Understand the importance of data providers and how they influence global finance…

March 24, 2022 by Louis DETALLE

Understand the importance of data providers and how they influence global finance…

In this article, Louis DETALLE (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023) explains the importance of data providers and how they influence global finance…

What are data providers?

A data provider is an intermediary between data and data users. Indeed, a data provider provides market data to financial firms, traders, and investors. The data distributed is previously gathered, organized and presented in an understandable way. Data providers collect the data from sources such as stock exchange feeds, brokers’ notes and dealer desks or regulatory filings. Some names will definitely ring a bell, such as Bloomberg, Thomson Reuters whereas some others will be less known as Moody’s Analytics.

The different types of data that are exchanged for financial purposes

When it comes to data used in finance, trading rooms are the best example as they contain almost nothing but data. Indeed, transaction prices, traded volumes of stocks and bonds are displayed at all times. But trading rooms are only one specific of example of data’s use in finance.

As mentioned, there are many different types of instruments (e.g., stocks, bonds, currencies, funds, options, futures, etc.) and hundreds of financial markets for investment, which leads to an extremely large flow of data exchanged.

The types of data offered vary by data provider. Generally, they cover information about companies and financial instruments (options, shares, bonds, treasury bonds and currencies) which companies might trade or issue.

The data can be updated every day or several times a day! Intraday data for instance are prices provided throughout the day and are usually released on a continuous basis.

The main dynamics of the Data Providers’ market

The explosion of financial data, enabled by the Internet tremendous potential, caused an explosion of demand for financial data. As evidenced in 2006 by the British mathematician and Tesco marketing mastermind Clive Humby’s quote, “Data is the new oil”, the data providers enjoy a market that seems to be limitless. Indeed, as data provider raw material’s amount is ever-increasing, it appears they will thrive for decades.

In addition, the market seems to be detained by only a few actors among which Bloomberg that acquired BNA and BusinessWeek. This contributes to curbing the number of data providers and improving the monopoly of Bloomberg on the data-providing market. Let’s review the market shares of the 4 major data providers: Bloomberg enjoys a comfortable 33,4% market share, Refinitiv Eiken follows with a 19,6% share of the market, Capital IQ has a 6,2% market share when FactSet closes the ranking with 4,5% of the market. (source:https://www.wallstreetprep.com/knowledge/bloomberg-vs-capital-iq-vs-factset-vs-thomson-reuters-eikon/)

Useful resources

About the author

The article was written in March 2022 by Louis DETALLE (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023).

The importance of data in finance

March 24, 2022 by Louis DETALLE

The importance of data in finance

In this article, Louis DETALLE (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023) explains the importance of data-management for corporations and how they are used to improve profitability.

According to a study published by CapGemini untitled: The data-powered enterprise: Why organizations must strengthen their data mastery, it is estimated that the gain from efficient data-management would represent 22% in terms of firm profitability.

Why is data used?

The use of data in finance can also be very useful in finance for various reasons.

Indeed, the multitude of data available allows for a deeper understanding of the market in terms of risks and opportunities. This knowledge is accompanied by an important consideration of political, social and economic factors.

As early as 2006, British mathematician and Tesco marketing mastermind Clive Humby stated “Data is the new oil.” The companies with the largest market capitalizations also bear witness to this importance of data. The ranking shows of tradingstat shows a podium of Apple, Microsoft and Google: the predominance of data-driven companies is clearly observable here.

In which finance-related fields is data used?

In finance, it is especially in the trading rooms that data has become an absolutely indispensable tool. Indeed, it is thanks to Big Data – i.e. increasingly exhaustive data, at an ever faster pace – that high frequency trading has been developed. In short, high-frequency trading makes it possible to place several thousand buy and/or sell orders in a few seconds, or even milliseconds, while optimizing risk management in order to adapt the strategy to market responses. This trading strategy allows for buying and selling in a sufficiently short period of time to avoid a potentially negative market movement during the operation.

On the other hand, retail banks (i.e. banks for individuals) are also confronted with the challenges of data-management. The development of online services offers them a better knowledge of their customers, which leads to a change in the bank’s relationship with its customers. In doing so, banks improve their ability to adapt their offer to the customer profile. Big Data also enables banks to fight fraud. Banks are now able to monitor all bank card transactions and be alerted when a user makes a payment (particularly in terms of amount, time or geographical area). For investment banks, whether it is the implementation of a more reliable scoring of credit files, the pooling of data between banks, analysis of the “sentiment” of investors for traders or the compliance of data and its processing, the indispensable character of data is no longer to be proven.

The importance of data regulation though

The use of data in finance is very useful but can be problematic when the data concerns the personal data of users or customers. In this context, financial actors are subject to ever increasing regulation and the adoption of the EU’s GDPR, in 2016, seems to be a step in this direction.

Useful resources

BlackRock L’utilisation du Big Data dans un processus d’investissement

About the author

The article was written in March 2022 by Louis DETALLE (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023).

The Monte Carlo simulation method for VaR calculation

January 30, 2024March 24, 2022 by Jayati WALIA

In this article, Jayati WALIA (ESSEC Business School, Grande Ecole – Master in Management, 2019-2022) explains the Monte Carlo simulation method for VaR calculation.

Introduction

Monte Carlo simulations are a broad class of computational algorithms that rely majorly on repeated random sampling to obtain numerical results. The underlying concept is to model the multiple possible outcomes of an uncertain event. It is a technique used to understand the impact of risk and uncertainty in prediction and forecasting models.

The Monte Carlo simulation method was invented by John von Neumann (Hungarian-American mathematician and computer scientist) and Stanislaw Ulam (Polish mathematician) during World War II to improve decision making under uncertain conditions. It is named after the popular gambling destination Monte Carlo, located in Monaco and home to many famous casinos. This is because the random outcomes in the Monte Carlo modeling technique can be compared to games like roulette, dice and slot machines. In his autobiography, ‘Adventures of a Mathematician’, Ulam mentions that the method was named in honor of his uncle, who was a gambler.

Calculating VaR using Monte Carlo simulations

The basic concept behind the Monte Carlo approach is to repeatedly run a large number of simulations of a random process for a variable of interest (such as asset returns in finance) covering a wide range of possible scenarios. These variables are drawn from pre-specified probability distributions that are assumed to be known, including the analytical function and its parameters. Thus, Monte Carlo simulations inherently try to recreate the distribution of the return of a position, from which VaR can be computed.

Consider the CAC40 index as our asset of interest for which we will compute the VaR using Monte Carlo simulations.

The first step in the simulation is choosing a stochastic model for the behavior of our random variable (the return on the CAC 40 index in our case).
A common model is the normal distribution; however, in this case, we can easily compute the VaR from the normal distribution itself. The Monte Carlo simulation approach is more relevant when the stochastic model is more complex or when the asset is more complex, leading to difficulties to compute the VaR. For example, if we assume that returns follow a GARCH process, the (unconditional) VaR has to be computed with the Monte Carlo simulation methods. Similarly, if we consider complex financial products like options, the VaR has to be computed with the Monte Carlo simulation methods.

In this post, we compare the Monte Carlo simulation method with the historical method and the variance-covariance method. Thus, we simulate returns for the CAC40 index using the GARCH (1,1) model.
Figure 1 and 2 illustrate the GARCH simulated daily returns and volatility for the CAC40 index.

Figure 1. Simulated GARCH daily returns for the CAC40 index.
img_SimTrade_CAC40_GARCH_ret
Source: computation by the author.

Figure 2. Simulated GARCH daily volatility for the CAC40 index.
img_SimTrade_CAC40_GARCH_vol
Source: computation by the author.

Next, we sort the distribution of simulated returns in ascending order (basically in order of worst to best returns observed over the period). We can now interpret the VaR for the CAC40 index in one-day time horizon based on a selected confidence level (probability).

For instance, if we select a confidence level of 99%, then our VaR estimate corresponds to the 1st percentile of the probability distribution of daily returns (the bottom 1% of returns). In other words, there are 99% chances that we will not obtain a loss greater than our VaR estimate (for the 99% confidence level). Similarly, VaR for a 95% confidence level corresponds to bottom 5% of the returns.

Figure 3 below represents the unconditional probability distribution of returns for the CAC40 index assuming a GARCH process for the returns.

Figure 3. Probability distribution of returns for the CAC40 index.
img_SimTrade_CAC40_MonteCarloVaR
Source: computation by the author.

From the above graph, we can interpret VaR for 99% confidence level as -3% i.e., there is a 99% probability that daily returns we obtain in future are greater than -3%. Similarly, VaR for 95% confidence level as -1.72% i.e., there is a 95% probability that daily returns we obtain in future are greater than -1.72%.

You can download below the Excel file for computation of VaR for CAC40 stock using Monte Carlo method involving GARCH(1,1) model for simulation of returns.

Advantages and limitations of Monte Carlo method for VaR

The Monte Carlo method is a very powerful approach to VAR due its flexibility. It can potentially account for a wide range of scenarios. The simulations also account for nonlinear exposures and complex pricing patterns. In principle, the simulations can be extended to longer time horizons, which is essential for risk measurement and to model more complex models of expected returns.

This approach, however, involves investments in intellectual and systems development. It also requires more computing power than simpler methods since the more is the number of simulations generated, the wider is the range of potential scenarios or outcomes modelled and hence, greater would be the potential accuracy of VaR estimate. In practical applications, VaR measures using Monte Carlo simulation often takes hours to run. Time requirements, however, are being reduced significantly by advances in computer software and faster valuation methods.

