. I also provided mentorship to analysts and senior executives, guiding them through the adoption of new tools, processes, and workflows.

Required skills and knowledge

This role required deep expertise in data architecture (Databricks, SQL), advanced analytics (Python, NLP, quantitative finance), and visualisation (PowerBI). The ability to translate complex business needs into scalable, maintainable, and user-friendly systems was critical. Equally important were leadership and stakeholder management skills, enabling me to bring together technical and non-technical teams to achieve common objectives.

What I learned

In this position, I learned how to combine cutting-edge technology with robust quantitative frameworks to address the evolving demands of a global investment business. I developed a stronger appreciation for the balance between innovation and operational stability—ensuring that every model, dashboard, or AI system could be trusted by those making high-stakes decisions. Most importantly, I saw firsthand how data strategy, when aligned with business objectives, can transform portfolio monitoring, risk management, and investor communication.

Financial concepts related to my role

Credit spread modelling

Credit spread modelling is the process of estimating the additional yield or premium investors require to compensate for the credit risk of a bond or loan compared to a risk-free benchmark, typically government securities. This spread reflects the market’s perception of the issuer’s default risk, liquidity risk, and other factors affecting creditworthiness. In my role, I built sophisticated credit spread models that integrated multiple layers of data, including macroeconomic variables (such as interest rates, GDP growth, and inflation), issuer-specific fundamentals (like leverage ratios, profitability, and cash flow stability), and real-time market indicators (credit default swap spreads, bond prices, and trading volumes). These models enabled risk managers and portfolio managers to estimate fair value spreads, detect deviations from expected spreads, and identify mispriced securities. The ability to quantify and forecast credit spreads was critical for pricing, risk management, and strategic asset allocation across private debt and distressed debt portfolios.

Stress testing

Stress testing involves evaluating how a portfolio or individual securities would perform under severe but plausible adverse market conditions. It is a key risk management tool that helps identify vulnerabilities and potential losses in extreme scenarios, such as economic recessions, interest rate shocks, or credit market disruptions. I developed full-stack stress testing models that allowed users to apply shocks and scenario analyses both at the individual security level and the aggregated portfolio level. These models incorporated changes in key variables including interest rates, credit spreads, default rates, and macroeconomic indicators. By simulating various stress scenarios, investment and risk teams could assess the resilience of portfolios, anticipate potential covenant breaches or defaults, and plan mitigation strategies. This was especially important for private debt and special opportunities portfolios, where cash flows and valuations can be highly sensitive to changing market environments.

Default probability modelling

Default probability modelling quantifies the likelihood that an issuer or specific security will fail to meet its financial obligations within a defined time horizon. Accurate default prediction is fundamental to credit risk management, pricing, and portfolio construction. I designed models leveraging a combination of financial statement ratios (such as debt coverage, liquidity, and profitability metrics), market-based indicators (equity volatility, credit spreads), and qualitative industry or sector factors to generate forward-looking default probabilities. These models powered watchlists and early-warning systems, enabling portfolio managers to identify issuers at risk of covenant breaches, coupon defaults, or bankruptcy. By anticipating potential defaults, the investment teams could proactively adjust exposures, engage with issuers, or hedge positions, thereby reducing portfolio losses and improving overall risk-adjusted returns.

Why should I be interested in this post?

This post offers valuable insights for students and professionals keen on the intersection of quantitative finance, data architecture, and AI-driven solutions within the asset management industry. It illustrates how leadership in data modelling and technology can directly impact critical investment functions such as portfolio strategy, risk assessment, and investor communications. Understanding how sophisticated models and automated analytics tools are developed and deployed equips aspiring quants, data scientists, and financial engineers with a clearer picture of real-world applications beyond theory—highlighting the importance of cross-functional collaboration, scalable system design, and continuous innovation in today’s complex financial markets.

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   ▶ Datastream

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Useful resources

Academic articles

Duffie, D., & Singleton, K. J. (2012). Credit Risk: Pricing, Measurement, and Management (illustrated edition). Princeton, NJ: Princeton University Press.

Business resources

Tikehau Capital

Tikehau Capital Solutions

Claessens S., Pazarbasioglu C., Laeven L., Dobler M., Valencia F., Nedelescu O., and Seal K. (2011) Crisis Management and Resolution: Early Lessons from the Financial Crisis, IMF

Preqin Alternative data platform

BlackRock eFront – Portfolio Management Solution

About the author

The article was written in August 2025 by Rohit SALUNKE (ESSEC Business School, Grande Ecole Program – Master in Management, 2018-2021).

