Why Retail Option Strategies Underperform: Payoffs, Probabilities, and the Cost of Speculation

Alexandre LANGEVIN

In this article, Alexandre LANGEVIN (ESSEC Business School, Global Bachelor in Business Administration (BBA), 2022-2026) examines why retail option strategies frequently underperform — that is, generate returns below a passive buy-and-hold benchmark or lose money outright — despite offering payoff profiles that appear attractive on paper. The article explains the structural mechanics behind four common strategies, identifies the sources of systematic drag, and illustrates how the gap between theoretical upside and realized performance emerges even before behavioral factors are considered.

Introduction

Options are among the most versatile yet complex instruments in financial markets. They can hedge risk, generate income, or express a directional view with defined downside (Hull, 2012). Yet a growing body of evidence suggests that retail investors who trade options systematically underperform both the market and their own expectations (Barber and Odean, 2000; de Silva, So and Smith, 2024). The question is not whether options are useful tools; they plainly are. The question is whether the specific strategies retail investors tend to favor are structurally suited to delivering the outcomes they expect.

The answer, in most cases, is that they are not. The gap between the payoff diagram and realized performance is not primarily attributable to adverse price realizations. It is embedded in the mechanics of how options are priced, how time erodes their value, and how the probability of profit is systematically lower than the shape of the payoff curve implies. Understanding these mechanics is the first step toward using options more deliberately.

How an Option Payoff Works

An option gives its buyer the right, but not the obligation, to buy (call) or sell (put) an underlying asset at a fixed price — the strike — on or before expiry. The buyer pays a premium for this right. At expiry, the profit or loss is determined entirely by the final price of the underlying relative to the strike.

For a long call: the option expires worthless if the underlying finishes below the strike. Above the strike, the buyer receives the difference between the final price and the strike. The buyer pays the premium upfront when entering the position; profit or loss at expiry therefore equals the intrinsic value minus this initial cost. The breakeven is therefore the strike plus the premium. For a long put, the logic is symmetric: the option has value if the underlying falls below the strike, and the breakeven is the strike minus the premium. Throughout this article, net profit or loss refers to the outcome at expiry after accounting for the premium paid upfront. The net profit or loss formula for a long call is:

Long call payoff formula

These payoff diagrams look appealing. The downside is capped at the premium paid; the upside is theoretically unlimited for calls and capped at the strike price minus the premium paid for puts (since the underlying cannot fall below zero) for puts. What the diagram does not show is the probability attached to each outcome.

The Four Strategies: Structure and Mechanics

The Excel model accompanying this article covers four strategies commonly used by retail investors. Each illustrates a distinct structural trade-off.

The following four strategies represent the most common approaches used by retail option traders, ranging from directional speculation to income generation.

Long Out-of-the-Money (OTM) Call. An option is out-of-the-money when exercising it immediately would produce no value — the strike is above the current price for a call, or below it for a put. In the illustrative example, SPY trades at $540. A call with a $560 strike costs $5.20. Breakeven is $565.20, requiring a 4.7% move in the underlying just to recover the premium. Below $560 at expiry, the entire $5.20 is lost. Above $565.20, the trade turns profitable. The net profit or loss is positively skewed and theoretically unlimited, which explains its appeal. The structural problem is that an OTM call requires the underlying to move by more than the market already expects, because the premium reflects that expected move.

A worked example illustrates the arithmetic. Suppose SPY closes at $575 at expiry. The intrinsic value of the $560 call is $575 − $560 = $15. Net profit per share = $15 − $5.20 = $9.80, or $980 per contract (one contract = 100 shares) — a return of 188% on the premium paid. Now suppose SPY closes at $550 instead. The call expires worthless; the loss is the full premium of $5.20 per share, or −$520 per contract. These two outcomes — $980 profit vs. −$520 loss — illustrate the asymmetry. The upside is real, but the full loss scenario is far more probable: SPY must rise more than 4.7% simply to break even, and more than that to generate meaningful profit.

Long OTM Put. A $520 put on SPY trading at $540 costs $4.80. Breakeven is $515.20, requiring a 4.6% decline. Like the OTM call, the put must overcome both the out-of-the-money gap and the premium cost before generating any return. In calm markets, the probability of hitting breakeven by expiry is well below what the payoff diagram implies.

Bull Call Spread. Buying the $550 call and selling the $570 call reduces the net cost to $5.30 (long premium $8.50 minus short premium $3.20). Breakeven falls to $555.30, and maximum profit is capped at $14.70 per share if SPY finishes above $570. The spread trades unlimited upside for a lower entry cost and a higher probability of profit compared to the naked call. The payoff formula is:

Bull call spread payoff formula

It is a more disciplined structure, but it still requires a meaningful directional move, and the profit ceiling is fixed regardless of how far the underlying moves above the upper strike.

Covered Call. An investor who holds 100 shares purchased at $540 sells a $560 call for $5.20. Breakeven falls from $540 to $534.80. If SPY finishes below $560, the investor keeps the premium and the position. If SPY finishes above $560, the shares are called away and the investor captures only $25.20 per share in total profit, regardless of how far the stock has risen. The strategy generates income but structurally caps the upside.

Figure 1. Payoff diagrams at expiry for the four strategies (illustrative inputs).
Option payoff diagrams
Source: computation by the author.

The Structural Sources of Underperformance

Three structural factors — theta decay, the volatility risk premium, and breakeven mechanics — explain why retail option strategies systematically underperform, independently of any behavioral bias.

Theta decay. Options lose value over time as expiry approaches. This decay is not linear; it accelerates sharply in the final weeks before expiry. A 30-day option that has lost 30% of its value in the first two weeks may lose the remaining 70% in the last two. Retail investors who buy short-dated options and hold them without a clear exit plan are running against the clock. The underlying must move quickly and decisively; a slow drift in the right direction is often not enough to overcome the daily erosion in time value. De Silva, So and Smith (2024) document that retail investors systematically purchase options ahead of anticipated volatility spikes, only to suffer double-digit percentage losses as volatility collapses and time value erodes post-announcement.

The volatility risk premium. Implied volatility — the level of volatility priced into an option’s premium — is persistently higher than realized volatility on average. This gap is the volatility risk premium, and it represents a systematic transfer of wealth from option buyers to option sellers. When you buy an option, you are paying for a level of volatility that, on average, does not materialize. Market makers and institutional sellers collect this premium consistently over time; retail buyers pay it. Broadie, Chernov and Johannes (2009) show that the apparently large returns to put-selling strategies are fully explained by compensation for bearing this volatility risk — what looks like alpha is largely a risk premium that option buyers are systematically on the wrong side of.

Breakeven mechanics. The breakeven calculation makes the structural difficulty explicit. For a long OTM call with a 4.7% breakeven requirement, the underlying must rise by 4.7% before expiry simply to recover costs. Historically, the probability of a large-cap equity index moving 5% or more in a given month is well below 50%. The payoff diagram shows what happens if the move occurs; it does not show how often it does. Most retail option buyers look at the profit region of the diagram without adequately pricing in the probability of reaching it. Barber and Odean (2000) document a closely related pattern in equity trading: retail investors systematically overestimate their ability to generate above-market returns, a bias that is amplified in options markets by the apparent leverage and lottery-like payoffs.

Transaction costs and taxes. A fourth source of drag, often overlooked, is the cost of trading itself. Retail investors typically pay per-contract commissions, and bid-ask spreads on options are wide relative to the premium — particularly for short-dated or illiquid contracts. On a $5.20 premium, a $0.10 spread represents nearly 2% of the position cost before any price move occurs. Capital gains taxes on short-term option profits further reduce net returns. These costs do not appear on payoff diagrams but compound the structural disadvantages described above.

Excel Model

The Excel model below contains four sheets — Long OTM Call, Long OTM Put, Bull Call Spread, and Covered Call — each following the same structure: an input table with yellow input cells, a payoff table across a range of expiry prices, and a payoff diagram with a breakeven marker. All inputs are illustrative and can be modified freely. The payoff columns and chart update automatically when inputs change.

Figure 2. Bull Call Spread sheet: inputs table and payoff formula.
Bull Call Spread inputs table
Source: computation by the author.

Download the Excel file

Why should I be interested in this post?

Options appear in equity research, derivatives desk interviews, and structured product discussions at banks and asset managers. Beyond the professional context, understanding why certain strategies structurally underperform is relevant for anyone who trades independently or advises clients on portfolio construction. The payoff diagram is the beginning of the analysis, not the end. Knowing how to read the probability distribution behind it is what separates informed use from speculation.

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Useful resources

Academic research

Barber, B.M. and Odean, T. (2000) Trading Is Hazardous to Your Wealth: The Common Stock Investment Performance of Individual Investors, Journal of Finance, 55(2), 773-806. Available at https://faculty.haas.berkeley.edu/odean/papers%20current%20versions/individual_investor_performance_final.pdf

de Silva, T., So, E.C. and Smith, K. (2024) Losing is Optional: Retail Option Trading and Expected Announcement Volatility, Review of Finance, 30(2), 489-535. Available at https://www.timdesilva.me/files/papers/losing_optional.pdf

Broadie, M., Chernov, M. and Johannes, M. (2009) Understanding Index Option Returns, Review of Financial Studies, 22(11), 4493-4529. Available at https://business.columbia.edu/sites/default/files-efs/pubfiles/3964/broadie_chernov_johannes.pdf

Hull, J.C. (2012) Options, Futures, and Other Derivatives, 8th edition, Pearson.

About the author

This post was written in April 2026 by Alexandre LANGEVIN (ESSEC Business School, Global Bachelor in Business Administration (BBA), 2022-2026). Alexandre is interested in derivatives markets, options trading, and quantitative approaches to portfolio analysis.

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The Shiller P/E (CAPE) Ratio: Measuring Long-Run Market Valuation

Alexandre LANGEVIN

In this article, Alexandre LANGEVIN (ESSEC Business School, Global Bachelor in Business Administration (BBA), 2022-2026) explains the Shiller P/E ratio, also known as the CAPE ratio: a valuation tool that adjusts for the business cycle to give a more reliable picture of whether equity markets are cheap or expensive.

Introduction

Every investor knows the price-to-earnings (P/E) ratio: divide the current market price by earnings per share and you get a simple measure of how much the market is paying for each dollar of profit. It is one of the most widely quoted metrics in equity analysis. But it has a structural flaw: earnings are cyclical. In a recession, they collapse, making the P/E look artificially inflated even when prices have barely moved. In a boom, they surge, making markets appear cheap when they may not be. A single year of earnings is a poor basis for a long-term valuation judgment.

Robert Shiller, a Yale professor and 2013 Nobel laureate in economics, proposed a simple fix. His ratio replaces one year of earnings with the average of the past ten years, adjusted for inflation. The result is a smoother, more stable measure of valuation that filters out the noise of the business cycle and allows for meaningful comparisons across time.

The Problem with Standard P/E

Consider the S&P 500 in 2009, shortly after the financial crisis. Prices had fallen sharply, but earnings had fallen even further, with many companies reporting losses. Standard P/E spiked above 100 at certain points, not because markets were expensive, but because the denominator had collapsed. An investor reading that number at face value might have concluded the market was dangerously overvalued, when it was near a generational buying opportunity.

The opposite problem occurs at cycle peaks. Strong earnings in boom years compress P/E ratios, making markets look reasonable just before a downturn. Standard P/E captures both price and the cyclical position of earnings simultaneously, making it hard to separate valuation from timing.

The CAPE Ratio: Construction and Formula

Shiller’s solution is to replace single-year earnings with the average of real earnings over the previous ten years. A ten-year window spans a full business cycle, smoothing out both recessions and booms. The formula is:

CAPE ratio formula

where P is the current market price, Et are reported earnings in year t, CPI0 is the current price index, and CPIt is the price index in year t. The inflation adjustment ensures that past earnings are expressed in today’s dollars, making them directly comparable to recent figures.

In the Excel model, each annual earnings figure is the average of the 12 monthly observations in Shiller’s dataset. Shiller himself constructs monthly earnings by interpolating S&P four-quarter totals, so the monthly series is a smooth continuous estimate rather than actual reported monthly results. The current S&P 500 price used is the April 9, 2026 closing price of $6,824.66, sourced from Yahoo Finance. The CPI reference is the February 2026 release from the U.S. Bureau of Labor Statistics.

Historical Record and Market Signals

Shiller’s dataset goes back to 1871, giving the ratio an exceptionally long historical record. The average CAPE over that full period is approximately 17.7 and the median around 16.6. These serve as rough benchmarks: readings significantly above the average suggest the market is expensive relative to long-run earnings capacity, while readings well below suggest the opposite.

The ratio’s most cited applications came before two of the largest crashes of the modern era. In December 1999, at the peak of the dot-com bubble, the S&P 500 CAPE reached 44.2, more than double its historical average. Shiller published Irrational Exuberance that same year, arguing on the basis of CAPE that US equities were severely overvalued. The S&P 500 subsequently fell by nearly 50% over the following two years. In August 2007, CAPE rose above 26 before the financial crisis and another major decline.

At the other extreme, CAPE dropped to around 8.5 in August 1982, one of its lowest post-war readings, preceding one of the strongest bull markets in US history. As of April 9, 2026, our model gives a CAPE of approximately 38.8, well above the historical average.

Figure 1. CAPE ratio at key historical market turning points (S&P 500, selected monthly readings). Source: Robert J. Shiller, econ.yale.edu; computation by the author.
CAPE historical chart
Source: computation by the author.

Excel Model

The Excel model below computes the CAPE ratio from Shiller’s raw data. It contains four sheets: a source data sheet copied directly from Shiller’s dataset, a CAPE Calculator that pulls ten-year annual averages and applies the inflation adjustment, a Historical Context sheet with key turning points, and a Read Me. The starting year of the ten-year window is adjustable, and the model updates automatically when price or CPI inputs are changed.

Figure 2. CAPE Calculator: ten-year window of inflation-adjusted earnings and resulting CAPE ratio.
CAPE calculator Excel screenshot
Source: computation by the author.

Download the Excel file

Interpretation and Limitations

What CAPE tells you. Shiller’s own research found a strong negative relationship between starting CAPE and subsequent 10-year real returns for the S&P 500: high CAPE tends to precede lower decade-long returns, and low CAPE tends to precede higher ones. The relationship is not mechanical and does not predict timing, but it is one of the more robust long-run return predictors in the academic literature.

The interest rate objection. The most common criticism is that CAPE ignores the level of interest rates. When rates are structurally low, investors rationally accept higher valuations because the alternatives offer little return. Some analysts argue that elevated CAPE readings since 2010 partly reflect lower rates rather than pure overvaluation. This debate is unresolved.

Accounting changes. Reporting standards for earnings have evolved significantly since the 1870s, particularly around goodwill and write-offs. Some researchers argue that modern reported earnings are not strictly comparable to historical figures, making century-long CAPE comparisons imperfect.

Not a timing tool. Investors who sold equities in 1996 because CAPE was already above its long-run average missed four more years of exceptional gains before the dot-com peak. CAPE is a signal about long-run expected returns, not a predictor of short-term price moves.

Why should I be interested in this post?

Valuation metrics appear in equity research, asset allocation decisions at investment managers, and macro discussions at private banks. The CAPE ratio is referenced in strategy notes, central bank research, and academic papers on return predictability. Understanding what it measures, how it is built, and what its limits are is practical knowledge for anyone working in equities or asset management — and one of the cleaner examples of how academic research translates directly into a practitioner tool.

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Useful resources

Academic research

Campbell, J.Y. and Shiller, R.J. (1988) Stock Prices, Earnings, and Expected Dividends, Journal of Finance, 43(3), 661-676. Available at scholar.harvard.edu.

Bunn, O. and Shiller, R.J. (2014) Changing Times, Changing Values: A Historical Analysis of Sectors within the US Stock Market 1872-2013, NBER Working Paper No. 20370. Available at nber.org.

Data sources

Shiller, R.J. Online Data, Yale University. S&P 500 price, earnings, CPI, and CAPE data from 1871 to present.

S&P 500 current price: Yahoo Finance.

CPI reference: U.S. Bureau of Labor Statistics, Consumer Price Index release.

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About the author

The article was written in April 2026 by Alexandre LANGEVIN (ESSEC Business School, Global Bachelor in Business Administration (BBA), 2022-2026).

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Duration and Convexity: Measuring Bond Price Sensitivity to Interest Rates

Alexandre LANGEVIN

In this article, Alexandre LANGEVIN (ESSEC Business School, Global Bachelor in Business Administration (BBA), 2022-2026) explains how duration and convexity allow investors and risk managers to measure and anticipate how bond prices react to changes in interest rates, and why the distinction between the two matters in practice.

Introduction

Bond markets sit at the heart of the global financial system, with outstanding fixed income markets exceeding $145 trillion worldwide (SIFMA, 2025). Yet one of the most fundamental challenges in fixed-income investing is deceptively simple to state: when interest rates move, bond prices move in the opposite direction. The harder question is by how much, and how accurately can we predict it?

Two risk measures answer that question: duration and convexity. Duration provides a first-order, linear approximation of price sensitivity to yield changes. Convexity accounts for the curvature in the price-yield relationship, improving accuracy when rate moves are large. Together, they form the analytical backbone of fixed-income risk management, from portfolio construction to regulatory capital requirements at banks.

Bond Pricing: The Starting Point

The price of a fixed-rate bond is the present value of all its future cash flows: periodic coupon payments and repayment of the face value at maturity, discounted at the bond’s yield-to-maturity. The yield-to-maturity (YTM) is the single discount rate that equates the present value of all cash flows to the current market price. With nominal value N, annual coupon rate c, maturity T, and YTM r, the bond price P is:

Bond price formula

As r rises, each discount factor grows, reducing the present value of every future cash flow and pushing the total price down. A useful benchmark: when the coupon rate equals the YTM, the bond prices at par. When the coupon rate exceeds the YTM, the bond trades above par — this is a premium bond, identifiable directly from the parameters before computing anything.

Duration

Macaulay Duration

Duration was formalized by Frederick Macaulay in 1938. Macaulay duration is the weighted average of the times at which a bond pays its cash flows, where each weight is the share of total present value arriving at that date. It answers: on average, how long does an investor wait to receive their money back?

A zero-coupon bond has a duration equal to its maturity, since all cash flow arrives at the end. A coupon bond always has a shorter duration than its maturity, because intermediate coupon payments pull the weighted average forward. For a given maturity, a higher coupon rate or a higher yield both reduce duration.

Modified Duration

Modified duration is Macaulay duration adjusted by dividing by (1 + r). It has a direct use as a price sensitivity measure: a bond’s percentage price change is approximately equal to minus its modified duration multiplied by the change in yield.

Modified duration definition

Duration price approximation

If a bond has a modified duration of 6, a 1% rise in yield reduces its price by roughly 6%. This is practical and widely used, but it is only a linear approximation and loses accuracy as yield changes grow larger.

In practice, traders and risk managers also use DV01 (Dollar Value of a Basis Point): the monetary price change for a 1 basis point (0.01%) shift in yield, equal to D* × P × 0.0001. DV01 is the standard unit for setting position limits on bond desks and for computing interest rate risk under Basel III.

Convexity

Why Duration Is Not Enough

The price-yield relationship of a bond is not a straight line — it is a convex curve. Duration approximates this curve with a tangent line at the current yield. For small yield moves this works reasonably well, but for larger moves the error accumulates in a predictable direction: duration always underestimates the true price. When rates fall, the actual price gain is larger than duration predicts. When rates rise, the actual price loss is smaller. This asymmetry, always working in the bondholder’s favor, is the essence of convexity.