Useful resources

Jorion P. (2007) Value at Risk, Third Edition, Chapter 12 – Monte Carlo Methods, 321-326.

About the author

The article was written in March 2022 by Jayati WALIA (ESSEC Business School, Grande Ecole Program – Master in Management, 2019-2022).

Implementing Black-Litterman asset allocation model

March 21, 2022 by Youssef LOURAOUI

In this article, Youssef Louraoui (Bayes Business School, MSc. Energy, Trade & Finance, 2021-2022) presents an implementation of the Black-Litterman model, used to determine the expected return of a portfolio by integrating investor’s views regarding the performance of the underlying assets selected in the investment portfolio.

This article follows the following structure: first, we introduce the Black-Litterman model. We then present the mathematical foundations of this model. We conclude with an explanation of the methodology to build the spreadsheet with the results obtained. You will find in this post an Excel spreadsheet which implement the model.

Introduction

The Black-Litterman asset allocation model, established for the first time in the early 1990’s by Fischer Black and Robert Litterman, is a sophisticated strategy for dealing with unintuitive, highly concentrated, and input-sensitive portfolios. The most likely reason that more portfolio managers do not use the Markowitz model, which maximises return for a given degree of risk, is input sensitivity, a well-documented issue with mean-variance optimization.

The Black-Litterman Model employs a Bayesian technique to integrate an investor’s subjective views of expected returns on one or more assets with the market equilibrium vector (prior distribution) of expected returns to obtain a new, mixed estimate of expected returns. The new vector of returns (the posterior distribution) is a weighted complex average of the investor’s views and market equilibrium.

Mathematical foundation

The idea of the Black Litterman estimates is not to find the optimum portfolio weights as in the Markowitz optimization framework, but instead to find the expected return that would be used as an input to compute the optimum portfolio weights. This approach is referred as reversion portfolio optimization technique. The idea behind is that optimum weights are already observed in the market and captured in the market portfolio. We can approach the reasoning by maximizing the following utility function adjusted to the risk:

img_SimTrade_mathematical_foundation_Black_Litterman_6

w^T = transposed of portfolio weights
Π = Implied equilibrium excess return vector
A = price of risk or risk aversion factor
Σ = variance-covariance matrix

We take the partial derivative of U in terms of weights (w) and we derive the following expression:

img_SimTrade_mathematical_foundation_Black_Litterman_5

By setting the partial derivative equal to zero, we can maximize the utility function in term of weights. The proposed approach in the Black Litterman approach is that instead of seeking the optimal weights, which are incorporated in the market portfolio and thus computable via the market capitalization of the equities in the portfolio, we’ll isolate the Π (implied equilibrium excess return) to obtain the optimal expected returns for the portfolio:

We can deconstruct the Black-Litterman model as

img_SimTrade_mathematical_foundation_Black_Litterman_3

τ= scalar
P = Linking matrix
∑ = Variance-covariance matrix
Π= implied equilibrium excess return
A = Price of risk
w = weight vector
Ω = uncertainty of views

The first term of the formula is introduced in order to respect the constraint that the portfolio weights should be equal to one:

The second term of the formula is to compute a weighted average of the implied equilibrium excess return adjusted to the uncertainty of the returns by the view vector weighted with the uncertainty of views:

The final output E(R) is a vector of return n x 1 that represent the equilibrium returns of the market adjusted to investors views.

Implementation of the Black-Litterman asset allocation model in practice

To model a Black-Litterman portfolio allocation, we obtained a large time series to obtain useful results by downloading the equivalent of 23 years of market data from a data provider (in this example, Bloomberg). We generate the variance-covariance matrix after obtaining all necessary statistical data, which includes the expected return and volatility indicated by the standard deviation of the returns for each stock during the provided period.

The data is derived from the Bloomberg terminal. The first step is to calculate the logarithmic returns and excess returns on the selected assets (returns minus the risk-free rate). After calculating the logarithmic returns on each asset, we can estimate the capital asset pricing model’s returns (CAPM) expected returns. This information will be used to calculate the Black-Litterman expected returns on a comparative basis.

1. The first input for the model is the price of risk A, which represents the risk aversion of investor and is obtained by subtracting the expected return of the market the risk-free rate and divided by the variance of the market:

img_SimTrade_Black_Litterman_formulas_for_spreadsheet_1

E(r_m)= expected market returns
r_f = risk-free rate
σ²_m = variance of market

2. We extract the respective market capitalization of each security to obtain their market weights in the portfolio. Given that our investable universe is made of five stocks, we can retrieve their respective market capitalization and compute the weights of each stock in relation to the sum of total market-capitalization in the portfolio.

img_SimTrade_Black_Litterman_formulas_for_spreadsheet_2

Table 1 depicts the optimal weights obtained from their respective market capitalisation, coupled with the respective expected return and volatility.

Table 1. Asset characteristics of Apple, Amazon, Microsoft, Goldman Sachs, and Pfizer.

img_SimTrade_Black_Litterman_spreadsheet_2

Source: computation by the author.

3. We compute the variance-covariance matrix of logarithmic returns using the data analysis tool pack available in Excel (Table 2).

Table 2. Variance-covariance matrix of asset returns

img_SimTrade_Black_Litterman_spreadsheet_5

Source: computation by the author.

4. We compute the implied equilibrium excess return (Π) as the matrix calculation of the price of risk (A) times the matrix multiplication of the weights computed in step 4 times the variance-covariance matrix computed in step 3.

img_SimTrade_Black_Litterman_formulas_for_spreadsheet_3

Π= implied equilibrium excess return
A = Price of risk
w = weight vector

5. The views are incorporated into the model. To achieve this, we provide three views to include into the model. If there are no views, the values will correspond to the market portfolio. The investment manager’s views for the expected return on certain of the portfolio’s assets regularly diverge from the Implied Equilibrium Return Vector (), which serves as the market-neutral starting point for the Black-Litterman model that quantifies the uncertainty associated with each view. The Black-Litterman Model can be used to depict such views in absolute or relative terms. As an illustration, let us suppose that the real and simulated portfolio will have the same views:

View 1: Apple will outperform Microsoft by .05 percent
View 2: Amazon will outperform Microsoft by .1 percent
View 3: Apple will outperform Amazon by .05 percent

To incorporate the vector Q of views, we create a link matrix P where the rows sum to zero. Figure 3 depicts the workings done in the spreadsheet.

Table 3. Views vector and Link Matrix (P)

img_SimTrade_Black_Litterman_spreadsheet_1

Source: computation by the author.

6. We compute omega to determine the degree of uncertainty associated with the views. While Black-Litterman paper used a value of tau equal to 0.25, an important number of academics went for calculating the tau equal to one. For the sake of simplifying the model, consider tau to be equal to one. This input is obtained by multiplying the Linking matrix by the variance-covariance matrix and transposing the Linking matrix (P).

img_SimTrade_Black_Litterman_formulas_for_spreadsheet_4

τ= scalar
P = Linking matrix
∑ = Variance-covariance matrix

7. We integrate all the values computed previously in the Black-Litterman model. Table 4 depicts the results obtained via the Black-Litterman allocation model.

Table 4. Results of the Black-Litterman allocation

img_SimTrade_Black_Litterman_spreadsheet_4

Source: computation by the author.

We can see that the results converge slightly to those from CAPM. Additionally, we can see that the views are reflected in the Black-Litterman expected returns. As a result, we can determine whether or not each view is satisfied. Indeed, Apple outperforms Amazon and Microsoft, while Amazon outperforms Microsoft.

You can download an Excel file to help you construct a portfolio via the Black-Litterman allocation model.

Why should I be interested in this post?

Modern Portfolio Theory is at the heart of modern finance, shaping the modern investing landscape. MPT has become the cornerstone of current financial theory and practice. MPT’s thesis is that winning the market is difficult and requires diversification and taking higher-than-average risks.

MPT has been around for nearly sixty years and shows no signs of slowing down. His theoretical contributions paved the way for more portfolio theory study. But Markowitz’s portfolio theory is sensitive to and depends on further ‘probabilistic’ expansion. This paper expanded on Markowitz’s previous work by incorporating investor views into the asset allocation process.

Useful resources

Academic research

Black, F. and Litterman, R. 1990. Asset Allocation: Combining Investors Views with Market Equilibrium. Goldman Sachs Fixed Income Research working paper

Black, F. and Litterman, R. 1991. Global Asset Allocation with Equities, Bonds, and Currencies. Goldman Sachs Fixed Income Research working paper

Black, F. and Litterman, R. 1992. Global Portfolio Optimization.Financial Analysts Journal, 28-43.

Idzorek, T.M. 2002. A step-by-step guide to Black-Litterman model. Incorporating user-specified confidence levels. Working Paper, 2-11.

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

About the author

The article was written in Mars 2022 by Youssef LOURAOUI (Bayes Business School, MSc. Energy, Trade & Finance, 2021-2022).

Implementing Markowitz asset allocation model

March 20, 2022 by Youssef LOURAOUI

In this article, Youssef LOURAOUI (Bayes Business School, MSc. Energy, Trade & Finance, 2021-2022) explains how to implement the Markowitz asset allocation model. This model is used to determine optimal asset portfolios based on the risk-return trade-off.

This article follows the following structure: first, we introduce the Markowitz model. We then present the mathematical foundations of this model. We conclude with an explanation of the methodology to build the spreadsheet with the results obtained. You will find in this post an Excel spreadsheet which implements the Markowitz asset allocation model.

