Contributors

Price is what you pay, value is what you get

by
Hadrien PUCHE

In this article, Hadrien PUCHE (ESSEC Business School, Grande École Program, Master in Management, 2023-2027) comments on Warren Buffett’s famous quote about the fundamental difference between price and value, and discusses how this distinction remains crucial for modern investors navigating today’s volatile markets.

About Warren Buffett

Warren Buffett is the chairman and CEO of Berkshire Hathaway, a financial holding company. He is widely considered one of the most successful long-term investors in history, known for his value-driven approach. Buffett often praised patience and a deep understanding of the intrinsic value of companies over speculation and short-term trends.

Warren Buffett
Warren Buffett
Source: Wikimedia Commons

About the quote

This quote, “price is what you pay, value is what you get”, is often attributed to Warren Buffett and is sometimes said to originate from one of his 1987 shareholder letters. However, its first verifiable appearance is in his 2008 shareholder letter, where Buffett uses it to emphasize the timeless lesson he learned from Benjamin Graham. The quote perfectly captures the essence of value investing, the philosophy that made Buffett so successful.

“Additionally, the market value of the bonds and stocks that we continue to hold suffered a significant decline along with the general market. This does not bother Charlie and me. Indeed, we enjoy such price declines if we have funds available to increase our positions. Long ago, Ben Graham taught me that ‘Price is what you pay; value is what you get.’ Whether we’re talking about socks or stocks, I like buying quality merchandise when it is marked down.”
(Warren E. Buffett, Berkshire Hathaway Shareholder Letter, 2008)

Analysis of the quote

Buffett’s quote highlights a key idea in both economics and finance: the difference between the price of an asset and its actual value.

The price is simply what you pay in a transaction, reflecting a market consensus at a specific moment in time. The value, however, is the abstract idea of the true worth of an asset, based on its capacity to generate future cash flows for its owner.

Buffett points out that market movements are largely shaped by human behavior, especially fear and greed, which can cause assets to be mispriced. The essence of long-term investing lies in identifying these inefficiencies and using them to build wealth over time.

Benjamin Graham, Buffett’s mentor, often illustrated this idea with the allegory of Mr. Market: a moody business partner who offers to buy or sell his stake in your company at changing prices every day. Sometimes he’s euphoric and offers too much, sometimes he’s depressed and offers too little. A wise investor listens politely, but never lets Mr. Market influence his view of the company.

Financial concepts related to the quote

We can relate this quote to three financial concepts: Intrinsic value, Efficient Market Hypothesis, and Market cycles.

Intrinsic value

The concept of intrinsic value suggests that any item can be given a “true worth”, based on its fundamentals: profits, growth, and overall future cash flows.

Models like the Discounted Cash Flow (DCF) analysis help investors estimate this value, by projecting how much cash an asset will generate and discounting it back to today’s money. Cash flows represent the real money a business produces — not just accounting profits, but the actual funds available to reinvest, repay debt, or distribute to shareholders.

The formula for the present value (PV) of a series of cash flows, denoted by CFt, discounted with the discount rate r, is given by:

Present value of a series of cash flows

The discount rate reflects both the time value of money — the idea that a euro today is worth more than a euro tomorrow, and the risk attached to the investment. The riskier the cash flows, the higher the discount rate investors will apply, and therefore the lower the intrinsic value. Understanding both the amount and the uncertainty of future cash flows is essential to determining what a company is truly worth.

Buffett’s philosophy is simple: always make investment decisions when you understand the intrinsic value and are therefore able to make informed and rational choices.

Efficient Market Hypothesis

According to the Efficient Market Hypothesis (EMH), formalized by Eugene Fama in 1970, asset prices in perfectly competitive markets instantly reflect all available information, implying that price always equals value.

However, value investing strongly challenges this idea. Markets often misprice assets, at least temporarily. Behavioral biases such as overconfidence, herd behavior or the tranquility paradox (a behavioral bias where prolonged stability increases risk-taking) can lead market prices to diverge from fundamentals, allowing some investors to buy undervalued assets and achieve superior long-term returns.

Market cycles

It is often believed that market prices move in cycles, driven by alternating periods of optimism and pessimism. When prices increase, investors are more optimistic and keep buying, pushing prices even higher. When prices start decreasing, investors start selling. All of this often happens without any major changes in the intrinsic value of the underlying companies.

Understanding these cycles may allow investors to act counter-cyclically: to buy when others are afraid and sell when they are greedy. Recognizing the difference between market emotion and fundamental value is what separates value investors from speculators.