The Convexity Correction

Convexity is the second derivative of the bond price with respect to the yield, divided by the price. Adding it as a second-order correction gives a substantially more accurate estimate:

Duration and convexity price approximation

The convexity term is always positive regardless of yield direction, which creates the favorable asymmetry: it always adds to the price estimate, making gains larger and losses smaller than the duration-only figure.

A Numerical Illustration

Consider a 7-year bond with a face value of $1,000, an annual coupon rate of 4%, and a current YTM of 3.5%. Since the coupon exceeds the yield, this is a premium bond. The Excel model gives a bond price of $1,030.57, a Macaulay duration of 6.26 years, a modified duration of 6.04, and a convexity of 44.91.

Figure 1. Cash Flow Analysis table and key results (N = $1,000, c = 4%, T = 7 years, r₀ = 3.5%).
Excel bond calculator screenshot
Source: computation by the author.

Now suppose the yield rises 2 percentage points, from 3.5% to 5.5%. The exact bond price falls to $914.76, a decline of 11.24%. The duration approximation predicts $906.00, overestimating the loss by nearly $9. The duration-convexity approximation gives $915.26, bringing the error down to under $0.50. Figure 2 shows this comparison across the full yield range.

Figure 2. Bond price as a function of YTM (N = $1,000, c = 4%, T = 7 years, r₀ = 3.5%): exact price (blue), duration approximation (red), duration + convexity approximation (green).
Bond price vs yield chart T=7
Source: computation by the author.

Excel Model

The Excel file below replicates these calculations for any bond. It contains a Cash Flow Analysis sheet computing present value, duration contribution, and convexity contribution for each year; a Price-Yield Chart comparing all three methods; and a Read Me tab. All inputs are editable in yellow cells, and the model supports maturities from 1 to 20 years.

Download the Excel file

A Note on Long-Duration Bonds

The limitations of the duration approximation become more pronounced for longer-maturity bonds. A 20-year bond with the same 4% coupon carries a modified duration of roughly 13-14 years. Applied to a large yield shift, the linear formula can produce a negative estimated price, because the correction term eventually exceeds the bond’s starting price. This does not happen in reality. It is simply a demonstration of how far the linear approximation strays when pushed outside its valid range. The duration-convexity approximation remains far better behaved across the same range. For long-duration bonds in volatile rate environments, accounting for convexity is not optional.

Figure 3. Price-Yield chart for a 20-year bond: the duration approximation turns negative at high yields while the convexity approximation tracks the exact price.
Bond price vs yield T=20
Source: computation by the author.

Applications in Fixed-Income Risk Management

Portfolio immunization. A portfolio manager protecting a bond portfolio against parallel rate shifts will match portfolio duration to the investment horizon. Price losses from rising rates are offset by higher reinvestment income on coupons, leaving total return roughly unchanged.

Risk limits and regulatory capital. Banks use DV01 to set position limits for fixed-income traders and to estimate interest rate risk under Basel III. A trader might be authorized to hold a maximum DV01 of $50,000, meaning no more than $50,000 of profit or loss per basis point move.

Convexity as a source of value. In volatile rate environments, investors seek bonds with high convexity. The asymmetric payoff profile — larger gains than losses for equal rate moves in either direction — is a property the market prices accordingly. Long-dated government bonds are a typical example.

Limitations. Both measures assume a parallel shift in the yield curve. In practice, the curve can steepen, flatten, or twist. For more granular risk measurement, practitioners use key rate durations, which isolate sensitivity at individual maturities. Duration and convexity remain the essential starting point.

Why should I be interested in this post?

Duration and convexity appear in fixed-income interviews, in the CFA curriculum, and in the daily work of bond traders and risk officers. Whether you are targeting investment banking, asset management, or financial risk management, these are concepts you will encounter early. The distinction between linear and non-linear sensitivity also recurs throughout quantitative finance, from option Greeks to credit portfolio models. Being able to work through it from first principles and build a functioning model is a meaningful differentiator at the MSc Finance level.

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Useful resources

Academic research

SIFMA (2025) Capital Markets Fact Book 2025. Available at sifma.org.

Cerovic, S., Pepic, M., Cerovic, S. and Cerovic, N. (2014) Duration and Convexity of Bonds, Singidunum Journal of Applied Sciences, 11(1), 52-66. Available at journal.singidunum.ac.rs.

Winkel, M. (2011) Duration, Convexity and Immunisation, Lecture Notes, Department of Statistics, University of Oxford. Available at stats.ox.ac.uk.

Crack, T.F. and Nawalkha, S.K. (2000) Common Misunderstandings Concerning Duration and Convexity, Working Paper. Available at ssrn.com.

Jeffrey, A. (2000) Duration, Convexity and Higher Order Hedging (Revisited), Yale International Center for Finance, Working Paper No. 00-22. Available at ssrn.com.

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About the author

The article was written in April 2026 by Alexandre LANGEVIN (ESSEC Business School, Global Bachelor in Business Administration (BBA), 2022-2026).

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“Markets can remain irrational longer than you can remain solvent” – John Meynard Keynes

Hadrien Puche

Is it possible to be right too early? In the world of finance, the answer is often yes. We frequently assume that if our analysis is sound, and if the data is on our side, profit is inevitable. However, history is littered with brilliant minds who correctly identified a market bubble, but got crushed by the weight of markets that refused to see their truth.

John Maynard Keynes, father of modern macroeconomics, learned this the hard way in the 1920s, as he nearly went bankrupt betting against the German Mark. He discovered that even his expert theories could be steamrolled by the sheer momentum of a crowd who does not care about mathematics or economics. In one sentence, “Markets can remain irrational longer than you can remain solvent”.

In this article, Hadrien Puche (ESSEC Business School, Grande École Program, Master in Management, 2023-2027) explores the limits of arbitrage, and why timing is just as important as being correct.

About Keynes and this quote

John Maynard Keynes
John Maynard Keynes
Source : Cambridge

John Maynard Keynes (1883–1946) was a British economist. In his 1936 work, The General Theory of Employment, Interest, and Money , he argued that aggregate demand (the total spending in an economy) is its primary engine of growth. He observed that during crises, a “liquidity trap” can occur, where individuals and businesses hoard cash, causing a cycle of stagnation that the “invisible hand” of the free market fails to fix without external intervention.

Another central pillar of his theory is the idea of “Animal Spirits,” the human emotions and instincts that drive financial decisions. Keynes argued that because the future is uncertain, investment is guided more by waves of optimism or pessimism than by cold calculation. To counter all of this, Keynes advocated active fiscal policy: governments should use deficit spending to stimulate demand. His focus was on short-run intervention, famously remarking that “in the long run, we are all dead.”

While the quote “Markets can remain irrational longer than you can remain solvent” is frequently linked to him, its true origin is a matter of historical debate. Some credit A. Gary Shilling, an American financial analyst who has claimed paternity of the phrase since the early 1970s.

Analysis of this quote

This quote is above all a warning against the limits of arbitrage.

Being right about, for example, a bubble, such as the Dutch tulip mania (1636) or the dot-com (2000), is irrelevant if you cannot survive the journey to the correction. A market can remain detached from reality for years, during which three specific pressures act against the contrarian investor:

  • Capital constraints and margin calls: if you short a stock at $100 because it is “irrationally” high, and it climbs to $200, your broker will ask for more collateral. If you cannot provide it, your position will be liquidated at a massive loss, even if you are just days ahead of the eventual crash.
  • Opportunity cost: tying up capital in a “correct” bet that takes five years to materialize can be devastating; losses incurred from inflation and missed gains in other sectors may outweigh the final profit of the trade.
  • Momentum and “animal spirits”: irrationality is frequently self-reinforcing. When prices rise, more and more less sophisticated investors enter the market, creating momentum that pushes valuations even further from fair value, and crushing those betting on a return to sanity.

The term ‘solvent’ in the quote is very important. It is about the investor’s ability to stay alive (at a financial level). In finance, being insolvent is almost the same as being dead. The market does not have to be rational on your timeline; it only has to stay irrational long enough to exhaust your resources.

The GameStop (GME) Short Squeeze

The 2021 GameStop saga remains the most violent modern illustration of Keynes’s warning. From a fundamental perspective, analysts were “right”: the company was a struggling brick-and-mortar retailer with a declining business model and falling revenues. However, “animal spirits” fueled by social media created a decoupled valuation where the stock price surged by over 2,700% in weeks.

This irrationality triggered a short squeeze, a technical phenomenon where rising prices force short sellers to buy back shares to cover their positions. This involuntary buying creates a self-reinforcing loop: the more short sellers exit to limit losses, the higher the price climbs, triggering further margin calls. This had lethal solvency consequences: hedge funds like Melvin Capital, despite their sound fundamental thesis, were caught in a liquidity squeeze. They were crushed not by being wrong about the company, but by being insolvent before the market’s timeline aligned with their own. This example highlights the brutal reality of timing: a short position has a “bleeding” cost that fundamental truth cannot always outrun.

Financial concepts linked to this quote

This quote is a perfect opportunity to go deeper into three financial concepts that you may find useful to know more about: short selling, the Efficient Market Hypothesis (EMH) and the time value of money and opportunity cost.

Short selling

To bet against an “overpriced” market, you can short sell something. If we keep the example of stocks, the idea is that you can borrow one Tesla share from someone, and then sell this share on the open market. If the price drops as you planned, you buy back the share for cheaper and give it back to its original owner, and pocket the difference (minus a borrowing fee for whoever owned the share).

Unlike buying a stock, where your risk is limited to your initial investment (the stock can’t be worth less than 0), shorting carries theoretically infinite risk, because there is no ceiling on how high a price can climb.

Short selling explanation
Source : IG Group

But maintaining a short position is not a passive endeavor; it is a “bleeding” process characterized by several layers of costs and pressure:

  • Stock borrow fees: shorting requires you to borrow shares from a lender. In highly speculative or “hard-to-borrow” markets, the interest rates on these loans can spike significantly, eroding your potential profits every day the market refuses to correct.
  • Dividend liability: if the company you are shorting pays a dividend, you need to pay this amount out of your own pocket to the person you borrowed the shares from.
  • The short squeeze risk: as an irrational market climbs, short sellers may be forced to buy back shares to cover their losses, creating even more buying pressure. If too many investors short-sold the stock, if they all want to buy back their positions at the same time, and if not enough shares are available on the market, prices can suddenly surge to absurd levels. This is what we discussed earlier with the GameStop example.

The Efficient Market Hypothesis (EMH) vs. the Keynesian reality

The Efficient Market Hypothesis (EMH) suggests that markets are always rational and instantaneously reflect all available information. Under this framework, there should not be any bubble in the market, because arbitrageurs would immediately correct any deviation from the “fair value”. Keynes’ quote serves as a direct challenge to this theory: it suggests that while markets should be rational, they are frequently driven by “animal spirits”; the human emotions and herd behavior that makes people take irrational decisions.

This creates a dangerous environment where the fundamental value remains decoupled from the market price for extended periods. This divergence is sustained by two primary factors that the EMH often overlooks:

  • Noise trading: Many participants buy based on trends, rumors, or social proof rather than data. This “noise” creates a momentum that rational analysis cannot easily break.
  • The “Greater Fool” theory: some (if not many) investors do not buy assets because they believe they are buying at a good price, but because they expect to be able to resell them at awhat we talked earlier higher price to someone else. Check out this article to see the example of NFTs.

Time Value of Money & Opportunity Cost

Identifying a 10% mispricing in the market is only half the work; you also need to actually profit from it. This means committing capital, and in finance, capital is never free. Every dollar tied up in a trade is a dollar that isn’t earning a return elsewhere. This means your trade must not only be “correct,” but it must also clear a specific hurdle rate to be considered a success.

  • The risk-free benchmark & opportunity cost: in a rational portfolio, the baseline for any investment is the risk-free rate (typically the yield on 10-year treasury bonds for US investors, or German bunds for EU investors). If the risk-free rate is 3% per year, you need to earn significantly more than an annualized 3% on any given trade to justify the risk of not simply sitting in “safe” government debt.
  • Time-adjusted returns: a practical way to see if your trade actually generated a real return is to use proper discounting through the present value formula. It allows you to calculate what a future sum of money (what you will have after the trade) should be worth to you today, to better compute your time-adjusted returns:

PV Formula

As a final example, if you identify a 10% mispricing today, but it takes you four years for the market to correct while the risk-free rate is 3%, your “safe” alternative would have grown to roughly 112.5% of your initial capital. By making only 10%, you have technically lost 2.5% in relative wealth, despite being “right” about the market’s irrationality.

My view on this quote

In addition to the structural limits of arbitrage, this quote serves as a stark reminder of the dangers of leverage. Whether through margin accounts or derivatives, leveraging capital allows you to trade as if you had a much larger balance; however, this acts as a double-edged sword that multiplies both gains and losses.

Because markets can stay irrational for an indefinite period, leverage significantly accelerates the path to insolvency. The market does not have to become rational on your specific timeline—or even at all. This becomes particularly dangerous when market irrationality persists longer than your loan agreement, your margin maintenance requirements, or your hedge fund mandate allows.

We see this frequently in highly speculative assets like cryptocurrencies or stocks with high price-to-earnings ratios, such as Palantir, MicroStrategy, or Tesla. You might be fundamentally correct that a specific valuation is a fantasy, but if you use borrowed money to bet against it, you are playing a high-stakes game. The house (the market) only needs to stay irrational one day longer than you can afford to pay your interest or meet your collateral calls.

Why should you keep this quote in mind?

For students, this is a vital warning against hubris. In your career, you will often see things that don’t make sense. You will be tempted to bet against them. But remember the following principles:

  • Risk management is key: never assume being “right” protects you from being “broke.” Always consider the possibility of being wrong for a very long time.
  • The market is a voting machine: in the short run, it doesn’t matter what the “fair value” is; what matters is what the average investor thinks. You most likely cannot sway the vote alone.
  • Solvency is survival: the most successful professionals are not those who are the most “right,” but those who are still standing when the correction finally arrives.

Ultimately, Keynes’ warning reminds us that the market is a psychological arena as much as a mathematical one. Surviving irrationality is the only way to eventually profit from the rationality.

Related posts on the SimTrade blog

Quotes

All posts about Quotes

   ▶ Hadrien PUCHE “The stock market is designed to transfer money from the impatient to the patient.” – Warren Buffett

   ▶ Hadrien PUCHE The market is never wrong, only opinions are.” – Jesse Livermore

   ▶ Hadrien PUCHE “The four most dangerous words in investing are, it’s different this time.” – John Templeton

Financial techniques

   ▶ Ian DI MUZIO Leverage in LBOs: How Debt Creates and Destroys Value in Private Equity Transactions

   ▶ Raphaël ROERO DE CORTANZE Gamestop: how a group of nostalgic nerds overturned a short-selling strategy

   ▶ Lang Chin SHIU The “lemming effect” in finance

Useful resources

Academic research

Shiller, R. J. (2000) Irrational Exuberance. Princeton: Princeton University Press.

Keynes, J. M. (1936) The General Theory of Employment, Interest, and Money. London: Macmillan.

Shleifer, A., Vishny, R. W. (1997) The Limits of Arbitrage The Journal of Finance, 52(1) 35-55.

Other resources

YouTube Video Fear the Boom and Bust: Keynes vs. Hayek – The Original Economics Rap Battle!.

About the Author

This article was written in April 2026 by Hadrien PUCHE (ESSEC Business School, Grande École Program, Master in Management, 2023-2027).

   ▶ Discover all articles by Hadrien PUCHE

“Diversification is protection against ignorance. It makes little sense if you know what you are doing.” – Warren Buffett

Hadrien Puche

In any asset management class, students are taught that diversification is a key to unlock mathematically optimal risk-adjusted returns. However, Warren Buffett, one of the world’s most successful investors, would beg to disagree: to him, “diversification is protection against ignorance. It makes little sense if you know what you are doing.”

In this article, Hadrien PUCHE (ESSEC Business School, Grande École Program, Master in Management, 2023-2027) discusses Buffett’s challenge to modern portfolio theory, and explains why, for a sophisticated investor, concentration may sometimes also be an option.

About Warren Buffett and this quote

Warren Buffett is the chairman and CEO of Berkshire Hathaway, a multinational holding company, that he transformed over the years into a conglomerate businesses (Geico, dairy queen…) and large equity stakes in listed companies (Coca-Cola, Apple…). He is widely considered the most successful value investor in history. He is known for his discipline, long-term perspective, and his ability to distinguish between market price and intrinsic value. This specific quote originates from his 1993 annual shareholder meeting, where he addressed the difference between a “know-nothing” investor and a “know-something” investor.

Warren Buffett

Source : CNBC

This also suggests that the reason Buffett said that isn’t to give a valuable lesson to investors, but to convince them that instead of looking for diversification and investing themselves, they should entrust their money to Berkshire Hathaway, because they have the informational edge to overperform a simply well-diversified portfolio.

Analysis of the quote

The core of Buffett’s idea is that risk is not a statistical measurement of price volatility, but rather a function of knowledge. If you have three companies you know perfectly (meaning you understand their business model, their management, and their competitive moat) then adding a fourth company “at random” just to diversify will actually increase your overall probability of loss.

Having more diversified portfolios lead to two critical issues:

  • The dilution of quality: your best investment idea is, by definition, better than your tenth best idea. By adding more stocks, you are moving away from your highest-conviction choices toward relatively more mediocre ones, watering down the potential returns of your portfolio.
  • Knowledge risk: spreading your attention across too many holdings dilutes your ability to monitor each one perfectly. You are more likely to miss a fundamental change in a business if you are tracking fifty companies instead of five.

Essentially, diversification only reduces risk when you add an asset you know nothing about to a portfolio of other assets you know nothing about. It is a great tool for the “ignorant” (in the financial sense) to protect themselves from a total wipeout, but it is a “downgrade” for anyone with a true informational edge.

Financial concepts linked to this quote

To better understand this tension between concentration and diversification, we can look at three key concepts that are very important to modern finance.

Modern Portfolio Theory (MPT) & Diversification

In every finance textbook, Modern Portfolio Theory (MPT) is presented as the “only free lunch” in investing. It suggests that by holding a large number of non-correlated assets, an investor can eliminate “idiosyncratic risk” (the risk specific to a company), leaving only the systematic risk of the market.

The Capital Market Line (CML) represents the most efficient combinations of the risk-free asset and the market portfolio. As shown in the graph below, every point on this line offers the highest possible (expected) return for a specific level of risk, effectively defining the “best” available trade-off. In the world of MPT, any portfolio falling to the right of this line is sub-optimal, while the area to the left remains mathematically unreachable.

The capital market line

However, MPT focuses almost entirely on the mathematical “co-variance” of stock prices rather than the underlying business quality. Buffett’s quote acts as a philosophical counter-weight to this academic standard: he suggests that MPT is a defensive tool, designed for those who cannot identify intrinsic value. If you cannot tell a good business from a bad one, MPT is your best protection; but if you can, it is nothing more than a constraint.

The Kelly Criterion

While MPT seeks to minimize variance, the Kelly Criterion seeks to maximize the growth of wealth. Originally developed by John Kelly at Bell Labs, this formula determines the optimal size of a series of bets based on the probability of success and the “edge” the bettor has.