My professional experience as Business & Data Analyst at Tikehau Capital

Rohit SALUNKE

In this article, Rohit SALUNKE (ESSEC Business School, Grande Ecole Program – Master in Management, 2018-2021) shares his professional experience as Business & Data Analyst at Tikehau Capital.

About the company

Tikehau Capital is a global alternative asset management group headquartered in Paris, France. Founded in 2004, it has become a multi-billion-euro investment house with expertise across private debt, real assets, private equity, and capital markets strategies. The company manages assets for institutional and private investors worldwide, relying on a long-term investment philosophy and strong entrepreneurial culture.

Tikehau Capital is a public company listed on Euronext Paris and operates in over a dozen countries. Its diversified investment strategies and robust risk management have contributed to consistent growth and resilience across market cycles.

Logo of Tikehau Capital.
Logo of Tikehau Capital
Source: Tikehau Capital.

I worked in the Information & Technology (IT) department of Tikehau Capital, and collaborated extensively across various teams within the organisation. My projects focused on building tools and processes that directly supported investment decision-making and portfolio monitoring.

My Experience

As a Business & Data Analyst in the IT department, my role was to design, develop, and deploy technology solutions for the company. I collaborated with several stakeholders such as Investment team, Risk, Operations, Sales and Marketing, Client Services and Top Management. Within the Investment teams I touched on topics across the TKO strategies, Private Debt, Private Equity, Tactical Strategies, Real Assets and Capital Market Strategies. This included working closely with investment professionals to understand their analytical needs and then translating those into scalable, automated systems for data processing, quantitative analysis and reporting.

My missions

As the technical lead and subject matter expert for a major digitisation project in an agile environment, I drove revenue and productivity enhancements through automation, data analysis, and improvements in data quality and reporting. My role involved analysing TKO’s portfolio of private investments, building automated reporting engines, developing quantitative analytics modules for portfolio monitoring, and creating ETL pipelines to consolidate data from multiple internal and external sources. I collaborated extensively across functions—including Sales, Product, Marketing, Finance, Client Services, Fund Operations, Risk, Investment, Private Debt, Private Equity, and Real Estate—to ensure the successful delivery of technology solutions aligned with business needs.

Key projects included: developing quarterly investor reporting automation tools for several business units (Python, Databricks, Microsoft PowerPoint, Word, Excel); leading the private markets data migration to Databricks and introducing company-wide KPI harmonisation, boosting efficiency by 12x; implementing dashboards for use by both cross-functional teams and top management (Databricks, PowerBI); and automating report delivery to clients (Power Automate). I also led analysts through project lifecycles, providing coaching to both junior and senior team members on in-house and external tools.

From an analytics perspective, I delivered asset performance analyses across funds and asset classes, supported fundraising and investor onboarding analytics, conducted risk assessments on asset performance under economic stress, and developed in-house benchmarking for Private Debt funds in collaboration with external partners. On the investor relations side, I handled institutional investor performance reporting requests. For data quality, I monitored internal platforms, managed escalation processes, and mitigated risks, issues, and dependencies to maintain the integrity and reliability of critical datasets.

Required skills and knowledge

In my role at Tikehau Capital, I developed a combination of technical, analytical, and cross-functional skills that enabled me to deliver technology solutions supporting investment decision-making across multiple asset classes. One of the key technical skills I applied was advanced programming in Python and SQL, which I used to design ETL pipelines, automate reporting processes, and integrate data from multiple sources into Databricks. I also gained deep expertise in dashboard creation and visualisation using PowerBI, allowing me to present complex portfolio performance metrics in a clear and actionable format for both senior management and investment teams.

I strengthened my understanding of private markets data structures, particularly within private debt, private equity, and real estate. This included learning how to manage and standardise KPIs across the organisation to ensure consistency in reporting and analysis. My work required strong knowledge of data governance and quality control, as I was responsible for monitoring internal platforms, managing data integrity issues, and implementing processes to improve accuracy and reliability.

