SimTrade blog -

My work-study experience in the Middle Office at Amundi

December 15, 2025 by Clara COMBELLES

In this article, Clara COMBELLES (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2021-2025) shares her professional experience as a Middle Office analyst at Amundi, Europe’s leading asset manager.

About the company

Amundi is the largest asset manager in Europe and among the top ten worldwide. Created in 2010 through the merger of Crédit Agricole Asset Management and Société Générale Asset Management, Amundi now manages more than €2,000 billion in assets for retail, institutional and corporate clients.

Logo of Amundi.

Source: the company.

Amundi offers a wide range of investment solutions including active management, passive strategies, responsible investment, real assets, and advisory services. The group operates in over 35 countries with more than 5,000 employees and is listed on Euronext Paris.

Within Amundi, I worked in the Middle Office Department, which plays a crucial operational role by ensuring the accuracy, reliability, and daily monitoring of all portfolio transactions. It acts as a bridge between portfolio managers, operations teams, accounting, risk management, and institutional clients.

My work-study program

My one-year work-study program immersed me in the operational heart of asset management. Working in the Middle Office gave me a transversal view of financial markets, portfolio management, and the full lifecycle of a transaction. I learned to manage time-sensitive tasks, handle large volumes of data, and collaborate with teams from different domains.

My missions

My daily tasks included several key responsibilities essential to the proper functioning of portfolios.

✔ Daily cash flow monitoring
Every morning, I reviewed all cash movements to ensure they aligned with portfolio expectations. This work prevents valuation errors and helps anticipate liquidity needs.

✔ Monthly securities position analysis
I carried out detailed monthly reconciliations between internal systems and accounting data to ensure that positions were accurately reflected and discrepancies were identified.

✔ Resolution of discrepancies and anomaly analysis
Whenever inconsistencies appeared — unexpected movements, trade errors, incorrect positions — I investigated their origin by liaising with operational teams such as trading, subscriptions/redemptions, fees, and accounting.

✔ Support to portfolio managers
I provided portfolio managers with operational insights, including historical flows and position analyses, to support their investment decisions.

✔ Production of client reporting
I contributed to the preparation of periodic reports sent to institutional clients, including asset allocation, performance, and risk indicators.

Required skills and knowledge

This role required strong analytical skills, attention to detail, and the ability to work under time pressure. On the technical side, I used portfolio management systems, Excel, and internal monitoring tools. Soft skills such as communication, teamwork, and problem-solving were essential to collaborate with multiple departments and resolve anomalies efficiently.

What I learned

This experience gave me a concrete understanding of the full lifecycle of a financial transaction, from order execution to final accounting. I developed strong operational and analytical skills, improved my ability to manage risks, and gained a transversal vision of asset management. I also learned the importance of precision, reliability, and responsiveness in the financial industry.

Financial concepts related to my internship

I present below three financial concepts related to my internship and explain their relevance to my missions: the lifecycle of a financial transaction, Net Asset Value (NAV), Operational risk management.

The lifecycle of a financial transaction

Every trade goes through several steps: order initiation, execution, settlement, booking, reconciliation and final reporting. My role in the Middle Office was directly linked to the final steps of this lifecycle, where accuracy and consistency are essential to ensure proper valuation of portfolios.

Net Asset Value (NAV)

Accurate valuation of funds depends on precise cash balances, up-to-date positions, and correctly recorded transactions. My checks on cash flows and monthly reconciliations ensured NAV reliability, which is crucial for investment decisions and client reporting.

Operational risk management

Operational errors—missing trades, incorrect positions, or inconsistent data—can lead to significant financial and reputational risks. By identifying anomalies, coordinating with teams, and resolving breaks, I actively contributed to reducing operational risk within the portfolios.

Why should I be interested in this post?

Students interested in finance often focus on front-office roles, but the Middle Office offers a unique opportunity to understand the full operational framework behind investment decisions. It is an excellent entry point into asset management, providing exposure to financial instruments, risk control, portfolio valuation, and cross-team collaboration. This experience builds a solid foundation for future careers in investment management, risk, operations, or financial analysis.

Useful resources

Amundi – Official Website

Basel Committee on Banking Supervision (2011) Principles for the Sound Management of Operational Risk.

About the author

The article was written in December 2025 by Clara COMBELLES (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2021-2025).

▶ Read all articles by Clara COMBELLES .

Historical Volatility

December 15, 2025December 14, 2025 by Saral BINDAL

In this article, Saral BINDAL (Indian Institute of Technology Kharagpur, Metallurgical and Materials Engineering, 2024-2028 & Research Assistant at ESSEC Business School) explains the concept of historical volatility used in financial markets to represent and measure the changes in asset prices.

Introduction

Volatility in financial markets refers to the degree of variation in an asset’s price or returns over time. Simply put, an asset is considered highly volatile when its price experiences large upward or downward movements, and less volatile when those movements are relatively small. Volatility plays a central role in finance as an indicator of risk and is widely used in various portfolio and risk management techniques.

In practice, the concept of volatility can be operationalized in different ways: historical volatility and implied volatility. Traders and analysts use historical volatility to understand an asset’s past performance and implied volatility as a forward-looking measure of upcoming uncertainties in the market.

Historical volatility measures the actual variability of an asset’s price over a past period, calculated as the standard deviation of its historical returns. Computed over different periods (say a month), historical volatility allows investors to identify trends in volatility and assess how an asset has reacted to market conditions in the past.

Practical Example: Analysis of the S&P 500 Index

Let us consider the S&P 500 index as an example of the calculation of volatility.

Prices

Figure 1 below illustrates the daily closing price of the S&P 500 index over the period from January 2020 to December 2025.

Figure 1. Daily closing prices of the S&P 500 index (2020-2025).

Source: computation by the author.

Returns

Returns are the percentage gain or loss on the asset’s investment and are generally calculated using one of two methods: arithmetic (simple) or logarithmic (continuously compounded).

Returns Formulas

Where R_i represents the rate of return, and P_i denotes the asset’s price at a given point in time.

The preference for logarithmic returns stems from their property of time-additivity, which simplifies multi-period calculations (the monthly log return is equal to the sum of the daily log returns of the month, which is not the case for arithmetic return). Furthermore, logarithmic returns align with the geometric mean thereby mathematically capture the effects of compounding, unlike arithmetic return, which can overstate performance in volatile markets.

Distribution of returns

A statistical distribution describes the likelihood of different outcomes for a random variable. It begins with classifying the data as either discrete or continuous.

Figure 2 below illustrates the distribution of daily returns for S&P 500 index over the period from January 2020 to December 2025.

Figure 2. Historical distribution of daily returns of the S&P 500 index (2020-2025).

Source: computation by the author.

Standard deviation of the distribution of returns

In real life, as we do not know the mean and standard deviation of returns, these parameters have to be estimated with data.

The estimator for the mean μ, denoted by μ̂, and the estimator for the variance σ², denoted by σ̂², are given by the following formulas:

Formulas for the mean and variance estimators

With the following notations:

R_i = rate of return for the i^th day
μ̂ = estimated mean of the data
σ̂² = estimated variance of the data
n = total number of days for the data

These estimators are unbiased and efficient (note the Bessel’s correction for the standard deviation when we divide by (n–1) instead of n).

Unbiased estimators of the mean and variance

For the distribution of returns in Figure 2, the mean and standard deviation calculated using the formulas above are 0.049% and 1.068%, respectively (in daily units).

Annualized volatility

As the usual time frame for human is the year, volatility is often annualized. In order to obtain annual (or annualized) volatility, we scale the daily volatility by the square root of the number of days in that period (τ), as shown below.

Annual Volatility formula

Where  is the number of trading days during the calendar year.

In the U.S. equity market, the annual number of trading days typically ranges from 250 to 255 (252 tradings days in 2025). This variation reflects the holiday calendar: when a holiday falls on a weekday, the exchange closes ; when it falls on a weekend, trading is unaffected. In contrast, the cryptocurrency market has as many trading days as there are calendar days in a year, since it operates continuously, 24/7.

For the S&P 500 index over the period from January 2020 to December 2025, the annualized volatility is given by

S&P500index Annual Volatility formula

Annualized mean

The calculated mean for the 5-year S&P 500 logarithmic returns is also the daily average return for the period. The annualized average return is given by the formula below.

Annualized mean formula

Where τ is the number of trading days during the calendar year.

For the S&P 500 index over the period from January 2020 to December 2025, the annualized average return is given by

Annualized mean formula

If the value of daily average return is much less than 1, annual average return can be approximated as

Annualized mean value

Application: Estimating the Future Price Range of the S&P 500 index

To develop an intuitive understanding of these figures, we can estimate the one-standard-deviation price range for the S&P 500 index over the next year. From the above calculations, we know that the annualized mean return is 12.534% and the annualized standard deviation is 16.953%.

Under the assumption of normally distributed logarithmic returns, we can say approximately with 68% confidence that the value of S&P 500 index is likely to be in the range of:

Upper and lower limits

The ranges calculated above are based on logarithmic returns (continuously compounded). To convert them into simple returns (effective annual rates), we use the following formula:

Effective rate formula

If the current value of the S&P 500 index is $6,830, then converting these log-return estimates into price levels gives:

Upper and lower price limits

Based on a 68% confidence interval, the S&P 500 index is likely to trade in the range of $6,534 to $9,172 over the next year.

Historical Volatility

Historical volatility represents the variability of an asset’s returns over a chosen lookback period. The annualized historical volatility is estimated using the formula below.

Historical volatility formula

With the following notations:

σ = Standard deviation
R_i = Return
n = total number of trading days in the period (21 for 1 month, 63 for 3 months, etc.)
τ = Number of trading days in a calendar year

Volatility calculated over different periods must be annualized to a common timeframe to ensure comparability, as the standard convention in finance is to express volatility on an annual basis. Therefore, when working with daily returns, we annualize the volatility by multiplying it by the square root of 252.

For example, for the S&P 500 index, the annualized historical volatilities over the last 1 month, 3 months, and 6 months, computed on December 3, 2025, are 14.80%, 12.41%, and 11.03%, respectively. The results suggest, since the short term (1 month) volatility is higher than medium (3 months) and long term (6 months) volatility, the recent market movements have been turbulent as compared to the past few months, and due to volatility clustering, periods of high volatility often persist, suggesting that this elevated turbulence may continue in the near term.

Unconditional Volatility

Unconditional volatility is a single volatility number using all historical data, which in our example is the entire five years data; It does not account for the fact that recent market behavior is more relevant for predicting tomorrow’s risk than events from past years, implying that volatility changes over time. It is frequently observed that after any sudden boom or crash in the market, as the storm passes away the volatility tends to revert to a constant value and that value is given by the unconditional volatility of the entire period. This tendency is referred to as mean reversion.

For instance, using S&P 500 index data from 2020 to 2025, the unconditional volatility (annualized standard deviation) is calculated to be 16.952%.

Rolling historical volatility

A single volatility number often fails to capture changing market regimes. Therefore, a rolling historical volatility is usually generated to track the evolution of market risk. By calculating the standard deviation over a moving window, we can observe how volatility has expanded or contracted historically. This is illustrated in Figure 3 below for the annualized 3-month historical volatility of the S&P 500 index over the period 2020-2025.

Figure 3. 3-month rolling historical volatility of the S&P500 index (2020-2025).

Source: computation by the author.

In Figure 3, the 3-month rolling historical volatility is plotted along with the unconditional volatility computed over the entire period, calculated using overlapping windows to generate a continuous series. This provides a clear historical perspective, showcasing how the asset’s volatility has fluctuated relative to its long-term average.

For example, during the start of Russia–Ukraine war (February 2022 – August 2022), a noticeable jump in volatility occurred as energy and food prices surged amid fears of supply chain disruptions, given that Russia and Ukraine are major exporters of oil, natural gas, wheat, and other commodities.

The rolling window can be either overlapping or non-overlapping, resulting in continuous or discrete graphs, respectively. Overlapping windows shift by one day, creating a smooth and continuous volatility series, whereas non-overlapping windows shift by one time period, producing a discrete series.

You can download the Excel file provided below, which contains the computation of returns, their historical distribution, the unconditional historical volatility, and the 3-month rolling historical volatility of the S&P 500 index used in this article.

You can download the Python code provided below, which contains the computation of returns, first four moments of the distribution, and experiment with the x-month rolling historical volatility function to visualize the evolution of historical volatility over time.

Alternatively, you can download the R code below with the same functionality as in the Python file.

Alterative measures of volatility

We now mention a few other ways volatility can be measured: Parkinson volatility, Implied volatility, ARCH model, and stochastic volatility model.

Parkinson volatility

The Parkinson model (1980) uses the highest and lowest prices during a given period (say a month) for the purpose of measurement of volatility. This model is a high-low volatility measure, based on the difference between the maximum and minimum prices observed during a certain period.

Parkinson volatility is a range-based variance estimator that replaces squared returns with the squared high–low log price range, scaled to remain unbiased. It assumes a driftless (expected growth rate of the stock price equal to zero) geometric Brownian motion, it is five times more efficient than close-to-close returns because it accounts for fluctuation of stock price within a day.

For a sample of n observations (say days), the Parkinson volatility is given by

Parkinson Volatility formula

where:

H_t is the highest price on period t
L_t is the lowest price on period t

Implied volatility

Implied Volatility (IV) is the level of volatility for the underlying asset that, when plugged into an option pricing model such as Black–Scholes–Merton, makes the model’s theoretical option price equal to the option’s observed market price.

It is a forward looking measure because it reflects the market’s expectation of how much the underlying asset’s price is likely to fluctuate over the remaining life of the option, rather than how much it has moved in the past.

The Chicago Board Options Exchange (CBOE), a leading global financial exchange operator provides implied volatility indices like the VIX and Implied Correlation Index, measuring 30-day expected volatility from SPX options. These are used by traders to gauge market fear, speculate via futures/options/ETPs, hedge equity portfolios and manage risk during volatility spikes.