Kelly criterion formula

Unlike the MPT, which would suggest a small allocation to any single stock to keep the portfolio “balanced,” the Kelly Criterion supports heavy concentration. It suggests that when the odds are heavily in your favor, the “bet” should be significantly larger, and can represent a significant portion of your capital. It is the mathematical foundation for the “betting big” philosophy that Buffett has applied throughout his career at Berkshire Hathaway.

Market Imperfection and Information Asymmetry

The Efficient Market Hypothesis (EMH) assumes that all information is already reflected in stock prices. However, Buffett’s success is built on the reality of market imperfections. For an investor to have a true edge, there must be a gap in how information is processed. If you spend hundreds of hours studying a specific niche, you may identify a ‘valuation gap’ that the average market participants missed. But you can’t do this work on all industries and all assets. Because of that, concentration allows you to maximize the financial value of that specific information.

Diversification, by contrast, “washes away” that hard-earned advantage, by blending your good insights with the general noise of the market average.

My view on this quote

While the logic of concentration is mathematically sound, its execution faces a major practical limit: intellectual honesty. To apply Buffett’s philosophy, you need to understand if you are yourself one of the professional managers who can overperform, or a simple retail saver who should go to diversification for protection against your own ignorance.

For an individual investor: humility as a strategy

For the vast majority of retail investors, diversification remains the “wisest default.” The “ignorance” Buffett mentions is not pejorative, but simply a realistic assessment of the time and resources one can dedicate to market analysis. Without a professional informational edge, concentration can often lead to a martingale trap, where an investor doubles down on loosing positions, based on an emotional conviction that the market is wrong and refusal to accept defeat. For this group, Modern Portfolio Theory (MPT) is not a constraint, but a necessary safeguard.

The institutional management problem

For an aspiring asset manager, the reality is a bit more complex, and highlights a structural paradox in the industry, where career incentives are more towards diversifying a portfolio than making a small number of concentrated bets.

  • Career risk versus absolute risk: If a concentrated portfolio underperforms, the manager risks being “wrong alone” and losing their job. If a diversified portfolio fails, they are “wrong with the crowd,” and no one will really consider that the loss is their responsibility.
  • The “closet indexing” trap: To minimize tracking error, many professionals choose the safety of the average. However, Buffett’s logic suggests that if you are not prepared to know your holdings better than the rest of the market, you are merely charging active management fees for a passive result, effectively selling the “market average” at a premium price.

Buffet’s call to invest with berkshire hathaway

Finally, we must consider context behind Buffett’s rhetoric. As we already stated, by framing diversification as a “protection against ignorance,” he is not just teaching finance, but also subtly positioning Berkshire Hathaway as the ideal destination for capital. He encourages investors to recognize their own limitations and, instead of buying a “know-nothing” index, to entrust their wealth to a firm that possesses the rare informational edge required to concentrate effectively. In essence, this quote is also a good lesson in brand positioning: it justified Berkshire Hattaway’s market concentration as the key to overperforming the market.

Why should you keep this quote in mind?

This principle forces you to ask a fundamental question: “Do I have a true edge, or am I just guessing?” If you are a student or a retail investor, recognizing your own ignorance is the first step toward safety. Diversification is your best friend when you are learning.

However, and this is where Buffett’s spirit is very important, if you want to achieve extraordinary results, you must first develop the analytical rigor to know your investments better than the rest of the market. Knowing the “average” only gets you the “average” return.

Related posts on the SimTrade blog

Business & Finance quotes

   ▶ All posts about Quotes

   ▶ Hadrien PUCHE Price is what you pay, value is what you get – Warren Buffett

   ▶ Hadrien PUCHE The stock market is designed to transfer money… – Warren Buffett

Useful resources

Academic research

Kelly J. L. Jr. (1956) A New Interpretation of Information Rate, Bell System Technical Journal 35(4) 917–926.

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

Sharpe W.F. (1991) The Arithmetic of Active Management, Financial Analysts Journal 47(1) 7-9.

Business resources

Buffett, W.E. Berkshire Hathaway Shareholder Letters

S&P Global. SPIVA Scorecards

About the Author

This article was written in April 2026 by Hadrien PUCHE (ESSEC Business School, Grande École Program, Master in Management, 2023-2027).

   ▶ Discover all articles by Hadrien PUCHE

Managing Corporate Risk: How Consulting and Financial Analysis Complement Each Other

Bochen LIU

In this article, Bochen LIU (Queen’s Smith School of Business, BCom 2023–2027; ESSEC BBA Exchange Program, Fall 2025) explains how corporate risk is understood, managed, and priced in practice, drawing on concrete experience from consulting frameworks and financial analysis at the Agricultural Bank of China.

What is corporate risk?

Corporate risk refers to the uncertainty that affects a firm’s ability to achieve its objectives. In practice, this includes credit risk, operational risk, market volatility, and strategic uncertainty. Rather than being purely theoretical, these risks directly influence financial performance, investment decisions, and long-term sustainability.

During my internship at the Agricultural Bank of China (ABC), risk was not treated as an abstract concept but as a measurable factor embedded in every lending decision. For example, when evaluating a corporate borrower, analysts examine cash flow stability, debt ratios, and industry exposure to determine the likelihood of default. This transforms uncertainty into a structured assessment.

From abstract risk to concrete decisions

One of the main limitations of theoretical discussions of risk is their level of abstraction. In practice, risk appears through specific operational situations. At ABC, I worked with customer financial data and observed how inconsistencies or missing information could directly affect credit evaluation. For instance, incomplete revenue records or irregular cash flows signaled higher uncertainty, which required further verification or stricter lending conditions.

This illustrates how risk is identified through data quality, financial transparency, and operational consistency. Rather than being a general concept, risk becomes visible through concrete indicators that influence real decisions such as loan approval, pricing, and collateral requirements.

Consulting: structuring and reducing uncertainty

Consulting plays a key role in transforming uncertainty into manageable components. In academic case work and consulting-style analysis, organizations improve risk exposure by refining reporting systems, standardizing processes, and strengthening internal controls.

A concrete example is the implementation of standardized reporting templates. During my internship, structured weekly reporting reduced inconsistencies in financial data and improved processing efficiency. This type of intervention does not eliminate uncertainty but reduces information asymmetry, making risks easier to monitor and manage.

Consulting therefore operates upstream: it improves the quality of information and decision-making structures, allowing firms to anticipate risks instead of reacting to them.

Financial analysis: measuring and pricing risk

While consulting structures risk, financial analysis quantifies and prices it. At ABC, credit assessment involved evaluating repayment capacity, industry volatility, and macroeconomic exposure. These factors were translated into measurable indicators such as probability of default and expected loss.

A concrete outcome of this process is interest rate determination. A firm with stable cash flows and low leverage receives favorable lending terms, while a firm with volatile earnings or weak financial transparency faces higher borrowing costs. In this sense, risk is directly converted into a financial price.

This demonstrates that risk is not only managed but monetized. Financial institutions assign a cost to uncertainty, aligning pricing with the level of exposure.

Risk vs uncertainty and the role of black swans

A deeper understanding of risk requires distinguishing it from uncertainty. Following Frank Knight’s framework, risk refers to situations where probabilities can be estimated, while uncertainty refers to events that cannot be predicted or quantified.

In practice, most financial models at ABC operate within the domain of measurable risk. Credit scoring, financial ratios, and industry benchmarks all assume that future outcomes can be approximated using historical data. However, these models have limits.

This is where the concept of “black swan” events, developed by Nassim Taleb, becomes critical. Events such as the 2008 financial crisis or the COVID-19 pandemic fall outside standard risk models yet have massive impacts on financial systems. These events expose the limitations of purely quantitative approaches.

From a practical perspective, this means that organizations must complement risk measurement with resilience. For example, banks require capital buffers and stress testing not because all risks can be predicted, but because extreme scenarios cannot be fully modeled.

From managing risk to building resilience

The interaction between consulting and financial analysis reveals a broader shift: firms no longer aim to eliminate risk but to manage and absorb it. Consulting improves internal structures and information quality, reducing controllable risks. Financial analysis evaluates and prices exposure, enabling informed decision-making.

However, neither approach fully addresses uncertainty. The presence of black swan events requires organizations to build adaptive capacity—through diversification, liquidity management, and strategic flexibility.

Risk management therefore evolves from a defensive function into a strategic capability. Firms that understand both measurable risk and unmeasurable uncertainty are better positioned to sustain performance in volatile environments.

Why should I be interested in this post?

For students and professionals in business and finance, understanding how risk operates in practice is essential. This post shows how theoretical concepts such as risk, uncertainty, and black swans translate into real-world decisions in consulting and banking.

It provides a concrete perspective on how organizations evaluate information, price uncertainty, and prepare for extreme events—skills that are directly relevant for careers in finance, consulting, and strategic management.

Related posts on the SimTrade blog

   ▶ Bryan BOISLEVE Principal Component Analysis (PCA) in Quantitative Finance

   ▶ Mathis HOUROU Client segmentation in private banking: marketing strategy or risk shield?

   ▶ Lokendra RATHORE Good-til-Cancelled (GTC) order and Immediate-or-Cancel (IOC) order

   ▶ Bochen LIU All posts by Bochen LIU

Useful resources

Agricultural Bank of China official website

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

Taleb, N. N. (2007). The Black Swan: The Impact of the Highly Improbable. Random House.

Hull, J. (2018). Risk Management and Financial Institutions. Wiley.

Bluhm, C., Overbeck, L., & Wagner, C. (2016). Introduction to Credit Risk Modeling. CRC Press.

Bank for International Settlements (BIS)

International Monetary Fund (IMF)

About the author

The article was written in April 2026 by Bochen LIU (Queen’s Smith School of Business, BCom 2023–2027; ESSEC BBA Exchange Program, Fall 2025).

   ▶ Discover all posts by Bochen LIU

April 2026: Inflation – Monthly Selection from the SimTrade blog

Most Read Articles about Inflation on the SimTrade Blog

This monthly selection highlights key articles on inflation, chosen based on their pedagogical value, practical relevance, and readership engagement. Inflation has been selected as a central theme due to its critical role in shaping monetary policy, asset pricing, and investment strategies in the current macro-financial environment. It is also a timely issue, as renewed geopolitical tensions—particularly involving Iran—may exert upward pressure on inflation through energy prices and supply chain disruptions.

   ▶ Anant JAIN Understanding Hyperinflation: Causes, Effects And Examples

   ▶ Raphaël ROERO DE CORTANZE Inflation & deflation

   ▶ Bijal GANDHI Inflation Rate

   ▶ Alexandre VERLET The return of inflation

Historical events about inflation

   ▶ Anant JAIN Hyperinflation in Hungary: 1945-1946

   ▶ Anant JAIN Hyperinflation In Argentina Since 2018: A Deep Dive Into The Economic Crisis

   ▶ Anant JAIN The Ongoing Hyperinflation In Turkey And Its Ripple Effects On European Union

A solid understanding of inflation is essential for interpreting macroeconomic developments, assessing monetary policy, and making informed financial decisions, which makes these articles particularly valuable for students and aspiring finance professionals.

Understanding the Order Book: Analyzing Market Liquidity

Bochen LIU

In this article, Bochen LIU (Queen’s Smith School of Business, BCom 2023–2027; ESSEC BBA Exchange Program, Fall 2025) explains the concept of the order book, how it functions in financial markets, and the insights it provides to traders.

What is an order book?

For anyone engaging in financial markets, understanding the order book is essential. The order book is a dynamic record of buy and sell orders for a given asset, reflecting the interaction between supply and demand in real time. Whether trading stocks, currencies, or digital assets, the order book allows market participants to visualize liquidity, identify potential price movements, and make informed decisions.

An order book lists all outstanding buy and sell limit orders for an asset, showing both the prices at which traders are willing to transact and the quantities they wish to trade. It provides a clear picture of market depth and the relative interest of buyers and sellers at different price levels. Unlike a simple price chart, the order book reveals where liquidity is concentrated and where potential support or resistance may occur, making it an indispensable tool for understanding short-term market dynamics.

How an order book functions

The order book is typically divided into two sections: the buy side (bid side) and the sell side (ask side). The buy side shows the highest prices buyers are willing to pay, while the sell side reflects the lowest prices sellers are willing to accept. Orders are listed by price and aggregated volume, and the book is continuously updated as trades are executed and new orders enter the market.

The difference between the best bid and best ask is known as the bid-ask spread, a key indicator of market liquidity. By monitoring changes in the spread and the distribution of orders, traders can gain insights into market sentiment and anticipate short-term price movements.

In practice, the organization of the order book allows traders to understand not just current prices but also the pressure from buyers and sellers at different levels. For example, a concentration of large buy orders may act as a support level, while clusters of sell orders can indicate resistance. The order book therefore acts as a living map of market intentions and is often used together with charts and other data sources.

Order book representation

The structure of the order book is often visualized through trading platforms that display the distribution of buy and sell orders at different price levels. A typical representation includes two columns: bids on the left and asks on the right. Each row shows the price level and the cumulative quantity available at that level.

Figure 1. Example of an order book (buy and sell parts presented side by side).
Example of an order book with buy and sell parts presented side by side
Source: screenshot from a trading platform.

Figure 1 presents one of the most common visual formats of the order book, in which bid orders are shown on the left and ask orders on the right. This side-by-side structure allows traders to compare the quantities available at different price levels and to identify the best bid and best ask immediately. It also makes the bid-ask spread visible, which is a key indicator of market liquidity and transaction cost.

Modern electronic trading platforms such as NASDAQ TotalView or cryptocurrency exchanges provide graphical representations of the order book. These interfaces often include a “depth chart,” where the cumulative buy and sell volumes are plotted against price levels. Such visualizations allow traders to quickly observe supply and demand imbalances.

Figure 2. Example of an order book (depth chart representation).
Example of an order book with a depth chart representation
Source: screenshot from a trading platform.

Figure 2 shows the order book in a format that combines tabular bid-ask information with a depth chart. The green area represents cumulative buy-side liquidity, while the red area represents cumulative sell-side liquidity. This representation helps traders visualize how orders are distributed across price levels and where supply-demand imbalances may emerge in the market.

Evolution of the order book

The order book constantly evolves as new orders arrive, existing orders are cancelled, and trades are executed. Two main types of orders influence this evolution: limit orders and market orders.

Limit orders add liquidity to the market by specifying both a price and quantity at which a trader is willing to buy or sell. When a trader places a buy limit order below the current market price or a sell limit order above it, the order enters the order book and waits to be matched.

Market orders, in contrast, remove liquidity. A market buy order immediately matches with the lowest available sell orders, while a market sell order matches with the highest available buy orders. As these trades execute, they reduce the quantities available in the order book and may shift the best bid and ask prices.

The interaction between incoming limit orders and market orders continuously reshapes the order book and drives short-term price movements.

Order priority rules

Electronic markets generally follow two key priority rules when matching orders: price priority and time priority.

Price priority means that orders offering better prices are executed first. For example, among buy orders, the highest bid has priority, while among sell orders the lowest ask has priority.

If multiple orders are placed at the same price level, time priority applies. The order that was submitted earlier will be executed before later orders. This rule encourages traders to submit orders quickly if they want to secure execution.

These priority mechanisms ensure fairness and transparency in electronic trading systems.

Price impact and transaction prices

The execution of orders can influence market prices, a phenomenon known as price impact. When large market orders consume multiple levels of liquidity in the order book, the transaction price may move significantly.

For example, if a large buy market order exceeds the quantity available at the best ask price, the trade will continue matching with higher ask prices. This process pushes the transaction price upward and illustrates how large orders can move markets.

Transaction prices and traded volumes therefore provide important information about market activity. High trading volumes often indicate strong participation and may reinforce price trends.

Liquidity characteristics of the order book

The order book provides several indicators that help measure market liquidity.

Bid-ask spread is the difference between the best bid and best ask price. A narrow spread typically indicates a liquid market with low transaction costs.

Market depth refers to the total quantity of buy and sell orders available at different price levels. A deep order book allows large trades to be executed without significantly affecting prices.

Market breadth describes how widely orders are distributed across price levels. A broad distribution indicates active participation from many traders.

Figure 3. Example of an order book (used to assess liquidity).
Example of an order book used to assess liquidity
Source: screenshot from a trading platform.

Figure 3 provides a mobile-style visualization of the order book, showing the best bid, the best ask, and the quantities available on both sides of the market. It is particularly useful for illustrating liquidity measures such as bid-ask spread, visible depth, and market breadth. By comparing the quoted quantities at different prices, traders can better evaluate the strength of buying and selling pressure.

Resilience measures how quickly the order book replenishes after large trades remove liquidity. A resilient market quickly attracts new orders and stabilizes prices.

These liquidity measures help traders evaluate the quality and stability of a market.

Why should I be interested in this post?

For ESSEC students interested in business and finance, understanding the order book is fundamental to analyzing financial markets and trading behavior. It provides practical insight into how prices are formed, how liquidity affects execution, and how real-time data informs strategic decisions.

Mastering order book analysis strengthens financial reasoning, improves understanding of market microstructure, and supports more informed investment or trading strategies. This knowledge is directly relevant for careers in finance, trading, investment analysis, and quantitative research.

Related posts on the SimTrade blog

   ▶ Federico DE ROSSI Understanding the Order Book: How It Impacts Trading

   ▶ Jayna MELWANI The impact of market orders on market liquidity

   ▶ Lokendra RATHORE Good-til-Cancelled (GTC) order and Immediate-or-Cancel (IOC) order

   ▶ Clara PINTO High-frequency trading and limit orders

Useful resources

SimTrade course — Trade orders

SimTrade course — Market making

SimTrade simulation — Market orders

SimTrade simulation — Limit orders

About the author

The article was written in April 2026 by Bochen LIU (Queen’s Smith School of Business, BCom 2023–2027; ESSEC BBA Exchange Program, Fall 2025).

   ▶ Discover all posts by Bochen LIU

AMM

Calculateur AMM

Calculateur AMM à produit constant

Cette application calcule le prix moyen de transaction et le prix final (prix marginal après transaction) pour un AMM de type x × y = k avec la convention suivante : achat = l’utilisateur achète l’actif x et paie en y, vente = l’utilisateur vend l’actif x et reçoit en y.

Paramètres du pool

Transaction

Résultats

Graphique

My Internship Experience as a Marketing Intern at XING QI Educational Institution

Bochen LIU

In this article, Bochen LIU (Queen’s Smith School of Business, BCom 2023–2027; ESSEC BBA Exchange Program, Fall 2025) shares his professional experience as a Marketing Intern at XING QI Educational Institution in Beijing, China.

About the company

XING QI is a private educational institution based in Beijing, China, specializing in after-school programs and supplemental learning for primary and secondary school students. Operating in a highly competitive market, the institution focuses on attracting students, improving enrolment, and maintaining high-quality educational services.

I worked within the marketing team, which was responsible for managing digital campaigns, promoting institutional events, analyzing marketing performance, and supporting student recruitment initiatives. The department ensured that promotional strategies reached potential students effectively and that marketing resources were allocated efficiently to support enrollment growth.

My internship

During my studies at ESSEC Business School, I joined XING QI Educational Institution as a Marketing Intern from 2021 to 2022. This experience provided hands-on exposure to digital marketing, campaign management, and event organization, offering insight into how strategic marketing decisions influence organizational growth.

The internship allowed me to observe how marketing activities are planned, executed, and evaluated, and how data-driven adjustments can improve performance and business outcomes.