On the soft skills side, I honed my ability to gather business requirements from diverse stakeholders and translate them into technical specifications. This meant working closely with colleagues from Sales, Product, Marketing, Finance, Client Services, Fund Operations, Risk, and Investment teams in an agile environment. I also developed leadership skills by guiding analysts through the project lifecycle and providing coaching to both junior and senior professionals on the use of in-house and external tools. Finally, I gained significant experience in investor relations support by preparing data for performance reporting, responding to institutional investor requests, and ensuring clear, professional communication of complex investment information.

What I learned

One of the most valuable lessons I learned at Tikehau Capital was how technology teams can act as strategic partners to investment teams across multiple asset classes, including Private Debt, Private Equity, Capital Market Strategies (CMS), Tactical Strategies, and Real Assets. Working in the IT department while collaborating closely with investment specialists taught me how to align technical solutions with diverse investment strategies and operational requirements. For example, during the digitisation project, I learned how to translate complex business requirements from each asset class into scalable automation, analytics, and reporting tools that directly improved portfolio monitoring, decision-making, and investor communication.

I gained a deeper understanding of asset class–specific analytics: in Private Debt and Private Equity, I worked on performance tracking, KPI harmonisation, and risk analytics; in CMS and Tactical Strategies, I learned how derivative positions and macro-driven strategies required different data models and stress-testing frameworks; and in Real Assets, I helped design systems that tracked physical asset performance alongside market and operational metrics. This cross-asset exposure enhanced my ability to adapt technical workflows to varied investment approaches.

From a technical perspective, I refined my proficiency in Python, SQL, Databricks, and PowerBI, using them to build ETL pipelines, automation workflows, and dashboards that served both analysts and senior management. I also honed my problem-solving skills by identifying bottlenecks in reporting processes and implementing solutions that improved efficiency by more than 10x in some areas. Additionally, I learned the importance of outcome evaluation—ensuring that every dataset, whether for an internal management dashboard or an institutional investor report, was accurate, consistent, and presented in a clear, actionable format tailored to the needs of each asset class.

Financial concepts related to my internship

I present below three financial concepts related to my internship and how they were applied in my work at Tikehau Capital: Portfolio monitoring, Credit risk metrics, and Cash flow forecasting.

Portfolio monitoring

Portfolio monitoring is the ongoing process of tracking an investment portfolio’s performance, risk profile, and compliance status to ensure it aligns with its strategic objectives. This involves assessing metrics such as returns, volatility, drawdowns, asset allocation shifts, and adherence to investment guidelines. Effective portfolio monitoring enables timely decision-making, allowing managers to rebalance, hedge, or adjust positions in response to market movements or changes in portfolio objectives. During my time in the IT department working closely with investment specialists across private debt, private equity, CMS strategies, tactical asset allocation, and real assets, I learned how technology can streamline this process. The reporting engines I built automated large parts of the workflow—integrating data from multiple sources, applying valuation models, and generating performance dashboards—allowing investment teams to access accurate, real-time insights without the delays and potential errors of manual data handling. This experience deepened my understanding of how portfolio monitoring supports not only performance measurement but also risk management, regulatory compliance, and informed strategic decision-making.

Credit risk metrics

Credit risk metrics are quantitative and qualitative measures used to evaluate the likelihood that a borrower will default on their obligations and to estimate the potential loss to the portfolio in such an event. These metrics include probability of default (PD), loss given default (LGD), exposure at default (EAD), credit spreads, and internal credit ratings, all of which help investment teams assess both individual counterparty risk and overall portfolio vulnerability. Accurate credit risk assessment is essential for pricing loans, structuring debt instruments, and determining capital allocation. While working closely with investment specialists across private debt, private equity, CMS strategies, tactical strategies, and real assets, I developed tools that integrated multiple credit risk data feeds into interactive dashboards. These systems consolidated financial statement data, market indicators, and external credit ratings into a unified view, enabling faster and more reliable risk assessments. By automating data aggregation and providing visual, real-time insights, these tools not only improved assessment accuracy but also allowed portfolio managers to respond more proactively to emerging credit concerns.