Introduction

Markowitz’s work is widely regarded as a pioneer work in financial economics and corporate finance due to its theoretical foundations and applicability in the financial sector. Harry Markowitz received the Nobel Prize in 1990 for his contributions to these disciplines, which he outlined in his 1952 article “Portfolio Selection” published in The Journal of Finance. His major work established the foundation for what is now commonly referred to as “Modern Portfolio Theory” (MPT).

To find the portfolio’s minimal variance, the Markowitz model uses a constrained optimization strategy. The goal of the Markowitz model is to take into account the expected return and volatility of the assets in the investable universe to provide an optimal weight vector that indicates the best allocation for a given level of expected return or the best allocation for a given level of volatility. The expected return, volatility (standard deviation of expected return), and the variance-covariance matrix to reflect the co-movement of each asset in the overall portfolio design are the major inputs for this portfolio allocation model for an n-asset portfolio. We’ll go over how to use this complex method to find the best portfolio weights in the next sections.

Mathematical foundations

The investment universe is composed of N assets characterized by their expected returns μ and variance-covariance matrix V. For a given level of expected return μ_P, the Markowitz model gives the composition of the optimal portfolio. The vector of weights of the optimal portfolio is given by the following formula:

With the following notations:

w_P = vector of asset weights of the portfolio
μ_P = desired level of expected return
e = identity vector
μ = vector of expected returns
V = variance-covariance matrix of returns
V^-1 = inverse of the variance-covariance matrix
t = transpose operation for vectors and matrices

A, B and C are intermediate parameters computed below:

img_SimTrade_implementing_Markowitz_2

The variance of the optimal portfolio is computed as follows

img_SimTrade_implementing_Markowitz_3

To calculate the standard deviation of the optimal portfolio, we take the square root of the variance.

Implementation of the Markowitz asset allocation model in practice

We generated a large time series to obtain useful results by downloading the equivalent of 23 years of market data from a data provider (in this example, Bloomberg). We generate the variance-covariance matrix after obtaining all necessary statistical data, which includes the expected return and volatility indicated by the standard deviation of the returns for each stock during the provided period. Table 1 depicts the expected return and volatility for each stock retained in this analysis.

Table 1. Asset characteristics of Apple, Amazon, Microsoft, Goldman Sachs, and Pfizer.
img_SimTrade_implementing_Markowitz_spreadsheet_1
Source: computation by the author.

We use the data analysis tool pack supplied in Excel to run a variance-covariance matrix for ease of computation (Table 2).

Table 2. Variance-covariance matrix of asset returns.
img_SimTrade_implementing_Markowitz_spreadsheet_4
Source: computation by the author.

We can start the optimization task by setting a desirable expected return after computing the expected return, volatility, and the variance-covariance matrix of expected return. With the data that is fed into the appropriate cells, the model will complete the optimization task. For a 10% desired expected return, we get the following results (Table 3).

Table 3. Asset weights for an optimal portfolio.
img_SimTrade_implementing_Markowitz_spreadsheet_2
Source: computation by the author.

To demonstrate the effect of diversification in the reduction of volatility, we can form a Markowitz efficient frontier by tilting the desired anticipated return with their relative volatility in a graph. The Markowitz efficient frontier is depicted in Figure 1 for various levels of expected return (Figure 1).

Figure 1. Markowitz efficient portfolio frontier.
img_SimTrade_implementing_Markowitz_spreadsheet_3
Source: computation by the author.

You can download the Excel file below to use the Markowitz portfolio allocation model.

Why should I be interested in this post?

Modern Portfolio Theory (MPT) is at the heart of modern finance, shaping the modern investing landscape. MPT has become the cornerstone of current financial theory and practice. MPT has been around for nearly sixty years and shows no signs of slowing down. His theoretical contributions paved the way for more portfolio theories. This post helps you to grasp the theoretical model and its implementation.

Useful resources

Academic research

Petters, A. O., and Dong, X. 2016. An Introduction to Mathematical Finance and Applications. Springer Undergraduate Texts in Mathematics and Technology.

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

About the author

The article was written in Mars 2022 by Youssef LOURAOUI (Bayes Business School, MSc. Energy, Trade & Finance, 2021-2022).

The incredible story of Nick Leeson & the Barings Bank

March 17, 2022 by Louis DETALLE

The incredible story of Nick Leeson & the Barings Bank

In this article, Louis DETALLE (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023) looks back at the bank fraud of Nick Leeson, a trader at Barings, which led to the collapse of the UK’s oldest investment bank…

History of Barings and Nick Leeson’s background

Barings was founded in 1762 in the UK, making it the oldest British bank, so renowned and prestigious that even the Queen of England was a client. It is therefore in this renowned institution that Nick Leeson will pursue his career after a spell at Morgan Stanley as an operations assistant. Ambitious and ready to do anything to make a name for himself within this prestigious institution, Nick Leeson multiplies risky operations and gradually climbs the ladder, greeted by a management admiring his results considering his young age.

The great fraud

In 1990, Barings chose Nick Leeson to head up the management of its Singapore subsidiary. Having spotted a flaw in the system for monitoring the compliance of traders’ market operations, Nick Leeson carried out speculative operations that were normally unauthorised and that brought in a lot of money for Barings. Nick Leeson was therefore engaged in a series of successful speculative trades, which is why management did not look into the matter. However, the day comes when the trader’s luck runs out: he makes bigger and bigger losses, as he hopes to make up for previous losses with each new trade.

With the trade tracking loophole still in use by Nick Leeson, he hides the losses from the failed trades in an error account, 88888. Nick Leeson also concealed documents from the bank’s auditor and continued to trade with losses accumulating over time. By the beginning of 1995, these losses reached £210 million, which represented half of Barings’ capital.

Eager to wipe out these very large losses, on the evening of January 16, 1995 Nick made a colossal trade – $7 billion – betting that the Nikkei would not fall overnight. Normally this would be considered a low-risk trade, but on the evening of 16 January an earthquake struck Kobe. On the morning of January 17, the Nikkei price collapsed and so did the trader’s positions.

Nick Leeson tried to make up for it by trying to make a quick recovery in the Nikkei, but this did not happen. Nick’s losses reach an abysmal $1.4 billion, which is twice the bank’s capital. Despite Nick’s ability to circumvent the bank’s internal controls, the level of losses is such that his entire scheme is uncovered. And the bank, faced with such losses, is forced to declare bankruptcy.

Conclusion

In conclusion, it was a major error in the compliance system that caused the Barings bankruptcy. Nowadays, enforcers can no longer supervise the tasks entrusted to them, and this is all the more true in banks where brand new departments have been created since the 2000s with the rise of compliance and banking regulation.

Useful resources

Mousli M. (2015) Quand un trader fait sauter une banque : Nick Leeson et la Barings L’Économie politique 68(4) 89-101.

Comprehensive history of the Barings bank

About the author

The article was written in March 2022 by Louis DETALLE (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023).

What happened between Bruno Iksil & JP Morgan

February 5, 2024March 17, 2022 by Louis DETALLE

What happened between Bruno Iksil & JP Morgan

In this article, Louis DETALLE (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023) explains how Bruno Iksil, a French trader working in London made inconsiderate trades in the name of the renown JP Morgan.

Bruno Iksil: background of a French trader based in London

Bruno Iksil, known as “The Whale”, is a French trader well known in London financial circles. A former student of Centrale Paris, this former Natixis employee built a reputation at JP Morgan for the size of the orders he placed. Bruno Iksil worked on the Credit Default Swaps (CDS) market, financial products that provide insurance against the non-repayment of loans.

Iksil’s activities at JP Morgan

Bruno Iksil’s reckless trading initially made JP Morgan Chase a lot of money, almost $100 million. His ability to succeed brilliantly in times of crisis and his boldness in business were praised and rewarded on numerous occasions by management, which made Iksil the highest paid trader in London. According to the Wall Street Journal, in recent years Bruno Iksil earned around $100 million a year at JPMorgan’s chief investment office (CIO).

And his nickname, linked to the enormity of the commitments he was making, was regularly on the front page of all the newspapers, along with the new positions taken by ‘The Whale’.

JP Morgan’s losses

Bruno Iksil was suspected of being involved in a colossal loss by JP Morgan Chase. According to the latest estimates, the risky bets of the Frenchman and his colleagues cost JP Morgan Chase 5.8 billion dollars. This triggered a real storm in the life of the trader who, according to the British journalist The Guardian, left the company.

Following the losses incurred by the American bank, Jamie Dimon – the Chief Executive Officer – had announced losses amounting to 2 billion dollars. In fact, nearly 4.4 billion dollars were lost as a result of the Whale’s operations.

Following these announcements, the bank’s market capitalization plunged by 25 billion dollars as the stock dived by 9%.

Conclusion and aftermath of the affair

The whale affair brought to light accusations of negligence against the bank, particularly in its internal controls. The risky positions in credit derivatives that Bruno Iksil and many other banks regularly took contributed to the subprime crisis. As a result, JP Morgan was fined $1 billion by the British and American authorities, on behalf of its management that enable the Whale to invest so much on financial markets.

Useful resources

Philippe Bernard (13/07/2015) A Londres, Bruno Michel Iksil échappe aux poursuites Le Monde.

JP Morgan

About the author

The article was written in March 2022 by Louis DETALLE (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023).

Monte Carlo simulation method

March 17, 2022 by Jayati WALIA

In this article, Jayati WALIA (ESSEC Business School, Grande Ecole – Master in Management, 2019-2022) explains the Monte Carlo simulation method and its applications in finance.