My missions

I managed online promotions and social media campaigns, contributing to a 35% increase in inquiries and a conversion rate of approximately 20% into enrollments. By redesigning advertising materials and conducting A/B testing, I helped improve campaign return on investment by about 18%, ensuring marketing resources were used efficiently.

In addition to digital campaigns, I supported campus events that attracted over 300 students and generated more than 50 new registrations. Organizing these events required coordination with team members, preparation of promotional materials, and direct engagement with students and parents. These activities demonstrated how marketing strategies directly influence customer behavior and institutional growth.

Required skills and knowledge

This internship required both technical marketing competencies and interpersonal communication skills. I used digital advertising tools, analytics platforms, and performance tracking methods to monitor campaign effectiveness and optimize promotional strategies. Applying marketing principles helped ensure campaigns were targeted and efficient.

Collaboration and communication were equally important, as I worked closely with the marketing team to coordinate campaigns, collect feedback, and refine event planning processes. Critical thinking and problem-solving were necessary when analyzing performance data and proposing improvements.

What I learned

This internship deepened my understanding of how marketing contributes to organizational growth. I learned the importance of continuously measuring campaign performance, understanding target audiences, and applying data insights to improve outcomes.

I also developed project management and coordination skills by working with multiple stakeholders during campaigns and events. These experiences strengthened my ability to organize tasks, manage timelines, and support team objectives effectively.

Furthermore, the internship highlighted the connection between marketing and finance. Digital campaigns and events generate revenue and influence institutional profitability, while evaluating campaign performance involves metrics similar to ROI calculations. My prior exposure to financial concepts through SimTrade helped me interpret marketing data quantitatively and understand how business decisions are assessed in terms of returns.

Business and financial concepts related to my internship

I present below three business and financial concepts related to my internship: marketing return on investment (ROI), conversion rate analysis, and data-driven strategic decision-making.

Marketing return on investment (ROI)

Marketing return on investment (ROI) measures the effectiveness of promotional spending relative to the results generated. By redesigning advertising materials and testing campaign variations, I contributed to improving ROI by increasing the efficiency of marketing expenditures and maximizing enrollment outcomes.

Conversion rate

Conversion rate analysis evaluates how effectively inquiries or leads are transformed into actual customers. Tracking inquiry growth and enrollment conversion rates allowed the marketing team to assess campaign performance and refine targeting strategies, demonstrating how quantitative metrics guide operational improvements.

Data-driven strategic decision-making

Data-driven strategic decision-making involves using performance metrics and analytical insights to guide organizational actions. Through analyzing campaign results and event outcomes, I observed how marketing data supports planning, resource allocation, and long-term institutional growth strategies.

Why should I be interested in this post?

This post provides insight into how marketing internships contribute to business performance and strategic development. Students interested in finance, business strategy, or management can understand how campaign analytics, performance metrics, and event coordination influence revenue generation and organizational growth.

The experience illustrates how analytical thinking, data interpretation, and structured planning are transferable skills valuable across marketing, finance, and broader business careers.

Related posts on the SimTrade blog

   ▶ All posts about Professional experiences

   ▶ Alexandre VERLET Classic brain teasers from real-life interviews

   ▶ Guylan ABBOU My Personal Experience in Marketing, and How It Links to Finance

   ▶ Fatimata KANE My internship experience as a marketing intern at Amazon

   ▶ Ines ILLES MEJIAS My professional experience as a marketing assistant at Auris Gestion

Useful resources

Beijing Weiqi Association official website

Kotler, P., & Keller, K. L. (2016) Marketing Management, 15th Edition, Pearson.

Farris, P. W., Bendle, N. T., Pfeifer, P. E., & Reibstein, D. J. (2010) Marketing Metrics: The Definitive Guide to Measuring Marketing Performance, Pearson.

About the author

The article was written in February 2026 by Bochen LIU (Queen’s Smith School of Business, BCom 2023–2027; ESSEC BBA Exchange Program, Fall 2025).

   ▶ Discover all posts by Bochen LIU

Deal Structuring in Investment Banking: How Earn-Outs, Rollover Equity, and Contingent Consideration Shape M&A Outcomes

Ian DI MUZIO

In this article, Ian DI MUZIO (ESSEC Business School, Master in Finance, 2025–2027) examines how investment banks structure consideration in M&A deals through earn-outs, rollover equity, and other forms of contingent consideration, and how these tools redistribute risk and return between buyer and seller.

Context and objective

In most introductory valuation courses, M&A is presented as if deals were paid in a single block of cash at closing, with maybe some stock mixed in. In practice, especially for private targets, the consideration structure can be highly engineered: part cash, part vendor rollover, part earn-out, sometimes with ratchets, performance-based options, or contingent value rights. These instruments are not cosmetic. They shift economic exposure to future performance, mitigate information asymmetry, and can literally decide whether a deal is financeable and acceptable to both sides.

The objective of this article is to provide a practical, technical lens on deal structuring from an investment banking perspective. We will:

  • Define earn-outs, rollover equity, and other forms of contingent consideration.
  • Explain how they affect valuation, incentives, and risk allocation between buyer and seller.
  • Show, via simple numerical illustrations, how these structures change internal rate of return (IRR) profiles and downside protection.
  • Discuss how investment banks help clients choose among structures, negotiate terms, and document them.

The target reader is a student or junior analyst who already understands basic discounted cash-flow (DCF) analysis and valuation multiples (e.g., EV/EBITDA) and wants to see how real‑world M&A uses structuring to solve problems that pure valuation cannot.

Why should I be interested in this post?

For ESSEC students targeting investment banking or private equity, deal structuring is one of the clearest markers of “on-the-job” knowledge. Many candidates can explain EV/EBITDA; far fewer can articulate when you would propose an earn-out instead of a price cut, how much rollover equity is typical in sponsor-backed deals, or how contingent payments are discounted and recorded.

Understanding these tools matters for three reasons:

  1. Interviews: Questions on earn-outs and vendor rollover appear frequently in technical and case interviews. Being able to speak in terms of incentives and risk, not just definitions, differentiates you.
  2. Live work: As a junior in M&A, you will build models where 10–40% of consideration is contingent. Mis-modelling that leg can distort valuation, internal rate of return (IRR), and leverage metrics.
  3. Client dialogue: CEOs and founders often care more about earn-out mechanics, governance, and downside protection than about abstract DCF outputs. Structuring is where banking becomes advisory, not just arithmetic.

Earn-outs – pricing uncertainty with contingent payments

An earn-out is a contractual arrangement where part of the purchase price is paid in the future if the target achieves predefined performance metrics (such as revenue, EBITDA, or users) over a measurement period. Economically, it converts part of the fixed price into a state-contingent claim on future outcomes.

Suppose a buyer and seller disagree on the sustainable EBITDA level. The seller believes the business can reach EUR 20m of EBITDA in three years; the buyer is only comfortable underwriting EUR 15m. An earn-out can bridge this gap by paying a base purchase price consistent with EUR 15m, plus a contingent payment if actual EBITDA falls within (or above) a specified range.

From a valuation perspective, the earn-out has three key components:

  • Performance metric and definition (EBITDA, revenue, gross profit; GAAP vs adjusted; FX treatment).
  • Pay-out function mapping metric values to consideration (for example, linear, step, or capped).
  • Discounting and probability-weighting of future pay-outs to compute present value.

The payout curve below shows that earnout payments rise as EBITDA improves, with a floor below the threshold and a cap beyond which additional performance does not yield further payment.

Earn-out payout profile as a function of EBITDA performance
Figure 1 – Example earn-out pay-out curve linked to EBITDA: below a threshold, the earn-out pays zero; between the threshold and the cap, pay-out increases with EBITDA; above the cap, additional performance does not increase consideration.
Il would add value for your post if you can provide the Excel (with the parameters to play with) that you used to create the figure

As Figure 1 illustrates, the earn-out can be seen as a call option written by the buyer on the future performance of the business. The seller receives upside if results exceed the base case, but bears downside if performance disappoints. For the buyer, this reduces the risk of overpaying based on optimistic projections and aligns seller incentives to support post-closing integration and growth.

In practice, the main challenges with earn-outs are not mathematical but behavioural and legal: defining metrics that cannot be easily manipulated, setting governance rules (who controls capex, pricing, hiring), and designing mechanisms for dispute resolution. Investment banks help by modelling multiple scenarios, benchmarking structures to market practice, and ensuring that legal drafting matches the economics in the spreadsheet.

Rollover equity – keeping the seller in the game

Rollover equity refers to the portion of the seller’s equity that is not sold for cash at closing, but reinvested into the new capital structure. In sponsor-backed deals, it is common for founders and management to roll over 20–40% of their pre-deal ownership. The rationale is twofold:

  • The buyer reduces the immediate cash outlay and increases alignment: the seller remains exposed to future value creation.
  • The seller keeps a “second bite of the apple”: if the PE fund executes its value-creation plan, rolled equity may be sold at a higher multiple at exit.

From a modelling standpoint, rollover equity affects both valuation and IRR attribution. Consider a deal where the implied enterprise value is EUR 200m, funded by EUR 120m of debt, EUR 50m of new equity from the sponsor, and EUR 30m of seller rollover. If the business is later sold for EUR 300m, the allocation of proceeds between sponsor and seller depends on their respective equity stakes and any preferred or ratchet instruments.

IRR comparison between all-cash sale and partial rollover equity for the seller
Figure 2 – Stylised IRR for the seller in two structures: (i) all-cash sale; (ii) 70% cash + 30% rollover equity. With strong post-deal value creation, the rollover structure produces a higher overall IRR for the seller.

As Figure 2 suggests, for sellers who believe in the buyer’s ability to grow the business, accepting rollover can increase expected IRR, even though it reduces immediate liquidity. For buyers, requiring some rollover is a signalling device: if the seller refuses to keep any skin in the game, that may indicate scepticism about the forecast.

Investment banks advising the seller will therefore frame the decision not just in terms of headline price, but in terms of risk-adjusted value and liquidity preferences. For founder-led companies, personal risk tolerance and diversification needs matter as much as expected uplift.

Contingent consideration in the valuation model

From the perspective of a valuation or LBO model, contingent consideration (earn-outs, contingent value rights (CVRs), deferred payments with performance triggers) must be integrated explicitly into the cash-flow profile for both parties. Conceptually, you proceed in three steps:

  1. Define states of the world (for example, downside, base, upside) with associated performance metrics (EBITDA, revenue, net promoter score (NPS)).
  2. Apply the contractual pay-out function to each state to compute the contingent leg of consideration.
  3. Probability-weight and discount each state back to closing, using a discount rate consistent with the risk of the contingent claim (typically higher than the buyer’s WACC).

On the buyer’s side, the expected cost of contingent consideration affects both sources & uses at closing and post-deal leverage metrics. On the seller’s side, it determines expected proceeds and IRR, but with higher dispersion than a pure cash deal.

Sources and uses diagram including cash, rollover equity, and contingent consideration
Figure 3 – Simplified sources & uses for a deal combining cash, seller rollover equity, and contingent consideration. The expected value of the earn-out is modelled separately and may be financed from future operating cash flows rather than funded entirely at closing.

Figure 3 shows a stylized sources & uses table where the base cash consideration is funded at closing, while the expected value of the earn-out is treated as an off-balance-sheet liability that will be funded over time from cash flows. Modelers must decide whether to treat this as debt-like (affecting leverage) or equity-like (affecting valuation but not covenants), depending on accounting treatment and negotiation.

How investment banks use these tools in practice

In live mandates, investment banks use structuring levers to solve concrete constraints:

  • Bridging valuation gaps: Earn-outs and seller notes allow deals to clear when buyer and seller have different expectations about growth or margin expansion.
  • Managing financing constraints: Deferring part of consideration via contingent payments can make a deal financeable within leverage limits and rating constraints.
  • Aligning incentives: Rollover equity and performance-based instruments keep key management motivated post-closing.
  • Signalling and negotiation: Willingness to accept rollover or contingent pay-outs signals confidence in the business to the other party and to co-investors.

On the execution side, junior bankers support this by:

  • Building flexible models where earn-out parameters, rollover percentages, and discount rates can be sensitized.
  • Preparing deal decks that show IRR profiles and downside cases across alternative structures.
  • Coordinating with legal counsel so that the SPA drafting matches the model (definitions of EBITDA, caps, floors, baskets, dispute mechanisms).

The key mindset shift is that price and structure are not independent. A buyer can pay more headline value if a larger share of that value is contingent. A seller can accept a lower base price if the earn-out and rollover offer enough upside. Good bankers are those who can use these levers to construct an efficient trade that both sides can sign.

Related posts on the SimTrade blog

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

   ▶ Ian DI MUZIO Valuation in Niche Sectors: Using Trading Comps and Precedent Transactions When No Perfect Peers Exist

   ▶ Roberto RESTELLI My Internship at Valori Asset Management

Useful resources

American Bar Association (2010) Model Stock Purchase Agreement – commentary on earn-out provisions and contingent consideration, Second Edition.

American Bar Association (2010) Model Stock Purchase Agreement – commentary on earn-out provisions and contingent consideration, Second Edition.

Koller, T., Goedhart, M., & Wessels, D. (2020) Valuation: Measuring and Managing the Value of Companies (7th edition). Hoboken, NJ: John Wiley & Sons.

McKinsey & Company (2025) Valuation: Measuring and Managing the Value of Companies 8th Edition, Wiley.

Rosenbaum, J., & Pearl, J. (2021) Investment Banking: Valuation, Leveraged Buyouts, and Mergers & Acquisitions (chapters on the M&A process and deal structuring).

Taleb, N. N. (2018) Skin in the Game: Hidden Asymmetries in Daily Life, Random House Publishing Group.

About the author

The article was written in January 2026 by Ian DI MUZIO (ESSEC Business School, Master in Finance (MiF), 2025–2027).

   ▶ Read all posts written by Ian DI MUZIO

February 2026: Derivatives – Monthly Selection from the SimTrade blog

Most Read Articles about Derivatives on the SimTrade Blog

This monthly selection highlights key articles on derivatives, chosen based on their pedagogical value, practical relevance, and readership engagement.

   ▶ Jayati WALIA Brownian Motion in Finance

   ▶ Akshit GUPTA Option Greeks – Vega

   ▶ Shengyu ZHENG Pricing barrier options with analytical formulas

   ▶ Tianyi WANG Understanding Snowball Products: Payoff Structure, Risks, and Market Behavior

   ▶ Saral BINDAL Implied Volatility and Option Prices

SimTrade Editorial Picks

In addition to the most read posts, the SimTrade editorial team highlights the following articles for their strong educational value in the world of option pricing and investment banking.

   ▶ Lucas BAURIANNE The Golden Boy: Une immersion dans l’univers des banques d’investissement

   ▶ Alexandre VERLET Classic brain teasers from real-life interviews

   ▶ Saral BINDAL Measures and statistics of business activity in global derivative markets

   ▶ Marie POFF Film analysis: Rogue Trader

A solid understanding of derivatives is essential for careers in trading, risk management, and corporate finance, making these articles particularly valuable for aspiring finance professionals.

My Internship Experience as an Accounting Intern at Municipal Road and Bridge Building Materials Group

Bochen LIU

In this article, Bochen LIU (Queen’s Smith School of Business, BCom 2023–2027; ESSEC BBA Exchange Program, Fall 2025) shares his professional experience as an Accounting Intern at Municipal Road and Bridge Building Materials Group in Beijing, China.

About the company

Municipal Road and Bridge Building Materials Group is a Beijing-based state-owned enterprise specializing in the production of asphalt mixtures, high-strength concrete, fiber-reinforced concrete, and other construction materials used in municipal infrastructure projects.

The company operates within the broader Beijing Municipal Road & Bridge system, a large infrastructure group formed through state-owned restructuring and joint investment by municipal entities. The broader group has registered capital exceeding RMB 2.2 billion, total assets around RMB 39 billion, more than 110 subsidiaries, and over 16,000 employees, reflecting the large operational scale of the infrastructure network in which the materials business operates.

As part of this infrastructure supply chain, the materials division supports road construction, bridge engineering, and urban maintenance projects by providing standardized building materials and technical support for municipal contractors.

Logo of Municipal Road and Bridge Building Materials Group.
Logo of Municipal Road and Bridge Building Materials Group
Source: the company.

I worked in the accounting department, which handled transaction recording, supplier invoice verification, project expense tracking, and preparation of monthly internal financial summaries to ensure operational data was accurately reflected in the accounting system and compliant with national regulations.

My internship

During the summer of 2022, I joined Municipal Road and Bridge Building Materials Group as an Accounting Intern. This experience provided hands-on exposure to corporate accounting practices, financial reporting processes, and internal workflow management, helping bridge the gap between academic learning and real-world financial operations.

The internship allowed me to understand how accounting systems function in practice and how accurate financial information supports management decisions and organizational efficiency.

My missions

My primary responsibility was preparing monthly debit and credit financial reports. This required collecting, verifying, and consolidating financial data from multiple departments, ensuring all entries were accurate and compliant with national accounting standards. Through this process, I became familiar with journal entries, reconciliation procedures, and the role of accurate reporting in corporate governance.

In addition to reporting tasks, I collaborated with senior accountants in reviewing financial records and identifying potential discrepancies. By participating in discussions and assisting with verification processes, I supported the team’s application of accounting principles and contributed to improving data reliability within the accounting workflow.

Required skills and knowledge

This internship required both technical and interpersonal competencies. On the technical side, I applied accounting principles, financial data verification methods, and report preparation techniques to present financial information clearly and accurately. I also learned how to structure reports so that they were informative, reliable, and useful for managerial review.

Soft skills were equally important. Communication and teamwork were necessary when coordinating with accountants and other departments, while attention to detail ensured data accuracy. These skills helped me contribute effectively to the accounting team and maintain smooth financial processes.

What I learned

This internship gave me a practical understanding of corporate accounting and financial reporting. Corporate accounting focuses on recording and verifying daily transactions, classifying expenses, and maintaining accurate internal financial data. Financial reporting, in contrast, involves summarizing this accounting information into structured monthly reports used by managers to monitor costs and evaluate project performance. Through my work checking invoices, reconciling entries, and helping prepare monthly summaries, I saw how accurate accounting records form the foundation for reliable financial reports and how errors at the transaction level can directly affect managerial decisions.

I also learned how structured reporting supports decision-making. By helping prepare monthly financial summaries, I saw how standardized reports allow managers to track project costs, compare spending across periods, and identify budget deviations. The experience also strengthened my collaboration skills, as I regularly coordinated with procurement and project teams to confirm invoice details and transaction information before the reports were finalized.

Additionally, the internship reinforced my interest in finance by connecting accounting practices with broader financial concepts. My prior exposure to financial markets through SimTrade helped me interpret accounting data in a strategic context and understand how corporate accounting interacts with financial decision-making.

Financial and business concepts related to my internship

I present below three financial and business concepts related to my internship: financial reporting accuracy, internal control and reconciliation, and corporate governance through accounting information.

Financial reporting accuracy

Financial reporting accuracy is fundamental in corporate accounting. Preparing monthly debit and credit reports required ensuring that all entries were properly recorded and verified. Accurate financial reports provide management with reliable information for monitoring performance, planning operations, and making strategic decisions.

Internal control and reconciliation

Internal control and reconciliation processes help prevent errors and detect discrepancies in financial records. By reviewing data with senior accountants and checking financial entries, I observed how structured verification procedures maintain data integrity and reduce operational risk within accounting systems.