Cash flow forecasting

Cash flow forecasting is the process of estimating the timing and magnitude of future inflows and outflows for an investment or portfolio. It is essential for assessing expected returns, ensuring sufficient liquidity to meet obligations, and supporting capital allocation decisions. Accurate forecasting allows investment teams to anticipate funding needs, optimise debt schedules, and stress test portfolios under different market conditions. This is particularly important in asset classes such as private debt, private equity, CMS strategies, tactical strategies, and real assets, where cash flows can be irregular and heavily dependent on deal structures, economic cycles, and market events. To support this, I built ETL pipelines that extracted deal-level data from multiple internal and external sources, transformed it into a consistent structure, and integrated it with dynamic forecasting models. These pipelines enabled investment teams to perform real-time scenario analyses, adjusting for variables such as interest rate changes, market shocks, and asset performance trends. By automating data preparation and linking it directly to forecasting tools, the process became faster, more transparent, and more adaptable to shifting market conditions.

Why should I be interested in this post?

For ESSEC students interested in finance and technology, this experience shows how a role in IT within an investment firm can offer direct exposure to financial markets, portfolio analytics, and data-driven decision-making—skills highly valuable in both finance and quantitative career paths.

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   ▶ Alexandre VERLET Classic brain teasers from real-life interviews

Useful resources

Academic articles

Altman, E. I., & Saunders, A. (1997). Credit risk measurement: Developments over the last 20 years, Journal of Banking & Finance, 21(11–12):1721–1742.

DeFond, Mark L. & Hung, Mingyi. (2003). An empirical analysis of analysts’ cash flow forecasts, Journal of Accounting and Economics, 35(1):73–100.

Business resources

Tikehau Capital

Tikehau Capital Solutions

Preqin Preqin – Alternative data platform

BlackRock eFront – Portfolio Management Solution

About the author

The article was written in August 2025 by Rohit SALUNKE (ESSEC Business School, Grande Ecole Program – Master in Management, 2018-2021).

Academic perspectives on optimal debt structure and bankruptcy costs

 Snehasish CHINARA

In this article, Snehasish CHINARA (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2025) explores the academic evolution of capital structure theory, focusing on the delicate balance between debt and equity financing. Starting from the foundational Modigliani and Miller propositions, this post delves into how the introduction of real-world frictions—particularly bankruptcy costs and financial distress—gave rise to the Trade-Off Theory.

Introduction to Capital Structure

Capital structure refers to the mix of debt and equity financing that a company uses to fund its operations and growth. It is a critical component of corporate finance, as it directly impacts a firm’s cost of capital, financial risk, and overall valuation.

Capital structure is reflected in a company’s balance sheet, which provides a snapshot of its financial position at a given point in time. Specifically, it is composed of two primary financing sources:

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

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

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

Table 1 – Simplified Balance Sheet Example

Table 1 shows that the firm finances its $350M in assets with $140M in debt (40%) and $210M in equity (60%), demonstrating a debt-to-equity ratio of 0.67 (=140/210). Additionally, the debt ratio, D/(D+E), measures the proportion of total financing that comes from debt 40% (=140/(140+210)). This indicates that a significant portion of capital is funded through borrowed money, allowing the company to take advantage of the use of debt, but also exposing it to higher financial risk if it faces difficulties in meeting debt obligations. These ratios are a few key indicators used to assess a company’s financial leverage and risk exposure.

Beyond Taxes — The Real-World Cost of Debt

Capital structure theory begins with Modigliani and Miller (1958), who argued that in a perfect market—with no taxes or distress costs—a firm’s value is unaffected by its mix of debt and equity. This implies that financing decisions are irrelevant: the firm’s cost of capital remains unchanged regardless of leverage.

Their later work in 1963 introduced corporate taxes, shifting the narrative. Since interest is tax-deductible, debt creates a tax shield that reduces taxable income, lowering the firm’s WACC and increasing its value. In theory, this would mean the more debt a firm uses, the better.

However, this doesn’t match real-world behaviour. Firms rarely use excessive debt. To explain this, Miller (1977) brought personal taxes into the picture. While firms benefit from interest deductibility, investors may face higher taxes on interest income compared to equity income. This reduces the net benefit of debt. At the market level, an equilibrium emerges where additional debt offers no further advantage—explaining why firms stop before 100% leverage.

Together, M&M and Miller show why debt can be attractive due to tax savings—but they don’t account for the costs of debt, which are crucial in practice. This article now turns to academic perspectives that build on these theories by introducing bankruptcy costs, financial distress, and agency issues, offering a more complete view of how firms decide on an optimal capital structure.