ARCH model

Autoregressive Conditional Heteroscedasticity (ARCH) models address time-varying volatility in time series data. Introduced by Engle in 1982, ARCH models look at the size of past shocks to estimate how volatile the next period is likely to be. If recent movements were big, the model expects higher volatility; if they were small, it expects lower volatility justifying the idea of volatility clustering. Originally applied to inflation data, this model has been widely used in to model financial data.

ARCH model capture volatility clustering, which refers to an observation about how volatility behaves in the short term, a large movement is usually followed by another large movement, thus volatility is predictable in the short term. Historical volatility gives a short-term hint of the near future changes in the market because recent noise often continues.

Generalized Autoregressive Conditional Heteroscedasticity (GARCH) extends ARCH by past predicted volatility, not just past shocks, as refined by Bollerslev in 1986 from Engle’s work. Both of these methods are more accurate methods to forecast volatility than what we had discussed as they account for the time varying nature of volatility.

Stochastic volatility models

In practice, volatility is time-varying: it exhibits clustering, persistence, and mean reversion. To capture these empirical features, stochastic volatility (SV) models treat volatility not as a constant parameter but as a stochastic process jointly evolving with the asset price. Among these models, the Heston (1993) specification is one of the most influential.

The Heston model assumes that the asset price follows a diffusion process analogous to geometric Brownian motion, while the instantaneous variance evolves according to a mean-reverting square-root process. Moreover, the innovations to the price and variance processes are correlated, thereby capturing the leverage effect frequently observed in equity markets.

Applications in finance

This section covers key mathematical concepts and fundamental principles of portfolio management, highlighting the role of volatility in assessing risk.

The normal distribution

The normal distribution is one of the most commonly used probability distribution of a random variable with a unimodal, symmetric and bell-shaped curve. The probability distribution function for a random variable X following a normal distribution with mean μ and variance σ² is given by

Normal distribution function

A random variable X is said to follow standard normal distribution if its mean is zero and variance is one.

The figure below represents the confidence intervals, showing the percentage of data falling within one, two, and three standard deviations from the mean.

Figure 4. Probability density function and confidence intervals for a standard normal varaible.
Standard normal distribution” width=
Source: computation by the author

Brownian motion

Robert Brown first observed Brownian motion was as the erratic and random movement of pollen particles suspended in water due to constant collision with water molecules. It was later formulated mathematically by Norbert Wiener and is also known as the Wiener process.

The random walk theory suggests that it’s impossible to predict future stock prices as they move randomly, and when the timestep of this theory becomes infinitesimally small it becomes, Brownian Motion.

In the context of financial stochastic process, when the market is modeled by the standard Brownian motion, the probability distribution function of the future price is a normal distribution, whereas when modeled by Geometric Brownian Motion, the future prices are said to be lognormally distributed. This is also called the Brownian Motion hypothesis on the movement of stock prices.

The process of a standard Brownian motion is given by:

Standard Brownian motion formula.

The process of a geometric Brownian motion is given by:

Geometric Brownian motion formula.

Where, dS_t is the change in asset price in continuous time dt, dX_t is a random variable from the normal distribution (N (0, 1)) or Wiener process at a time t, σ represents the price volatility, and μ represents the expected growth rate of the asset price, also known as the ‘drift’.

Modern Portfolio Theory (MPT)

Modern Portfolio Theory (MPT), developed by Nobel Laureate, Harry Markowitz, in the 1950s, is a framework for constructing optimal investment portfolios, derived from the foundational mean-variance model.

The Markowitz mean–variance model suggests that risk can be reduced through diversification. It proposes that risk-averse investors should optimize their portfolios by selecting a combination of assets that balances expected return and risk, thereby achieving the best possible return for the level of risk they are willing to take. The optimal trade-off curve between expected return and risk, commonly known as the efficient frontier, represents the set of portfolios that maximizes expected return for each level of standard deviation (risk).

Capital Asset Pricing Model (CAPM)

The Capital Asset Pricing Model (CAPM) builds on the model of portfolio choice developed by Harry Markowitz (1952), stated above. CAPM states that, assuming full agreement on return distributions and either risk-free borrowing/lending or unrestricted short selling, the value-weighted market portfolio of risky assets is mean-variance efficient, and expected returns are linear in the market beta.

The main result of the CAPM is a simple mathematical formula that links the expected return of an asset to its risk measured by the beta of the asset:

CAPM formula

Where:

E(R_i) = expected return of asset i
R_f = risk-free rate
β_i = measure of the risk of asset i
E(R_m) = expected return of the market
E(R_m) − R_f = market risk premium

CAPM recognizes that an asset’s total risk has two components: systematic risk and specific risk, but only systematic risk is compensated in expected returns.

Returns decomposition fromula.

Where the realized (actual) returns of the market (R_m) and the asset (R_i) exceed their expected values only because of consideration of systematic risk (ε).

Decomposition of risk.
Decompositionion of risk

Systematic risk is a macro-level form of risk that affects a large number of assets to one degree or another, and therefore cannot be eliminated. General economic conditions, such as inflation, interest rates, geopolitical risk or exchange rates are all examples of systematic risk factors.

Specific risk (also called idiosyncratic risk or unsystematic risk), on the other hand, is a micro-level form of risk that specifically affects a single asset or narrow group of assets. It involves special risk that is unconnected to the market and reflects the unique nature of the asset. For example, company specific financial or business decisions which resulted in lower earnings and affected the stock prices negatively. However, it did not impact other asset’s performance in the portfolio. Other examples of specific risk might include a firm’s credit rating, negative press reports about a business, or a strike affecting a particular company.

Why should I be interested in this post?

Understanding different measures of volatility, is a pre-requisite to better assess potential losses, optimize portfolio allocation, and make informed decisions to balance risk and expected return. Volatility is fundamental to risk management and constructing investment strategies.

Useful Resources

Academic research

Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity, Journal of Econometrics, 31(3), 307–327.

Engle, R. F. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation, Econometrica, 50(4), 987–1007.

Fama, E. F., & French, K. R. (2004). The Capital Asset Pricing Model: Theory and Evidence, Journal of Economic Perspectives, 18(3), 25–46.

Heston, S. L. (1993). A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options, The Journal of Finance, 48(3), 1–24.

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

Parkinson, M. (1980). The extreme value method for estimating the variance of the rate of return. Journal of Business, 53(1), 61–65.

Sharpe, W. F. (1964). Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk, The Journal of Finance, 19(3), 425–442.

Tsay, R. S. (2010). Analysis of financial time series, John Wiley & Sons.

Other

NYU Stern Volatility Lab Volatility analysis documentation.

Extreme Events in Finance Risk maps: extreme risk, risk and performance.

About the author

The article was written in December 2025 by Saral BINDAL (Indian Institute of Technology Kharagpur, Metallurgical and Materials Engineering, 2024-2028 & Research Assistant at ESSEC Business School).

▶ Read all articles by Saral BINDAL.

The “lemming effect” in finance

December 14, 2025 by Lang Chin SHIU

In this article, SHIU Lang Chin (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2024-2026) explains the “lemming effect” in financial markets, inspired by the animated movie Zootopia.

About the concept

The “lemming effect” refers to situations where individuals follow the crowd unthinkingly, just as lemmings are believed to follow one another off a cliff. In finance, this idea is linked to herd behaviour: investors imitate the actions of others instead of relying on their own information or analysis.

The image above is a cartoon showing a line of lemmings running off a cliff, with several already falling through the air. The caption “The Lemming Effect: Stop! There is another way” warns that blindly following others can lead to disaster, even if “everyone is doing it.” The message is to think independently, question group behaviour, and choose an alternative path instead of copying the crowd.

In Zootopia, there is a scene where lemmings dressed as bankers leave their office and are supposed to walk straight home after work. However, after one lemming notices Nick selling popsicles and suddenly changes direction to buy one, the rest of the lemmings automatically follow and queue up too, even though this is completely different from their original route and plan. This illustrates how individuals can abandon their own path and intentions simply because they see someone else acting first, much like investors may follow others into a trade or trend without conducting independent analysis.

Watch the video!

Source: Zootopia (Disney, 2016).

The first image shows Nick Wilde (the fox) holding a red paw-shaped popsicle. In the film, Nick uses this eye‑catching pawpsicle as a marketing tool to attract the lemmings and earn a profit.

zootopia lemmings
Source: Zootopia (Disney, 2016).

The second image shows a group of identical lemmings in suits walking in and out of a building labelled “Lemming Brothers Bank.” This is a parody of the real investment bank “Lehman Brothers,” which collapsed during the 2008 financial crisis. When one lemming notices the pawpsicle, it immediately changes direction from going home and heads toward Nick to buy the product, illustrating how one individual’s choice triggers the rest to follow.

zootopia lemmings
Source: Zootopia (Disney, 2016).

The third image shows Nick successfully selling pawpsicles to a whole line of lemmings. Nick is exploiting the lemmings’ herd‑like behaviour: once a few begin buying, the others automatically copy them and all purchase the same pawpsicle. The humour lies in how Nick profits from their conformity, using their predictable group behaviour—the “lemming effect”—to make easy money.

zootopia lemmings
Source: Zootopia (Disney, 2016).

Behavioural finance uses the lemming effect to describe deviations from perfectly rational decision-making. Rather than analysing fundamentals calmly, investors may be influenced by social proof, fear of missing out (FOMO) or the comfort of doing what “everyone else” seems to be doing.

Understanding the lemming effect is important both for professional investors and students of finance. It helps to explain why markets sometimes move far away from fundamental values and reminds decision-makers to be cautious when “the whole market” points in the same direction.

How the lemming effect appears in markets

In practice, the lemming effect can be seen when large numbers of investors buy the same “hot” stocks simply because prices are rising, they assume that so many others doing the same thing cannot be wrong.

It applies in reverse during market downturns. Bad news, rumours, or sharp price declines can trigger a wave of selling. The fear of being the last one can push them to copy others’ behaviour rather than stick to their original plan.

Such herd-driven moves can amplify volatility, push prices far above or below intrinsic value, and create opportunities or risks that would not exist in a purely rational market. Recognising these dynamics helps investors to step back and question whether they are thinking independently.

Related financial concepts

The lemming effect connects naturally with several basic financial ideas: diversification, risk-return trade-off, market efficiency, Keynes’ beauty contest and gamestop story. It shows how human behaviour can distort these textbook concepts in real markets.

Diversification

Diversification means not putting all your money in the same blanket (asset or sector), so that the poor performance of one investment does not destroy the whole. When the lemming effect is strong, investors often forget diversification and concentrate on a few “popular” stocks. From a diversification perspective, following the crowd can increase risk without necessarily increasing expected returns.

Risk and return

Finance said that higher expected returns usually come with higher risk. However, when many investors behave like lemmings, they may underestimate the true risk of crowded trades. Rising prices can create an illusion of safety, even if fundamentals do not justify the move. Understanding the lemming effect reminds investors to ask whether a sustainable increase in expected return really compensates the extra risk taken by following the crowd.

Market efficiency

In an efficient market, prices should reflect all available information. Herd behaviour and the lemming effect demonstrate that markets can deviate from this ideal when many investors react based on emotions or social cues rather than information. Short-term mispricing created by herding can eventually be corrected when new information becomes available or when rational investors intervene. For students, this illustrates why theoretical models of perfect efficiency are useful benchmarks but do not fully capture real-world behaviour.

Keynes’ beauty contest

Keynes’ “beauty contest” analogy describes investors who do not choose stocks based on their own view of fundamental value, but instead try to guess what everyone else will think is beautiful.Instead of asking “Which company is truly best?”, they ask “Which company does the average investor think others will like?” and buy that, hoping to sell to the next person at a higher price. This links directly to the lemming effect: investors watch each other and pile into the same trades, just like the lemmings all changing direction to follow the first one who goes for the pawpsicle.

GameStop story

The GameStop short squeeze in 2021 is a modern real‑world illustration of herd behaviour. A large crowd of retail investors on Reddit and other forums started buying GameStop shares together, partly for profit and partly as a social movement against hedge funds, driving the price far above what traditional valuation models would suggest. Once the price started to rise sharply, more and more people jumped in because they saw others making money and feared missing out, reinforcing the crowd dynamic in a very “lemming‑like” way.

Why should I be interested in this post?

For business and finance students, the lemming effect is a bridge between psychology and technical finance. It helps explain why prices sometimes move in surprising ways, and why sticking mindlessly to the crowd can be dangerous for long-term wealth.

Whether you plan to work in banking, asset management, consulting or corporate finance, understanding herd behaviour can improve your judgment. It encourages you to combine quantitative tools with a critical view of market sentiment, so that you do not become the next “lemming” in a crowded trade.

Useful resources

BBC Five animals to spot in a post-Covid financial jungle

Tversky, A., & Kahneman, D. (1973). Availability: A heuristic for judging frequency and probability. Cognitive psychology, 5(2), 207-232.

Gupta, S., & Shrivastava, M. (2022). Herding and loss aversion in stock markets: mediating role of fear of missing out (FOMO) in retail investors. International Journal of Emerging Markets, 17(7), 1720-1737.

Gupta, S., & Shrivastava, M. (2022). Argan, M., Altundal, V., & Tokay Argan, M. (2023). What is the role of FoMO in individual investment behavior? The relationship among FoMO, involvement, engagement, and satisfaction. Journal of East-West Business, 29(1), 69-96.

About the author

The article was written in December 2025 by SHIU Lang Chin (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2024-2026).

▶ Read all articles by SHIU Lang Chin.

Time value of money

December 13, 2025December 13, 2025 by Lang Chin SHIU

In this article, SHIU Lang Chin (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2024-2026) explains the time value of money, a simple but fundamental concept used in all areas of finance.

Overview of the time value of money

The time value of money (TVM) is the idea that one euro today is worth more than one euro in the future because today’s money can be invested to earn interest. In other words, receiving cash earlier gives more opportunities to save, invest, and grow wealth over time. This principle serves as the foundation for valuing loans, bonds, investment projects, and many everyday financial decisions.