Corporate governance

Corporate governance relies on transparent and reliable accounting information. Well-prepared financial reports allow organizations to comply with regulations, demonstrate accountability, and support informed decision-making. My work on monthly reporting illustrated how accounting functions contribute directly to organizational stability and managerial oversight.

Why should I be interested in this post?

This post provides insight into how corporate accounting operates within a large infrastructure-related enterprise. Students interested in accounting, corporate finance, or financial analysis can understand how financial reporting, verification procedures, and structured accounting systems support organizational decision-making.

The experience demonstrates how early internships can strengthen both technical accounting knowledge and professional skills, forming a solid foundation for careers in finance and business.

Related posts on the SimTrade blog

   ▶ All posts about Professional experiences

   ▶ Alexandre VERLET Classic brain teasers from real-life interviews

   ▶ Samia DARMELLAH My experience as an accounting assistant at Dafinity

   ▶ Alessandro MARRAS My professional experience as a financial and accounting assistant at Professional Services

Useful resources

Municipal Road and Bridge Building Materials Group official website

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

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

Drury, C. (2018) Management and Cost Accounting, 10th edition, Cengage Learning EMEA.

About the author

The article was written in February 2026 by Bochen LIU (Queen’s Smith School of Business, BCom 2023–2027; ESSEC BBA Exchange Program, Fall 2025).

   ▶ Discover all posts by Bochen LIU

Structured products: what’s behind them?

Jules HERNANDEZ

In this article, Jules HERNANDEZ (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2021-2025) writes about structured products, the different types of products sold to institutional and retail investors. This article aims to introduce structured products, explore their various types, and explain how these instruments are engineered by structurers. This article will also study the so-called Greeks, which are used by traders after the issuance of these products.

What is a structured product ?

A structured product is a type of financial investment whose return is tied to the performance of one or more underlying assets and defined by pre-specified features and scenarios. It is not a simple buy-and-hold portfolio in equities and bonds, but rather a customized investment instrument created by combining multiple financial products to achieve a particular risk-return profile (we will explore in detail, in further sections, how these combinations are built). According to BNP Paribas Wealth Management, in an article written the 27/07/2021, structured products can be broadly defined as “a savings or investment product where the return is linked to an underlying asset with pre-defined features (maturity date, coupon dates, capital protection level …)”. These instruments belong to the category of non-traditional investment strategies and are typically constructed by packaging together a bond, one or more underlying assets, and financial instruments such as derivatives. “It can serve as a tool for portfolio diversification and an alternative to traditional investments”, according to an article written on the Société Générale France Website by Yaël Eljarrat-Ouakni, Head of Structured Products offerings at Societe Generale Private Banking France. What makes structured products distinctive is that their payoff is conditioned on market outcomes rather than simply the passage of time. The return an investor receives (whether it involves coupon payments, principal protection, or participation in underlying asset performance) is determined at the product’s launch and depends on how the reference markets evolve relative to the conditions set in the product’s terms. In essence, structured products are tailor-made solutions that allow investors to express specific market views or achieve particular investment goals while defining the precise risk and return mechanics in advance. However, because they combine multiple financial instruments and scenarios, these products are considered more sophisticated than traditional securities and require careful understanding before investment.

Main parameters of a structured product

A structured product is defined by a set of key parameters that determine its payoff structure and risk-return profile. Each product has different settings that are tailored to the risk-return ratio wanted by the investor. The main components are the following:

Underlying asset

Each structured product is linked to an underlying asset whose performance determines the product’s payoff. The underlying can be a single stock, an equity index, a basket of shares, an interest rate, a credit entity, a commodity, or a currency pair. All asset classes can be underlying assets of a structured product. Some structured products may also be linked to a combination of two or more underlying assets. For instance, a product can be indexed on a basket of equities, such as Apple, Microsoft, and Tesla. More complex structures may even combine different asset classes, for example providing exposure to both a single stock and an interest rate, such as Apple and the French 10-year government bond yield (OAT 10Y). The characteristics of the underlying, such as volatility or correlation (in the case of a basket of two or more assets), and overall market conditions, play a key role in determining the product’s pricing, risk profile, and potential return. The nature of the underlying is therefore a central element in understanding the behavior of a structured product.

Coupons

Similarly to a bond, a coupon is the pre-agreed (before the product is bought) potential income paid to the investor during the life of the structured product. They may be fixed or conditional, and in many structures, they are paid only if the underlying remains above a predefined barrier on specific observation dates. The level of coupons offered depends on several market factors, including volatility of the underlying, interest rates, maturity, dividends in case of a stock or index underlying, and the level of protection embedded in the structure. We will see later in further details how these factors impact the level of the coupon.

Maturity

Maturity is the predetermined date on which the structured product expires, and its final payoff is calculated. It may range from short-term (around one year) to long-term (up to ten years or more). Any capital protection mechanism typically applies only at maturity. Certain products also include early redemption features, such as autocall mechanisms, which allow the product to terminate before its scheduled maturity if specific market conditions are satisfied.

Capital protection level

The capital protection level defines the extent to which the initial investment is protected at maturity. Protection may be full, partial, or conditional upon the underlying not falling below a specified barrier. If the protection condition is breached, the investor may be exposed to partial or total loss of capital. This parameter is fundamental, as it largely determines the downside risk embedded in the product. We will explore later why this protection matters and how by reducing the capital protection, an investor can increase its coupon.

Observation frequency

Observation frequency refers to how often the product’s conditions are assessed. Observations may occur annually, semi-annually, quarterly, monthly, or even daily, depending on the structure. Coupon payments, barrier monitoring, and early redemption triggers are evaluated on these predefined dates. For instance, the frequency of observation affects the probability of coupons being paid and the likelihood of early redemption.

Issuer

A structured product is issued by a financial institution, typically a bank. The most known issuers on the market are JP Morgan, Goldman Sachs, BNP Paribas and Société Générale. In France, a study from SRP Investors are therefore exposed to issuer credit risk, meaning that the repayment of capital and any coupons depends on the issuer’s financial strength and ability to meet its obligations. In the event of issuer default, investors may incur losses regardless of the performance of the underlying asset. Assessing the creditworthiness of the issuer is therefore essential.

Liquidity conditions

Liquidity conditions refer to the ability to sell the structured product before maturity. Although many issuers usually provide secondary market pricing under normal market conditions, liquidity is not guaranteed. The product’s market value before maturity can fluctuate significantly due to changes in the underlying asset, volatility, interest rates, and credit spreads. As a result, exiting early may lead to gains or losses that differ substantially from the payoff expected at maturity.

The different families of products

Capital growth products

Capital growth products are structured products designed primarily to enhance the value of the initial investment at maturity rather than to generate regular income during the life of the product. Returns are typically paid at maturity and depend on the performance of the underlying asset according to predefined participation rates, leverage factors, or payoff formulas. These products may offer full or partial capital protection, or they may provide enhanced upside participation in exchange for limited or conditional downside protection. They are generally suitable for investors seeking medium- to long-term capital appreciation and who do not require periodic income. Most of these products bear the name of “Athena products” and usually have autocall features, which we’ll explain in further sections.

Yield products

Yield or income products are designed to generate regular conditional coupons during the life of the investment. These coupons are typically paid periodically (for instance, quarterly, or annually) if certain market conditions are met. The income offered is usually higher than traditional fixed-income instruments because investors accept conditional and additional downside risk. In many cases, capital is only protected if the underlying asset does not breach a predefined barrier at maturity. Common examples of yield products are Phoenix products or reverse convertibles, which we will explain in further sections.

Main Types of Structured Products

This section will now explore what are the different types of structured products issued by banks. Many standardized products exist, and we will explore the main ones.

Autocall

Autocallable notes (often simply called “Autocalls”) are structured products that offer conditional coupons and include an automatic early redemption feature. On predefined observation dates, if the underlying asset trades at or above a specified level (the autocall barrier), usually the strike of the underlying, the product is redeemed early, and the investor receives the nominal amount plus the accrued coupon. As a reminder, the strike price is the price at which the underlying asset trades when the structured product is issued. The strike price is often expressed as a percentage of the initial level, which is always 100, representing the initial level set at inception. If the underlying does not trade above the strike level at the observation date, e.g. 90, the product continues until the next observation date or until maturity. Autocalls are among the most widely distributed structures in Europe. According to the AMF report “Markets and Risk Outlook” of 2025, “The most common structure for structured products distributed in Europe, as in the rest of the world, is the autocall” and “In France, in 2024, autocalls accounted for almost two-thirds of the structured products distributed.”

Let’s illustrate this product with a concrete example. Consider a retail investor purchasing an autocall linked to LVMH stock. The product has a 5-year maturity, pays an annual coupon of 5%, and features an autocall barrier set at 100% of the strike price. Additionally, the investor opts for capital protection at 50%, which limits potential losses in adverse scenarios. The three scenarios below demonstrate the possible outcomes under different market conditions.

Bullish Scenario : Early redemption of the product
Bullish Scenario : Early redemption of the product

In this first scenario, thanks to favorable market conditions, the price of LVMH at the year 1 observation date is above its initial level. As a result, the product is redeemed early, and the investor receives both the coupon and the nominal.

Bearish Scenario
Bearish Scenario

In this scenario, the market conditions didn’t allow an early redemption of the product, because the price of LVMH decreased. Since the product wasn’t redeemed, no coupon was paid. However, at maturity, since the price of LVMH is still within the “capital protected zone, the investor receives back the full nominal of his investment.

Market crash Scenario : The capital is at risk
Market crash Scenario : The capital is at risk

The worst scenario happened for the investor. The price of LVMH dived, and it reached a price below the 50% capital protection barrier. Therefore, the investor did not receive any coupon and the investor suffered a loss of capital. At maturity, in this example, the price of LVMH observed at maturity was 45%, therefore the investor only got 45% back of his initial investment.

Worst of products

“Worst of” structured products are linked to a basket of underlyings, and their performance is determined by the worst-performing asset in the basket. Imagine a worst of product with 3 underlying assets, Apple, Microsoft, Amazon. At observation date, we will take into consideration for the payment of the coupon (and the autocall feature if the product is a Autocall worst of) the least performative asset. For instance, if Apple is at 70% of the strike, Microsoft at 80% and Amazon at 65%, only Amazon’s performance will be taken into account. While this structure allows for higher coupon payments due to increased risk, it also significantly raises downside exposure because capital protection and coupon conditions depend on the weakest underlying. It is in the investor’s interest to select a basket of underlyings whose correlation is as close as possible to 1. Ideally, all the assets should move in the same direction. A correlation of -1 would be completely detrimental to the investor since if one stock performs well, the other stock has a high probability of opposite performance. Some banks also issue “Best of” products which are less risky, because the underlying taken into account is, here, the strongest asset, reducing therefore the risk probability.

Bearish products

Bearish products are designed for investors with a negative or moderately bearish market view. In simpler words, the investor is going against the market, betting the market will go down. In these structures, coupons or early redemption may be triggered if the underlying remains below or declines toward certain predefined levels. They allow investors to monetize a non-bullish market scenario while still embedding conditional risk protection mechanisms. These products are not common, but for certain investors those can be interesting for tactical diversification or hedging positions.

Phoenix products

Phoenix products are income-generating structured products that pay periodic conditional coupons, often featuring an autocall barrier. However, unlike standard autocalls, coupon payments do not necessarily require early redemption. Coupons may accumulate and be paid later if conditions are subsequently met. At maturity, all the coupons accumulated are paid and the capital is refunded if the underlying is not below the capital protection barrier. Phoenix structures are widely used in private banking for investors seeking regular yield.

To illustrate this kind of products, let’s imagine the following product : a Phoenix product index on the NVIDIA stock is bought by an investor with the following parameters: a 5-year maturity, a coupon of 5%, an autocall barrier set at 100% of the strike, a coupon barrier of 70%, and a capital protection barrier set at 50%. The three scenarios below demonstrate the possible outcomes under different market conditions.

Bullish Scenario : Early redemption of the product
Bullish Scenario : Early redemption of the product

In this first scenario, thanks to favorable market conditions, the price of NVIDIA at the year 1 observation date is above its initial level. As a result, the product is redeemed early, and the investor receives both the coupon of 5% and the nominal.

Bearish Scenario
Bearish Scenario

In this scenario, the market conditions did not allow for early redemption of the product, because the price of NVIDIA decreased. However, the investor still received 4 out of 5 available coupons, since the price of NVIDIA lied above the coupon barrier every year except at the year 3 observation date. At maturity, even if NVIDIA is trading at 95% if its initial level, the entire nominal is totally refunded to the investor, thanks to the capital protection barrier. Therefore, in this scenario, at maturity, the investor received 120% of its initial investment.

Market crash scenario
Market crash scenario

The worst scenario happened for the investor. The price of NVIDIA dived to 45% of its initial level, a price below the 50% capital protection barrier. Here, we can observe that, despite this tremendous decline, two coupons were still paid to the investors (at year 1 and year 3). However, in this example, the price of NVIDIA observed at maturity was 45%, therefore the investor only got 45% back of his initial investment. Therefore, at maturity, the investor received 45% (adjusted nominal) + 10% (coupons) = 55% of its initial investment. The investor suffered a significant loss of capital.

Credit Linked Note (CLN)

Credit Linked Notes are structured products that provide exposure to the credit risk of one or several reference entities. Instead of being primarily linked to equity performance, CLNs are tied to the occurrence of predefined credit events (such as default or restructuring). Investors receive enhanced yield in exchange for assuming the credit risk of the reference entity. If a credit event occurs, the investor may suffer partial or total loss of capital depending on the recovery rate. On a more technical point of view, in the case of a CLN, the investor is selling Credit Default Swaps (CDS) to finance the coupon he’s supposed to receive if no credit default occurs. These CLN can be linked to more than one company and are tools commonly used for yield enhancement and credit diversification strategies.

Reverse Convertible

Reverse convertibles are yield-enhancement products that offer high fixed coupons in exchange for conditional exposure to the downside of an underlying asset. In these products, regardless of the performance of the underlying asset, the coupon will always be paid. But, on the other hand, if the underlying falls below the capital protection barrier, repayment may occur in shares (or at a value linked to the underlying’s final level), leading to potential capital loss. Otherwise, if the underlying remains above a predefined strike or barrier at maturity, the investor receives full nominal repayment. Therefore, these products always run until maturity. Depending on the maturity, the investor is taking an illiquidity risk (this risk is associated with every type of structured products, even if there might be liquidity conditions that can allow the investor to sell his position on a secondary market).

Let’s illustrate this product with a real example. Consider a retail investor purchasing a reverse convertible linked to Apple stock. The product has a 5-year maturity and pays an annual coupon of 5%. Additionally, the investor opts for capital protection at 70%, which limits potential losses in adverse scenarios. The three scenarios below demonstrate the possible outcomes under different market conditions.

Bullish Scenario
Bullish Scenario

In this first scenario, the investor received the 5 coupons and its initial investment since the price of Apple is trading at maturity at a higher level than at the inception of the product. Therefore, in this case, the investor has received, at maturity, 125% of its initial investment.

Bearish Scenario
Bearish Scenario

In this second scenario, all coupons have been paid to the investors and the investor received here also, at maturity, its full investment since the price of Apple, ended above the capital protection barrier of 70%. Therefore, in this case, the investor has received, at maturity, 125% of its initial investment.

Market crash scenario
Market crash scenario

The worst scenario happened for the investor. The price of Apple was trading at maturity at 60% of the initial level. Therefore, as always, all the coupons were paid, but the investor suffered a loss of capital of 40%. Indeed, only 60% of the nominal was refunded to the investor since the price of Apple ended below the capital protection barrier. Therefore, in this case, the investor has received, at maturity, 60% (adjusted nominal) + 25% (coupons) = 85% of its initial investment.

Key features of structured products

Structured products are engineered using specific mechanisms that shape their risk-return profiles. By playing with the parameters, we’ll explore in this section, an investor is able to shape an ideal product, that replicates its market view. By tailoring these mechanisms, an issuer can adjust the risk/return ratio of a structured product. Overall, taking more risks means greater coupons for the investor (as always, if the conditions for payment are met).

Capital protection barriers

A capital protection barrier is a predefined level of the underlying asset below which the investor may incur a loss of capital. If the underlying never breaches this barrier during its observation period (or at maturity, depending on the structure), the investor can benefit from full or partial protection of their initial investment. Barriers are usually expressed as a percentage of the initial underlying level (set at 100). For instance, if a structured product sets a capital protection barrier at 70%. This means that, at maturity, if the underlying lies below this barrier, the investor will suffer a capital loss, proportional to how deep he is. If the underlying is trading at 65% of the strike at maturity, the investor will lose 35% of its invested capital. Otherwise, if the underlying asset closes at 71%, the entirety of the nominal invested will be repaid to the investor.

Investors should understand that the lower the capital protection barrier, the “safer” the investment, and therefore the lower the coupon offered. Conversely, the higher the capital protection barrier, the riskier the product becomes, as the probability of incurring a capital loss increases, and accordingly, the higher the coupon offered. It is also possible to remove all kinds of capital protection, but this rarely the case since it offers full exposure to the underlying asset and is therefore very risky.

Total capital protection

Total capital protection means that the investor’s principal is guaranteed at maturity regardless of the performance of the underlying. In fully capital-protected products, the investor will receive at least the nominal amount back at maturity. The products with this feature are considered “safe”, but the investor bears a huge illiquidity risk depending on the maturity. Even though he can exit the product under certain liquidity circumstances but recall that these conditions are not always in favor of the investor. The issuer is not willing to lose money by providing these exit possibilities. Therefore, exiting a structured before maturity goes almost always with a discount.

Decrement indices as underlying

This feature is one the most complex features of structured products and is very often misunderstood by investors, but also by wealth managers. This feature is extremely risky as the Central Bank of Ireland tried to warn investors but also finance professionals with a letter in March 2023 to warn about these decrement indices. A decrement index is a type of financial index that gradually decreases by a fixed amount at regular intervals, such as daily, monthly, or annually. Often, this fixed reduction represents dividends paid by the underlying stocks or a pre-specified amount chosen by the index provider. Essentially, the index is designed to drift downward over time in a predictable way. To price a structured product, the issuer (the bank and its traders/structurers) must anticipate two parameters, the risk-free rate and the expected dividends of the underlying in case of a stock or an index. The issue with dividends is that their level is uncertain. They are rarely stable, and companies decide to adjust it depending on their results or their financing needs. This uncertainty makes the anticipation of the dividends really complex for structurers and this uncertainty must be paid by the investors. What offer the banks to avoid the investor to “pay” this uncertainty is to anticipate these dividends by decreasing by a fixed amount. The coupon for the investor becomes therefore more interesting for the investor but the investment becomes significantly riskier. As a matter of fact, let’s imagine that an investor buys a product linked to the European Stoxx 50 (SX5E), with a decrement of 5% yearly. Each year, 5 points will be removed from the performance of the SX5E. This reduction increases a lot the probability that, at maturity, the underlying asset lies under the capital protection barrier.