My opinion about this quote

I believe that this quote is often forgotten by many investors. With the democratization of trading apps and financial content on social media, anyone can trade, but not many understand what they are doing.

I find value investing particularly challenging in today’s market. Take Nvidia as an example: as of 2025, the company is a global leader in graphics processing units (GPUs) and AI computing. With a Price-to-Earnings ratio around 51, can its intrinsic value truly justify a $4 trillion market capitalization? Perhaps, if you believe that the AI ecosystem will sustain the massive profits investors currently expect, but there is no doubt that the stock is currently expensive.

P/E ratio comparison of Nvidia, Amazon, Microsoft, Apple, Google

The challenge of identifying true value is not new : a historical parallel can be drawn with the dot-com bubble of the late 1990s, when companies with minimal earnings, like pets.com and America Online, saw sky-high valuations, that perhaps were disconnected from their actual values. Such episodes should remind investors of the importance of nuancing enthusiasm with careful analysis.

Graph of the cyclically adjusted P/E ratio of the SP500, from 1930 to today

The point isn’t to be pessimistic, but to make every investment decision with a clear and well-reasoned understanding of the underlying business and how it will evolve.

A similar question arises with Bitcoin. What is one Bitcoin worth? The only serious answer to this question is that one Bitcoin is worth whatever someone else is willing to pay for it. Value investors, like Buffett, avoid these hype-driven assets and choose to focus on assets that have fundamentals they can understand.

Price of bitcoin from 2010 to sept 2025

Why should you be interested in this post?

This quote offers a timeless lesson for anyone studying or working in finance: investing is all about discipline, critical thinking, and analytical rigor.

Whether you want to work in trading, M&A, private equity, private debt, asset management, or any other financial field, understanding the distinction between price and value is essential.

As a final thought, you may find it very interesting to apply this principle to your own career choices. Look beyond appearances and seek roles that align with your long-term values and curiosity.

Related posts on the SimTrade blog

   ▶ All posts about Quotes

Useful resources

Business

Berkshire Hathaway

Warren Buffett (2024) Warren Buffett’s 2024 letter to investors, Berkshire Hathaway.

Academic research

Fama E. (1970) Efficient Capital Markets: A Review of Theory and Empirical Work, Journal of Finance, 25, 383-417.

Fama E. (1991) Efficient Capital Markets: II Journal of Finance, 46, 1575-617.

Hou K., H. Mo, L. Zhang (2017) The Economics of Value Investing, NBER Working paper 25563.

About the author

The article was written in October 2025 by Hadrien PUCHE (ESSEC Business School, Grande École Program, Master in Management, 2023-2027).

The Business Model of Proprietary Trading Firms

by
Anis MAAZ

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

Context and objective

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

What a prop firm is

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

The Proprietary Trading Industry: Origins and Scale

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

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

Core Characteristics of prop firms

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

How the business model works

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

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

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

Who Are the Traders?

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

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

What successful traders share :

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

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

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

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

Success rates: what is typical and why most traders fail

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

Trading Strategies, Markets, and Tools

Which Markets?

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

Common Strategies

Prop traders typically employ “intraday strategies”:

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

Tools and Platforms

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

The key performance idea

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

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

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

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

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

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

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

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

Why traders choose prop firms (psychology and practical reasons)

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

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

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

Regulation: A Gray Zone

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

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

Conclusion

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

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

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

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

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

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

Why should I be interested in this post?

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

Related posts on the SimTrade blog

   ▶ Theo SCHWERTLE Can technical analysis actually help to make better trading decisions?

   ▶ Michel VERHASSELT Trading strategies based on market profiles and volume profiles

   ▶ Vardaan CHAWLA Real-Time Risk Management in the Trading Arena

Useful Resources

Topstep payout policy and FAQs (current rules and examples)

The Funded Trader statistics on pass/payout rates

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

General overviews of prop trading mechanics and risk controls

About the author

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

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

by
Yann TANGUY

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

Creation of Modern Portfolio Theory (MPT)

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

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

A Practical Example

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

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

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

Mathematical explanation

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

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

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

Portfolio Variance Formula

Where:

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

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

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

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

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

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

Quantifying performance

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

Sharpe Ratio Formula

Where:

  • E(RP) = expected return of portfolio P
  • Rf = risk-free rate
  • σP = standard deviation of returns of portfolio P

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

Limitations of MPT

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

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

Emergence of Post-Modern Portfolio Theory (PMPT)

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

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

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

Calculating Downside Deviation

To compute downside deviation, we carry out the following:

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

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

Download the Excel file for the data for MPT and PMPT

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

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

Diversification effect on Sharpe Ratio (High correlation)

Diversification effect on Sharpe Ratio (Low correlation)

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

Diversification effect on Downside Deviation (High correlation)

Diversification effect on Downside Deviation (Low correlation)

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

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

Conclusion

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

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

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

Why should I be interested in this post?