To work with TVM, finance uses a few key tools: present value (the value today of future cash flows), future value (the value in the future of money invested today),etc. With these elements, it becomes possible to compare different cash-flow patterns that occur at various dates consistently.

Future value

The future value (FV) of money answers the question: if I invest a certain amount today at a given interest rate, how much will I have after some time? Future value uses the principle of compounding, which means that interest earns interest when it is reinvested.

For a simple case with annual compounding, the formula is:

Future Value (FV)

where PV is the amount invested today, r is the annual interest rate, and T is the number of years.

For example, if 1,000 euros are invested at 5% per year for 3 years, the future value is FV = 1,000 × (1.05)^3 = 1,157.63 euros. This shows how even a modest interest rate can increase the value of an investment over time.

Compounding frequency can also change the result. If interest is compounded monthly instead of annually, the formula is adjusted to use a periodic rate and the total number of periods. The more frequently interest is added, the higher the future value for the same nominal annual rate, illustrating why compounding is such a powerful mechanism in long-term investing.

Compounding mechanism with monthy and annual compounding.

You can download the Excel file provided below, which contains the computation of an investment to illustrate the impact of the frequency on the compounding mechanism.

Present value

Present value (PV) is the reverse operation of future value and answers the question: how much is a future cash flow worth today? To find PV, the future cash flow is “discounted” back to today using an appropriate discount rate that reflects opportunity cost, risk and inflation.

For a single future cash flow, the present value formula is:

Present Value (PV)

Where FV is the future amount, r is the discount rate per period, and T is the number of periods.

For example, if an investor expects to receive 1,000 euros in 2 years and the discount rate is 5% per year, the present value is PV = 1,000 / (1.05)^2 = 907.03 euros. This means the investor would be indifferent between receiving 907.03 euros today or 1,000 euros in two years at that discount rate.

Choosing the discount rate is a key step: for a safe cash flow, a risk-free rate such as a government bond yield might be used, while for a risky project, a higher rate reflecting the required return of investors would be more appropriate. A higher discount rate reduces present values, making future cash flows less attractive compared to money today.

Applications of the time value of money

The time value of money is used in almost every area of finance. In corporate finance, it forms the basis of discounted cash-flow (DCF) analysis, where the expected future cash flows of a project or company are discounted to estimate the net present value. Investment decisions are typically made by comparing the present value to the initial cost.

DCF

In banking and personal finance, TVM is essential to design and understand loans, deposits and retirement plans. Customers who understand how interest rates and compounding work can better compare offers, negotiate terms and plan their savings. In capital markets, bond pricing, yield calculations and valuation of many other instruments depend directly on discounting streams of cash flows.

Even outside professional finance, TVM helps individuals answer simple but important questions: is it better to take a lump sum now or a stream of payments later, how much should be saved each month to reach a future target, or what is the true cost of borrowing at a given interest rate? A good intuition for TVM improves financial decision-making in everyday life.

Why should I be interested in this post?

As a university student, understanding TVM is essential because it underlies more advanced techniques such as discounted cash-flow (DCF) valuation, bond pricing and project evaluation. It is usually one of the first technical topics taught in introductory corporate finance and quantitative methods courses.

Useful resources

Harvard Business School Online Time value of money

Investing.com Time value of money: formula and examples

About the author

The article was written in December 2025 by SHIU Lang Chin (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2024-2026).

▶ Read all articles by SHIU Lang Chin.

Deep Dive into evergreen funds

December 12, 2025 by Emmanuel CYROT

In this article, Emmanuel CYROT (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2021-2026) introduces the ELTIF 2.0 Evergreen Fund.

Introduction

The asset management industry is pivoting to democratize private market access for the wealth segment. We are moving from the rigid Capital Commitment Model (the classic “blind pool” private equity structure) to the flexible NAV-Based Model, an open-ended structure where subscriptions and redemptions are executed at periodic asset valuations rather than through irregular capital calls. For technical product specialists, the ELTIF 2.0 regulation isn’t just a compliance update, it’s the architectural blueprint for the democratization of private markets. Here is the deep dive into how these “Semi-Liquid” or “Evergreen” structures actually work, the European landscape, and the engineering behind them.

The Liquidity Continuum: Solving the “J-Curve” Problem

To understand the evergreen structure, you have to understand what it fixes. In a traditional Closed-End Fund (the “Old Guard”):

The Cash Drag: You commit €100k, but the manager only calls 20% in Year 1. Your money sits idle.
The J-Curve: You pay fees on committed capital immediately, but the portfolio value drops initially due to costs before rising (the “J” shape).
The Lock: Your capital is trapped for 10-12 years. Secondary markets are your only (expensive) exit.

The Evergreen / Semi-Liquid Solution represents the structural convergence of private market asset exposure with an open-ended fund’s periodic subscription and redemption framework.

Fully Invested Day 1: Unlike the Capital Commitment model, your capital is put to work almost immediately upon subscription.
Perpetual Life: There is no “end date.” The fund can run for 99 years, recycling capital from exited deals into new ones.
NAV-Based: You buy in at the current Net Asset Value (NAV), similar to a mutual fund, rather than making a commitment.

The difference in investment processes between evergreen funds and closed ended funds

Source: Medium.

The European Landscape: The Rise of ELTIF 2.0

The “ELTIF 2.0” regulation (Regulation (EU) 2023/606) is the game-changer. It removed the extra local rules that held the market back in Europe. These rules included high national minimum investment thresholds for retail investors and overly restrictive limits on portfolio composition and liquidity features imposed by national regulators.

Market Data as of 2025 (Morgan Lewis)

Volume: The market is rapidly expanding, with over 160+ registered ELTIFs now active across Europe as of 2025.
The Hubs: Luxembourg is the dominant factory (approx. 60% of funds), followed by France (strong on the Fonds Professionnel Spécialisé or FPS wrapper) and Ireland.
The Arbitrage: The killer feature is the EU Marketing Passport. A French ELTIF can be sold to a retail investor in Germany or Italy without needing a local license. This allows managers to aggregate retail capital on a massive scale.

Structural Engineering: Liquidity

This section delves into the precise engineering required to reconcile the illiquidity of the underlying assets with the promise of periodic investor liquidity in Evergreen/Semi-Liquid funds. This is achieved through a combination of Asset Allocation Constraints and robust Liquidity Management Tools (LMTs).

The primary allocation constraint is the “Pocket” Strategy, or the 55/45 Rule. The fund is structurally divided into two distinct components. First, the Illiquid Core, which must represent greater than 55% of the portfolio, is the alpha engine holding long-term, illiquid assets such as Private Equity, Private Debt, or Infrastructure. Notably, ELTIF 2.0 has broadened the scope of this core to include newer asset classes like Fintechs and smaller listed companies. Second, the Liquid Pocket, which can be up to 45%, serves as the fund’s buffer, holding easily redeemable, UCITS-eligible assets like money market funds or government bonds. While the regulation permits a high 45% pocket, efficient fund operation typically keeps this buffer closer to 15%–20% to mitigate performance-killing “cash drag”.

Crucial to managing liquidity risk is the Gate Mechanism. Although the fund offers conditional liquidity (often quarterly), the Gate prevents a systemic crisis if many investors attempt to exit simultaneously. This mechanism works by capping redemptions at a specific percentage of the Net Asset Value (NAV) per period, commonly set at 5%. If aggregate redemption requests exceed this threshold (e.g., requests total 10%), all withdrawing investors receive a pro-rata share of the allowable 5% and the remainder of their request is deferred to the next liquidity window.

Finally, managers utilize Anti-Dilution Tools like Swing Pricing to protect the financial interests of the long-term investors remaining in the fund. In a scenario involving heavy redemptions, where the fund manager is forced to sell assets quickly and incur high transaction costs, Swing Pricing adjusts the NAV downwards only for the exiting investors. This critical mechanism ensures that those demanding liquidity—the “leavers”—bear the transactional “cost of liquidity,” thereby insulating the NAV of the “stayers” from dilution.

Why should I be interested in this post?

Mastering ELTIF 2.0 architecture offers a definitive edge over the standard curriculum. With the industry pivoting toward the “retailization” of private markets, understanding the engineering behind evergreen funds and liquidity gates demonstrates a level of practical sophistication that moves beyond theory—exactly what recruiters at top-tier firms like BlackRock or Amundi are seeking for their next analyst class.

Useful resources

Société Générale Fonds Evergreen et ELTIF 2 : Débloquer les Marchés Privés pour les Investisseurs Particuliers

About the author

The article was written in December 2025 by Emmanuel CYROT (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2021-2026).

▶ Read all articles by Emmanuel CYROT.

How to stay up to date with financial news

December 12, 2025 by Zineb ARAQI

In this article, Zineb ARAQI (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2021-2025) shares advice about how to keep up with financial news for all aspiring finance professionals.

Why It Is Important to Stay Up to Date with Financial News

Financial news is the lens through which we understand how the world’s economies, companies, and markets evolve. Every major financial decision from central bank rate changes to corporate mergers or geopolitical shocks has immediate and long-term effects on asset prices and business trends. Staying informed allows you to interpret these signals early, understand their implications, and make better strategic decisions.

For aspiring finance professionals, this habit is essential. Recruiters expect candidates to follow markets closely and to demonstrate an ability to connect recent news to broader macroeconomic themes. Whether you are preparing for interviews in investment banking, private equity, asset management, consulting, or fintech, the ability to discuss current events intelligently can significantly strengthen your profile. Beyond interviews, developing strong market awareness helps you stand out in internships and early career roles, where teams rely on juniors who can quickly contextualize news and anticipate its impact on clients, sectors, or investment strategies.

How to Stay Up to Date with Financial News

A practical guide to staying informed in fast-moving markets

Why Staying Informed Matters

Financial markets evolve at incredible speed. A policy announcement, an earnings release, or a geopolitical event can move markets within minutes, shaping investment decisions, risk perception, and overall confidence. Staying updated is essential for investors, analysts, entrepreneurs, and business leaders, and it is especially critical for students who aspire to build a career in finance.

For future financiers, staying informed is non-negotiable. Whether you aim to work in investment banking, private equity, asset management, consulting, or sustainable finance, you will be expected to understand market trends, macroeconomic developments, and sector dynamics. Interviewers routinely test candidates on their financial awareness, and teams rely on juniors who can connect the dots between daily news and strategic decisions. Developing this habit early provides a strong competitive advantage and prepares you for the fast-paced environment of modern finance.

Use Traditional Media

Newspapers

Established financial newspapers remain among the most reliable sources for in-depth reporting, analysis, and opinion pieces.

Financial Times – excellent global coverage and ESG/sustainable finance insights.
The Wall Street Journal – strong focus on U.S. markets and corporate news.
Les Échos – the go-to source for French and European economic updates.

Digital Tools & Apps

Digital platforms offer free and accessible ways to follow markets on the go.

Google Finance – clean dashboards for watchlists and news.
Yahoo Finance – good for company pages and basic charts.
Investing.com – economic calendars, real-time quotes, and commodity data.
MarketWatch – accessible journalism and market commentary.

Subscribe to Quality Newsletters

Newsletters provide concise daily updates that fit easily into your morning routine.

Bloomberg – “Five Things to Start Your Day” – short, sharp, and market-focused.
FT Moral Money – For sustainable finance and ESG trends.
Morning Brew (Markets) – fun and accessible, it is a great source for beginners.
The Economist Weekly – broader macro and geopolitical analysis.

Listen to Podcasts & Watch Videos

Audio and video formats are perfect for learning while commuting, cooking, or working out.

Bloomberg Surveillance – expert interviews and macroeconomic analysis.
FT News Briefing – a concise summary of global business news.
Planet Money (NPR) – accessible, entertaining explanations of complex topics.
CNBC Squawk – real-time market commentary.

Follow Trusted Sources on Social Media

Social media delivers information in real time, but the key is following verified and credible accounts.

Twitter/X: Bloomberg Markets, Reuters Business, Holger Zschaepitz, Morgan Stanley Research.
LinkedIn: Economists, asset managers, and thought leaders.
YouTube: Bloomberg, WSJ, CFA Institute, finance educators.

The Jamie Dimon Way: How a Top CEO Stays Informed

One of the most respected figures in global finance, Jamie Dimon, CEO of JPMorgan Chase has built a disciplined routine around staying informed. His approach is simple but extremely rigorous: every morning, he wakes up before 5 a.m. and reads multiple newspapers in a precise order to get a balanced, global perspective.

Dimon has shared in interviews that he starts with The Washington Post and The New York Times to understand national headlines and political dynamics. He then moves to The Wall Street Journal for corporate and market-focused coverage. Finally, he reads the Financial Times for a more international and less U.S.-centric viewpoint. On weekends, he adds The Economist, which he considers essential for deep macroeconomic and geopolitical insights.

Beyond what he reads, Dimon’s philosophy is equally revealing. He avoids distractions, rarely checks his phone during the day, and refuses to let notifications drive his attention. Instead, he prioritizes thoughtful reading, focused work, and long-term thinking.

For students and young professionals aiming for a career in finance, Dimon’s approach offers a clear lesson: serious finance careers require serious information habits. The ability to understand market movements, connect events across regions, and think strategically starts with a consistent, deliberate daily routine grounded in high-quality, diverse sources of information.

How I Stay Informed

As a finance student preparing for interviews, case studies, and technical assessments, staying informed quickly became a daily habit rather than an academic requirement. During my time at ESSEC, and later through interviews for finance roles, I realized that strong market awareness often makes the difference between a good candidate and an outstanding one.

To keep up, every morning, I scan the Financial Times to get a first sense of macroeconomic movements, overnight market performance, and key corporate stories. I then check Yahoo Finance to review charts, earnings updates, and sector-specific developments. Throughout the day, I rely on newsletters such as Bloomberg’s “Five Things to Start Your Day” and FT Moral Money for ESG trends, which are particularly relevant to my academic and professional interests.