Autocall barriers

Autocall barriers are features used in every autocall products. An autocall barrier is a trigger level set for early redemption. On each observation date, if the underlying asset’s price is at or above this barrier, the product is redeemed early and the investor receives the nominal amount plus an accrued coupon. If the barrier is not reached, the product continues until the next observation date or maturity. The probability of early redemption is influenced by volatility, time to maturity, barrier level, and observation frequency. Lower volatility increases the likelihood that the underlying remains near its initial level and therefore increases the probability of being called. Higher observation frequency increases the number of opportunities for redemption. Lower autocall barriers raise the probability of early termination but reduce the coupon that can be offered, as the option budget must reflect the increased likelihood of payout.

Degressive or step-down barriers

Degressive barriers (also called step-down barriers) are barrier levels that decrease over time according to a predetermined schedule (not to be confused with decrement, which is totally different). This feature can affect coupon barriers and/or autocall barriers. This mechanism makes it easier for the product to maintain capital protection or coupon conditions as time passes, since the barrier getting lower, it becomes less risky for the investor and easier to get the coupon even if the underlying has a negative performance. Step-down features are commonly used to balance downside protection with attractive coupon levels.

Leveraged products

Leveraged products amplify the exposure to the underlying’s performance. Instead of offering a one-for-one participation in gains or losses, they provide a multiple (e.g., 2×) of the underlying’s movement above or below a certain level. Leveraged structures can offer higher potential returns but also involve significantly greater risk and complexity, especially in volatile markets. These investments are highly risky and are not common in France or in Europe due to legislation.

Memory effect

The memory effect is a feature found in some structured products, particularly Phoenix, where missed coupon payments can be “remembered” and paid later if conditions are subsequently met. For example, if the product fails to meet the coupon condition on one observation date but satisfies it on subsequent dates, the investor may receive the accumulated unpaid coupons at that later time. This mechanism enhances the probability of ultimately receiving the anticipated income. This feature makes the product less risky and therefore reduces the amount of the coupon.

Technical composition of a structured product: What’s behind the scene?

Structured products may appear complex, but from a financial engineering perspective, most of them can be broken down into two fundamental building blocks: a fixed-income component and a derivatives component. Understanding this decomposition is key to understanding pricing, risk, and payoff mechanics. The fixed-income component corresponds to a zero-coupon bond, and the derivatives component is made of one or multiple options.

Zero-coupon bond

The zero-coupon bond is the capital preservation engine of the structured product. To build a structured product, a zero-coupon bond is purchased at a discount and repays its full nominal value at maturity. In structured products, part of the investor’s initial capital is allocated to buying a zero-coupon bond issued by the bank. If held until maturity, this bond grows back to the nominal amount, thereby ensuring full or partial capital protection (depending on the structure). For example, if interest rates are positive, the issuer does not need to invest 100% of the investor’s capital to guarantee 100% repayment at maturity. A portion (say 85–95%) may be sufficient to secure the nominal amount at maturity, because when a zero-coupon is bought, it is bought a discount. Indeed, the formula for this instrument is as follows: PV = N/(1+r)T, with N, the nominal, r, the interest rate, T, the number of years, while PV is the present value or simply the price. For example, if interest rates are 3% and maturity is five years, the issuer needs approximately 86.3% of the invested capital to guarantee repayment of 100 at maturity. The remaining 13.7% constitutes the option budget that will finance the derivative component of the structure. This simple discounting mechanism explains why the interest rate environment plays a crucial role in structured product design. When interest rates are high, the present value of the guaranteed capital is lower, leaving a larger budget to purchase optionality. Conversely, in a low-rate environment, capital protection becomes more expensive, reducing the amount available to enhance coupons or upside participation. Moreover, the longer the maturity, the cheaper the bond. This allows the investor to have a greater budget for the other component, that shapes the payoff. The bigger budget for the options you have, the greater your coupon can be.

Finally the investor has to remember that the zero-coupon bond is not necessarily a risk-free investment. Since the issuer of the bond is the bank that also issues the structured product, the investor bears the issuer’s credit risk default. Therefore, a higher issuer credit spread reduces the cost of the funding leg and mechanically increases the option budget, which may result in more attractive coupons, although at the expense of higher credit risk for the investor.

Options

The performance component of a structured product is constructed through a portfolio of options. Once the funding leg has secured the desired capital protection level, the remaining capital is allocated to buying and/or selling derivative instruments that shape the payoff profile. The option portfolio may include long call options to provide upside participation, short put options to finance enhanced coupons, digital options to generate fixed conditional payments, and barrier options to create knock-in or knock-out features. We will now explore deeper how the mechanisms we explained before are replicated with options.

What about capital protection barriers?

Capital protection barriers are engineered primarily through put options. Consider a structure offering full capital protection as long as the underlying does not fall below 60% of its initial level at maturity. Economically, this is equivalent to the issuer being short a put down-and-in (PDI) option at 60% of the initial level. If the underlying finishes above that level, the put expires worthless, and the investor receives full nominal repayment. If it finishes below, the put is in the money and the investor participates in the downside beyond the strike, typically through physical delivery. Therefore, the sale of this PDI brings cash to the investor that allows to buy more options to increase the potential payoff. By bearing a downside risk with the investor being short a PDI, the premium of the option brings cash to finance other options. The price of these put options varies a lot depending on many factors: volatility of the underlying, maturity but also type of barrier. As a matter of fact, a PDI with a European barrier is cheaper than a PDI with an American barrier. Let’s break it down. European barriers can only be triggered at the end of the product life, at the maturity, but an American put can be exercised at any time before maturity. Ultimately, an American option gives more in-the-moneyness probabilities to the investor who is long the put.

Moreover, there is a concept that matters a lot for structurers: the skew. Skew simply states that the downside protection is more expensive than upward protection. In other words, put are more expensive than call for a same (opposite) strike. This is explained because investors fear more the loss than the gains. This concept affects therefore the price of a PDI option, in the advantage of the investor if he’s willing to take a riskier standpoint. Finally, another alternative to PDI to gain downside protection, is the Gear Put, which is a leveraged put. As I mentioned earlier, these protections are not common since the European and French regulators do not want that retail investors take leveraged downside positions.

How do structurers build autocall barriers ?

As an reminder, an autocall is triggered if, at the observation date, the underlying trades above the autocall barrier. This barrier is synthetized by structurers by using knock-out digital options, calls here, also called barrier options. These tools simply say that, at the observation date, if the underlying asset trades above the strike price, then the digital call is triggered and pays a fixed pre-determined amount. The payoff of these instruments is therefore simply 1 or 0 depending of the level of the underlying. Without going too deep into the technical side of these digitals. Due to the liquidity of these options, a structurer creates these barrier options using call spreads.

The Greeks, what sensitiveness do traders look at?

Structured products are not static instruments. Once issued, they are dynamically hedged by the structuring or trading desk. The risk of these products is managed through sensitivities known as “Greeks,” which measure how the product’s value changes in response to variations in market parameters. Because most structured products embed optionality, understanding these sensitivities is crucial for risk management. Traders continuously monitor delta, gamma, vega, and theta in order to hedge their positions and control their P&L (Profit & Loss).

Delta

The delta measures the sensitivity of the product’s price to small changes in the underlying asset. For instance, if a product has a delta of 0.4, a one-unit increase in the underlying leads approximately to a 0.4 increase in the product’s value. In structured products, delta is rarely constant. For capital-protected products with upside participation, delta is positive but typically less than one. For yield products such as autocalls or reverse convertibles, delta can vary significantly depending on proximity to barriers. Autocalls structures often shows complex delta behavior. When the underlying approaches the autocall barrier, delta may increase sharply due to the higher probability of early redemption (if the product is triggered, the product ends, and there is no more delta-hedging since the investor is paid). Conversely, if the underlying approaches the capital protection barrier, delta can become more negative, reflecting increasing downside exposure. Trading desks hedge delta dynamically by buying or selling the underlying asset (or futures). Because delta changes continuously, hedging must be adjusted frequently, especially in volatile markets.

Gamma

Gamma measures the sensitivity of delta to changes in the underlying price (it is the second derivative of the product value with respect to the underlying). Gamma reflects how quickly delta changes. High gamma means that delta is unstable and requires frequent rebalancing. Same as the delta, structured products with embedded barrier options often exhibit high gamma near barrier levels. For example, when the underlying trades close to a knock-in or knock-out barrier, small price movements can significantly change the probability of barrier activation, causing sharp shifts in delta. In summary, gamma risk is particularly acute near maturity or near barrier levels.

Vega

Vega measures sensitivity to changes in implied volatility. Implied volatility is not the historical volatility, but the volatility that is anticipated by the market. This implied volatility affects, by a lot, option prices. Vega indicates how much the product’s value changes when market-implied volatility moves by one percentage point. Most structured products distributed to investors are structurally short volatility. This is because enhanced coupons are financed by selling optionality, such as puts. When implied volatility rises, the value of those short options increases, negatively impacting the product’s market value. An investor has to remember that during market crises, volatility spikes can significantly deteriorate the value of structured product inventories due to their short vega profile.

Theta

Finally, the last Greek that an investor must understand is Theta. It measures the sensitivity of the product’s value to the passage of time. It represents time decay. For a long option position, theta is typically negative, as options lose value over time. For a short option position, theta is positive, reflecting the fact that the seller benefits from time passing without adverse movement. For autocall products, time decay also influences the probability of early redemption. As maturity approaches, the distribution of potential outcomes narrows, and risk becomes more concentrated around barrier levels.

Why should I be interested in this post?

You may be interested in this article for several reasons. It summarizes a wide range of key concepts related to financial products. It will therefore be particularly useful if you are an investor seeking investment solutions aimed at growing your wealth. This article provides a solid foundation for understanding these products, which are very often misunderstood. Naturally, these investments involve risks, and I strongly encourage you to fully acknowledge them, as partial or total loss of capital may be associated with this type of product. This article will also help you understand how issuers design and structure these products.

Moreover, the number of structured products sold and issued has increased a lot for few years. According to SRP and their report on the European market, in 2020, the sales volume of structured products in Europe was about more than USD$75 billion, for less than 50 000 structured products issued. In 2024, the number of structured products issued rose to more than 350 000 and the sales volume exploded to reach more than USD$250 billions. You can find below the sales volume evolution of structured products in Europe between 2020 and 2024 :


Structured products sales volume in Europe between 2020 and 2024 Structured products sales volume in Europe between 2020 and 2024

Finally, this article may prove highly valuable if you are a student looking to build your knowledge of these financial products. It will also be beneficial if you are preparing for interviews for trading floor positions at investment banks or for roles as a structured products broker. All the elements covered in this article provide relevant material to help you prepare for the technical questions typically asked by recruiters.

Related posts on the SimTrade blog

Professional experiences

   ▶ All posts about Professional experiences

   ▶ Mickael RUFFIN My Internship Experience as a Structured Finance Analyst at Société Générale

   ▶ Wenxuan HU My experience as an intern of the Wealth Management Department in Hwabao Securities

   ▶ Mathis HOUROU Client Segmentation and Private Banking: Marketing Strategy or Risk Shield?

   ▶ Lang Chin SHIU My internship experience at HSBC

Financial techniques

   ▶ Tianyi WANG Understanding Snowball Products: Payoff Structure, Risks, and Market Behavior

   ▶ Mahé FERRET Selling Structured Products in France

   ▶ Akshit GUPTA Equity structured products

   ▶ Shengyu ZHENG Reverse Convertibles

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

   ▶ Shengyu ZHENG Capital Guaranteed Products

Useful resources

Yaël Eljarrat-Ouakni What is a Structured Product? Société Générale Private Banking France.

BNP Paribas Wealth Management (07/2021) Understanding Structured Products

Autorité des Marchés Financiers (AMF) (24/05/2025) 2025 Markets and Risk Outlook

SRP (18/03/2025) Global Market review 2024, Europe Market review 2024

Central Bank of Ireland (03/03/2023) MiFID Structured Retail Product Review – Supervisory Guidance (Decrement Index warnings)

About the author

The article was written in February 2026 by Jules HERNANDEZ (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2021-2025).

   ▶ Discover all articles by Jules HERNANDEZ.

My Internship Experience as an Investment Intern at Kylin Hall Capital

Bochen LIU

In this article, Bochen LIU (Queen’s Smith School of Business, BCom 2023–2027; ESSEC BBA Exchange Program, Fall 2025) shares his professional experience as an Investment Intern at Kylin Hall Capital.

About the company

Kylin Hall Capital is a Beijing-based venture capital firm investing in early- to growth-stage technology companies in China. The firm focuses on sectors such as advanced manufacturing, clean technology, and deep-tech innovation, targeting startups with strong technological differentiation and scalable business models.

Founded to support innovation-driven companies in China’s rapidly evolving technology ecosystem, the firm operates in a venture capital market characterized by intense competition and high selectivity. Venture investors typically review a large number of potential opportunities each year while only investing in a small fraction of them. Investment decisions, therefore, rely on rigorous screening, structured due diligence, and detailed market analysis.

Kylin Hall Capital evaluates companies based on factors such as market size, technological feasibility, competitive positioning, and long-term growth potential. Its investment process involves industry research, expert interviews, competitor benchmarking, and preparation of analytical reports used by partners to assess opportunities and risks before allocating capital.

Logo of Kylin Hall Capital.
Logo of Kylin Hall Capital
Source: the company.

I worked within the investment research and analysis function, supporting the firm’s deal evaluation process. The team conducted market research, synthesized expert insights, benchmarked competitors, and prepared structured investment reports for partners. This role ensured that investment decisions were supported by reliable information, clear documentation, and consistent analytical reasoning.

My internship

As a third-year student at Queen’s Smith School of Business and an exchange student at ESSEC Business School, I joined Kylin Hall Capital as an Investment Intern in Beijing during 2024–2025. This experience gave me hands-on exposure to investment analysis, market research, and strategic decision-making within a venture capital environment.

Through this internship, I gained firsthand insight into how early-stage investment decisions are grounded in research, critical thinking, and strategic judgment. Investment analysis is not only about numerical evaluation but also about synthesizing diverse information into concise, actionable guidance for decision-makers.

My missions

My missions included summarizing expert interviews, authoring initiation reports, producing investment recommendation reports, and conducting sector research to support the firm’s investment pipeline.

I conducted and summarized expert interviews across multiple technology sectors, identifying market trends, adoption challenges, and competitive dynamics. These summaries created structured knowledge resources supporting ongoing investment analysis.

I authored an initiation report on NL-Tech, in which I analyzed the company’s target market size, customer segments, and revenue model using industry reports, public financial information, and competitor benchmarking tables. I compared NL-Tech’s product positioning, pricing logic, and technological features with key competitors and summarized the findings in a structured memo including market maps, competitor matrices, and a synthesis of expert interview insights. This report provided partners with a clear overview of the company’s market environment, differentiation, and potential strategic risks before moving forward in the evaluation process.

I also prepared an investment recommendation analysis for SAI GAN Technology, examining its business model, technological capabilities, and commercial scalability. I reviewed available company materials, analyzed its competitive advantages and operational challenges, and structured the results into a recommendation note outlining strengths, risks, and potential growth scenarios. The document translated research findings into a concise decision-support format used internally to facilitate discussion among partners regarding the firm’s investment positioning and next steps.

In addition, I researched the nuclear fusion sector, analyzing technological progress, regulatory developments, and competitive landscapes. This work helped identify long-term strategic opportunities and informed the firm’s understanding of emerging investment themes.

Required skills and knowledge

This internship required strong technical and analytical capabilities. I used Excel, data visualization tools, market research methodologies, and professional report-writing techniques to analyze complex information. I also learned how to structure investment memos, synthesize findings clearly, and present insights in formats suitable for senior partners.

Soft skills were equally important. Critical thinking was necessary to interpret incomplete or evolving information. Time management ensured deadlines were met, while effective communication allowed me to translate complex analysis into concise recommendations and collaborate efficiently with team members.

What I learned

This experience provided a realistic understanding of how venture capital investment analysis operates in practice.

I learned the importance of structured research and synthesis. Investment decisions rely on combining quantitative metrics with qualitative insights and presenting them in a concise and actionable manner.

I also understood how investment recommendations function as decision-making tools. Reports guide capital allocation, risk assessment, and strategic prioritization, bridging analytical work and actual investment actions.

Additionally, I gained confidence in professional reporting and data presentation. I developed workflows for summarizing interviews, benchmarking competitors, and organizing projections, enabling the team to focus on strategic discussions rather than raw data processing.

Overall, this internship strengthened my interest in venture capital and investment strategy and provided a foundation for future roles in investment analysis, corporate strategy, or portfolio management.

Financial concepts related to my internship

I present below three financial concepts related to my internship: market and competitor analysis, investment recommendation frameworks, and strategic opportunity identification.

Market and competitor analysis

Market and competitor analysis is fundamental to venture capital investment evaluation. When preparing initiation and recommendation reports, I benchmarked companies within sectors such as NL-Tech, identifying differentiating product features, strategic positioning, and competitive advantages. These analyses informed the firm’s prioritization of investment opportunities.

Investment recommendations

Investment recommendations follow structured evaluation frameworks combining market potential, financial projections, scalability, and risk assessment. I learned to evaluate indicators such as projected revenue growth, technological scalability, and exit potential, integrating these elements into cohesive recommendations supporting partner-level decisions.

Strategic opportunity identification

Strategic opportunity identification involves analyzing emerging industries to anticipate future growth areas. While researching nuclear fusion technologies, I examined technological breakthroughs, regulatory trends, and industry gaps. This process illustrates how venture capital firms align investment strategies with long-term innovation and market evolution.

Why should I be interested in this post?

This post provides insight into how venture capital firms evaluate investment opportunities and transform research into actionable decisions. Students interested in investment management, venture capital, or corporate strategy can understand how structured research, analytical reasoning, and professional reporting support real-world investment processes.

The experience also highlights foundational skills such as structured thinking, communication, and strategic analysis, which are essential for careers in finance, consulting, and investment-related fields.

Related posts on the SimTrade blog

   ▶ All posts about Professional experiences

   ▶ Classic brain teasers from real-life interviews

Useful resources

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

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

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

About the author

The article was written in February 2026 by Bochen LIU (Queen’s Smith School of Business, BCom 2023–2027; ESSEC BBA Exchange Program, Fall 2025).

   ▶ Discover all posts by Bochen LIU

“Time in the market beats timing in the market.” – Kenneth Fisher

There are two primary approaches to investing in the stock market. Some market participants adopt a trading-oriented strategy; they believe that financial gains depend on their ability to predict the evolution of the market (when to enter and when to exit), or in other words, they try to time the market. Other market participants favor a long-term investment approach: they expect their investments to compound over 10 or 20 years, by spending as much time in the market.

Time in the market vs. timing the market is a classic debate in the investment world. Kenneth Fisher had a very strong opinion on this debate. To him, “Time in the market beats timing the market”. The duration on an investment (the time in the market) is a significantly better factor of success for your investments that the quality of your attempts to optimize entry and exit points (timing the market).

For the vast majority of market participants, the effort to outmaneuver daily fluctuations is not just difficult, but a statistically losing game.

Hadrien PUCHE

In this article, Hadrien PUCHE (ESSEC, Grande École Program, Master in Management, 2023-2027) explores the behavioral and financial foundations of Fisher’s principle, analyzing why the “cost of being out” can often exceed the risks of staying in through market cycles.

About Kenneth Fisher and the quote

Kenneth Fisher

Source: Fisher Investments

Kenneth Fisher is a billionaire investment analyst, who founded Fisher Investments. He also is a long-time columnist for Forbes. He is well known for his contributions to investment theory, particularly in popularizing the use of the Price-to-Sales ratio. Throughout his career, Fisher has been a vocal critic of the “market timing” fallacy, arguing that most investors hurt their returns by trying to avoid downturns.