The Trade-Off Theory: Balancing Tax Shields and Bankruptcy Costs

While M&M (1963) and Miller (1977) emphasize the tax advantages of debt, real-world firms don’t pursue unlimited leverage. Why? Because with higher debt comes higher financial risk. This leads to the Trade-Off Theory—a more realistic and widely taught framework in modern corporate finance.

At the heart of this theory lies a simple question: How much debt is too much?

The Trade-Off Theory proposes that firms weigh the benefits of debt (primarily the interest tax shield) against the costs of debt (most notably bankruptcy risk and financial distress costs). The optimal capital structure is achieved when the marginal benefit of taking on more debt equals its marginal cost. Therefore, firms pick capital structure by trading off the benefits of the tax shield from debt against the costs of financial distress (including agency costs of debt).

This framework leads to a simple but powerful relationship:

where:

  • VL is the value of a levered firm using debt.

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

  • Tax shield benefits: arising from the tax deductibility of interest payments

  • Expected bankruptcy cost: a function of both the probability of distress and the magnitude of associated losses (probability of distress x cost if distress occurs)

The Trade-Off Theory argues that the value of a firm initially increases with debt due to tax savings from interest deductibility, but only up to a point. Beyond that, the probability of bankruptcy and the costs associated with financial distress begin to outweigh the benefits of the tax shield. Due to this, there exists an optimal capital structure where the marginal benefit of debt exactly equals its marginal cost.

Tax shield benefit: This term represents the annual tax saving due to deductible interest.

where:

  • TC: Corporate tax rate

  • rD: Interest rate on debt

  • D is the amount of debt of the firm

Expected Bankruptcy Cost:

This cost includes:

  • Direct costs (legal, administrative): typically 2–5% of firm value

  • Indirect costs (reputation, supplier reactions, customer attrition): potentially far higher

How Firm Value Changes with Debt

According to the Trade-off Theory, the total value of a levered firm equals the value of the firm without leverage plus the present value of the expected tax savings from debt, less the present value of the expected financial distress costs.

It is represented as follows:

where:

  • VL is the value of a levered firm using debt.

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

Firms should increase leverage until they reach the optimal level where the firm value (and the net benefit of debt) is maximized. At the optimal leverage, the marginal benefits of interest tax shields that result from increasing leverage are perfectly offset by the marginal costs of financial distress.

Figure 1 – Trade-Off Theory Optimal Leverage

Source: “The Static Theory of Capital Structure” Brealey, Myers, & Allen

Figure 1 above illustrates the Trade-Off Theory of capital structure, which posits that a firm’s value is influenced by two opposing forces: the benefits of debt and the costs of financial distress.

  • The upward-sloping blue line represents the firm’s value if we consider only the corporate tax advantage of debt, where each additional unit of debt increases firm value by the present value of the tax shield (Tc×D). However, this idealized trajectory (as per M&M 1963) does not account for real-world frictions.

  • As leverage rises, so too does the probability of financial distress, bringing with it both direct costs (legal and administrative expenses) and indirect costs (reputation damage, lost sales, supplier concerns). These rising costs are reflected by the gap between the blue and pink curves.

  • The pink curve represents the actual value of a levered firm after subtracting distress costs. It shows the actual value of the firm once these financial distress costs are taken into account. Initially, this curve rises along with the tax shield benefits. But after a certain point, the marginal cost of debt begins to exceed its marginal benefit, causing the curve to flatten and then decline.

  • The pink curve peaks at the point marked D∗—the firm’s optimal capital structure. The point D∗ is the optimal amount of debt where the marginal benefit of the tax shield is exactly offset by the marginal expected cost of financial distress.

  • To the left of D∗, adding more debt increases firm value; to the right of it, further leverage diminishes value. The curve therefore reflects a concave relationship between debt and firm value, with the maximum point corresponding to the firm’s optimal capital structure.

Figure 1 delivers three core insights:

  • Leverage is a double-edged sword—it creates value through tax savings but erodes it through risk.

  • The optimal debt level is not universal—it depends on a firm’s industry, asset type, cash flow stability, and access to capital markets.

  • Real-world capital structure decisions are about finding a balance, not maximizing one benefit in isolation.

Static vs. Dynamic Trade-Off Models: From Simplified Theory to Real-World Complexity

The traditional Trade-Off Theory provides a powerful intuition: firms balance the benefits of debt (tax shields) against its costs (financial distress). However, how firms actually make capital structure decisions over time is more complex than the simple static view. This brings us to an important academic distinction: static vs. dynamic trade-off models.