My commute has also become part of my learning routine. When I take the tube, I often listen to The Clark Howard Podcast, a show focused on personal finance, smart money habits, and consumer insights. Although it is not a markets podcast, it helps me better understand everyday financial decisions, interest rates, and economic trends from a practical perspective. It is one of the easiest ways to stay informed without feeling like I’m “studying,” and it keeps me grounded in real-world financial thinking even during busy weeks.

Before interviews, I also prepare short summaries on major themes such as inflation trends, geopolitical risks, and standout M&A deals. This practice not only helped me perform well during recruiting processes, but also strengthened my analytical thinking and confidence when discussing financial topics with professionals.

Staying informed, for me is about building intuition. Over time, this routine has helped me better understand how markets react, how narratives evolve, and how events connect across regions. It is a habit that continues to shape my education and my career aspirations in finance.

Conclusion

Staying up to date with financial news does not require hours of daily reading. With the right combination of traditional media, digital tools, and consistent habits, you can easily stay informed and understand the major trends shaping markets. Start small, be consistent, and over time you will build strong financial awareness that gives you a real edge in both academic and professional settings.

Useful resources

About the author

This article was written in December 2025 by Zineb ARAQI (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2021–2025).

▶ Read all articles by Zineb ARAQI.

My internship experience at HSBC

December 9, 2025 by Lang Chin SHIU

In this article, SHIU Lang Chin (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2024-2026) shares her professional experience at HSBC in Hong Kong.

About the company

HSBC (The Hongkong and Shanghai Banking Corporation) is one of the world’s largest banking and financial services organisations, serving millions of customers through Retail Banking, Wealth Management, Commercial Banking, Global Banking and Markets, and other specialised businesses.

In Hong Kong, HSBC plays a key role as a leading provider of corporate and investment banking, trade finance, and wealth management products, making it a central player in the regional and global financial system.

Logo of HSBC.

Source: the company.

My internship

During my internship in the Wealth and Personal Banking team in Hong Kong, I assisted with daily operations supporting client relationship managers and investment advisors. My work involved preparing client onboarding documents, updating records in the bank’s management system, and ensuring compliance with Know Your Customer (KYC) and internal policy requirements. I also helped compile client portfolio summaries, draft investment proposals, and conduct market research to support financial planning and investment recommendations.

Beyond these tasks, I gained exposure to a wide range of wealth management products including mutual funds, equity and bonds, structured products, and insurance solutions. I participated in internal meetings to observe how product specialists, compliance officers, and relationship managers collaborate to deliver integrated services for clients. Additionally, I contributed to the preparation of client presentations and market updates, which strengthened my understanding of how macroeconomic trends influence individual investment strategies.

My missions

My missions included supporting relationship managers and product managers with the preparation of client materials, such as simple financial summaries and presentation slides for internal and external meetings. I also assisted with internal reports, helped update client information in our internal systems, and observed calls and meetings to understand client needs and identify follow-up actions.

Required skills and knowledge

This internship required strong analytical skills, attention to detail and a solid foundation in finance and banking concepts, such as understanding financial statements, basic risk metrics and common banking products. At the same time, soft skills such as communication, time management, and professionalism were crucial, as I had to collaborate with different team members, handle confidential information carefully, and deliver work under tight deadlines.

What I learned

Through this experience, I learned how front-office and support teams interact to serve clients and manage risks within a large universal bank. I developed a more concrete understanding of how theoretical concepts from corporate finance and financial markets are applied in real transactions and client discussions, and I improved my ability to structure quantitative information clearly in reports.

Financial concepts related to my internship

Three financial concepts related to my internship: relationship banking, risk-return and capital allocation, and regulation and compliance. These concepts help explain how my daily tasks fit into the broader functioning of the bank.

Relationship banking

Relationship banking refers to building long-term relationships with clients rather than focusing only on individual transactions. In practice, this means understanding clients’ businesses, industries and strategic priorities to provide tailored solutions over time. By helping prepare client materials and following up on information requests, I contributed to the relationship-building process that supports client retention and opportunities.

Risk-return and capital allocation

Banks constantly balance risk and return when they grant loans, underwrite deals or hold assets on their balance sheet, subject to capital and liquidity constraints. Internal analyses, credit information, and financial ratios are used to assess whether the expected return of a client or transaction justifies the associated risk and capital consumption. My exposure to simple financial analysis and internal reporting showed how data and models support these risk-return decisions.

Regulation and compliance

Banking is a highly regulated industry, with strict rules on capital, liquidity, anti-money laundering (AML), know-your-customer (KYC) and conduct. Many processes in the bank, from onboarding to reporting and product approval, are shaped by these regulatory requirements. During my internship, I observed how documentation, data accuracy, and internal controls are integrated into daily workflows to ensure that business growth aligns with regulatory expectations and internal risk appetite.

Why should I be interested in this post?

An internship at HSBC offers exposure to a global banking environment, sophisticated financial products and real client situations. It also provides a strong platform to develop quantitative skills, professional communication and an understanding of how large financial institutions create value while managing complex risks—skills that are highly transferable to careers in banking, consulting, corporate finance and risk management.

Useful resources

HSBC – Official website

HSBC Internships for students and graduates

HSBC Financial Regulation

About the author

The article was written in December 2025 by SHIU Lang Chin (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2024-2026).

▶ Read all articles by SHIU Lang Chin.

My Apprenticeship Experience at Capgemini Invent

December 9, 2025 by Zineb ARAQI

In this article, Zineb ARAQI (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2021-2025) shares her experience as an apprentice at Capgemini Invent within the Data & AI practice for Financial Services, where she contributed to major digital transformation programs across global banking institutions.

About the company

Capgemini Invent is the digital innovation, design and transformation brand of the Capgemini Group. Created in 2018, it combines strategy, technology, data, and creative design to help organizations reinvent their business models. Capgemini Invent operates in more than 30 countries and brings together over 10,000 consultants, data scientists, designers, and industry experts.

Capgemini Invent works at the intersection of strategy and execution, supporting clients through their end-to-end transformation journeys. Its expertise spans digital transformation, artificial intelligence, cloud modernization, sustainability strategy, customer experience, and data-driven operating models.

Within the wider Capgemini Group (over 340,000 employees worldwide), Invent plays a critical role in bridging management consulting with advanced technological execution. This unique positioning allows consultants to work on strategic topics while staying close to the technical realities of implementation, particularly in fast-evolving domains like AI, data governance, and digital banking.

Logo of Capgemini.
Logo Capgemini
Source: Capgemini Invent

About the department: Data & AI for Financial Services

I completed my apprenticeship within the Data & AI Financial Services practice, the division supporting major French and international banks in their data strategy and AI-driven transformation. This department works closely with Chief Data Officers (CDOs), Chief Analytics Officers, and executive committees to design, deploy, and govern enterprise-wide data architectures and AI solutions.

During my apprenticeship, I worked on strategic missions covering Europe, Middle East, and Africa, the Americas, and Asia-Pacific. Our team addressed high-impact topics such as data governance, regulatory compliance and Environmental, Social, and Governance reporting, customer intelligence, risk modelling, AI use-case acceleration, cloud migration, and the operationalization of large-scale data platforms. The practice serves flagship clients across retail banking, corporate & investment banking, insurance, and payments.

My apprenticeship experience at Capgemini Invent

My Missions

Throughout my apprenticeship, I contributed to large digital transformation programs for top French banks. My work spanned across all regions, EMEA, the Americas, and Asia reflecting the global scale of modern banking operations and the cross-regional governance challenges faced by CDOs.

My missions included:

Supporting Chief Data Officers in defining and implementing enterprise-wide data governance frameworks (metadata, lineage, quality, operating models).
Designing AI use-case portfolios, including prioritization matrices, feasibility assessments, and Return on Investment evaluations for retail and corporate banking.
Analyzing cross-regional data issues across APAC, the Americas, and EMEA to harmonize data standards and reporting structures.
Contributing to ESG & sustainable finance reporting, helping banks adapt to emerging CSRD (the EU’s new mandatory sustainability reporting directive), TNFD (the global framework for nature-related risk disclosures) and ESRS (the detailed European sustainability reporting standards) requirements using improved data pipelines.
Supporting cloud transformation initiatives by assessing data migration readiness and defining new operating models for data platforms.
Supporting cloud transformation initiatives by assessing data migration readiness and defining new operating models for data platforms.
Building dashboards and analytics tools using SQL, PowerBI, and Python to transform raw data into clear insights that support risk, compliance, and business teams in their decision-making.

These projects exposed me to the complexity of financial data ecosystems, the challenges of legacy infrastructures, and the role of AI in reshaping operational models at scale.

Required skills and knowledge

Working at the intersection of consulting, data governance, and financial services required a combination of analytical, technical, and communication skills. On the technical side, I relied on knowledge of banking business lines (retail, Corporate & Investment Banking, payments), data modelling fundamentals, SQL, cloud concepts, and AI/ML logic. Understanding regulatory frameworks and risk data aggregation standards was essential, especially when advising CDOs on compliance or data lineage workflows.

Soft skills were equally important: client communication, structured problem-solving, stakeholder management, and the ability to translate complex data topics into actionable recommendations. Working across multiple regions strengthened my adaptability and cross-cultural communication, as I collaborated with teams in Europe, the U.S., and Asia.

What I learned

This apprenticeship taught me how central data has become to the competitiveness and resilience of financial institutions. I learned how banks leverage data to enhance customer experience, reduce risk, improve compliance, and accelerate digital transformation. I also gained firsthand exposure to how global banks structure their operating models, from governance to platforms to analytics, and how AI can be responsibly integrated into decision-making processes.

Most importantly, working with CDO organizations helped me understand the strategic importance of data leadership and the challenges of transforming legacy institutions into data-driven organizations. This experience reinforced my interest in financial technology, analytics, and sustainable finance.

Business concepts related to my internship

I present below three financial, economic, and management concepts related to my apprenticeship. These concepts illustrate how data strategy, regulatory expectations, and AI-driven transformation shape the operating models of large financial institutions and how my work experience aligned with these challenges.

Data Governance and Regulatory Compliance (BCBS 239, CSRD, ESRS)

During my missions, the concept of data governance was central. Financial institutions operate under strict regulatory expectations such as BCBS 239 (risk data aggregation), CSRD (corporate sustainability reporting), and ESRS (European sustainability standards). These frameworks require banks to demonstrate full control of their data including lineage, quality, documentation, accessibility in order to produce reliable regulatory reports. My role consisted in helping banking groups structure governance models, build data quality controls, and harmonize data definitions across regions. This concept is at the heart of banking transformation, as regulatory pressure and data modernization are now inseparable.

AI Use-Case Prioritization and ROI Evaluation

A second concept I applied throughout my apprenticeship is the prioritization of AI use-cases based on business value, feasibility, and risk. Banks often have dozens of potential AI initiatives, but only a fraction deliver measurable Return on Investment (ROI). My work involved constructing prioritization matrices, evaluating data readiness, estimating financial impact, and supporting executive committees in building realistic AI roadmaps. This required balancing quantitative evaluation (cost savings, efficiency gains) with qualitative factors (regulatory risk, bias mitigation, ethical constraints). This concept is fundamental to ensuring that AI programs are scalable, responsible, and aligned with strategic objectives.

Operating Model Transformation for Data Platforms and Cloud Migration

The third concept closely linked to my missions is the transformation of operating models for data platforms migrating to the cloud. Banks are progressively replacing legacy infrastructure with modern cloud-based architectures to improve scalability, reduce costs, and accelerate analytics capabilities. My work consisted in assessing migration readiness, defining roles and responsibilities, and designing new governance processes adapted to cloud environments. This concept is essential because technology alone cannot transform an organization, it must be accompanied by clear processes, change management, and redesigned workflows.

About the author

This article was written in December 2025 by Zineb ARAQI (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2021–2025).

▶ Read all articles by Zineb ARAQI.

My apprenticeship experience as a Junior Financial Auditor at EY

December 9, 2025 by Iris ORHAND

In this article, Iris ORHAND (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2021-2026) shares her professional experience as a Junior Financial Auditor at Ernst & Young.

About the company

EY (Ernst & Young) is one of the “Big Four” professional services firms, supporting companies across audit, consulting, strategy, tax, and transactions. In audit, EY’s mission is to provide reasonable assurance on financial statements, bringing together financial analysis, an understanding of risks, internal control review, and clear, structured documentation to back audit opinions and reinforce stakeholder trust. Today, the firm brings together nearly 400,000 professionals across more than 150 countries and generated around USD 51.2 billion in revenue in its 2024 fiscal year.

Logo of EY

Source: the company.

My internship

In 2024, I joined EY in Paris La Défense as a Junior Financial Auditor on a 12-month apprenticeship. This experience gave me hands-on exposure to the audit cycle, from planning to fieldwork to final deliverables, and helped me understand how auditors balance technical rigor, deadlines, and client interaction.

My missions

Over the year, I worked on the financial analysis of seven companies, ranging from €10 million to €1.5 billion in revenue. I was part of a business unit focused on associations and the public sector, which allowed me to discover organizations with very different missions and financial setups. My largest and longest engagement was with Universal, where I really had the chance to follow a full audit cycle and understand how such a large structure operates. On a daily basis, I reviewed financial statements like the P&L, balance sheet and cash flow, identified unusual trends, dug into variances, and tried to understand the story behind the numbers. I also prepared financial analyses and draft audit conclusions for internal teams as well as for client discussions.

Even though my main focus was on the non-profit and public sector, EY gives motivated juniors the chance to work with other business units from time to time, and I really wanted to take advantage of that. Thanks to this, I was able to join a mission in the defense sector for Thalès, which was a completely different environment and pushed me to adapt quickly and broaden my understanding of industry specific risks.

Throughout the year, I relied a lot on audit tools and automation, using audit software, macros and advanced Excel to structure testing, make our work more traceable, and gain efficiency during busy periods. I was also involved in internal control assessments and risk management topics, which helped me understand how processes and day to day workflows can directly impact the reliability of financial reporting. I also participated in reviewing management forecasts, comparing them with historical results, challenging assumptions and pointing out areas where further evidence was needed. Overall, this experience helped me build a strong analytical mindset and gave me a much clearer view of how different types of organizations operate behind their financial statements.