This quotes originates from a 2018 USA Today article where Kenneth Fisher wrote :

“Even the greatest investors are wrong maybe a third of the time. But here’s some good news: You don’t need perfect timing to achieve marvelous returns. Time in the market beats timing the market – almost always.”

Analysis of the quote

The fundamental question every investor face is: How to invest? While the allure of “buying low and selling high” sounds simple, executing it consistently is nearly impossible. Fisher’s quote highlights that the market is not a puzzle to be solved daily, but a vehicle to be ridden over years.

“Timing the market” requires two perfect decisions: knowing exactly when to get out and exactly when to get back in. “Time in the market,” conversely, requires only one decision: to start. By staying invested, you capture the total return of the market, including dividends and the recovery phases that follow volatility. Fisher’s principle suggests that the “missed opportunity” of being on the sidelines is the greatest risk of all.

Furthermore, Fisher’s insight also implies that an investment’s duration is very often more important than the yield. Too many investors are obsessed over finding the best “alpha” (a few extra percentage points of return) but forget about duration. A moderate return sustained over decades will always outperform a spectacular return that gets interrupted all the time.

Similarly, another thing to consider is the heavy “cost of inaction” that comes with searching for the perfect entry point. By waiting for the ideal market conditions or trying to identify the absolute best opportunities, you are losing time (and therefore compounding); a cost that is rarely justified by the improved entry point.

Three Financial Concepts Linked to the Quote

We now introduce three financial concepts that are related to this quote, and that you may find useful to understand the mechanics behind Fisher’s principle: the long-term drivers of the market growth, the danger of missing the “Best Days”, and the Dollar Cost Averaging (DCA) to find a good compromise between timing and time.

The long-term drivers of the market growth

To understand why “time in the market” works, we have to look at what actually drives the market’s long-term upward trajectory. Unlike a casino, the stock market is a vehicle for productive capital, and its growth is fueled by fundamental economic forces:

  • GDP Growth & Corporate Earnings: As the global economy expands and companies become more efficient, they generate higher profits. Over decades, stock prices tend to track this fundamental growth in value.
  • Inflation: Since stocks represent ownership in real assets and businesses, they act as a natural hedge. As prices for goods and services rise, nominal corporate revenues and asset values follow suit.
  • The Equity Risk Premium: This is the “extra” return investors demand for choosing stocks over “risk-free” assets like government bonds. To earn this premium, you simply have to be present.

By staying in the market, you aren’t just “hoping” for a rise; you are capturing the compounding effect of global productivity and inflation.

The danger of missing the “Best Days”

When it comes to the statistical distribution of the market returns, it is important to understand that it is highly skewed, with the bulk of annual gains often concentrated in a handful of trading sessions. This concentration creates a massive “cost of being out” for any investor that happens to miss such days.

The figure below shows the distribution of the returns on the S&P 500 index compared to the estimated normal distribution. What matters here is that the S&P500 distribution has much fatter tails than the normal ones; meaning that very high and very low returns happen more than one would expect with a normal distribution.

Figure 1. Distribution of the returns on the S&P 500 index
Distribution of markets returns for the S&P500
Source: Seeking Alpha

The issue with these fatter tails is that missing a small number of high-returns days can be catastrophic for an investor’s terminal wealth. Historically, missing just the 10 best days in a decade is enough to cut an investor’s total return by half, and missing the best 30 days end can turn the returns negative, even in a bull market.

The paradox of the “Time in the Market” is that these “best days” usually occur within weeks or days of the “worst days.” By trying to avoid the worst days, many also miss the best days, and this is where the true opportunity cost lies. The only proven way to make sure that you are present for the best days is to stay invested through the worst ones

DCA: The Compromise Between Timing and Time

Let’s say you have €10,000 today, and you want to invest them in the market. You do not want to “time the market” and want instead to spend “time in the market”. However, you are facing the “entry dilemma”: should you go all-in now or wait for a better price in a few days?

Going all-in (Lump Sum investing) means immediate exposure, but it can make many investors uncomfortable. To make it easier and more manageable, many investors choose to rather do a Dollar Cost Averaging (DCA): investing their money progressively at set intervals (monthly, weekly, etc.).

The DCA approach is psychologically attractive, because it removes the paralysis that comes with the fear of “buying the top.” If the market drops the day after your first investment, you actually benefit by buying the next “tranche” at a lower price. However, financial literature suggests a different reality.

Most academic research, including the study by Brennan, Li, and Torous (2005), argues that Lump Sum investing outperforms DCA roughly 75% of the time. This is because markets have a positive “expected return” (they go up more often than they go down). By holding cash on the sidelines to “average in,” you are essentially betting against the market’s natural upward trajectory.

Brennan’s core argument is that “Dollar-Cost Averaging just means taking risk later.” By choosing DCA, you aren’t avoiding market risk; you are simply delaying your full participation in the market’s growth. The “cost” of this delay is often higher than the benefit of potentially catching a lower entry price.

So why do so many professionals still recommend DCA?

The choice is ultimately a psychological one. While a Lump Sum is mathematically superior, it carries a high “regret risk.” If an investor puts €10,000 in on Monday and the market crashes on Tuesday, they might panic and sell everything, violating Fisher’s principle of staying in the market. DCA acts as a behavioral bridge: it may yield slightly lower returns on average, but it ensures the investor actually stays the course.

Ultimately, the “best” strategy is the one that prevents you from exiting the market prematurely. How much stress do you feel at the idea of a short-term loss? If that stress leads to bad decisions, the “insurance” provided by DCA is well worth the mathematical trade-off.

Why you should always keep this quote in mind

Fisher’s perspective extends far beyond financial advice. It is a reminder that in most cases, in both your personal and professional life, consistency matters more than intensity. While the modern world often rewards the pursuit of the “perfect” moment, this mantra suggests that the duration of your efforts is a far more reliable predictor of success than the timing of your actions.

At its core, this quote is a reminder that time will always be your greatest asset. You may not always secure the highest yields, or the most prestigious returns in the short term, but as long as you maintain a longer presence, the cumulative effect of being active will eventually outweigh the benefits of a single, well-timed move.

Consider your own professional career. As a student, your immediate returns may not be that great, and you may fail at “timing the market” by not landing the perfect role in the perfect company in your first attempt. But as long as you spend more “time in the market” (by building skills, networking, gaining experience…), you will eventually reach your objectives.

There is also a significant (and often overlooked) cost to trying too hard to find the perfect opportunities. When you obsess over timing, you risk analysis paralysis and the exhaustion of your mental capital. Sometimes the most strategic move is to accept the path currently before you, proceed with discipline, and allow the future to unfold. By focusing on your tenure rather than your timing, you trade the stress of the unknown for the certainty of cumulative growth.

In the long run, the most successful individuals are rarely those who waited for the wind to be perfect; they are those who kept their sails up regardless of the weather. By internalizing this quote, you adopt a mindset that values patience as a form of hidden strength, ensuring that your capital (both financial and intellectual) has the necessary room to breathe, and expand.

Related Posts on the SimTrade Blog

   ▶ All posts about Quotes

   ▶ Hadrien PUCHE “The stock market is designed to transfer money from the impatient to the patient” – Warren Buffett

   ▶ Hadrien PUCHE “Price is what you pay, value is what you get” – Warren Buffett

Useful resources

Fisher Investments Market Commentary. Insights from Ken Fisher’s firm on why staying the course matters.

Academic literature

Fama E.F. (1965) Random Walks in Stock Market Prices, Financial Analysts Journal, 21(5), 55-59.

Brinson G.P., L.R. Hood, and G.L. Beebower (1986) Determinants of Portfolio Performance, Financial Analysts Journal, 42(4), 39-44.

Brennan M.J., F. Li, and W.N. Torous (2005) Dollar-Cost Averaging Just Means Taking Risk Later, Review of Finance, 9(4), 509–535.

About the Author

This article was written in February 2026 by Hadrien PUCHE (ESSEC Business School, Grande École Program, Master in Management, 2023-2027).

   ▶ Discover all articles by Hadrien PUCHE

My Internship Experience at the Agricultural Bank of China (ABC)

Bochen LIU

In this article, Bochen LIU (Queen’s Smith School of Business, BCom 2023–2027; ESSEC BBA Exchange Program, Fall 2025) shares his professional experience as a Financial Intern at the Agricultural Bank of China.

About the company

The Agricultural Bank of China is one of China’s “Big Four” commercial banks, serving hundreds of millions of customers across retail, corporate, and rural banking segments. With thousands of branches nationwide, ABC plays a major role in financing agricultural development, supporting SMEs, and delivering a wide range of financial services, including deposits, loans, wealth management products, and payment solutions.

Operating at this scale requires robust internal processes such as standardized reporting, regulatory compliance, risk management, and precise handling of customer information. The finance and operations teams ensure that front-line activities align with corporate strategy and risk guidelines, making accuracy and efficiency essential qualities in daily operations.

Logo of the Agricultural Bank of China.
Logo of Agricultural Bank of China
Source: the company.

I worked within the branch environment responsible for financial reporting, operational risk checks, client data processing, and financial product monitoring. This unit coordinated information from multiple departments to prepare performance reports, verify customer records for compliance purposes, and support analysis of retail and corporate banking products. Its role was to ensure that operational data remained accurate, standardized, and available for supervisors, thereby supporting internal control, risk monitoring, and informed managerial decision-making across the branch.

My internship

As a student from Queen’s Smith School of Business participating in the ESSEC BBA Exchange Program, I had the opportunity to join the Agricultural Bank of China as a Financial Intern in Beijing from 2023 to 2024. This experience exposed me to financial operations, reporting workflows, client data management, and retail product analysis within one of China’s largest state-owned commercial banks.

This internship allowed me to witness firsthand how financial operations are supported by structured information flows. Financial reporting and client data processing are not merely administrative tasks; they form the backbone of internal control systems, enabling managers to make timely and informed decisions across the bank’s branches and business units.

My missions

My missions ranged from streamlining weekly management reporting—reducing turnaround time and improving decision-making efficiency—to processing large volumes of client records for daily risk assessment, and analyzing a variety of financial products across retail and corporate banking.

A core responsibility of my role was assisting with weekly management reporting for the branch. I collected financial and operational data from multiple departments, standardized the format, verified accuracy, and prepared consolidated reports for supervisors. By automating portions of the Excel templates and cleaning data more efficiently, I helped reduce the report turnaround time by approximately 20%. This improvement enabled managers to make decisions more quickly and with clearer visibility on the branch’s performance trends.

I also supported the bank’s daily operational risk assessment by processing and verifying large volumes of client records. This included reviewing transaction histories, updating customer information, and ensuring that all files met regulatory and internal compliance requirements. Handling hundreds of records demanded accuracy, confidentiality, and discipline, as small errors could lead to compliance discrepancies or delays during internal audits.

In addition to reporting and operations, I conducted research on over ten retail and corporate financial products, including personal loans, SME credit lines, savings instruments, and investment-linked products. By comparing product structures, pricing, and customer segments, I gained insight into how banks differentiate offerings and balance profitability with client needs.

Required skills and knowledge

This internship required both technical and interpersonal skills. On the technical side, I worked extensively with Excel to automate report templates, validate performance indicators, and clean datasets efficiently. I strengthened my understanding of banking products, compliance procedures, and risk management systems.

Equally important were soft skills such as attention to detail, time management, communication, and reliability. Weekly reporting deadlines demanded discipline, while client data processing required precision and structured thinking to avoid compliance-related issues. Through these responsibilities, I developed habits that are essential for a career in finance.

What I learned

This experience provided me with a realistic understanding of operational finance inside a major commercial bank. First, I learned the importance of accuracy. Whether preparing reports or updating client files, even small inconsistencies could affect decision-making or regulatory compliance. This taught me to double-check all figures and maintain clear documentation.

Second, I discovered how reporting frameworks support managerial decision-making. Weekly performance reports acted as diagnostic control systems that helped managers assess branch performance, track deviations, and prioritize resources.

Third, I gained confidence in data processing and product analysis. Working through real client files and financial products strengthened my understanding of commercial banking operations and the financial mechanisms supporting customer services. Finally, this experience enhanced my interest in finance and provided a solid foundation for future roles in financial analysis, banking, or corporate finance.

Financial concepts related to my internship

I present below three financial concepts related to my internship: management reporting, operational risk assessment, and financial product analysis. These concepts illustrate the connection between my daily tasks and broader financial management practices.

Management reporting

Management reporting is a core component of internal management control. At ABC, weekly reports enabled supervisors to track metrics such as loan growth, customer acquisition, overdue accounts, and product sales. By optimizing reporting workflows, I contributed to more efficient decision-making and improved information flow within the branch.

Operational risk assessment

Operational risk includes failures in processes, systems, or human error. My work processing client data reflected how banks mitigate this risk through documentation checks, standardized records, and compliance verification. Understanding operational risk is essential for evaluating the stability and effectiveness of financial institutions.

Financial product analysis

Financial product analysis involves comparing product structures, pricing mechanisms, customer segments, and risk-return characteristics. Researching retail and corporate banking products helped me understand how banks refine pricing strategies, innovate offerings, and position themselves competitively while respecting regulatory constraints.

Why should I be interested in this post?

This post provides a realistic view of a financial internship inside a major commercial bank. Students interested in banking, corporate finance, or financial analysis can gain insight into the operational foundation supporting financial institutions.

The experience highlights the value of structured reporting, data accuracy, and understanding financial products—skills that form the backbone of careers in finance, analytics, and investment management.

Related posts on the SimTrade blog

   ▶ All posts about Professional experiences

   ▶ Alexandre VERLET Classic brain teasers from real-life interviews

Useful resources

Agricultural Bank of China official website

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

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

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

About the author

The article was written in February 2026 by Bochen LIU (Queen’s Smith School of Business, BCom 2023–2027; ESSEC BBA Exchange Program, Fall 2025).

   ▶ Discover all posts by Bochen LIU

Client Segmentation and Private Banking: Marketing Strategy or Risk Shield?

Mathis HOUROU

In this article, Mathis HOUROU (ESSEC Business School, Global Bachelor in Business Administration (GBBA) 2022-2026) explains why Private Banking’s client segmentation is not just about sales, but also a crucial risk management tool for banks.

Introduction

In business school, we learn that client segmentation is a commercial tool. In Private Banking, it is used to group clients by wealth to offer them better services and adjust the pricing. However, during my experience at Société Générale Private Banking, I realized that segmentation is also a powerful risk management tool. Bankers have to take into account regulatory requirements such as KYC (Know Your Customer), as well as the client’s profile, risk tolerance, investment time, and biases.

This becomes even more important with complex products, such as structured products, where a mismatch can lead a client to losses he wasn’t ready to take. Such a situation can cause reputational issues, and regulatory risk for the bank.

This article will show how putting clients in different “boxes” helps banks to control risks and avoid potential disasters.

What is Client Segmentation?

In the context of Private Banking, client segmentation is the classification of clients into different categories in order to adapt the relationship and the level of risk.

Clients are not only grouped according to their AUM (Assets Under Management), but also on their financial experience, investment goals, time horizon, and their risk acceptance. This is all regulated by KYC and investor questionnaires.

On the first layer of this segmentation are the retail clients. These clients represent a vast majority of the clients and often have a low investing capacity.

Then you have the High-Net-Worth Individuals (HNWI), they have investing power and need advice.

Finally, the Ultra-High-Net-Worth Individuals (UHNWI). They are extremely wealthy clients with complex and various needs.

While this may look like a typical marketing pyramid, it is actually a security tool. Each level has strict rules on what the banker can and cannot sell and at what price.

The Global Wealth Pyramid.
The Global Wealth Pyramid
Source: UBS Global Wealth Report.

Segmentation as a Shield against Bad Investments

The most important link between segmentation and risk is suitability. Not every client can handle the same risk. Segmentation helps the bank to define the limits, both to protect the client from unsuitable investments and to protect the bank from regulatory and reputational risk. For example, a risk-averse client in the “retail” segment shouldn’t have access to very volatile products like derivatives or Private Equity.

By using these segments, the bank avoids a “mismatch.” If a bank sells a risky product to a client who doesn’t understand it and loses money, they can have legal problems. Segmentation acts like a filter to prevent this from happening.

Managing Regulatory and Legal Risk

Today, regulations like MiFID II in Europe are very strict. Banks have to prove that the product is good for the client. Segmentation simplifies this, if a product is rated “Risk 5/5” for example, it will be automatically unavailable for clients in the “Conservative” segment.

This reduces the risk of lawsuits and fines and it ensures that the bank is really protecting the client, sometimes even against their own will, by refusing to sell them something too dangerous for their profile.

Risk vs Return relationship.
Risk vs Return JPM
Source: J.P. Morgan Asset Management.

The Human Factor: Behavioral Risk

Risk is not just about numbers; it is also about psychology. In fact, behavioral risk is often underestimated because difficult to measure.

For example, when the market crashes, clients can react differently. An educated investor is going to see an opportunity to buy and will stay calm and steady, while a less experienced client might panic and sell everything at the bottom in fear of losing everything he has.

Finally, segmentation helps bankers to anticipate these reactions because they know that one specific segment needs reassurance and phone calls during a crisis, while another segment is going to be more resilient and will want updates on new opportunities.

Conclusion

To conclude, client segmentation is often shown in Business Schools as a way to maximize profits, but in Private Banking, it is a good way to minimize risks.

It protects the client and the bank at the same time. It makes sure that complicated products are only sold to the clients who understand them, and it helps bankers manage the emotions of the investors.

For us finance students, this is a great lesson: risk management is not just about Excel sheets and formulas. It starts with knowing exactly who your client is.

Related posts on the SimTrade blog

   ▶ Mathis HOUROU My internship experience as a Counterparty Risk Analyst at Société Générale

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

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

   ▶ Michel VERHASSELT Risk comes from not knowing what you are doing

Useful resources

J.P. Morgan Asset Management Guide to the Markets (Europe)

UBS UBS Global Wealth Report 2025

About the author

The article was written in February 2026 by Mathis HOUROU (ESSEC Business School, Global Bachelor in Business Administration (GBBA)).

   ▶ Discover all articles by Mathis HOUROU.

“Investing is stupid if you’re more worried about short-term volatility than long-term quality.” – Charlie Munger

Investing is often a battle with our own emotions. We see prices rise sharply and crash just as fast, and this can lead to very bad investment decisions. However, Charlie Munger’s wisdom comes once again handy, to remind us to avoid overlooking at prices all day-long, because “Investing is stupid if you’re more worried about short-term volatility than long-term quality”.

Hadrien PUCHE

In this article, Hadrien PUCHE (ESSEC Business School, Grande École Program, Master in Management, 2023-2027) explores why Munger’s wisdom serves as a welcome reminding of the difference between short-term price and long-term value.

Charlie Munger: the architect of quality investing

Charlie Munger
Charlie Munger
Source : Fortune

Charlie Munger (1924–2023) was far more than a mere lieutenant to Warren Buffett; he was the primary intellectual catalyst who shifted Berkshire Hathaway’s strategy away from the traditional “cigar butt” school of Benjamin Graham. While Graham sought “fair businesses at a great price,” Munger convinced Buffett of the immense power found in “great businesses at a fair price”. The 1989 Letter to Shareholders is particularly famous for the “Mistakes of the First Twenty-Five Years” where Munger’s influence is clear.