Introduction

The Monte Carlo method was invented by John von Neumann (Hungarian-American mathematician and computer scientist) and Stanislaw Ulam (Polish mathematician) during World War II to improve decision making under uncertain conditions. It is named after the popular gambling destination Monte Carlo, located in Monaco and home to many famous casinos. This is because the random outcomes in the Monte Carlo modeling technique can be compared to games like roulette, dice and slot machines. In his autobiography, ‘Adventures of a Mathematician’, Ulam mentions that the method was named in honor of his uncle, who was a gambler.

How Monte Carlo simulation works

The main idea is to repeatedly run a large number of simulations of a random process for a variable of interest (such as an asset price in finance) covering a wide range of possible situations. The outcomes of this variables are drawn from a pre-specified probability distribution that is assumed to be known, including the analytical function and its parameters. Thus, Monte Carlo simulations inherently try to recreate the entire distribution of asset prices.

Example: Apple stock

Consider the Apple stock as our asset of interest for which we will generate stock prices according to the Monte Carlo simulation method.

The first step in the simulation is choosing a stochastic model for the behavior of our random variable (the Apple stock price in our case). A commonly used model is the geometric Brownian motion (GBM) model. The model assumes that future asset price changes are uncorrelated over time and the probability distribution function of the future price is a log-normal distribution. The movements in price in GBM process can be expressed as:

img_SimTrade_GBM_process

with dS being the change in asset price in continuous time dt. dW is the Wiener process (W_t+1 – W_t is a random variable from the normal distribution N(0, 1)). σ represents the price volatility considering the unexpected changes that can result from external effects (σ is assumed to be constant over time). μdt together represents the deterministic return within the time interval with μ representing the growth rate of the asset price or the ‘drift’.

Integrating dS/S over a finite interval, we get :

img_SimTrade_simulated_asset_price

Where ε is a random number generated from a normal distribution N(0,1).

This equation thus gives us the evolution of the asset price from a simulated model from day t-1 to day t.

We can now generate a simulation path for 100 days using the above formula.

The figure below shows five simulations for the price of the Apple stock over 100 days with Δt = 1 day. The initial price for Apple stock (i.e, price at t=0) is $146.52.

Figure 1. Simulated Apple stock prices according to the Monte Carlo simulation method.
img_SimTrade_Apple_MonteCarloSim
Source: computation by author.

Thus, we can observe that the prices obtained by just these five simulations range from $100 to over $220.

You can download below the Excel file for generating Monte Carlo Simulations for Apple stock.

Applications in finance

The Monte Carlo simulation method is widely used in finance for valuation and risk analysis purposes.

One popular application is option pricing. For option contracts with complicated features (such as Asian options) or those with a combination of assets as their underlying, Monte Carlo simulations help generate multiple potential payoff scenarios for the option which are averaged out to determine the option price at the issuance date.

The Monte Carlo method is also used to assess potential risks by generating simulations of market variables affecting portfolios such as asset returns, interest rates, macroeconomic factors, etc. over different time periods. These simulations are then assessed as required for risk modelling and to compute risk metrics such as the value at Risk (VaR) of a position.

Other applications include personal finance planning and corporate project finance where simulations are generated to construct stochastic financial models for sensitivity analysis and net present value (NPV) projections.

Useful resources

Hull, J.(2008) Risk Management and Financial Institutions, Fifth Edition, Chapter 7 – Valuation and Scenario Analysis.

About the author

The article was written in March 2022 by Jayati WALIA (ESSEC Business School, Grande Ecole Program – Master in Management, 2019-2022).

My professional experience as financial research assistant in a green finance institute

June 15, 2025March 16, 2022 by Haiyuan XU

My professional experience as financial research assistant in a green finance institute

In this article, Haiyuan Xu (ESSEC Business School, Master in Strategy & Management of International Business (SMIB), 2021-2022) presents her personal internship experience as assistant financial research analyst in the sector of green finance.

About the company

The International Institute of Green Finance (IIGF) is the first international research institute in China with the goal of promoting the development of green finance. It was established in September 2016 by a donation from Tianfeng Securities Company (Tianfeng Securities issued the first green corporate bond of a private listed company in the market). Its research topics include green finance, climate finance, energy finance and health finance. The IIGF is committed to building a domestic first-class and world-leading green finance think-tank with Chinese characteristics.

Logo of the International Institute of Green Finance (IIGF).
Logo of IIGF
Source: IIGF.

What about my internship？

Job missions

My position was financial research assistant in the green bonds sector.

My work can be summarized as the construction of a financial database about green bonds, and the writing of financial research reports.

Regarding the database construction, the relevant data of green bonds were mainly collected from public channels such as the WIND database (WIND is a financial data and analysis tool service company providing financial data, information, and software service in mainland China). For the database of the institute, I collected information such as the type of green bonds (most of the green bonds were convertible bonds), green bond issuers, use of raised funds, certification and underwriting to update and maintain the existing green bond database. In addition, I participated in the establishment of the standard for the definition of non-labeled green bonds, so that the green investment of general bonds is also included in the database, which makes the database more convincing.

In terms of financial report writing, I participated in on-site research, collected data, and wrote the first draft of the green bond report. Finally, the institute issues the annual green bond market development report, which is of great help to the development of green finance in China.

Requirements for this internship

The job was a team job and required cooperation and communication skills (soft skills). Of course, this job also required me to be financially literate. In addition, I needed to have the ability to collect data, process data and analyze financial markets (hard skills).

What I have learnt from the internship

What impresses me most is the knowledge I have learnt from the business of green finance. I think the development of China’s green finance market is relatively imperfect, and many products can be innovated. According to recent data released by the People’s Bank of China, as of the end of 2021, China’s green loan balance in local and foreign currencies was 15.9 trillion yuan, a year-on-year increase of 33%. But at the same time, driven by the “dual carbon” goal, the scale of China’s green credit products is far from meeting the relevant investment and financing needs, and there is still huge room for growth. At present, the Chinese government is strongly supporting the development of green finance. China is rich in green energy such as wind energy and hydropower. Therefore, based on the above conditions, I believe that China’s green financial market has great potential for development. I look forward to setting up a green public fund company to raise funds for the development of green finance in China.

Earn money in a green way.

Source: IIGF.

Financial Concepts

I explain below some financial concepts that I found useful during my internship at IIGF.

Convertible bonds

A convertible bond is a fixed-income corporate debt security that yields interest payments, but can be converted into a predetermined number of common stock or equity shares. The conversion from the bond to stock can be done at certain times during the bond’s life and is usually at the discretion of the bondholder.

Green bonds

Green bonds are fixed-income financial instruments which are used to fund projects that have positive environmental and/or climate benefits. They follow the Green Bond Principles stated by the International Capital Market Association, and the proceeds from the issuance of which are to be used for the pre-specified types of projects.

Underwriting

Underwriting (UW) services are provided by some large financial institutions, such as banks, insurance companies and investment houses, whereby they guarantee payment in case of damage or financial loss and accept the financial risk for liability arising from such guarantee.

Useful resources

International Institute of Green Finance (IIGF)

The report published by the IIGF

Green finance for developing countries

About the author

The article was written in March 2022 by Haiyuan_Xu (ESSEC Business School, Master in Strategy & Management of International Business (SMIB), 2021-2022).

Excel functions for mortgage

February 5, 2024March 14, 2022 by Liangyao TANG

Excel functions for mortgage

In this article, Liangyao TANG (ESSEC Business School, Master in Strategy & Management of International Business (SMIB), 2021-2022) explains the functions in Excel that are useful to study a mortgage. Mastery of Excel is an essential skill nowadays in financial analysis and modelling tasks. Proficiency in using Excel formulas can help analysts quickly process the data and build the models more concisely.

Mortgage

A mortgage is the type of loan used in real estate, vehicles, and other types of property purchasing activities. There are two parties in the mortgage contract: the borrower and the lender. The contract sets the terms and conditions about the principal amount, interest rate, interest type, payment period, maturity, and collaterals. The borrower is contracted to pay back the lender in a series of payments that contains part of the principal as well as the interests before the maturity date.

The mortgage is also subject to different terms according to the bank’s offers and macroeconomic cycle. There are two types of interest rates: the fixed-rate loan and the floating (variable) rate loan, in which the interest rate is a pre-determined rate (at the beginning of the period) and post-determined rate (at the end of the period).

Example of repayment schedule.

In this post, I will use the following example: a mortgage of $300,000 for property purchasing. The mortgage specifies a 5% fixed annual interest rate for 30 years, and the borrower should pay back the loan on a monthly basis. We can use Excel functions to calculate the periodic (monthly) payment and its two components, the principal repaid and the interests paid for a given period. The calculations are shown in the sample Excel file that you can download below.

PMT

The “PMT” (Payment) Excel function calculates the periodic mortgage payment.

The periodic repayment for a fixed-rate mortgage includes a portion of repayment to the principal and an interest payment. Since the mortgage has a given maturity date, the payment is calculated on a regular basis, for example, every month. All repayments are of equal amount throughout the loan period.