Required skills and knowledge

This role required a combination of both hard and soft skills, and I quickly realized how important it was to balance the two. On the technical side, I relied a lot on advanced Excel, basic automation and macro logic, and a structured approach to financial analysis. A solid understanding of accounting fundamentals was essential, as well as developing strong documentation habits to keep our work clear, traceable, and easy for reviewers to follow. But beyond the technical knowledge, soft skills mattered just as much, if not more. Attention to detail was key, as was maintaining a sense of professional skepticism without falling into mistrust. Clear and calm communication helped a lot, especially when dealing with tight deadlines or last-minute requests during busy periods. I also learned how important it is to be pedagogical and professional with clients. Sometimes, audit questions can make clients feel like they are being challenged or judged, even when that’s not the intention. Taking the time to explain why we need certain information, reassuring them, and keeping the conversation constructive made the whole process smoother and helped build trust. Overall, this mix of technical rigor and human sensitivity was at the core of the role.

What I learned

This apprenticeship strengthened my ability to turn raw financial data into meaningful audit insights. Over time, I became much more comfortable linking business reality to accounting outcomes, understanding why a number moved, what it implied, and what kind of evidence was needed to support it. I also learned to think with a risk-based mindset, focusing my attention on the areas that had the greatest impact on the reliability of the financial statements. Finally, working under tight deadlines taught me how to stay organized and efficient while still maintaining high quality standards and keeping my work clear and ready for review. This combination of technical understanding, prioritization, and discipline is something I really developed throughout the year.

Financial concepts related to my internship

I present below three financial concepts related to my internship: financial statement analysis, internal control and audit risk, and forecasts, assumptions and professional skepticism.

Financial statement analysis

Audit work involves understanding not only the numbers, but also the story behind them and the operational reality that drives financial performance. Financial statement analysis played a central role throughout my apprenticeship. Trend analysis, ratio analysis, and variance explanations were essential tools to detect anomalies, identify risks, and guide the direction of our testing. By comparing periods, analyzing shifts in key indicators, and questioning unusual movements, I learned to form a more accurate picture of how an organisation truly operates.

This analytical process goes far beyond reading figures. It requires understanding the client’s business model, the context behind certain decisions, and the internal processes that ultimately shape the financial statements. Through this approach, I learned to prioritize the most sensitive areas, challenge assumptions that did not align with expectations, and connect accounting outcomes to the real functioning of the organisation. This ability to translate raw numbers into meaningful insights became one of the most valuable skills I developed during the apprenticeship.

Internal control and audit risk

Internal control quality plays a key role in shaping audit strategy. Throughout my apprenticeship, I saw how understanding a client’s processes, identifying where the risks lie, and evaluating the controls in place helps determine the likelihood of misstatements. When controls are strong and consistently applied, the risk is lower, which allows auditors to adjust their testing. When controls are weak or not operating as intended, the audit must be more detailed and rely on additional evidence.

In practice, this involved mapping processes, speaking with client teams, and observing how transactions were handled on a daily basis. It also required professional judgment to identify the areas where real vulnerabilities might exist. This experience helped me understand how internal control and audit risk are linked, and how this relationship influences the entire audit approach.

Forecasts, assumptions and professional skepticism

Comparing forecasts with historical figures is a practical way to assess the reasonableness of management’s assumptions, whether they relate to growth, margins, or cash generation. This exercise helps identify when projections are aligned with past performance and market dynamics, and when they seem overly optimistic or require stronger supporting evidence. It is also a direct application of professional skepticism, since the auditor must question the logic behind the assumptions without falling into mistrust. Over time, this analysis strengthens judgment and helps determine what is reasonable, what needs to be challenged, and where additional documentation or explanations are necessary.

Why should I be interested in this post?

This experience is especially valuable for anyone interested in audit, accounting, corporate finance, risk, or advisory. It gave me a strong understanding of financial statements, but also taught me discipline, structure, and a more analytical way of thinking. Throughout the year, I learned how to interpret numbers in a real-life context, how to stay organised under pressure, and how to communicate clearly with both clients and team members. What I liked is that these skills are not limited to audit. They can be applied in many areas such as transaction services, FP&A, or even banking. Being able to analyze financial data, understand risks, and form a well-reasoned judgment is useful in almost any finance role, which makes this apprenticeship a great foundation for whatever comes next in a finance-related career.

Useful resources

EY Official website

L’Expert-comptable.com La méthodologie d’audit : Les assertions

Wikipedia EY (entreprise)

Wikipedia Big Four (audit et conseil)

About the author

The article was written in December 2025 by Iris ORHAND (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2021-2026).

▶ Read all articles by Iris ORHAND

The four most dangerous words in investing are, it’s different this time.

December 9, 2025 by Hadrien PUCHE

Financial markets are filled with stories of bubbles, crashes, and periods of extreme optimism or pessimism. Yet human nature remains surprisingly constant, as we are prone to believe that “this time is different.” Sir John Templeton’s famous quote reminds investors that historical patterns, lessons, and cautionary tales are often ignored in the face of conviction, novelty, or excitement.

In this article, Hadrien Puche (ESSEC, Grande École, Master in Management, 2023 / 2027) comments on this quote, exploring why believing that history will not repeat itself can be one of the most dangerous biases in investing.

About Sir John Templeton

Sir John Templeton

Source: John Templeton Foundation

Sir John Templeton was a legendary investor and philanthropist, renowned for his disciplined approach to value investing, a strategy that involves seeking out companies, markets or assets that are deeply undervalued compared to their true long term potential. Rather than following the crowds, value investors analyze the fundamentals of companies (earnings, balance sheets, management…) to make investment decisions.

Born in 1912 in the United States, he built a global investment career by seeking opportunities where others saw only risk. In 1939, at the outbreak of WW2, he borrowed money to buy shares when the market was at its lowest, including shares in 34 bankrupted companies, only 4 of which turned out to be worthless. In 1954, he founded the Templeton Growth Fund, a diversified mutual fund that sought bargains in depressed markets around the world.

Although the exact origin of this quote is unclear, it reflects Templeton’s belief that market cycles tend to repeat themselves. Investors often dismiss historical lessons when conditions seem unprecedented. In periods of optimism, they believe innovation or policy changes make downturns impossible. But Templeton argued this mindset is even more dangerous during crises: each time recession, war or financial turmoil hits, people insist the situation is entirely different from past downturns and ignore proven patterns of recovery. This leads to panic selling and missed opportunities at the moment of greatest long term value. Markets may change, but human psychology and systemic risks tend to repeat in predictable ways.

Analysis of the quote

At the heart of Templeton’s statement lies a timeless observation about human behavior. Investors frequently convince themselves that new technologies, policies, or financial instruments render past risks irrelevant. They see bubbles in real time but rationalize them as unique and unrepeatable events.

This attitude is perilous. By assuming “it is different this time,” investors often take excessive risk, neglect proper analysis, and overvalue assets. History shows that the same patterns, including leverage, speculation, overconfidence, and panic, tend to recur regardless of the era or instrument. The global financial crisis of 2008, the dot com bubble of 2000, and the 1929 crash illustrate the consequences of ignoring these lessons.

Templeton’s advice is simple yet profound. Treat each investment with humility, respect historical precedents, and avoid the hubris of believing novelty exempts you from risk. Recognizing that “this time” may not be different is not a rejection of innovation or change. It is an acknowledgment of patterns, limits, and the laws of risk.

Economic and financial concepts related to the quote

Let’s go into more details over three interesting financial concepts that are linked to this quote.

Market cyclicity

Financial markets naturally tend to move in cycles. Bull markets are followed by corrections; recessions are followed by recoveries. This inherent cyclicity explains why Templeton’s warning is so critical: periods of euphoria are often followed by downturns regardless of how unique the circumstances appear.

This cyclical pattern is most vividly illustrated by the formation of financial bubbles; situations where asset prices rise far above their intrinsic value due to speculation and excessive optimism. Investors frequently underestimate these cycles when past trends have been unusually profitable. For example, during the dot com boom, many believed that technology’s growth would render traditional valuation metrics irrelevant. The result was a speculative bubble followed by a sharp market correction.

As documented by economist Charles P. Kindleberger in his classic work, Manias, Panics, and Crashes: A History of Financial Crises, these bubbles follow a predictable, recurring pattern.

Stages of a market bubble

He argued that financial crises typically progress through phases of displacement, boom, euphoria, and eventually distress and panic. By ignoring history and assuming that novelty exempts them from these fundamental laws, investors risk participating in the formation and painful bursting of the bubble.

Understanding market cyclicity encourages investors to remain vigilant, diversify their holdings, and respect the natural flow of markets even when conditions seem unprecedented.

The Tranquility Paradox and Minsky’s Hypothesis

The tranquility paradox describes a simple but dangerous human habit: when the economy feels stable for long enough, we start believing that this stability will last forever. Rising markets, low volatility, and strong indicators give investors a sense of comfort. They begin to assume that risk has disappeared, that the system is safer than ever, and that the future will look just like the present.

This mindset is exactly what Templeton warned against, and it sits at the center of economist Hyman P. Minsky’s Financial Instability Hypothesis. Minsky’s core idea is counterintuitive: periods of stability create the conditions for instability. In other words, stability is not the end of risk, it’s the beginning of the next one.

The graph below illustrates this dynamic. When things look calm for long enough, investors slowly shift from safe financing to riskier forms, without even realizing it.

Graph of the Minsky moment

Minsky identified three stages:

Hedge financing, the safe zone: Cash flow covers both interest and principal.
Speculative financing, the risky zone: Cash flow covers interest only; principal is rolled over.
Ponzi financing, the danger zone: Cash flow covers neither interest nor principal. Survival depends on continuous borrowing or rising asset prices.

Over time, more and more activity moves into those speculative and Ponzi stages, pushing the system closer to what Minsky called a Minsky Moment, the sudden realization that debts can’t be serviced, asset values drop, confidence collapses, and panic selling begins.

This is the heart of the paradox: calm markets create overconfidence, overconfidence leads to excessive risk taking, and excessive risk taking triggers the crisis. Understanding this pattern helps investors maintain discipline, stay cautious during good times, and avoid falling for the seductive idea that “this time is different.”

Historical bias in personal finance

Templeton’s warning is not limited to market professionals; personal finance and long term investing are equally susceptible to the belief that history will not repeat itself. This risk is rooted in historical bias, a cognitive shortcut where many individuals assume that high past returns on stock indexes, real estate, or other assets will continue indefinitely, often ignoring the possibility of lower future growth or structural changes in the economy.

This bias, a form of extrapolation bias, can be highly dangerous in retirement planning, risk allocation, and portfolio construction. Relying solely on historical equity returns may lead to severe overestimation of future wealth and underestimation of risks during periods of low growth or inflation.

As articulated by economist Burton Malkiel in A Random Walk Down Wall Street, the historical record provides valuable context, but it must not be treated as a definitive forecast. Malkiel’s work supports the idea that, in an efficient market, all available information is already reflected in current prices, meaning past price movements hold no predictive power for the future.

Therefore, Templeton encourages reflection: a disciplined investor balances cautious optimism about the future with a realistic understanding of historical realities, recognizing that past performance of market indexes does not guarantee future results.

My opinion about this quote

Templeton’s insight is essential for both students and seasoned professionals. It serves as a reminder that neither euphoria nor fear should dictate investment decisions. Markets will always fluctuate, and history often rhymes if it does not repeat exactly.

However, it is also true that sometimes conditions are different, and excessive caution can prevent individuals from capitalizing on genuine opportunities. Innovation, technological change, and macroeconomic shifts can justify deviations from historical trends. The challenge lies in distinguishing between real novelty and wishful thinking.

In personal finance, this principle is particularly relevant. Many investors assume that past returns on broad indexes such as the S&P 500 are a reliable guide for the future. Structural changes, low interest rates, and demographic shifts may produce different outcomes.

Market performance of the SP500 over 30 years and different crises

Although global stock markets have historically recovered after crises, this cannot be taken as definitive evidence that they will always do so in the future.

Balancing historical awareness with flexibility and critical thinking is the essence of sound investing.

Why should you be interested in this post?

Templeton’s warning is not only a lesson in investing. It is a lesson of humility, discipline, and critical thinking. Believing “this time is different” can blind both students and professionals to risks, patterns, and opportunities. Studying history, understanding cycles, and acknowledging psychological biases improves decision making in finance and beyond.

Whether you are building a portfolio, analyzing market trends, or planning for the future, this insight encourages you to respect the lessons of the past while remaining vigilant and adaptable.

Useful resources

Investment Wisdom & Discipline

These resources provide practical advice on long term, non emotional investing and avoiding market fads.

Templeton, John. The Templeton Plan.
Malkiel, Burton G. A Random Walk Down Wall Street.

History of Financial Crises

These essential books and papers explain why markets crash and the patterns those crises follow.

Kindleberger, Charles P. (1978). Manias, Panics, and Crashes: A History of Financial Crises.
Minsky, Hyman P. (1992). The Financial Instability Hypothesis, Working Paper No. 74, Jerome Levy Economics Institute.

Market Psychology & Valuation

These sources examine the role of human behavior, psychology, and valuation issues in speculative bubbles.

Shiller, Robert. Irrational Exuberance.
Blanchard, Olivier J., and Mark W. Watson. (1982). “Bubbles, Rational Expectations and Financial Markets.”
Tirole, Jean. (1982). On the Possibility of Speculation under Rational Expectations, Econometrica, 50(5) 1163–1181.

About the Author

This article was written in 2025 by Hadrien Puche (ESSEC, Grande École, Master in Management 2023 / 2027)

My apprenticeship experience as an Executive Assistant in Internal Audit (Inspection Générale) at Bpifrance

December 8, 2025December 8, 2025 by Iris ORHAND

In this article, Iris ORHAND (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2021-2026) shares her professional experience as an Executive Assistant in Internal Audit (Inspection Générale) at Bpifrance (January – December 2025).