He achieved this by integrating a multidisciplinary framework (incorporating insights from psychology, biology, and physics) to decode the complexities of the financial world, ultimately arguing that the quality of a business is the only reliable engine for long-term wealth.

It has to be said that there is no record of Charlie Munger saying these exacts words, but it does summarize well his investment philosophy.

An analysis of this quote

Munger’s philosophy rests upon the bedrock observation that the stock market operates as a “weighing machine” in the long run, even if it behaves like a “voting machine” in the short term. He famously dismissed the academic obsession with volatility as a proxy for risk, arguing instead that a twenty-percent drawdown is not a “loss” unless the investor is forced to sell, or if the fundamental earning power of the business has permanently deteriorated. Real risk, in the Munger school of thought, is defined strictly as the permanent loss of capital (the inability to recover one’s initial investment), which has almost no correlation with the standard deviation of daily price movements.

Furthermore, Munger recognized that investors are often their own worst enemies, due to “loss aversion” (a biological vestige of our evolutionary past where a declining stock price triggers a “fight or flight” response). He suggested that if an individual lacks the temperament to ignore these short-term signals, they are effectively paying an “emotional tax” that prevents them from reaching the higher echelons of compounding.

Indeed, the first rule of compounding is to never interrupt it unnecessarily; by reacting to volatility, investors often liquidate high-quality assets during temporary market drawdowns, effectively resetting their exponential growth clock and sacrificing future prosperity.

Financial concepts related to the quote

This quote reminds me of a few very interesting financial concepts that you may be interested in.

The flaw with Beta in the modern portfolio theory

In the world of academic finance (specifically within the Capital Asset Pricing Model, or CAPM), risk is mathematically defined as Beta (β), which measures the sensitivity of an asset’s returns relative to the broader market.

As a reminder, the CAPM expresses the expected return of an asset as a function of the risk-free rate, the beta of the asset, and the expected return of the market. The main result of the CAPM is a simple mathematical formula that links the expected return of an asset to these different components. For an asset i, it is given by:

CAPM risk beta relation

Where:

  • E(ri) represents the expected return of asset i
  • rf the risk-free rate
  • βi the measure of the risk of asset i
  • E(rm) the expected return of the market
  • E(rm)- rf the market risk premium.

The risk premium for asset i is equal to βi(E(rm)- rf), that is the beta of asset i, βi, multiplied by the risk premium for the market, E(rm)- rf.

In this model, the beta (β) parameter is a key parameter and is defined as:

CAPM beta formula

Where:

  • Cov(ri, rm) represents the covariance of the return of asset i with the return of the market
  • σ2(rm) the variance of the return of the market.

However, Munger viewed this as a fundamental intellectual error. From an analytical standpoint, if a company’s intrinsic value remains stable while its price drops significantly, the “risk” (the probability of overpaying) has actually decreased, even though the “volatility” (the Beta) has technically increased.

For the rational investor, volatility should be viewed as a provider of liquidity and favorable entry points rather than a threat. When the market overreacts to macro-economic data or geopolitical tension, it creates a “Rationality Gap” where high-quality firms are temporarily mispriced. Munger argued that those who can remain stoic during these periods are the ones who capture the “premium of patience.”

In essence, while the academics are busy calculating standard deviations, the Munger-style investor is busy calculating whether the business’s ability to generate cash remains intact.

”A
What really matters for Charlie Munger is to buy the stock when it is underpriced, and selling it when it is overpriced. Source: Elearnmarkets Blog

ROIC, and the dynamics of the “Economic Moat”

For Munger, “Quality” was not a vague descriptor but a quantifiable financial phenomenon centered on one main metric: Return on Invested Capital (ROIC). The formula is elegant in its simplicity:

ROIC = NOPAT ÷ Invested Capital

Munger observed that over a forty-year holding period, a stock’s total return will inevitably converge toward its ROIC. Crucially, for value to be created, this ROIC must be higher than the Weighted Average Cost of Capital (WACC). If you hold a business that earns six percent on its capital for decades—barely matching its cost of capital—you will ultimately earn a six percent return, regardless of whether you bought it at a “bargain” or a “fair” price. Conversely, if a business earns eighteen percent on capital, the positive spread over its WACC creates a compounding effect that will eventually dwarf any initial valuation premium you paid.

However, high ROIC is a magnet for competition, which is why Munger prioritized companies with a “Economic Moat.” This refers to a structural barrier (such as the brand equity of Coca-Cola, the network effects of Alphabet, or the high switching costs of Microsoft) that prevents competitors from eroding those high returns. Without a moat, the spread between ROIC and WACC is merely a temporary state before mean-reversion takes hold. Therefore, analyzing a business involves a deep dive into its competitive advantages to ensure that its high ROIC is sustainable over decades, and not just over a few quarters.

Time Arbitrage and Tax Efficiency

One big advantage that an individual investor has over a professional fund manager is the concept of “Time Arbitrage.” Most institutional managers are constrained by quarterly benchmarks, and the pressure to avoid “tracking error” (falling behind the index), which forces them to react to short-term volatility to protect their career longevity. However, by extending the time horizon to ten or twenty years, an investor exit this hyper-competitive arena where most traders operate, and enters a space where patience is the primary competitive edge.

This long-term orientation also creates a significant (yet often overlooked) financial benefit: tax efficiency. By refusing to sell during volatile periods, the investor avoids triggering capital gains taxes, which allows the “unpaid taxes” to remain within the investment, and compound for free.

As Munger frequently noted, the “big money” is found in the waiting. By minimizing turnover, you maximize the terminal value of your portfolio, by ensuring that the engine of compounding is never throttled by unnecessary friction or tax leakage.

My opinion on this quote

In my view, this quote is a very interesting take on financial rationality. It is a rejection of the “noise” that defines modern electronic trading. What I find most compelling is Munger’s insistence that volatility is not a hazard, but rather the price of admission for superior returns (a concept many students struggle to internalize when they first encounter the volatility-centric models of academic finance).

To me, Munger is arguing that the market is often a theatre of the absurd where prices decouple from reality due to human emotion; therefore, the only logical response for a serious investor is a disciplined focus on the structural integrity of the business (the quality) rather than the erratic pulse of the stock price.

I believe that the “stupidity” Munger refers to is the intellectual laziness of letting a falling price dictate your perception of a business’s value. It is far easier to look at a chart and feel fear, than it is to dig into a 10-K filing to verify the Return on Invested Capital (ROIC), or the durability of a competitive advantage. By prioritizing quality over volatility, we are essentially choosing to be owners of productive assets rather than gamblers on price movements; and this shift in perspective is, in my opinion, the single most important transition a young financier can make.

Why should this quote matter to you

Whether you aspire to work in Asset Management, Private Equity, or Equity Research, Munger’s perspective is a vital toolkit for professional survival. In the institutional world, you will be constantly bombarded with requests to explain “why the market is down today” or “why a portfolio company underperformed this month.”

If you focus on these short-term “wiggles” in the data, you risk becoming a mere weather reporter. Understanding Munger allows you to move beyond superficial queries and focus on the real metrics: the cash flow margins, the structural moat, the capital allocation of management…

The “ROIC” of your career path

This principle transcends stock picking and applies directly to your own professional trajectory. Think of your career through the lens of Investment vs. Volatility:

  • Career Volatility: These are the temporary setbacks: a tough performance review, a project that stalls, or a hiring freeze. If you overreact to this volatility, you risk making impulsive “trades” with your career that interrupt your progress.
  • Career Quality: This is the compounding value of your technical skills, your network, and your intellectual rigor. These are the assets that generate a high “Return on Invested Capital” (ROIC) for your time and effort.

In finance, the most dangerous mistake you can make is interrupting a compounding process unnecessarily. By prioritizing the “long-term quality” of your professional output over the “short-term noise” of the job market, you ensure that you are building a career that is structurally sound and capable of weathering any economic storm.

Related posts

Quotes

   ▶ All posts about Quotes

   ▶ Hadrien PUCHE “The big money is not in the buying and selling, but in the waiting.” – Charlie Munger

   ▶ Hadrien PUCHE “The market is never wrong, only opinions are.” – Jesse Livermore

Financial techniques

   ▶ Saral BINDAL Historical Volatility

   ▶ Jayati WALIA Capital Asset Pricing Model (CAPM)

   ▶ Youssef LOURAOUI Markowitz Modern Portfolio Theory

   ▶ Youssef LOURAOUI Beta

Useful resources

Kaufman, P.D. (2005) Poor Charlie’s Almanack: The Essential Wit and Wisdom of Charles T. Munger, Third Edition, Virginia Beach, VA: Donning Company Publishers.

Buffett W.E. Berkshire Hathaway Shareholder Letters Omaha, NE: Berkshire Hathaway Inc.

Frazzini A., D. Kabiller, and L.H. Pedersen (2013) Buffett’s Alpha, Working paper.

About the Author

This article was written in February 2026 by Hadrien PUCHE (ESSEC Business School, Grande École Program, Master in Management, 2023-2027).

   ▶ Discover all articles by Hadrien PUCHE

“The market is a continuously unfolding process of discovery.” – Peter Steidlmayer

Financial markets move every second, reacting to every new piece of information, every financial statement, every geopolitical event. Prices rise, fall, move too far, come back again, and for anyone observing from the outside, this constant motion can easily appear chaotic.

Yet, behind this apparent disorder lies a deeper structure, a logic shaped by the continuous exchange between buyers and sellers who negotiate and adjust their positions in real time.

Hadrien PUCHE

In this article, Hadrien PUCHE (ESSEC Business School, Grande École Program, Master in Management, 2023-2027) explains why Peter Steidlmayer’s quote is so meaningful.

Peter Steidlmayer and the origin of market profile

Peter Steidlmayer
Peter Steidlmayer
Source : Profile trading

Peter Steidlmayer is known above all as the creator of the Market Profile methodology, introduced at the Chicago Board of Trade in the early 1980s. His goal was both simple and ambitious: to provide market participants with a clearer understanding of where the market was “accepting” value, rather than simply where prices happened to appear on a linear chart.

The quote that defines his philosophy first gained prominence in his seminal work:

“The market is a continuously unfolding process of discovery. Price is not value in itself, but the market’s best guess at a given moment. Only through the passage of time, with sufficient volume at a given range, can value be established.”

— Peter Steidlmayer, Markets and Market Logic: Trading and Investing with a Sound Understanding and Approach (1986).

Until this publication, most analysis focused on time-based charts that displayed the sequence of prices but said little about the intensity of trading at each level. Steidlmayer added a decisive dimension by incorporating Volume at Price, revealing how the market behaves like a continuous auction. Buyers and sellers negotiate, the market explores different levels, and value emerges where transactions cluster and where time confirms acceptance.

The quote takes its meaning directly from this framework. For Steidlmayer, markets discover value in the same way an auction settles a fair price: not through a single print, but through repeated interaction. A sudden spike tells us very little; it is merely a “probe.” But when the market spends time around a certain level with significant volume, it offers a reliable indication of Accepted Value.

To learn more about market profiles, check out this article by Michel Verhasselt on Market Profiles.

The graph below presents Steidlmayer’s market price distribution. The curve is constructed by dividing the trading session into equal time intervals (typically 30 minutes) and recording each price level traded during every interval. Each instance of a price occurring within a given bracket is labeled a Time Price Opportunity (TPO). The distribution is then formed by aggregating the total number of TPOs at each price level across the session, thereby producing a time-weighted empirical distribution of prices. Under conditions of relative balance between supply and demand, this process often yields a bell-shaped profile. In this framework, price discovery exhibits an ordered structure: the central region of the curve (characterized by a high concentration of TPOs) reflects sustained trading activity and temporary equilibrium, commonly interpreted as the market’s most accepted estimate of fair value. Conversely, the tails correspond to price levels traversed quickly, signaling rejection, imbalance, and potential disequilibrium (often associated with emotional trading).

The distribution curve of prices, a way to estimate the actual value of an asset

Analysis of the Quote

“The market is a continuously unfolding process of discovery” captures the very essence of Steidlmayer’s thinking. By framing the market this way, he reminds us that it is not a static mechanism but a living process in perpetual motion. Prices are not definitive statements of value; they are temporary judgments, mere snapshots of the market’s collective opinion at one precise moment.

Price reflects the most recent consensus, influenced by news, emotion, and short-term liquidity. It is the market’s best guess, but never its final conclusion. True value, on the other hand, does not reveal itself instantly. It appears gradually through the accumulation of transactions that demonstrate where participants genuinely agree. This requires sufficient volume and visible acceptance to prove that a broad set of participants (and not just a few aggressive traders) concurs on a price level.

This distinction explains why short-term volatility often expresses emotion more than fundamentals. In contemporary terms, price discovery is fast and exploratory, while value discovery is slow, deliberate, and shaped by consensus. This is what Warren Buffet meant when he said “Price is what you pay, value is what you get”.

Understanding that the market is a “continuously unfolding” conversation helps investors remain focused on the durable signal of value rather than reacting to the transient noise of price.

Three Financial Concepts Linked to the Quote

Market microstructure and auction theory

Financial markets operate in many ways like auctions. Buyers raise their bids, sellers adjust their offers, and the market constantly seeks the level at which both sides find balance. This is the essence of Market microstructure, the study of how a market’s participants and their behavior determine the price of an asset. Just like in an auction, participants negotiate in real-time, until the highest price someone is willing to pay and the lowest price someone is willing to sell meet.

Steidlmayer’s vision aligns perfectly with the principles of market microstructure: during periods of uncertainty, the market enters a “discovery” phase, where prices move rapidly and vertically to find new participants. This is the market effectively “probing” for the limits of supply and demand, in a continuously unfolding process of discovery.

When a price is found, the market stops moving vertically and starts moving horizontally, spending more time at a specific level to facilitate the maximum amount of trade. These consolidation areas are visual representation of agreement. It shows that the market has stopped searching and has found a temporary equilibrium where both buyers and sellers are satisfied with the price.

”Graph
As you can see here, the price moves in a range until a market event causes an auction. When an appropriate price is found, the market resumes moving in a new range again. Source : Jump trading.

Liquidity

Liquidity plays a decisive role in determining whether a price reflects genuine value or merely a temporary distortion. In finance, liquidity is defined as the ability to buy or sell an asset quickly without significantly affecting its price. It is a multi-dimensional concept, analyzed through several key components:

  • Tightness: Refers to the cost of a transaction, typically measured by the width of the bid-ask spread.
  • Depth: The volume of orders available at various price levels above and below the current market price.
  • Breadth: The number and diversity of market participants, indicating a wide range of interests.
  • Resiliency: The speed at which prices recover to “fair value” after a large, potentially disruptive trade.

In Steidlmayer’s framework, a price level reached on minimal volume is considered “unfair” or an outlier; it tells us very little because it lacks the support of the broader market. On the other hand, a price level traded repeatedly with strong participation speaks with far more authority. It indicates that a large number of participants have agreed on this price, and that is therefore “fair”.

This is why professional investors rely on measures such as the Volume Weighted Average Price (VWAP), the volume profile, and value area boundaries. These tools help separate the meaningful “signal” of institutional conviction from the surrounding “noise” of retail emotion. In essence, price is the discovery mechanism, but volume is the validation.

Without volume, a price movement is a mere suggestion; with volume, it becomes a confirmed consensus of value.

Market efficiency

The quote also relates to the Efficient Market Hypothesis (EMH), which suggests that asset prices reflect all available information. There are three distinct forms of market efficiency:

  • Weak form: Assumes that current prices reflect all information contained in past prices and trading volumes, meaning technical analysis cannot consistently produce excess returns.
  • Semi-strong form: Assumes that prices adjust instantly to all publicly available information, such as earnings announcements or economic data.
  • Strong form: Assumes that prices reflect all information, including private or insider information, according to which price should always equal value.

Steidlmayer proposes a more nuanced and realistic vision: markets are constantly searching for value, and they do not find it immediately. Because the “process of discovery” is driven by human participants with varying expectations, the market often overshoots or undershoot its true value before settling into a new equilibrium.

Mean reversion of a price over time
We can clearly see the market’s propensity for emotional excess, where price extends far beyond fair value before reverting to the mean. These oscillations prove that price discovery is a non-linear process driven by temporary imbalances in supply and demand. Source: dailypriceaction.com

This concept is essential for students. It explains why markets may be broadly efficient over long horizons, but still display irrational behavior in the short term. These “inefficiencies” are not market failures, but proof that the discovery process is in action.

By understanding that the current price is a search (and not a final answer), an investor can remain calm when the market overreacts, knowing that prices will eventually pull back towards the established value area.

My opinion on this Quote

I believe this quote offers a very accurate description of market behavior. It captures, with remarkable clarity, the difference between instantaneous price and durable value. What I find particularly compelling is the way it reframes volatility as part of the market’s natural process of exploration (rather than a source of confusion).

This perspective encourages patience, and reinforces the idea that investors should focus on context and ranges rather than individual specific prices.

However, it is important to nuance this perspective in the context of today’s modern markets. Steidlmayer’s logic was developed in the 1980s, long before the dominance of High-Frequency Trading (HFT) and algorithmic execution. Today, the “process of discovery” often happens in milliseconds, particularly on large cap stocks. While the fundamental principles of auction theory still apply, the transition from price to value is now much faster.

Despite this technological shift, the core lesson remains: the market is a conversation, and even if that conversation is now partly led by machines, the ultimate consensus still requires time and volume to establish true value.

Why should this quote matter to you ?

This quote is a good way of adding an additional level of complexity to your understanding of how markets truly function. Understanding the market’s process of discovery helps better understand markets movements, distinguish noise from genuine information, and avoid reacting impulsively to volatility. It teaches you to appreciate the essential roles of time, liquidity, and volume in revealing value, in order to make better decisions.

Ultimately, this quote conveys a profound lesson. The market is more of a conversation than a verdict, a continuous exchange of perspectives that gradually converges toward value. For any student who hopes to approach markets with discipline and understanding, mastering this idea is both a practical and an intellectual advantage.

Related posts

Famous quotes about valuation

   ▶ All posts about Quotes

   ▶ Hadrien PUCHE “Price is what you pay, value is what you get” – Warren Buffett

   ▶ Hadrien PUCHE “The stock market is filled with individuals who know the price of everything, but the value of nothing.” – Philip Fisher

Other famous quotes

   ▶ Hadrien PUCHE “The big money is not in the buying and selling, but in the waiting.” – Charlie Munger

   ▶ Hadrien PUCHE “Don’t look for the needle in the haystack. Just buy the haystack.” – John C. Bogle

About market profile

   ▶ Michel VERHASSELT Market profiles

   ▶ Michel VERHASSELT Difference between market profiles and volume profiles

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

   ▶ Raphael TRAEN Volume-Weighted Average Price (VWAP)

Useful resources

Steidlmayer P.J. and K. Koy (1986) Markets and Market Logic: Trading and Investing with a Sound Understanding and Approach, Porcupine Press.

Steidlmayer P.J. and S.B. Hawkins (2003) Steidlmayer on Markets: Trading with Market Profile, John Wiley & Sons, Second Edition;

TPO versus Volume Profiles

Trader Dale Volume Profile vs. Market Profile – What Is The Difference? YouTube video

About the Author

This article was written in February 2026 by Hadrien PUCHE (ESSEC Business School, Grande École Program, Master in Management, 2023-2027).

   ▶ Discover all articles by Hadrien PUCHE