The mathematical formula for the periodic mortgage payment is:

Formula for the periodic mortgage payment

With the following notations:

PMT: the payment
P: the principal value
r: the interest rate
N: the total number of periods

The repayment schedule is a table which gives the periodic payment, and the principal repaid and the interests paid for a given period. It can be a large table. For example, the repayment schedule of a loan with 30 year maturity and monthly payment has 180 lines. In formal terms, the payment schedule would be:

Repayment schedule of a mortgage

The repayment schedule shows the payment amount of each period, and the remaining principal balance after each payment. The ‘P’ represents the principal amount at the beginning of the mortgage, and the remaining principal is subjective to an (1+r) times interests at each period. The remaining principal is the principal balance from last period minus the current payment. Therefore for period 1, the remaining balance is equal to P(1+r), which is the principal with one year of interest, minus the PMT value, which is the payment of the current period.

The syntax for the Excel function to calculate the periodic payment is: PMT(rate, nper, pv, [fv], [type]).

With the following notations:

PMT: the periodic payment of the loan
Nper: the total number of periods of the loan
PV : the principal (present value) of the loan
[fv]: the future value of the loan (optional parameter). Default equal to 0
[type]: when payments are due (optional parameter). 0 = end of period, 1 = beginning of period. Default is 0

The function is used explicitly in the case of a fixed interest rate to compute the (constant) periodic payment.

The PMT function will calculate the loan’s payment at a given level of interest rate, the number of periods, and the total value of the loan for principals at the beginning of the period (principal + interest).

When using the function, it is essential to always align the time unit of the interest rate and the unit of Nper. If the mortgage is compounding on a monthly basis, the number of periods should be the total number of months in the amortization, and the rate should be the monthly interest rate, which equals the annual rate divided by 12. . In the above example, the interest should be paid in a monthly basis, therefore the number of period (Nper) is equal to 12 month x 30 year = 360 periods. Since the annual interest rate is 5%, the monthly interest rate would equal to 5% divide by 12, which is 0.42% per month.

IPMT and PPMT

To supplement on the information about the monthly payment, we can also use the function IPMT and PPMT to calculate the principal repaid and the interest rate paid for a given period.

IPMT

IPMT is the Excel function that calculates the interest portion in each of the periodic payment.

The syntax of the Excel function to calculate the interest portion of the periodic payment is: IPMT(rate, per, nper, pv, [fv], [type]).

With the following notations:

IPMT: interest payment
rate: interest rate
per: current period number
nper: total number of periods
pv: present value
[fv]: the future value of the loan (optional parameter). Default equal to 0
[type]: when payments are due (optional parameter). 0 = end of period, 1 = beginning of period. Default is 0

The rate refers to the periodic interest rate, while the “nper” refers to the total number of payment periods, and the “per” refers to the period for which we want to calculate the interest.

PPMT

PPMT is the Excel function that calculates the principal portion of a periodic payment.

The syntax of the Excel function to calculate the principal portion of a periodic payment is: PPMT(rate, per, nper, pv, [fv], [type]).

With the following notations:

PPMT: principal payment
rate: interest rate
per: current period number
nper: total number of periods
pv: present value
[fv]: the future value of the loan (optional parameter). Default equal to 0
[type]: when payments are due (optional parameter). 0 = end of period, 1 = beginning of period. Default is 0

Those of the results should be consistent with the amortization schedule shown above. The principal repayment should equal to PMT per period minus the interest rate paid (IPMT).

RATE

Contrarily, if the user is given the periodic payment amount information and wants to find out about the interest rate used for the calculation, he/she can use the RATE function in Excel.

The syntax of the Excel function to calculate the rate is: RATE(nper, pmt, pv, [fv], [type], [guess]).

With the following notations:

RATE: the interest rate
nper: the total number of payment periods
pmt: the constant periodic payment
pv: the principal amount
[fv]: the future value of the loan (optional parameter). Default equal to 0
[type]: when payments are due (optional parameter). 0 = end of period, 1 = beginning of period. Default is 0
[guess]: your guess on the rate (optional parameter). Default is 10%

The RATE Excel function will automatically calculate the interest rate per period. The time unit of the interest rate is aligned with the compounding period; for example, if the mortgage is compounding on a monthly basis, the RATE function also returns a monthly interest rate.

Example with an Excel file

The use of the Excel functions PMT, IPMT, PPMT and RATE is illustrated in the Excel file that you can download below.

Useful resources

Forbes What is a mortgage

Rocket mortgage Types of mortgage

Ramsey How Do Student Loans Work?

Prof. Longin’s website Echéancier d’un crédit (mortgage calculator in French)

About the author

The article was written in March 2022 by Liangyao TANG (ESSEC Business School, Master in Strategy & Management of International Business (SMIB), 2021-2022).

My experience as a financial analyst at CASIM

March 13, 2022 by Liangyao TANG

My experience as a financial analyst at CASIM

In this article, Liangyao TANG (ESSEC Business School, Master in Strategy & Management of International Business (SMIB), 2021-2022) discusses the valuation methodologies in private equity and venture capitals based on her experience at the CAS Investment Management Co., Ltd.

CASIM

Chinese Academy of Sciences Holdings Co., Ltd., also referred to as CAS Investment Management Co., Ltd., is the first state-owned asset management company approved by the Assets Supervision and Administration Commission of the State Council (SASAC). Its predecessor is the Chinese National Economic Council-CAS Scientific and Technological Promotion & Economic Development Foundation. On April 12, 2002, CAS investment completed the industrial and commercial registration and was restructured into a limited liability asset management company.

Logo of CAS Investment Management.

Source: CAS Investment Management.

Since 2008, CAS Investment has carried out Private Equity fund investment business as an institutional investor. It had directly invested in many strategic emerging industries that helped shape China’s technological development. As of the end of 2018, the registered capital of CAS Investment was 5.1 billion RMB, holding more than 50 companies, which mainly distributed in information technology, high-end equipment manufacturing, environmental technology, and new materials. For the past 25 years, CASIM has supported over 300 successful commercialization and industrialization of technological development, some of which have become the industrial bellwethers and have contributed to promoting scientific achievement conversion in China. The Investment portfolio include Cambricon Technologies, Farasis Energy, ThunderSoft Technologies, and SIASUN Robostics.

My Mission

Passionate about creating value for enterprises and gaining a more on-edge understanding of the business world, I carried out a four-month internship as a Financial Analyst in the CAS Investment Management Co., Ltd. During that time, I joined the team and worked together with a Managing Director and a senior analyst. My responsibility is to conduct market research and desktop analysis for the project, which has allowed me to quickly dive into the TMT industry, especially the e-commerce sector. I conducted more than 20 financial statement analyses, operation strategies analyses, and market analyses to evaluate different investment opportunities. The qualitative analysis includes competitor benchmarking, business model analysis, supply chain and distribution analysis. Quantitatively includes building financial models to calculate future cash flow and income stream, and building market models to estimate the potential market size and market share. In addition, I also performed due diligence by conducting more than 50 interviews with industry experts on multiple projects. During the financial due diligence, I utilized Excel VBA to process and analyze ratios in the massive inventory and operation data to verify the authenticity of the target company’s financial performance. Those analytical work have greatly improved my hands-on ability to build market models and identify potential investment risks. I co-delivered the formal investment proposals for distribution to the Investment Committee, an infrequent task given to an intern during the past several years.

Knowledge and skills required

This investment analyst role requires the candidate to have a deep understanding of the business world and the target industry to be able to value the future potential of a business. The candidate also has to have the critical thinking ability to identify the risks and make rational decisions. During this experience, I have significantly reinforced my knowledge in financial statement analysis and market analysis. I also gained a solid improvement on my fast-learning abilities, analytical skills, and logical thinking capacities, which could be very beneficial for me moving forward in my career path.

Key financial indicators to understand valuation

When and what valuation method should be used has always been a matter of constant debate in the financial industry. The valuation methods depend on different stages of development and different rounds of financing of companies. This brings great differences in the estimated value of firms, resulting in no unified framework in the industry. The valuation can be said to be the most difficult part of a project. The valuation methods that are often used in Private Equity and Venture Capital firms include price-earnings ratio (P/E), price-book ratio (P/B), price-earnings growth (PEG), discounted cash flows (DCF), etc. I describe a few of them below.

P/E

The P/E method is one of the most common valuation methods. There are usually two types of price-earnings ratios for listed companies: historical price-earnings ratio (also called Trailing P/E) and forecasted price-earnings ratio (also called Forward P/E). The historical P/E ratio uses the current market value to divide by the earnings of the company in the previous financial year (or earnings in the previous 12 months). On the contrary, the forward P/E ratio uses current market capitalization, divided by the company’s earnings for the current fiscal year (or earnings for the next 12 months). Investors invest in the company’s future and estimate the present value for the company’s future operating capabilities. Therefore, the calculation formula using the price-earnings ratio method is:

Enterprise value = forecast price-earnings ratio × company profit for the next 12 months.

The price-earnings (P/E) ratio can be chosen from those of the comparable companies, their direct competitors, or the average price-earnings ratio of the industry in which the target company is located. The price-earnings ratio mainly depends on the expected growth rate of the company. The company’s valuation at its growing stage usually is much higher than that of a company at its mature stage. Also, it depends on the business’s risk level or the risk tolerance from the investors. A lower price-earnings ratio is used for a risker business because investors would demand a higher return from the investment.

EV/EBITDA

Another commonly used ratio in private company valuation is the EV/EBITDA ratio. The ratio is used to compare the enterprise value (EV) with the Earnings Before Interest, Taxes, Depreciation & Amortization (EBITDA). This gives investors a sense of how many times of the EBITDA they will have to pay if acquiring the target enterprise. Comparing the EV/EBITDA ratio of a company with the industry average and direct rivals also gives investors an idea of a fair target price. This method could leave out the effect of the company’s depreciation, inventory, non-recurring income and expenditure. Therefore, it is a good supplement to the price-to-earnings ratio valuation method.