About the company

Bpifrance is France’s public investment bank, created in 2012 through the merger of several state-backed institutions, and today it plays a central role in financing and supporting French companies at every stage of their development. With around €60 billion deployed in 2024 and a workforce of roughly 2,300 employees, Bpifrance combines public policy objectives with financial expertise to help businesses innovate, grow, and expand internationally. Its mission goes far beyond lending, as it also provides guarantees, equity investments, innovation funding, export support, and advisory services, making it a one-stop partner for entrepreneurs. Because it operates at the intersection of public funds and financial markets, strong governance and a solid control environment are essential, which is why functions such as Risk, Compliance, Internal Control and Internal Audit play a crucial role in ensuring responsible decision-making, transparency and the long-term protection of public interests.

Logo of Bpifrance

Source: the company.

My internship

In 2025, I completed a 12-month apprenticeship as an Executive Assistant in the Internal Audit Department, known at Bpifrance as “Inspection Générale”. This department is responsible for independently assessing the quality of the bank’s processes, controls and risk management, and for providing recommendations to strengthen the organization’s overall governance. My role combined operational coordination, process improvement and analytical support, which gave me practical exposure to how an internal audit function prepares and delivers missions, follows strict methodologies and ensures the consistency and quality of its work. Through this experience, I had the opportunity to see how internal auditors challenge processes, analyze risks, and help the organization operate more securely and efficiently.

My missions

During my apprenticeship, I contributed to the strategic optimization of internal audit processes, participated in internal audit missions, developed indicators and reporting tools, and implemented and executed a new internal audit quality review process, which is now used to assess the work of more than 30 internal auditors at each end-of-mission review period.

Required skills and knowledge

What I learned

During the year, I contributed to several projects aimed at improving both efficiency and audit quality within the Internal Audit Department. I worked on initiatives that strengthened the organization and standardization of internal audit processes, which helped teams work more consistently across missions. I also took part in internal audit assignments, supporting the different steps of the mission lifecycle and helping prepare and structure the deliverables. Another part of my work involved developing indicators and reporting tools to give management better visibility over activity levels, deadlines and key metrics. Finally, I helped implement and run a new internal audit quality review process, now used by more than thirty internal auditors, which significantly improved consistency, clarity and review readiness across the department.

Financial concepts related to my internship

I present below three financial concepts related to my internship: credit risk and portfolio quality, liquidity risk, and market risk.

Credit risk and portfolio quality

Credit risk refers to the possibility that a borrower may be unable to meet its obligations, which makes it one of the core risks for any bank. In internal audit, the objective is not to take or challenge credit decisions, but to assess whether the credit process itself is robust and well controlled. This involves reviewing how credit approvals are granted, whether delegation levels are respected, and whether all required documentation is complete, coherent and properly justified. Internal Audit also examines how exposures are monitored over time, looking at the quality of follow-up procedures, the detection of early warning indicators and the responsiveness of teams when a situation starts to deteriorate. Together, these elements help determine whether the bank’s credit processes provide a reliable framework for managing risk and maintaining a healthy loan portfolio.

Liquidity risk

Liquidity risk refers to the possibility that a financial institution may not be able to meet its short-term obligations when they fall due. In traditional banks, this risk is often linked to customer deposits, which can fluctuate and create sudden funding pressures. At Bpifrance, liquidity risk exists as well, but in a different form. The organisation does not rely on retail deposits and instead operates with stable funding sources such as the State, the Caisse des Dépôts or long-term market issuances. This structure makes liquidity risk generally less acute than in commercial banks. However, it remains a critical area because Bpifrance must still manage significant cash outflows related to loans, guarantees and investment operations, and must ensure that its funding plans and liquidity buffers remain robust and aligned with its long-term missions.

Market risk

Market risk is the risk of losses arising from changes in market variables such as interest rates, exchange rates or the value of financial assets. In many banks, it is closely linked to trading activities and exposure to volatile financial markets. At Bpifrance, market risk is present but within a much narrower scope. The institution does not operate trading desks and does not take speculative positions. Instead, its exposure comes from treasury management, the valuation of certain financial instruments and, more importantly, the evolution of the value of its equity investments. For this reason, market risk at Bpifrance is less about short-term volatility and more about the prudent management of long-term financial assets and the stability of the institution’s balance sheet over time.

Why should I be interested in this post ?

This role is highly relevant for students interested in risk, governance, internal control, compliance, audit or operational excellence. It provides a concrete view of how financial institutions identify vulnerabilities, strengthen their control environment and improve resilience over time. Working at Bpifrance also adds a meaningful dimension to the experience, because the organisation supports the french economy and operates with a clear public mission. It is also known as a responsible employer with strong working conditions and a culture that values collaboration, learning and employee wellbeing. Altogether, this makes the experience both professionally valuable and personally rewarding.

Useful resources

Bpifrance Official website

About the author

The article was written in December 2025 by Iris ORHAND (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2021-2026).

▶ Read all articles by Iris ORHAND

My internship experience at HKTDC

December 8, 2025December 8, 2025 by Lang Chin SHIU

In this article, SHIU Lang Chin (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2024-2026) shares her professional experience as a summer intern in the Exhibition and Digital Business Department at the Hong Kong Trade Development Council (HKTDC) in Hong Kong, China.

About the company

The Hong Kong Trade Development Council (HKTDC) is a statutory body established in 1966 to promote, assist and develop Hong Kong’s trade. It serves as the international marketing arm for Hong Kong-based traders, manufacturers and service providers, with a strong focus on supporting small and medium-sized enterprises.

HKTDC operates a global network of around 50 offices, including multiple offices in Mainland China, to position Hong Kong as a two-way global investment and business hub. Through international exhibitions, conferences and business missions, it creates business opportunities for companies by connecting them with partners and buyers worldwide.

Logo of HKTDC.

Source: the company.

My internship

I joined HKTDC as a summer intern in the Exhibition and Digital Business Department in Hong Kong, which is responsible for organising large-scale trade fairs and public exhibitions. During my internship at the Hong Kong Trade Development Council (HKTDC), I joined the Exhibition and Digital Business Department, which is responsible for organising large-scale trade fairs and public exhibitions connecting global enterprises and Hong Kong’s business community. The HKTDC is a statutory body that promotes Hong Kong as an international business hub, with over 30,000 exhibitors and 400,000 trade buyers participating in its annual exhibitions.

The department I served in manages both B2B and B2C events, such as the Hong Kong Book Fair, Sports and Leisure Expo, and World of Snacks, which together attracted over 1 million visitors in 2024. These fairs not only generate significant foot traffic and publicity but also foster cross-sector collaboration and cultural exchange. For instance, the Hong Kong Book Fair alone featured more than 760 exhibitors from 30 countries and regions, drawing over 990,000 visitors across seven days at the Hong Kong Convention and Exhibition Centre (HKCEC), with estimated sales revenue exceeding HK$50 million in direct transactions and book sales.

My missions

My main missions were to assist in organising three of HKTDC’s public exhibitions — the Hong Kong Book Fair, World of Snacks, and the Hong Kong Sports & Leisure Expo. I supported the planning, coordination, and on-site execution of these events, including exhibitor liaison, logistics management, and handling visitor enquiries. My responsibilities also involved preparing fair materials, checking booth setups, coordinating with contractors and internal teams, and ensuring each exhibition zone operated smoothly throughout the event period.

In addition to operational tasks, I assisted in marketing and promotional efforts, such as preparing sponsorship materials for the Book Fair Lucky Draw and helping the marketing team create social media posts and reels to attract younger visitors. I also served as an emcee for public seminars and workshops, enhancing event engagement and communication between speakers and the audience. Through these assignments, I gained valuable exposure to event management processes, from preparation to live execution, and developed a deeper understanding of how the HKTDC integrates marketing, logistics, and stakeholder relations to deliver large-scale exhibitions.

Working at an exhibition

Required skills and knowledge

This internship required a combination of soft and hard skills. On the soft-skills side, communication, teamwork, adaptability, and customer orientation were essential, as I interacted with colleagues from different units, exhibitors from diverse backgrounds, and a high volume of visitors within tight time constraints. On the hard skills side, I benefited from having a basic knowledge of marketing and event management, as well as an understanding of how trade fairs support business development and branding.

What I learned

During the internship, I learned how large exhibitions are structured from planning stages to on-site execution and post-event follow-up. I also gained confidence in handling operational issues under pressure, prioritising tasks and communicating clearly with stakeholders who have different expectations and constraints. Ultimately, the experience deepened my interest in marketing, events, and digital business by demonstrating how well-designed exhibitions can create value for both companies and the general public.

Business and economic concepts related to my internship

I present below three business and economic concepts related to my internship: market matching and platforms, experiential marketing, and capacity/operations management. Each helps to understand how HKTDC create value for participants and how my daily tasks are connected to broader economic mechanisms.

Market matching and platforms

HKTDC is a platform that facilitates matching between buyers and sellers, particularly for SMEs looking to reach new markets. Trade fairs reduce search and transaction costs by concentrating information, products and potential partners in one place. In my missions, supporting exhibitor coordination and visitor flow contributed to making this matching process smoother and more efficient.

Experiential marketing

The Hong Kong Book Fair, World of Snacks and the Hong Kong Sports & Leisure Expo are strong examples of experiential marketing in practice. These fairs are not only about selling products; they create immersive experiences through themed zones, demonstrations, workshops and special programmes that engage visitors emotionally and physically. By helping with on-site operations and visitor interactions, I saw how layout, signage, activities and staff behaviour influence the customer journey and can strengthen brand perception and purchase intention.

Capacity and operations management

Large exhibitions require careful capacity and operations management to handle fluctuating visitor numbers while maintaining safety and service quality. Concepts such as peak-load management, queuing, crowd control, and resource allocation are evident in the way entrances, halls, and activity zones are organised. My tasks related to monitoring visitor traffic, guiding flows and coordinating with different teams were directly linked to these operational decisions, which ultimately affect exhibitors’ satisfaction and the overall performance of the event.

Why should I be interested in this post?

For a business student interested in careers related to marketing, events, consulting or trade promotion, an internship at an organisation like HKTDC offers a unique combination of public and private sector exposure. You can observe how strategic objectives are translated into concrete event formats and marketing actions, while developing practical skills in project management, communication, and data-driven decision-making. This type of experience can be a strong asset when applying for roles in event management, business development, corporate marketing or international trade-related positions.

Useful resources

Hong Kong Trade Development Council

HKTDC Hong Kong Book Fair

HKTDC World of Snacks

HKTDC Hong Kong Sports & Leisure Expo

About the author

The article was written in December 2025 by SHIU Lang Chin (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2024-2026).

▶ Read all articles by SHIU Lang Chin.

My Internship as a Junior Consultant in Marketing & Finance Studies at Eres Gestion

December 7, 2025 by Emmanuel CYROT

In this article, Emmanuel CYROT (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2021-2026) shares his professional experience as Junior Consultant in Marketing & Finance Studies at Eres Gestion.

About the company

Eres Gestion is a leading independent player in the French employee savings (épargne salariale) and retirement savings (épargne retraite) markets. The company is part of the Eres Group, which offers a unique open-architecture approach, allowing them to select and combine the best investment funds from various management companies. With over €7 billion in assets under management (as of 12/31/2024), Eres is known for its expertise in designing and implementing profit-sharing schemes, employee share ownership plans, and individual retirement solutions (Plan Epargne Retraite or PER). Eres Gestion places a strong emphasis on socially responsible investing (SRI) and solidarity funds.

Logo of Eres Gestion

Source: the company.

My internship

I worked as a Junior Consultant in charge of Marketing & Finance Studies at Eres Gestion from September 2024 to February 2025 in Paris. I was within the company’s dual-focused research team, bridging the gap between deep financial analysis and market strategy. My role involved quantitative modeling, competitive benchmarking, and the creation of strategic content aimed at supporting sales and marketing efforts. I was reporting to Mirela Stoeva, Head of Studies and Offer at Eres Gestion.

My missions

My primary technical mission involved comprehensive Regulatory Intelligence and Data Analysis, specifically leading the update of the L’Observatoire Européen des Retraites study. This required consolidating data to quantify the evolution of retirement savings assets focusing on the post-Loi Pacte growth of the PER (Plan d’Épargne Retraite). I also conducted crucial Competitive Benchmarking by analyzing various third-party funds based on their retrocession rates to optimize Eres’s offerings. Finally, I supported the firm’s thought leadership on Employee Share Ownership (SBF 120 companies) by drafting expert articles and maintaining all key analytical supports, including the Le panorama de l’actionnariat salarié. I was tasked by the Marketing Director to conduct an internal study on the Retail’s Structured Product Environment in France.

Required skills and knowledge

My experience as a Junior Consultant in Marketing & Finance Studies at Eres Gestion was characterized by a high degree of autonomy and a constant curiosity, which were essential for navigating the complex sector of employee savings (épargne salariale) and employee share ownership. The role required me to conduct in-depth studies on the Pacte Law (Loi Pacte), fund performance analysis, and the valuation of unlisted companies. The intensive work on Excel to model these assets and flows cultivated methodical rigor and discipline, enabling me to become perfectly fluid with numbers and ensure the accuracy of strategic deliverables for the teams.

What I learned

This experience provided me with a comprehensive understanding of the French employee savings and retirement ecosystem, particularly the strategic implications of the Loi Pacte and the development of value-sharing initiatives. I significantly enhanced my skills in quantitative market analysis, competitive benchmarking, and translating complex financial information into accessible, strategic content for both internal and external stakeholders. Working closely with both the finance and marketing teams offered invaluable insight into the product life cycle, from regulatory impact assessment to market positioning.

Business and financial concepts related to my internship

I present below three business and financial concepts related to my internship: The French Retirement Savings Reform (Loi Pacte), Employee Share Ownership Plans (ESOPs), and Structured Products.