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

Related posts on the SimTrade blog

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   ▶ Raphael TRAEN Understanding Correlation in the Financial Landscape: How It Drives Portfolio Diversification

   ▶ Rishika YADAV Understanding Risk-Adjusted Return: Sharpe Ratio & Beyond

   ▶ Youssef LOURAOUI Minimum Volatility Portfolio

   ▶ All posts about Financial techniques

Useful resources

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

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

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

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

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

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

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

About the author

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

Understanding Risk-Adjusted Return: Sharpe Ratio & Beyond

by
Rishika YADAV

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

Risk-adjusted return

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

The Sharpe Ratio

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

Sharpe Ratio = (E[RP] − Rf) / σP

where Rp = portfolio return, Rf = risk-free rate (e.g., T-bill yield), and σp = standard deviation of portfolio returns (volatility).

Interpretation

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

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

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

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

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

The equation of the CAL is:

E[RQ] = Rf + ((E[RP] − Rf) / σP) × σQ

where:

  • E[Rp] = expected return of the combined portfolio
  • Rf = risk-free rate
  • E[RP] = expected return of risky portfolio P
  • σP = standard deviation of P
  • σQ = resulting standard deviation of the combined portfolio (proportional to weight in P)

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

Slope(CAL) = (E[RP] − Rf) / σP = Sharpe(P)

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

The equation of the CML is:

E[RQ] = Rf + ((E[RM] − Rf) / σM) × σQ

where M denotes the market portfolio.

The slope of the CML, (E[RM] − Rf) / σM, is the Sharpe ratio of the market portfolio.

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

Figure 1. Capital Allocation Line (CAL), Capital Market Line (CML) and the Sharpe ratio.
Capital Allocation Line and Sharpe ratio
Source: computation by author.

Strengths of the Sharpe Ratio

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

Limitations of the Sharpe Ratio

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

Beyond Sharpe: Alternative measures

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

Applications in portfolio management

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

Why should I be interested in this post?

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

Related posts on the SimTrade blog

   ▶ Capital Market Line (CML)

   ▶ Understanding Correlation and Portfolio Diversification

   ▶ Implementing the Markowitz Asset Allocation Model

   ▶ Markowitz and Modern Portfolio Theory

Useful resources

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

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

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

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

About the author

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

My Internship Experience as a Financial Analyst at Ophéa

by
Anis MAAZ

In this article, Anis MAAZ (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2023-2027) shares his professional experience as a Financial Analyst at Ophéa.

Introduction

As a Global BBA student at ESSEC Business School, I had the opportunity to join Ophéa as a Financial Analyst Intern during the summer of 2024. This four-month experience gave me hands-on exposure to financial reporting, budget monitoring, and compliance work within the public housing sector.

My missions ranged from participating in profit and loss commissions overseeing €68 million in annual investments, to preparing balance sheets and income statements, and conducting property tax audits. In this post, I will share my professional journey at Ophéa and reflect on how financial analysis supports decision-making in mission-driven organizations.

This experience allowed me to see firsthand how management control systems function in practice. Management control is the process by which managers influence organizational members to implement strategy. At Ophéa, financial analysis was not just about numbers, it was about creating information flows that enabled strategic decision-making in resource allocation, cost optimization, and compliance management.

About Ophéa

Ophéa is a public housing authority (Office Public de l’Habitat) based in Strasbourg, serving the Grand Est region of France. As a social landlord, Ophéa manages thousands of affordable housing units and oversees approximately €68 million in annual investments dedicated to property maintenance, renovations, and new construction projects.

Logo of OPHEA.
Logo of OPHEA
Source: the company.

Public housing authorities in France operate under strict regulatory frameworks, balancing social missions with financial sustainability. This requires rigorous budget management, transparent financial reporting, and careful cost optimization, all areas where the finance team plays a central role.

My Internship

During my internship at Ophéa, my missions focused on three main areas: Budget Monitoring and Profit & Loss Commissions, Balance Sheets and Income Statements, and Property Tax Analysis and Compliance Audits.

Budget Monitoring and Profit & Loss Commissions

I contributed to monthly profit and loss commissions, where the finance team reviewed budget execution across all departments. With €68 million in annual investments, tracking variances between forecasted and actual spending was critical. I extracted data from the accounting system, identified significant deviations, and prepared summary reports for management review.