P/B

The price-to-book (P/B) ratio is the ratio of market value/ tangible net assets value, or the ratio of share price per share to total book value per share. The EV of the firm is the same as discussed above in the P/E ratio section, and the book value is equal to total assets minus the intangible assets and total liabilities. The P/B ratio demonstrated important information about its market value compared to its accounting measures.

The advantages of this method are that the net assets’ data is easy to obtain, and the book value of net assets usually is authentic and less manipulated than net profit. Additionally, even if the company hasn’t been profitable, the price-to-book ratio is rarely negative, so it’s easier to calculate. However, the book value is affected by the choice of accounting policies. If companies implement different accounting standards, the price-to-book ratio will lose comparability. Secondly, the P/B ratio will not be a good measure for those companies with very few fixed assets. Those companies normally have a bigger portion of intangible assets, such as goodwill, intellectual property, brand reputation, and so on, which are not taking into consideration in the book value and could be easily manipulated. For example, for the service providers, media and entertainment producers, and IT companies, the company’s potential has very minimal relevance with its fixed assets. Therefore, the P/B ratio can be meaningless in some cases.

To sum up, the valuation of private enterprises, especially for start-up companies, is a unique and challenging task. The process is usually a combination of scientific computation and some flexibilities. No valuation method is perfect; analysts need to be very flexible and sharp with the method and the supplementary information they use to draw an objective conclusion.

Useful resources

McKinsey & Company (23/10/2019) Pricing: The next frontier of value creation in private equity

Insider Intelligence (06/01/2022) Financial Services Industry Overview in 2022: Trends, Statistics & Analysis

BVCA Private Equity Explained

About the author

The article was written in March 2022 by Liangyao TANG (ESSEC Business School, Master in Strategy & Management of International Business (SMIB), 2021-2022).

Eurobonds

March 7, 2022 by Akshit GUPTA

Eurobonds

This article written by Akshit GUPTA (ESSEC Business School, Grande Ecole Program – Master in Management, 2019-2022) explains Eurobonds traded in financial markets.

Introduction

In financial markets, bonds are debt securities used by issuers to raise capital from investors. In return investors get an interest payment on the principal invested over the life of the bond. The bonds can be issued by governments, municipalities, financial institutions, and companies. The duration of the bonds can cover different time periods.

Eurobonds are a special kind of bonds issued by companies or governments to raise capital from financial markets. These bonds are denominated in a currency different from the currency of the country where they originated. The Eurobonds help issuers to raise capital in a foreign currency and at a lower cost. Let’s take the example of an American company which would like to issue debt in euros to finance its operations in Europe. If it borrows in European markets, it will get a higher interest rate as it is less well known in the foreign markets that in the domestic market. With Eurobonds, the company can benefit from the same level of interest rates as for its domestic bonds, thereby lowering its cost of capital.

These instruments have a medium to long term maturity and are highly liquid in the market. They are traded over the counter (OTC) and the market for Eurobonds is made up of several financial institutions, issuers, investors, government bodies, and brokers. Many brokerages across the world provide trading platforms facilities to investors and borrowers for trading in different kinds of Eurobonds.

Characteristics of Eurobonds

Eurobonds are unsecured instruments and investors demand high yields on these instruments based on the credit ratings of the issuer. The issuer can issue Eurobonds in a foreign currency and a foreign land based on their capital needs. The name of a Eurobond carries the name of the currency in which they are dominated. For example, a French company willing to do business in the United States, can issue a Eurobond in the UK financial market denominated in US dollars which will be called as euro-dollar bond.

A Eurobond should not be confused with a foreign bond issued by an issuer in the foreign market denominated in the local currency of the investor. A Eurobond can be issued in a foreign country and can be denominated in a currency different from the local currency of the issuer. For example, a French company willing to invest in Japan can issue a Euro-yen bond in the US markets denominated in the local currency of Japan.

These bonds are traded electronically on different platforms and can have maturities ranging from 5 years to 30 years. The bonds can have fixed or floating interest rates with semi-annual or annual payments. These bonds have a relatively small face value making it attractive even to small investors.

Benefits of Eurobonds

Eurobonds can serve different benefits to issuers and investors.

Major advantage of Eurobonds for the issuers

Access to capital at lower rates – Companies can choose countries with lower interest rates to issue Eurobonds, thereby avoiding interest rate risks
Access to different bond maturities – As Eurobonds can have maturities ranging from 5 years to 30 years, companies can have a wide range of maturities to choose from depending on their requirements
Access to international markets – By issuing Eurobonds denominated in a different currency, companies can access different markets with more ease with a wide investor base.

Major advantage of Eurobonds for the investors

Access to international markets – By buying Eurobonds, investors can gain easy access to international markets thereby diversifying their fixed income portfolios.
Access to different bond maturities – As Eurobonds can have maturities ranging from 5 years to 30 years, borrowers can have a wide range of maturities to choose from depending on their investment profile.
High liquidity – As the market size for Eurobonds is very large, investors can enjoy higher liquidity and can exit their positions as per their needs.

Example

The figure below gives an example of Eurobonds issued by the Federal Republic of Nigeria.

Characteristics of the Eurobonds issuance.

Example of Eurobond issuance

Source: FMDQ.

▶ Akshit GUPTA Green bonds

▶ Jayati WALIA Fixed-income products

▶ Jayati WALIA Credit Risk

Useful resources

International Capital Market Association (ICMA) History of Eurobonds

About the author

Article written in March 2022 by Akshit GUPTA (ESSEC Business School, Grande Ecole Program – Master in Management, 2019-2022).

Green bonds

March 7, 2022 by Akshit GUPTA

Green bonds

This article written by Akshit GUPTA (ESSEC Business School, Grande Ecole Program – Master in Management, 2019-2022) explains Green bonds traded in financial markets.

Introduction

A green bond is a fixed-income product that works like a conventional bond, except that the money invested in them is used exclusively to finance green projects that support environment preservation, sustainability and reduction of climate change (low-carbon economy). Green projects can include renewable energy such as solar and wind power, energy-efficient infrastructure, clean transportation and waste management and recycling.

In 2007, the European Investment Bank (EIB) issued the world’s first ever green bond under the name Climate Awareness Bond (CAB), which focused on renewable energy and energy efficiency projects. This was followed by the World Bank issuing its own green bonds, until 2012 when the first corporate green bond was issued. Since then the market for green bonds has grown tremendously creating all-time highs with every passing year. The greatest issuer of green bonds in 2020 was the French government with a combined issue size of nearly 13 billion USD.

Types of green bonds

Green bonds can be classified as the following: green “use of proceeds” bonds, green “use of proceeds revenue” bonds, green project bonds, and securitized green bonds.

Green “use of proceeds” bonds

The funds raised by these green bonds are invested in green projects but they are backed/secured by issuer’s assets. Hence, their ratings are the same as other debt instruments by the issuer. For instance, the Climate Awareness Bond issued by EIB is one such green bond.

Green “use of proceeds revenue” bonds

The funds raised are assigned to eligible green projects. However, bondholders have recourse to a specified revenue stream from the issuers which may or may not be related to the eligible green projects.

Green project bonds

Proceeds from green project bonds are used for specific projects, investors having a direct exposure to the green project itself.

Securitized green bonds

These bonds are backed by a large group of green projects or assets.

Benefits of investing in green bonds for issuers

Lower cost of capital

Green bonds help environment focused companies to raise large amount of initial and working capital at lower costs to fund their ESG activities which require heavy initial investments. For example, companies can raise capital to fund a project focused towards generating renewable energy.

Brand value

Companies issuing green bonds enjoy an increase in the brand value and favourable reputation amongst the investors, as they are becoming more inclined towards sustainability.

Benefits of investing in green bonds for investors

Diversification

Over the years, the financial markets have seen an increased demand for green bonds amongst investors. Various factors have contributed to this increase including portfolio diversification, focus on socially responsible investments opportunities, fulfilment of ESG mandates of the financial institutions, etc.

Tax benefits

Investors can enjoy tax incentives on the investments made in green bonds. The interest incomes generated on these bonds are generally tax exempt or provide tax reductions to the investors. Thus, the issuers also benefit from lower interest rates due to the tax benefits.

Increase in liquidity

As the market size for green bonds is increasing, investors can enjoy higher liquidity and can exit their positions as per their needs.

Examples

The image below shows the listing of green bonds on Euronext.

Listing of green bonds on Euronext.

Listing of green bonds

Source: Euronext.

Useful resources

Corporate Finance Institute Eurobonds

ICMA History of Eurobonds

Euronext Listing of green bonds

About the author

Article written in March 2022 by Akshit GUPTA (ESSEC Business School, Grande Ecole Program – Master in Management, 2019-2022).

Wirecard: At the heart of the biggest German financial scandal of the 21st century

March 7, 2022 by Louis DETALLE

Wirecard: At the heart of the biggest German financial scandal of the 21st century

In this article, Louis DETALLE (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023) explains what happened with Wirecard, the German company that caused a major scandal in the German financial place.

Quick review of the Wirecard company

The Wirecard case takes its name from a German start-up specializing in online payment solutions. Listed on the Frankfurt stock exchange in 2018, this company has experienced a meteoric growth with more than 300,000 corporate customers at its peak in June 2020.