The French Retirement Savings Reform (Loi Pacte)

The 2019 Pacte Law (Plan d’Action pour la Croissance et la Transformation des Entreprises) is a major French reform aimed at simplifying the country’s complex retirement savings landscape. Its main component is the creation of the Retirement Savings Plan (Plan d’Épargne Retraite or PER), a unified and portable product replacing previous schemes. The law aimed to channel more of the French population’s savings into long-term investments, including unlisted assets like private equity, to support corporate financing and economic growth.

Employee Share Ownership Plans (ESOPs)

Employee Share Ownership Plans (ESOPs) are incentive programs that allow employees to acquire shares in their company. In France, this is a key component of the employee savings system (épargne salariale). The benefits include aligning the interests of employees and shareholders, increasing organizational commitment, and strengthening the company’s capital structure. Recent French legislation also focuses on developing and simplifying various value-sharing and profit-sharing schemes.

Structured Products

Structured products are complex financial instruments whose performance is linked to an underlying asset, index, or basket of assets. They are typically issued by banks and are essentially a combination of a “riskless” bond (to provide capital protection) and one or more derivative instruments (like options) (to provide market exposure and enhance return). They are customized to offer a specific risk/return profile, but their complexity necessitates thorough internal analysis, which was a core part of my mission.

Why should I be interested in this post?

The experience provides unique Business Intelligence training: you won’t just be supporting one study but rather working on at least two of the four major annual publications, such as the L’observatoire Européen des Retraites or the Le panorama de l’actionnariat salarié. This direct involvement gives you a unique, 360-degree insight into the strategic data, market trends, and competitive landscape of French employee savings and share ownership that few junior roles offer. Furthermore, the requirement for high autonomy and rigorous Excel work on fund benchmarking and asset modeling forces the development of methodical discipline and fluency with numbers necessary for demanding quantitative roles after graduation.

Useful resources

Blog Eres Gestion

H24 Finance

About the author

The article was written in December 2025 by Emmanuel CYROT (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2021-2026).

▶ Read all articles by Emmanuel CYROT.

My Internship as a Product Development Specialist at Amundi ARA

December 7, 2025December 4, 2025 by Emmanuel CYROT

In this article, Emmanuel CYROT (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2021-2026) shares his professional experience as a Product Development Specialist within the marketing team at Amundi ARA under the Senior Product Development Specialist and the Director of Marketing and Communication.

About the company

Amundi Alternative & Real Assets (ARA) is a specialized business line within the Amundi Group dedicated to private market investment solutions, managing approximately €66.1 billion in assets as of late 2025. Formally established in 2016 to consolidate the group’s capabilities, ARA employs a team of roughly 330 professionals operating across eight European investment hubs (including Paris, London, Milan, and Zurich). The division provides institutional and retail investors with access to the real economy through a diverse range of products, including real estate (its largest segment), private debt, private equity, and infrastructure, as well as fund of funds strategies and Hedge Funds UCITS (Undertakings for Collective Investment in Transferable Securities) which are a liquid versions of hedge fund strategies to a broad base of retail investors in Europe.

Logo of Amundi Investment Solutions.

Source: the company.

The Marketing team at Amundi ARA, comprising approximately 15 members (including a robust cohort of interns and apprentices), acts as a specialized bridge connecting Clients with the Sales team. Their primary mandate is to translate complex private market strategies into commercially viable investment solutions tailored for both institutional and retail investors. The team utilizes a highly structured support model where every specific area of expertise is represented by a dedicated “Investment Specialist,” each of whom is directly supported by an assigned intern.

My internship

The internship lasted 6 months between March and August 2025 and was reporting directly mostly reporting to the Senior Product Development Specialist in the team and monitoring new fund launches across to all teams within ARA: Sales, structuring, Investments Teams, Business Development, etc.

My missions

My primary mission was to participate in the conception, structuring, and launch of two new funds within the ARA range. To support this, I conducted detailed market analyses and competitive studies, specifically benchmarking French and Luxembourgish evergreen funds using professional terminals like Preqin, Pitchbook, and Bloomberg, which provided access to crucial data on performance, management fees, Assets under management, redemption gates, lock-up periods, etc.

I was also responsible for the collection, analysis, and dissemination of sectoral Business Intelligence data. I produced reports designed for the Sales, Marketing, Management teams to aid in decision-making for meetings internally and externally.

Another major part of my mission was the creation and updating of marketing materials, including pitchbooks, brochures, and product sheets. This ensured that the sales teams had accurate and compelling documentation to promote the funds to investors.

Required skills and knowledge

This role required strong communication and organizational skills to coordinate effectively across diverse teams and manage product launch deadlines. Intellectual curiosity and discipline were essential for synthesizing complex market studies without external AI assistance, alongside the ability to filter relevant business intelligence from general noise. Finally, technical proficiency in Excel and data providers (Bloomberg, Preqin, Pitchbook) was critical, coupled with a rapid understanding of the specificities of Private Assets vehicles.

What I learned

Through the benchmarking and product launch support, I gained a systematic understanding of how private asset funds are structured and positioned in a competitive market. I developed the ability to assess market needs and translate them into product features.

My contribution helped streamline the flow of Business Intelligence between the structuring and sales teams. I also deepened my understanding of the regulatory and commercial requirements for launching funds in the European market. Overall, this internship strengthened my skills in market analysis, product marketing, and strategic communication.

Financial concepts related to my internship

I present below three financial concepts related to my internship: Evergreen funds, UCITS hedge funds, and Private Equity funds.

Evergreen Funds

Unlike traditional closed-ended Private Equity or Private Debt funds with finite terms and J-curve effects, evergreen funds function as semi-liquid open-ended vehicles allowing for continuous capital recycling. My work focused on benchmarking the liquidity management mechanisms of French and Luxembourgish vehicles such as ELTIF 2.0, which is the European Long-Term Investment Fund regulation designed to increase retail and institutional investor participation in long-term, illiquid assets. I analyzed key technical features including NAV (Net asset Value) calculation frequency, the calibration of redemption gates, notice periods, and the implementation of liquidity sleeves to mitigate the asset-liability mismatch inherent in offering liquidity on illiquid underlying assets.

UCITS Hedge Funds

Alternative UCITS (often referred to as “Liquid Alts”) democratize access to hedge fund strategies (e.g., Long/Short Equity, Global Macro) by wrapping them in a regulated UCITS framework. My benchmarking work involved analyzing how these funds offer weekly or daily liquidity and high transparency to investors, unlike their offshore Cayman or BVI counterparts which often impose lock-up periods and gates.

Private Equity Funds

A central part of this role involved the strategic overhaul and tailoring of investor pitchbooks and marketing materials. This required translating complex fund structures and performance data into clear, compelling narratives for both institutional and retail investors. In that context, I learned the key concepts of Private Equity Funds alongside helpful formations that were provided by Amundi. This allowed me to familiarize well with metrics used to analyze PE funds (DPI, TVPI, J-Curve…) and different strategies (Mid-market, Impact).

Why should I be interested in this post?

This post is for you if you want to be at the forefront of asset management, specializing in the growing world of Private Markets (Private Equity, Infrastructure, Impact). It’s an excellent chance to learn deeply about product structuring and the commercial lifecycle of funds, all within a honestly great, supportive environment that ensures you gain hands-on experience and valuable strategic insight.

Useful resources

Opalesque Alternative Market Briefing

Citywire

France Invest

About the author

The article was written in December 2025 by Emmanuel CYROT (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2021-2026).

▶ Read all articles by Emmanuel CYROT.

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

December 3, 2025 by Emanuele BAROLI

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

Context and objective

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

What are interest rates

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

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

M&A industry — a definition

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

Use of leverage

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

How interest rates impact the M&A industry

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

Evidence from 2021–2024 and what the chart shows

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

Source: Fed.

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

What does academic research say

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

Useful resources

Academic articles

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

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

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

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

Financial data

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

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

OECD Data Long-term interest rates

About the author

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

▶ Read all articles by Emanuele BAROLI.

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

December 3, 2025 by Emanuele BAROLI

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

What is an Info Memo

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

Executive summary

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

Key investment highlights

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

Market overview

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

Business overview

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

Historical financial performance and budget

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

Business plan

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

Appendix

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

Why should you be interested in this post?

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

Useful resources

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

DealRoom How to Write an M&A Information Memorandum

About the author

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

▶ Read all articles by Emanuele BAROLI.

“It’s not whether you’re right or wrong that’s important, but how much money you make when you’re right and how much you lose when you’re wrong.” – George Soros

December 3, 2025 by Hadrien PUCHE

In financial markets, everyone wants to be right. The temptation to make accurate predictions, about earnings, interest rates, recessions, or stock prices, is universal. But as George Soros reminds us, accuracy alone is meaningless. What truly matters is how much you profit when you’re right, and how much you lose when you’re wrong.

This quote challenges one of the deepest misconceptions in trading: the belief that success depends on predicting the future. In reality, trading success mostly depends on risk management, position sizing, and the discipline to adjust when the market proves you wrong.

About George Soros

George Soros
Warren Buffett

Source: EU

George Soros (born in 1930) is a Hungarian-American investor and philanthropist. He founded Soros Fund Management, a global macro hedge fund known for making large, directional bets across currencies, bonds, equities, and commodities.

Soros became globally famous in 1992 when he “broke the Bank of England” by shorting the British pound, a trade widely reported to have earned over $1 billion.

The European Exchange Rate Mechanism (ERM) was created to stabilize European currencies ahead of the future monetary union by keeping exchange rates within narrow fluctuation bands. When the UK joined, it agreed to maintain the pound within this band, but entered at a rate that many considered overvalued.

Seeing this imbalance, George Soros spent months building a large short position against the pound. On “Black Wednesday” in 1992, the British government failed to defend the currency through interest-rate hikes and interventions, forcing a devaluation. Soros reportedly earned over $1 billion and became known as “the man who broke the Bank of England.”

Not all of Soros’s trades were successful. In 2016, he reportedly lost close to $1 billion after wrongly predicting that markets would fall following Donald Trump’s election.

Beyond trading, Soros developed the theory of reflexivity, which argues that markets are shaped by feedback loops between perceptions and fundamentals. His philosophy emphasizes uncertainty, adaptability, and the psychological drivers behind market behavior.

The context behind this Quote

This quote is not actually from Soros. It comes from Stanley Druckenmiller—Soros’s former chief strategist—in The New Market Wizards (1994). Druckenmiller explains that the most important lesson he learned from Soros was not the importance of being right, but of structuring trades so that being right pays off and being wrong costs little.

Book cover of the new market wizards

The quote therefore reflects Soros’s investment philosophy: markets cannot be predicted with certainty, so success depends more on managing risk than on forecasting.

This mindset is foundational to modern risk management and a key reason Soros is considered one of the most influential investors of the past century.

Analysis of the Quote

The quote captures three essential ideas:

asymmetric returns
risk management
intelligent position sizing

Being right doesn’t matter unless it pays. For example, even if you forecast Nvidia’s earnings perfectly, you may still fail to profit because:

You may not have any position.
Your position may be too small.
The market may behave irrationally.
Losses on other trades may outweigh this one win.

This is the essence of risk management: structuring positions so that winners meaningfully contribute to performance while losers remain contained.

Let’s introduce three key financial ideas that relate to this quote.

1. Diversification and Position Timing

Even if your analysis is correct, the market might not react as expected, or not at the right time. This is where the distinction between trading and investing matters.

Soros’s quote speaks the language of trading: position sizing, timing, and controlling downside on each bet.

Investing, by contrast, relies less on precise timing and more on diversification, which reduces exposure to unpredictable events and smooths returns across different regimes.

Mathematically, diversification lowers portfolio variance because asset returns are imperfectly correlated. Even when individual positions behave unpredictably, a well-constructed portfolio can achieve far better risk-adjusted results than any single trade. In that sense, diversification plays a similar role for investors as stop-losses and disciplined position sizing do for traders: it manages the impact of being wrong.

The following graph illustrates how adding more independent positions reduces overall portfolio risk.

A graph representing the overall risk of a portfolio as a function of the number of positions

2. Avoid cutting winners to reinforce losers

This behavioral trap affects most investors. Soros’s approach is the opposite:

cut losing positions quickly
let winners run

Yet, due to loss aversion (as formalized by Kahneman & Tversky (1979) in Prospect Theory), investors often do the reverse:

sell winners too early
hold losers too long

This pattern is well-documented in the literature. Shefrin & Statman (1985) termed it the disposition effect: the systematic tendency to “sell winners too early and ride losers too long.” The emotional discomfort of realizing a loss often outweighs the rational need to exit a bad position.

Momentum works partly for this reason. Rising prices attract reluctant investors who delayed selling their winners, amplifying trends; meanwhile, stubbornly held losers can drift downward for longer than fundamentals alone would justify.

3. Quantitative trading: the power of averaging out

Quantitative trading is built on making many small, systematic bets with a positive expected value. The goal is not to win every trade, but to win more (or bigger) on average.

This is the practical application of the idea that:

being right occasionally with large wins
is more valuable than
being right frequently with small gains.

This also echoes Jesse Livermore’s famous line: “The market is never wrong, only opinions are.” (link)

My view on this quote

One limitation of Soros’s statement is that it implicitly assumes the reader is an active trader. In reality, today’s markets are dominated by algorithms, quantitative models, and high-frequency strategies, an environment in which most individuals are unlikely to outperform professional traders. For traders, Soros’s point is straightforward: you will often be wrong, so what matters is how you size positions and manage risk when you are.

At a literal level, the quote may also seem paradoxical: you cannot know in advance which trades will be winners or losers. But the message isn’t about prediction, it’s about discipline.

This distinction becomes especially clear when you contrast trading with investing.

Traders live in a world of short-term uncertainty and constant position adjustments, where the asymmetry between gains and losses determines survival.
Investors, on the other hand, think in years, not minutes. They rely less on timing and more on letting fundamentals and compounding work over time. For them, the “how much you lose when you’re wrong” part translates into diversification, staying invested, and avoiding irreversible mistakes rather than optimizing each individual decision.