Balance Sheets and Income Statements

A core responsibility was preparing and updating the company’s balance sheets and income statements using Excel. I consolidated financial data from various sources, ensured consistency across accounts, and formatted reports according to public accounting standards. This required attention to detail and understanding how transactions flow through financial statements.

Property Tax Analysis and Compliance Audits

I conducted detailed property tax analyses for Ophéa’s property portfolio, auditing over 150 individual tax assessments. My role was to verify tax calculations, identify discrepancies, and ensure regulatory compliance. Through systematic review, I identified more than €20,000 in annual overpayments across multiple properties, primarily due to outdated property classifications and incorrect square footage assessments. By filing correction requests with tax authorities, I helped recover these funds and optimize ongoing tax expenses by roughly 3% for the affected properties..

Required Skills and Knowledge

This internship demanded both hard and soft skills. On the technical side, I worked extensively with Excel, pivot tables, advanced formulas (VLOOKUP, INDEX-MATCH, SUMIFS), and data validation. I also learned public accounting principles according to French standards.

Beyond tools, discipline and consistency were essential. In financial reporting, small errors compound quickly. I learned to double-check figures, reconcile accounts systematically, and maintain clear documentation. Speed also mattered: closing processes run on tight schedules, and producing accurate reports quickly became a key skill.

What I Learned

This experience gave me a realistic view of financial analysis in practice, especially in a regulated environment.

First, I learned that discipline is the foundation of financial work. When preparing statements that will be audited by public authorities, every line must be traceable and justified. I developed habits around data validation and structured workflows that will serve me throughout my career.

Second, I understood how financial reporting operates as a diagnostic control system, where variance reports don’t just track performance but enable strategic conversations about resource allocation, project prioritization, and operational challenges. Financial data became the language connecting operational reality with strategic objectives.

Third, I gained confidence with Excel as a professional tool, moving beyond basic spreadsheets to building dynamic models and automating repetitive tasks. For instance, I created a pivot-table that reduced my reporting time by approximately 30%, allowing the team to focus on analysis rather than data compilation.

Finally, this internship confirmed my interest in investment-related careers. While operational, it gave me a solid foundation in financial mechanics, how companies track performance, manage budgets, and ensure compliance. These skills are directly transferable to roles in investment analysis or corporate finance.

Financial Concepts Related to My Internship

I present below three financial concepts related to my internship experience: budget variance analysis, accrual accounting, and working capital management.

Budget Variance Analysis

During monthly reviews of Ophéa’s €68 million investment budget, I analyzed variances across 5+ active projects. For example, I identified a renovation project that exceeded its initial budget by 10% (approximately €150,000) due to unforeseen structural issues. By documenting this variance and presenting it to management with supporting data, I helped facilitate timely discussions with project managers about cost controls and timeline adjustments.

Variance analysis is fundamental in public institutions where financial transparency is required, and it is a core skill in investment analysis when evaluating company performance against guidance.

Accrual Accounting

Accrual accounting records revenues and expenses when they are incurred, not when cash changes hands. At Ophéa, if we invoiced rent in December but received payment in January, revenue was still recorded in December. This provides a more accurate picture of financial performance but requires careful tracking. I learned to reconcile accounts receivable and payable, ensuring that transaction timing aligned with service delivery. Understanding accruals is essential for analyzing financial statements in investment decisions.

Working Capital Management

Working capital is the difference between current assets and current liabilities, which measures short-term financial health. Ophéa’s operations required careful cash flow planning: rent collection had to cover ongoing expenses. During property tax audits, I identified €20,000 of overpayments that tied up cash unnecessarily. By correcting these and claiming refunds,we improved cash availability for priority investments. Investors closely monitor working capital trends because deteriorating metrics can signal operational problems.

Why Should You Be Interested in This Post

This post offers a realistic view of a financial analyst internship in a mission-driven organization. If you are considering careers in corporate finance or investment analysis, understanding budget processes, financial reporting, and compliance work is essential.

This internship also highlights the value of foundational skills like Excel proficiency, attention to detail, and working with structured data. For students interested in sustainable finance, the public housing sector shows how financial analysis supports social outcomes.

Conclusion

My internship at Ophéa sharpened my analytical, technical, and organizational skills. Through budget monitoring, financial reporting, and compliance work, I learned how financial analysis translates into strategic decisions.

This experience confirmed my interest in investment-related careers. I now have a solid foundation in financial mechanics and understand the importance of rigor and consistency in financial work. Looking ahead, I want to pursue roles that combine financial analysis with strategic decision-making, whether in corporate finance, equity research, or portfolio management.