Wirecard’s logo

However, as early as 2015, doubts were raised about the effectiveness of the Wirecard model and suspicions of irregularities arose. It was not until June 2020 that the management admitted that €1.9 billion of its consolidated balance sheet did not exist.

Several actors bore the brunt of the scandal

The consequences of this announcement were terrible for several actors:

Firstly, for the company, whose share price lost 90% of its value in a few days (see chart below). Rating agencies such as Moody’s are removing Wirecard’s rating due to the falsification of the information on which the rating was based. At management level, the former CEO of Wirecard, Markus Braun, resigned and was arrested by the German justice system. He ended up in prison along with two other executives of the German company. Jan Marsalek, another Wirecard executive, has been wanted since June 2020 by Interpol to be brought before the Munich court.

Evolution of Wirecard’s stock’s value.

For EY, Wirecard’s auditor, this case is reverberating through the financial ecosystem. Indeed, the auditor EY is also in great difficulty since the teams of the big firm of the Big 4 have been certifying the accounts of the company for several years and missed important frauds. The Financial Times, for example, accused the firm of failing to check for accounting irregularities in the balance sheet, which should have been done. There are numerous legal actions against the auditor for malpractice, such as the complaint by the German law firm Schirp & Partner. The German authorities have also launched a preliminary investigation against EY, whose head of the German branch has announced his resignation following the scandal.

Finally, the Federal Financial Supervisory Authority, the regulatory and supervisory body for the financial sector in Germany, is also affected by the affair. The German Minister of Finance therefore announced a plan to reform the BaFin, which also saw its director step down.

Conclusion

In conclusion, the Wirecard affair is considered to be one of the most important scandals of the 21st century as it has called into question the structures and statutes of a company, a Big 4 firm as well as German regulatory bodies.

Useful resources

La Tribune (21/04/2021) Scandale Wirecard : Merkel et son ministre des Finances contraints de se justifier (in French).

About the author

The article was written in March 2022 by Louis DETALLE (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023).

The 3 biggest corporate frauds of the 21st century

March 7, 2022 by Louis DETALLE

In this article, Louis DETALLE (ESSEC Business School, Programme Grande Ecole – Master in Management, 2020-2023) presents three major corporate frauds of the 21^st century.

Enron (2001)

Enron was a leader in the raw material and energy sector in 2000. Kenneth Lay and Jeffrey Skilling were the two leaders of the group who have disguised the accounts of the company for years. As an example, Enron’s Directors hid never-ending debts in subsidiaries so as to display a healthy Head company whose liabilities were very limited because hidden in the subsidiaries’ accounts. In 2001, when hiding the truth was not possible anymore, Enron collapsed and dragged down the auditing firm Arthur Andersen as well as the pension funds designed for the retirement of its employees that were all made redundant at the same time.

enron-stock-price
The stock price and logo of Enron

Parmalat (2003)

Parmalat was an Italian company that was rocked in 2003 by a financial scandal that forced it to declare bankruptcy. During 1990s, Parmalat had been losing over $300 million per year, and decided to wipe this debt off the company’s financial records by using 3 shell companies situated in the Caribbean. However, a €14 billion euro hole was discovered in the Parmalat books. Calisto Tanzi and the financial director, Fausto Tonna had set up six shell companies in Luxemburg and falsified banking documents stating the company had accounts with millions of dollars at Bank of America…

Parmalat logo Logo of Parlamat

Volkswagen and the DieselGate (2015)

In fact, Volkswagen had launched in 2006 a project that aimed at manufacturing engines adapted to the American emission norms that were tougher than Europe’s. As they couldn’t implement their solution, the employees and engineers developed a software that enabled cars to bypass the control. In fact, the car’s software was able to detect when it was being measured and it curbed the car’s emissions accordingly. In 2014, an American study measured the emission levels of Volkswagen that reached nearly 40 times the authorized levels. The DieselGate exploded in 2015 when the US Environmental Protection Agency accused Volkswagen of having bypassed the anti-pollution regulation with a software able to bypass the control.

Events accelerate and Volkswagen admits to having equipped 11 million of its vehicles worldwide with fraudulent software. Volkswagen’s CEO, Martin Winterkorn, is pushed out and resigns and the German justice system opens a criminal investigation against the Group. As a result of this scandal, Volkswagen’s share price falls by 50% (see chart below), the Group records its first annual loss for 20 years and commits to paying a fine of 1 billion euros, the largest ever paid by a company in Germany.

Volkswagen stock chart

Conclusion

As a conclusion, one can identify 2 different types of mechanism at stake: for Enron & the Parlamat, the fraud occurred because the leaders falsified accounting documents & financial statements. For Volkswagen, it’s a bypass of the US authorities that ended up having the German Group sanctioned. Board of Directors are often tempted to manipulate the financial statements or their knowledge of the sector to have the better of the regulators and the markets, however, as evidenced by the stock charts of the 3 companies, when the fraud is unveiled, the firms lose a lot as investors’ confidence plummet.

Useful resources

AM Today (30/09/2019) Origine et conséquences : ce qu’il faut savoir sur le dieselgate

Le Monde Diplomatique (February 2004) Le scandale Parmalat

About the author

The article was written in March 2022 by Louis DETALLE (ESSEC Business School, Programme Grande Ecole – Master in Management, 2020-2023).

Senior banker (coverage)

March 5, 2022 by Frédéric ADAM

Senior banker (coverage)

In this article, Frédéric ADAM (ESSEC Business School, Grande Ecole Program – Master in Management, 1990-1993) explains the job of (dedicated) salespeople in banks, who deal with global corporate customer needs. There are generic terms for this type of job : we are going to speak either about “senior banker” or “(global) relationship manager”, that can be included in the so-called “coverage” .

Introduction: Coverage at the “center of the playing field »

The term “Coverage” (or more commonly “customer relationship manager”), stemming from the Anglo-Saxon world of finance, has become an increasingly essential cog in the banks to serve their customers, as finance business became more and more complex from the 1980s, giving rise to the advent of the main commercial business lines, embodying expertise/financial engineering in different forms, which can be grouped into the five main following families:

Transaction banking: cash management, correspondent banking, receivable & supply chain finance, trade & export finance
Financial markets: treasuries & bonds, derivatives & FX, stocks & commodities
Project finance: real estate, shipping & aeronautics, infrastructure
Mergers and acquisitions (M&A)
Corporate & Structured finance: LBO, debt restructuring & advisory, securitization

At the same time, the so-called “support” or “transversal” functions in banks have undergone a similar evolution (development and complexification). These professions have gained in increasingly varied and specialized skills, but also in number, especially in the risks, legal and compliance departments.
This evolution contributed to put the “coverage” at the “center of the playing field”. These “support business lines” can also be grouped into the five main following families:

Back-office
Risks, Permanent Control & Audit Department
Finance & Procurement Department (including asset and liability management (ALM))
Compliance & Legal Department
IT Department

Coverage players: common points and differences between banks

There is an initial invariant in all banks, whether they operate as retail banking actors (for individuals and small businesses) or investment banking actors (for large corporates). You can only access the senior banker after a first (successful) experience, mostly, in commercial business lines. Knowledge of the offers made by the commercial business lines, but also of the internal processes of the banks, in particular on the risk component, are logical prerequisites.

Then, depending on the typology of the banks, these prerequisites may differ (in number, or even in duration of experience), namely:

Retail banking: a preliminary experience in Transaction Banking or on (Corporate) Risks will be appreciated
Corporate & Investment banking: Transaction Banking remains important, but it will often be supplemented with a 2nd commercial line experience / skill, the trend being to seek more M&A profiles for Large Cap coverage in recent years

Finally, the organization of coverage is not the same according to the typology of banks:

Retail Banking: customer relationship management is often carried out by one person, named « chargé d’affaires entreprises » (or business manager), supported by 1 or 2 assistants, dealing with the risk aspects (monitoring of banking commitments) and day-to-day flow business issues.
Corporate & Investment Banking: on large customers, the model is often available with a pair of senior banker (lead) / relationship manager (back-up) type, who monitors the risks (commitments) of the customer, but also is in a lead position on various commercial issues, except for the high value-added component, namely the M&A assigned to the senior banker.

The (common) mission of Coverage players: embody & simplify “One Stop Shopping”.

What is “One Stop Shopping”? It aims at identifying the customer’s needs and, to orient / put in contact, like a “guidance service”, the customer, towards the right expert(s) in the five main commercial lines. To do this, the pitch is a recurring tool, which is a client presentation, outlining “what I understood of your needs” and the business line solutions that the bank makes available to the client to address these needs. A “coverage pitch” can thus often lead to a more detailed/specific “business (line) pitch”, with an indicative / dedicated commercial proposal, often called an “early bird” (pricing is then given provided…).
This ability to articulate coverage with the commercial business lines, and thus allow the customer to do their shopping at a one-stop shop / single entry point, contributes to the attractiveness of the job, in particular with Large Cap, which have complex and numerous issues, and demand a very high level of responsiveness. Thus, for a Large Cap, having valuable and responsive coverage in particular, with which it maintains a close (trustfully) relationship, can prove to be a decisive competitive advantage… For example, during an external growth operation, the financing of a major international contract or a radical transformation of its business model (and its associated capital expenditure needs).

Useful resources

Soraya Haquani (February 24, 2011) Les nouveaux visages des banquiers conseils L’AGEFI Hebdo.

Alumneye (September 7, 2021) 5 questions sur le Coverage en banque d’investissement L’AGEFI Hebdo.