Static Trade-Off Models: A Snapshot of Capital Structure

A static trade-off model is a one-period, one-time optimization framework. It assumes that a firm evaluates all its financing options at a single point in time and selects the capital structure that maximizes firm value. The firm is thought to instantly move to its optimal leverage ratio and maintain it indefinitely.

This model gives us a clean formula for firm value:

While this is helpful in teaching and early analysis, it oversimplifies real-world decision-making. Firms don’t reset their debt every day based on a formula. Instead, they must plan, adjust, and adapt—which is where dynamic models offer deeper insight.

Dynamic Trade-Off Models: Capturing Real-World Decision-Making

Dynamic trade-off models build on the static framework by recognizing that:

Capital structure is adjusted over time, not all at once. Firms face adjustment costs when issuing new debt or equity (e.g., flotation costs, signaling effects).

Business conditions, tax environments, interest rates, and risk evolve. Managers are forward-looking—they consider not only current benefits and costs but also future risks, taxes, and financing needs.

In these models, the optimal debt level is not a fixed point. Instead, firms operate within a target range of leverage and make gradual adjustments toward it when the benefits outweigh the costs of doing so.

For example:

A firm may not issue new debt today even if it’s slightly under-leveraged, because issuing comes with costs. It might instead wait for a better interest rate, a tax law change, or an internal cash flow event.

Dynamic models are particularly well-captured in the work of:

  • Fischer, Heinkel, and Zechner (1989) – who modelled how firms behave in a stochastic environment where recapitalization is costly.

  • Leland (1994) – who showed that default thresholds and optimal leverage depend on firm value and volatility over time.

Types of Bankruptcy Costs: The Hidden Burden of Excessive Leverage

While tax benefits of debt are quantifiable and immediate, the costs of financial distress—especially bankruptcy—are more nuanced, less visible, and often underestimated. These costs are central to the Trade-Off Theory, and understanding their components is essential for evaluating real-world capital structures.

Bankruptcy costs can be broadly classified into three types:

1. Direct Bankruptcy Costs

These are the explicit, out-of-pocket expenses incurred during legal bankruptcy proceedings.

  • Legal fees, court costs, bankruptcy consultants

  • Administrative expenses, such as auditing and trustee services

Empirical studies suggest that direct costs range from 2–5% of firm value, though they may be higher in complex bankruptcies. While these are easier to measure, they are not necessarily the largest component.

Example: A manufacturing firm with a $500 million valuation that enters Chapter 11 could incur $10–25 million in legal and court-related costs alone.

2. Indirect Bankruptcy Costs

These are opportunity costs or value losses incurred even if bankruptcy does not occur—simply being in distress can harm the business.

  • Loss of customers: Buyers lose trust in a distressed brand.

  • Supplier tightening: Suppliers demand advance payments or withdraw credit.

  • Employee turnover: Top talent exits due to job insecurity.

  • Delayed investments: Management focuses on liquidity over strategy.

Indirect costs are often much larger than direct ones—estimated at 10–20% of firm value in some studies.

Example: A hotel chain facing debt pressure may see a fall in bookings, reduced vendor support, and higher staff attrition—impacting operations even before legal proceedings begin.

3. Agency Costs of Debt

As financial distress increases, so do agency conflicts between debt holders and equity holders.

Two prominent issues are:

  • Asset Substitution Problem – Shareholders may prefer riskier projects with higher upside (but higher default risk) because they capture the gains, while losses are partially borne by creditors.

  • Underinvestment Problem – Highly leveraged firms might pass on positive NPV projects because the gains would go to debt holders, not shareholders. Thus, debt discourages investment when it is most needed.

These agency costs distort management incentives, especially when firms are close to violating debt covenants or already under pressure.

Academic Contributions on Bankruptcy and Alternative Views on Capital Structure

Pecking Order Theory and Information Asymmetry

Contrasting the Trade-Off Theory, Myers and Majluf (1984) introduced the Pecking Order Theory, which prioritizes financing sources based on information asymmetry. Firms prefer internal financing (retained earnings) first, then debt, and issue equity only as a last resort. This hierarchy arises because managers possess more information about the firm’s value than external investors, leading to adverse selection concerns when issuing new equity.