Seen this way, Soros’s line applies to both groups, just at different scales: traders manage outcomes trade by trade; investors manage them across decades. Either way, the principle holds: success depends less on being right and more on controlling the cost of being wrong.

Why should you care about this quote ?

The lesson is not about predicting markets or mastering sophisticated position sizing. The deeper message is:

Don’t rely on being right.
Structure your trades so that mistakes are limited and successes compound.

A diversified ETF strategy naturally achieves this.
In cap-weighted indices:

winners grow in weight
losers shrink, limiting their impact
the portfolio trends with long-term market growth

This simple, robust approach aligns with Soros’s philosophy: control the downside, let the upside work.

Useful Resources

Soros, George (1987). The Alchemy of Finance. Soros explains reflexivity, asymmetry of payoff, and his macro-trading framework.
Schwager, Jack (1994). The New Market Wizards. Contains Stanley Druckenmiller’s interview where the famous quote originates.
The Disposition to Sell Winners Too Early and Ride Losers Too Long: Theory and Evidence — Hersh Shefrin & Meir Statman (Journal of Finance, 1985, 40(3), 777–790).
Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263–291.

To learn more about Soros’s famous 1992 British pound trade:

Eichengreen, Barry & Wyplosz, Charles (1993). “The Unstable EMS.” A leading academic analysis of why the European Exchange Rate Mechanism (ERM) became vulnerable and how the 1992 crisis unfolded.
Bank of England (1993). Report on the Withdrawal of Sterling from the ERM. Official institutional account of the events surrounding Black Wednesday.

About the Author

This article was written in December 2025 by Hadrien Puche (ESSEC, Grande École Program, Master in Management – 2023–2027).

At what point does diversification becomes “Diworsification”?

December 1, 2025November 30, 2025 by Yann TANGUY

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

The Concept of Diworsification

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

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

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

Practical Example

Let’s assume there are two investors.

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

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

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

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

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

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

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

Correlated portfolio returns over volatility

Non-Correlated portfolio returns over volatility

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

Target number of assets for a diversified portfolio

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

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

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

Crossover from Diversification to Diworsification

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

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

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

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

Causes of Diworsification

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

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

How to avoid Diworsification

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

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

Conclusion

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

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

Why should I be interested in this post?

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

Useful resources

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

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

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

About the author

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

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

November 29, 2025 by Tianyi WANG

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

Introduction

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

Snowball Structure Product .

Source: public market data.

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

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

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

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

Who buys Snowball products?

Snowball products are purchased mainly by:

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

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

What is a Snowball product?

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

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

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

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

Market behavior: what does it mean?

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

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

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

Key barriers in Snowball products

Knock-out (Autocall) barrier

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

Knock-in barrier

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

How Snowball payoffs work

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

There are three typical outcomes:

Knock-out outcome (early exit)

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

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

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

Knock-in triggered (risky outcome)

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

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

Why Snowball products are risky

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

Key risks include:

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

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

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

As a result:

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

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

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

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

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

Snowball Take Out.

Source: public market data.

How market behavior affects Snowball performance

Volatility

High volatility increases the likelihood of crossing both barriers.

Trend direction

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

Liquidity and investor flows

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

Snowball knock-in chart.

Source: public market data.

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

Conclusion

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

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

About the author

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

My internship Experience at Bloomberg

December 8, 2025November 28, 2025 by Zineb ARAQI

In this article, Zineb ARAQI (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2021-2025) shares her professional experience as a Summer Intern at Bloomberg LP within the Sales & Analytics department

About the company

Bloomberg L.P. is a leading global provider of financial data, analytics, media and software services. The firm was co-founded on October 1, 1981 by Michael Bloomberg along with Thomas Secunda, Duncan MacMillan and Charles Zegar. Headquartered in the Bloomberg Tower at 731 Lexington Avenue, New York, the company has expanded massively since its founding, as of 2025, it operates globally with over 26,000 employees across roughly 159 offices in more than 69 countries.

One unique aspect of Bloomberg L.P. is that it is a privately held company. It has never gone through an IPO and remains majority-owned by Michael Bloomberg. Being non-public allows Bloomberg to focus on long-term strategic goals rather than quarterly earnings pressure, reinvesting heavily in data innovation, infrastructure, and client service.

Bloomberg’s flagship product is the Bloomberg Terminal, a real-time financial data and analytics platform that remains central to the workflows of banks, asset managers, hedge funds, and other institutional investors worldwide. The Terminal enables users to access live market data, historical price series, fixed-income yield curves, equity and credit analytics, news feeds, messaging.

Fun fact: The Bloomberg terminal pioneered real-time communication in financial markets with the launch of IB Chats.

Over time, Bloomberg has diversified beyond terminals. The group now encompasses a broad media and information-services ecosystem: a global news agency, television and radio networks, newsletters, and research & analytics services for legal, tax, government, and energy sectors.

Financially, Bloomberg remains a powerhouse in its industry. The company’s main competitors in the financial information & analytics industry include Refinitiv, FactSet Research Systems, Dow Jones & Company, and other specialized vendors such as Capital IQ. However, the terminal has been deeply embedded in financial institutions for decades. It’s breadth of data, analytics, and real-time functionality make it the most comprehensive and indispensable platform in the industry

Thanks to its combination of real-time data services, analytics platforms, global media reach, and multi-asset coverage, Bloomberg L.P. occupies a central place in financial markets infrastructure powering investment decisions, regulatory research, corporate finance, media coverage and more.

Beyond its core business, Bloomberg is also recognized for its major contribution to global philanthropy through Bloomberg Philanthropies. Founded by Michael Bloomberg, the foundation donates billions of dollars to public health, climate action, education, the arts, and government innovation. It is one of the largest philanthropic organizations in the world. In 2024, Bloomberg Philantropies invested $3.7 billion around the world. Over his lifetime, Mike has so far given $21.1 billion to philanthropy.

Logo of Bloomberg.

Source: the company.

I completed a 10-week internship in Bloomberg’s Sales & Analytics department, at the very heart of global capital markets. Sales & Analytics departments, are often called the bread and butter of the company

This division sits at the heart of Bloomberg’s business model, as it supports clients using the Bloomberg Terminal and ensures they can fully leverage its data, analytics, and market intelligence. During my internship, I rotated between the Sales and Analytics teams, which allowed me to understand both the technical problem-solving side and the commercial relationship-building side of the job. We also followed intensive finance and product courses, giving all interns, regardless of previous background, a strong foundation. One of the aspects I loved most was the diversity of profiles in the cohort: many interns came from humanities or non-quantitative degrees and had never touched a terminal before, yet the team valued curiosity, communication, and adaptability just as much as financial knowledge. This made the experience dynamic, collaborative, and intellectually stimulating.

My internship experience as a summer intern at Bloomberg HQ, London

My Missions

From day one, I was immersed in a fast-paced, data-driven environment where real-time information, market microstructure, and client strategy intersect. The internship, ranked among Glassdoor’s Best Internships for 2025, gave me direct exposure to the workflows of traders, portfolio managers, and investment strategists across multiple asset classes.

Throughout the summer, I supported clients across Fixed Income, Equities, and FX, analysing their use cases to optimise workflows on the Bloomberg Terminal. I handled incoming requests, troubleshot data discrepancies, mapped liquidity fragmentation across venues, and helped clients interpret complex analytics such as yield curve construction, fair-value curves, relative-value screens, and multi-factor equity models. Working in real time with market participants strengthened my ability to think fast, communicate clearly, and translate technical concepts into actionable insights for users.

I also worked on several technical initiatives. I placed second in the BQuant project by engineering a Python model to forecast dividend behaviour using historical regimes, percentile-based distributions, volatility clustering patterns, and price-dividend spread diagnostics. With my team, I also developed a UN SDGs portfolio alignment tool, building a scoring engine that maps holdings to SDG targets using company-level disclosures, sector baselines, and ESG controversy filters helping portfolio managers assess the sustainability profile of their books.

On the product side, I pitched a feature enhancement for the Terminal: an audio-pronunciation function for client names to support global coverage teams and reduce communication friction. The proposal was selected for implementation after technical feasibility review. I additionally explored workflow gaps between Sales and Enterprise Solutions, analysing how data pipelines, entitlement systems, and API usage influence client onboarding and retention.

Beyond the technical work, the internship offered unforgettable moments: meeting Mike Bloomberg, attending senior leadership sessions on data, AI, and market evolution, and joining client visits to observe how relationships are built at scale in a highly competitive industry. This experience placed me at the intersection of analytics, markets, and client strategy, sharpening both my technical capabilities and my commercial intuition.

Required skills and knowledge

My role required a combination of hard and soft skills. On the technical side, a strong understanding of capital markets was essential particularly yield curve mechanics, equity valuation logic, and the functioning of foreign exchange markets. I relied heavily on analytical skills to diagnose client issues, read market diagnostics, and navigate complex datasets across functions like YAS (bond pricing), EQS (equity screening), and FXFM (FX forwards). In parallel, I needed strong communication skills to articulate solutions clearly, ask precise diagnostic questions, and adapt technical explanations to traders, PMs, or analysts under time pressure. The role also required resilience, curiosity, and the ability to build trust quickly with clients. This combination of market knowledge, fast problem-solving, and client-centric communication was central to succeeding in Sales & Analytics.

What I learned

The internship taught me the importance of deep technical knowledge when speaking to clients, especially traders who rely on speed and accuracy. I learned how the Bloomberg Terminal integrates data, analytics, and market infrastructure into a seamless workflow, and how small optimizations can materially improve a client’s decision-making process. I also discovered the strategic role of Sales & Analytics in connecting client needs with product development, which reinforced my interest in financial technology and market analytics.

Financial concepts related to my internship

I present below three financial concepts related to my internship. These concepts reflect the analytical tools and market mechanisms I interacted with daily, and demonstrate how my work required understanding both financial theory and real-world applications.

Yield Curves and Term Structure of Interest Rates

A major part of supporting Fixed Income clients involved helping them analyse the term structure of interest rates. I frequently used the Bloomberg function YCRV, which constructs and visualizes sovereign yield curves using benchmark bonds or swaps. Understanding the shape of the curve upward sloping, flat, or inverted allowed clients to assess market expectations for inflation, monetary policy, and recession risk. My role was to explain how yield curves are calibrated, why certain instruments are used as pillars, and how shifts in the curve affect duration, convexity, and bond valuation. This concept was central to my interactions with rates traders and portfolio managers.

Relative Value Analysis in Equities

Equity clients often asked about screening methods to identify mispriced securities. I worked extensively with EQRV (Equity Relative Value), which compares companies across valuation metrics such as EV/EBITDA, P/E ratios, or free-cash-flow yield. Mastering this concept was essential to explain how traders and analysts use relative value strategies to detect pricing discrepancies within a sector or region. My work involved guiding clients through constructing peer sets, interpreting valuation z-scores, and integrating forward earnings revisions into their screens, illustrating how quantitative equity analysis informs investment decisions.

FX Forward Pricing and Interest Rate Parity

In FX, one of the most frequent topics was the pricing of forward contracts. Using functions like FXFW and FXFM, I helped clients compute forward points, measure carry, and understand deviations from covered interest rate parity. The concept links interest rate differentials to expected currency movements and determines the fair value of forward exchange rates. My role required explaining how forward curves are built, how central bank rate expectations feed into pricing, and why liquidity varies across tenors. This concept was crucial when assisting FX traders and corporate clients in hedging currency exposures.

Useful resources

Bloomberg

Bloomberg Rates & Bonds

Bloomberg Currency Implied Yield Indices Methodology (PDF)

Bloomberg Global roles

Bloomberg Bloomberg Philanthropies

Corporate Finance Institute Bloomberg functions & shortcuts list

Financial data

LSEG (Refinitv)

Factset

Dow Jones & company

Internships

Glassdoor A Guide to the Best Internships

About the author

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

▶ Read all articles by Zineb ARAQI.

About the company

My work-study program

My missions

Required skills and knowledge

What I learned

Financial concepts related to my internship

The lifecycle of a financial transaction

Net Asset Value (NAV)

Operational risk management

Why should I be interested in this post?

Related posts on the SimTrade blog

Useful resources

About the author

Introduction

Practical Example: Analysis of the S&P 500 Index

Prices

Returns

Distribution of returns

Standard deviation of the distribution of returns

Annualized volatility

Annualized mean

Application: Estimating the Future Price Range of the S&P 500 index

Historical Volatility

Unconditional Volatility

Rolling historical volatility

Alterative measures of volatility

Parkinson volatility

Implied volatility

ARCH model

Stochastic volatility models

Applications in finance

The normal distribution

Brownian motion

Modern Portfolio Theory (MPT)

Capital Asset Pricing Model (CAPM)

Why should I be interested in this post?

Related posts on the SimTrade blog

Risk and Volatility

Portfolio Theory and Models

Financial Indexes

Useful Resources

Academic research

Other

About the author

About the concept

How the lemming effect appears in markets

Related financial concepts

Diversification

Risk and return

Market efficiency

Keynes’ beauty contest

GameStop story

Why should I be interested in this post?

Related posts on the SimTrade blog

Useful resources

About the author

Overview of the time value of money

Future value

Present value

Applications of the time value of money

Why should I be interested in this post?

Related posts on the SimTrade blog

Useful resources

About the author

Introduction

The Liquidity Continuum: Solving the “J-Curve” Problem

The European Landscape: The Rise of ELTIF 2.0

Market Data as of 2025 (Morgan Lewis)

Structural Engineering: Liquidity

Why should I be interested in this post?

Related posts on the SimTrade blog

Useful resources

About the author

Why It Is Important to Stay Up to Date with Financial News

How to Stay Up to Date with Financial News

Why Staying Informed Matters

Use Traditional Media

Newspapers

Digital Tools & Apps

Subscribe to Quality Newsletters