Related posts on the SimTrade blog

   ▶ All posts about Professional experiences

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

   ▶ Martin VAN DER BORGHT My experience as an intern in the Corporate Finance department at Maison Chanel

   ▶ Pierre BERGES My first experience in corporate finance inside a CAC40 group

Useful Resources

Academic references

Ophéa website

Academic references

Anthony, R. N., & Govindarajan, V. (2007) Management Control Systems (12th ed.). McGraw-Hill.

Horngren, C. T., Datar, S. M., & Rajan, M. (2015) Cost Accounting: A Managerial Emphasis (15th ed.). Pearson.

Drury, C. (2018) Management and Cost Accounting (10th ed.)

About the author

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

Reinventing Wellness: How il Puro Brings Personalization to Nutrition

by
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

by
Nithisha CHALLA

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

Introduction

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

What Are Treasury Bonds?

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

Types of Treasury Securities

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

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

Historical Performance of Treasury Bonds

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

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

Investing in Treasury Bonds

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

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

Factors Affecting Treasury Bond Prices

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

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

Relationship between bond price and current bond yield

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

Formula for the price of the bond
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

by
Nithisha CHALLA

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

History of the Herfindahl-Hirschmann Index(HHI)

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

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

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

What is the Herfindahl-Hirschman Index (HHI)?

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

Calculation of the HHI

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

Formula for the Herfindahl-Hirschman Index (HHI).
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

by
Nithisha CHALLA

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

Introduction

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

What are US Treasuries?

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

Types of US Treasuries

There are four main types of US Treasuries:

Treasury Bills (T-bills)

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

Treasury Notes (T-Notes)

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

Treasury Bonds (T-Bonds)

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

Treasury Inflation-Protected Securities (TIPS)

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

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

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

by
Dawn DENG

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

Why learn to do a stock pitch?

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

Choosing a company: balance understanding and interest

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

Industry analysis: space, structure, and cycle

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

Investment thesis: three key arguments

(1) Strategic Layer – “Why now”

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

(2) Competitive Layer – “Why this company”

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

(3) Financial Layer – “Why it matters”

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

Valuation: turn logic into numbers

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

Relative Valuation

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

Absolute Valuation (Discounted Cash Flow)

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

Risks and catalysts: define uncertainty

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

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

by

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

by
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

by
Cornelius HEINTZE

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

Why this is important

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

The Two-Stage Model

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

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

    PV(Growth phase) = 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

by
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

by
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.

Related posts on the SimTrade blog

Professional experiences

   ▶ 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

   ▶ Nithisha CHALLA Job description – Financial analysts

   ▶ Alexandre VERLET Classic brain teasers from real-life interviews

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

by
Samuel BRAL

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

Context of the project

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

Understanding the DCF method

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

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

Step-by-step technical implementation

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

1. Revenue Forecasting

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

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

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

2. EBIT Estimation

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

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

EBIT = Revenue – Operating Costs

3. Tax Adjustment and NOPAT

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

NOPAT = EBIT × (1 – Tax Rate)

4. Depreciation and CAPEX

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

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

5. Net Working Capital (NWC)

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

6. Terminal Value and Discounting

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

TV = FCF_2028 × (1 + g) / (WACC – g)

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

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

7. Equity Value per Share

Enterprise Value – Net Debt + Cash = Equity Value

Equity Value / Number of Shares = Value per Share

WACC and Beta calculation

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

  • Cost of equity was derived using the Capital Asset Pricing Model (CAPM):
    • Cost of Equity = Risk-Free Rate + Beta × Market Risk Premium
  • Beta was computed by unlevering and relevering betas of comparable firms in China, Taiwan, and Malaysia. This accounts for business and financial risk.
  • Cost of debt was based on comparable bond yields and company-specific risks.
  • Capital structure weights were based on BWI’s most recent financial statements.

The photos below are showing how I proceeded

WACC Computation

Beta Computation

Key results and analysis

The model output was:

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

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

Download the Excel file

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

Download the Excel file for WACC and Beta analysis

Why should I be interested in this post?

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

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   ▶ Andrea ALOSCARI Internship: Corporate & Investment Banking (Intesa Sanpaolo)

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

by
Samuel BRAL

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

Context of the project

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

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

What is a Monte Carlo simulation?

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

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

Simulation approach and methodology

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

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

Here is how each component was modeled:

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

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

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

Key results and insights

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

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

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

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

by
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

by
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

by
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.

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   ▶ 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).