Dynamic Models of Capital Structure

Recognizing that capital structure decisions are not static, researchers have developed dynamic models to reflect real-world complexities. Leland (1994) incorporated factors such as agency costs, taxes, and bankruptcy costs into a continuous-time framework, providing insights into how firms adjust their leverage over time in response to changing conditions.

Human Capital and Bankruptcy Risk

Recent studies have explored the interplay between human capital and capital structure. For instance, research by Berk, Stanton, and Zechner (2010) examines how firms with significant human capital considerations may adopt lower leverage to mitigate the adverse effects of financial distress on their workforce and overall operations.

Empirical Evidence and Contemporary Reviews

Empirical studies have tested these theories across various contexts. For example, research published in the Journal of Finance investigates how bankruptcy risk influences firms’ capital structure choices, revealing an inverse relationship between bankruptcy risk and leverage. Comprehensive literature reviews, such as those by Cerkovskis et al. (2022) and Visinescu and Micuda (2023), provide critical analyses of the evolution and empirical validation of capital structure theories, offering valuable insights for both scholars and practitioners.

Practical Considerations in Capital Structure Decisions

While theories like Modigliani-Miller (M&M), the Trade-Off Theory, and Agency Cost Theory provide useful frameworks for understanding capital structure, real-world evidence shows that firms consider multiple factors beyond theory when making financing decisions. Empirical studies highlight how industries, economic conditions, credit ratings, and market perceptions influence a company’s choice between debt and equity.

Real-World Capital Structure Choices

Empirical research supports the idea that firms do not strictly follow any single capital structure theory but instead balance tax advantages, financial flexibility, and risk. Some key observations from real-world studies include:

  • Myers (1984) found that firms follow a “Pecking Order” when raising funds, preferring internal financing (retained earnings) first, followed by debt, and issuing equity as a last resort due to information asymmetry.

  • Graham (2000) estimated that firms use only about 60% of the potential tax benefits of debt, indicating that firms hesitate to take on excessive leverage due to bankruptcy risks.

  • Frank & Goyal (2009) confirmed that larger, more profitable firms tend to have higher leverage, while smaller, riskier firms avoid debt due to financial distress concerns.

These studies suggest that firms do not maximize leverage, but rather choose a debt level that balances benefits and risks based on firm size, profitability, and market conditions.

Why Should I Be Interested in This Post?

Understanding a firm’s optimal debt structure is essential for anyone involved in finance, strategy, or investment analysis. Whether you’re an investor evaluating risk, a finance professional shaping capital decisions, or a student building foundational knowledge, the trade-off between debt and equity lies at the core of corporate financial strategy. This post offers a deep dive into the academic perspectives on capital structure, highlighting how bankruptcy costs, financial distress, and tax considerations influence real-world financing decisions. By mastering these concepts, you’ll be better equipped to assess firm value, understand risk-return dynamics, and make more informed financial judgments in a world where leverage can both create and destroy value.

Related posts on the SimTrade blog

   ▶ Snehasish CHINARA Optimal capital structure with corporate and personal taxes: Miller 1977

   ▶ Snehasish CHINARA Optimal capital structure with taxes: Modigliani and Miller 1963

   ▶ Snehasish CHINARA Optimal capital structure with no taxes: Modigliani and Miller 1958

   ▶ Snehasish CHINARA Solvency and Insolvency in the Corporate World

   ▶ Snehasish CHINARA Illiquidity, Liquidity and Illiquidity in the Corporate World

   ▶ Snehasish CHINARA Illiquidity, Solvency & Insolvency : A Link to Bankruptcy Procedures

   ▶ Snehasish CHINARA Chapter 7 vs Chapter 11 Bankruptcies: Insights on the Distinction between Liquidations & Reorganisations

   ▶ Snehasish CHINARA Chapter 7 Bankruptcies: A Strategic Insight on Liquidations

   ▶ Snehasish CHINARA Chapter 11 Bankruptcies: A Strategic Insight on Reorganisations

   ▶ Akshit GUPTA The bankruptcy of Lehman Brothers (2008)

   ▶ Akshit GUPTA The bankruptcy of the Barings Bank (1996)

   ▶ Anant JAIN Understanding Debt Ratio & Its Impact On Company Valuation

Useful resources

US Courts Data – Bankruptcy

S&P Global – Bankruptcy Stats

Statista – Bankruptcy data

About the author

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