Bogler Marc (June 4, 2021) Le Coverage en Banque de Financement et d’Investissement LinkedIn.

About the author

Article written in February 2022 by Frédéric ADAM (ESSEC Business School, Grande Ecole Program – Master in Management, 1990-1993).

The regulation of cryptocurrencies: what are we talking about?

March 5, 2022 by Hugo MEYER

The regulation of cryptocurrencies: what are we talking about?

In this article, Hugo MEYER (ESSEC Business School, Master in Strategy & Management of International Business (SMIB), 2020-2021) presents the regulation of cryptocurrencies.

Introduction

The first cryptocurrency – Bitcoin – launched in 2008 by Satoshi Nakamoto had for ambition to “break the rules and change the world”.

Thirteen years later, cryptocurrency represents a 2$ trillion market, with an increasing institutional presence, from crypto hedge funds to large banks. Behind this bewildering evolution, public authorities lagged behind, slowly empowering with feverish regulation actions.

The lack of regulation in this burgeoning area has created an opening for boundless fraud and money laundering, forcing some countries to get to grips with the cryptocurrency’s pitfalls.

What are cryptocurrencies?

Cryptocurrencies are at the edge of revolutionizing the way we’ve been trading since thousands of years. By definition, a cryptocurrency is an encrypted, digital, and decentralized medium of exchange that allows two parties that could be located everywhere on the globe to transfer funds directly, without relying on any trusted third party.

Instead of being secured by public institutions and/or companies, these transfers are carried out on the Blockchain which is a “digital database or ledger containing information that can be simultaneously used and shared within a large decentralized, publicly accessible network”.

Example: To make it simple, let’s say that A wants to send money to B. This transaction is included in a ‘block’. This block is broadcasted to every member in the network, and then validated or not by them. Once validated, the block is added to the chain, triggering the money to move from A to B.

Figure 1. Process of transaction with the blockchain
Schema of the blockchain
Source: Institut des actuaires.

This distributed network provides an indelible and transparent record of transactions as the chain cannot be counterfeited. If someone tried to change any information contained in one block, the different parties of the network would not approve the transaction as they could check the whole history of the blockchain and compare it to the new one.

Thus, many cryptocurrencies such as Bitcoin, Ethereum and Monero rely on public blockchains to allow transactions in complete security and transparency.

“I do think Bitcoin is the first money that has the potential to do something like change the world” – Peter THIEL.

What is the regulation about?

By definition, regulation tally with the act of controlling something, or enacting an official rule. What does it imply for cryptocurrencies?

A cryptocurrency is entirely defined by its creator, that must foremost determine its characteristics. This creation process is divided into three steps:

Pick or create its blockchain platform
Choose a consensus algorithm
Design the blockchain architecture.

His creator defines the rules around it, while the ecosystem built accordingly to these rules regulate it and make it functional. Once the crypto is launched, it is impossible to modify its architecture and the rules. In this way, a cryptocurrency cannot be regulated, even by his founder. Thus, authorities do not have any grip with cryptocurrencies in themselves. They are auto-regulated by their initial algorithms, and nothing else.

Thereby, what are we talking about when dealing with the regulation of cryptocurrency?

Cryptocurrencies are mainly exchanged through platforms called “exchanges” such as Coinbase, Binance or eToroX. The first existing regulation framework is the accessibility to these platforms. For most of them, requirements like providing its identity are requested, following the Know Your Customer (KYC) compliance.

Secondly, the regulatory framework for these platforms depends on where they are based. Each country has a different approach of cryptocurrency, meaning that the regulation can be different in any of them.

For example, cryptocurrency exchanges are legal in the United States and fall under the regulatory scope of the Bank Secrecy Act (BSA). Therefore, exchanges service providers must register with FinCEN, implement an anti-money laundering (AML) and combating the financing of terrorism (CFT) program, maintain appropriate records, and submit reports to the authorities. It does not mean their trading activities are regulated.

These requirements permit exchanges to operate as licensed Money Service Businesses (MSBs), leading regulators to focus on anti-money laundering (AML) and due diligence measures, but not trading (and all the aspects of market manipulation).

Given the lack of significant regulatory oversight of actual trading activity, it is not surprising to see many cryptocurrency exchanges carry out questionable activities, such as offering leverage to their clients and wash trading, during times of market instability. But these are not the only problems raised by the lack of regulation.

Why should exchanges be more regulated?

The blockchain is a recent technology, understood by a few. As regulation always comes after innovation, the crypto market has been sidelined by public authorities for many years. The question of regulating it has recently appeared in response to the many downsides incurred to cryptos.

Customer protection

When investing in cryptocurrencies, the customer is lacking protection. An investor could be facing fake websites, hacking, and platform bankrupts without any legal recourse to recover his money. These situations could never happen in a traditional investment as it is institutionally regulated. To become more secure, exchanges must follow the example of itBit, an US-based exchange oversighted by the New York Department of Financial Services (DFS) and registered as a bank.

Illegal Financial flows & crime

Cryptocurrency can be used for illicit transactions and for laundering criminal proceeds that may or may not have started as cryptocurrency. These illicit transactions occur on the dark web, including the purchase/sale of illicit drugs and debit and credit card information. According to a study published in 2019 by Oxford Academics, 76$ billion of illegal activity per year involve bitcoin, which represents half of total Bitcoin transactions.

Cryptos can also be used for ransomware attacks, like the one that shut down the Colonial Pipeline in May 2021. This attack was one of many others high-profile instances of hackers seeking Bitcoin ransoms, that should tend to multiply in the upcoming years.

Price stability

Blockchain technology has increasingly become a speculative tool for investing and achieving high returns in the short term, leading to market volatility. These fast and unpredictable price changes also have a direct impact on the velocity, where more and more people hold their cryptocurrencies instead of selling or using them.

Plus, the volatility of cryptos prices may let the market suffer from illiquidity. The notion of liquidity for a financial asset refers to the ease with which an asset can be bought or sold (without a strong price impact, e.g., limit implicit transaction costs).

Tax evasion

One of the first problem that arise from tax evasion is taxation. Many countries have their own regulatory framework, either taxing cryptos as an asset (Israel), a financial asset (Bulgaria), or even a foreign currency (Switzerland). Once the taxation rule found out, authorities will tackle another problem: The investors resistance to report their gains.

Taking the example of USA, authorities ask filers on their income tax forms – like any form of income – whether they received or made any transactions with cryptocurrency. However, third-party reporting in the sector is scarce; making it even more difficult to attribute gains to one natural person.

Thus, how can regulation allow the crypto market to take over these pitfalls?

Worldwide market regulation

“Bitcoin is not unregulated. It is regulated by algorithm instead of being regulated by government bureaucracies” – Andreas Antonopoulos

Despite being a global phenomenon, every country does not hang up with the same type of regulation.

First, some countries have expanded their laws on money laundering, counterterrorism, and organized crimes to include cryptocurrency markets, and require banks and other financial institutions that facilitate such markets to conduct all the due diligence requirements imposed under such laws. For instance, Australia and Canada recently enacted laws to bring cryptocurrency transactions and institutions that facilitate them under the ambit of money laundering and counter-terrorist financing laws.

Some jurisdictions have gone even further and imposed restrictions on investments in cryptocurrencies. Some countries – Algeria, Bolivia, Morocco, Vietnam – explicitly ban any and all activities involving cryptocurrencies. Qatar and Bahrain consider that their citizens are forbidden from engaging in any kind of activities involving cryptocurrencies locally but allow them to do so outside their borders.

There are also countries that, while not banning their citizens from investing in cryptocurrencies, impose indirect restrictions by hindering transactions involving cryptocurrencies, such as China, Iran, or Thailand.

A limited number of countries regulate initial coin offerings (ICOs), which use cryptocurrencies as a mechanism to raise funds. Of the jurisdictions that address ICOs, some (mainly China, Macau, and Pakistan) ban them altogether, while most tend to focus on regulating them.

When it comes to taxation, the challenge appears to be how to categorize cryptocurrencies and the specific activities involving them. This matters primarily because whether gains are categorized as income or capital gains invariably determines the applicable tax bracket. For instance, in Israel, cryptos gains are taxed as assets, while there are subject to income tax in Spain and Argentina.

Advocates of digital currencies say that accepting cryptocurrencies is much more relevant than rejecting it. For instance, El Salvador became the 7th of September 2021 the first country in the world to make Bitcoin a legal tender. One day after, the “Regulation of the Bitcoin Law” entered into force, that establishes standards of conducts supervised by the Superintendency of the Financial System (SSF), the equivalent of the Securities Exchange Commission (SEC) in the United States or the Autorité des Marchés Financiers (AMF) in France. This regulation will bring much more protection to Bitcoin users, while setting up numerous programs in cybersecurity, anti-money laundering, and tax evasion.

Conclusion

As Bitcoin – and other cryptocurrencies – become more and more popular, regulation will have to step up altogether, despite asking extensive questions on its bounding by International Authorities.

Economic threat, exacerbated risks and investigation complications are all issues that can be counteracted by regulation laws on the crypto market. Central banks will play a major role in this governance, going along with their traditional missions such as ensuring price stability and a proper operating financial system.

Nevertheless, regulation may lead to underestimated consequences. As it goes on, crypto investment will progressively become “mainstream” and dismiss the first and most powerful investors. This trend might also push innovators to take a step back from it, thus decreasing the number of cryptocurrencies created and newly innovative blockchains.

Useful resources

Academic research

Sean, F. Jonathan, R K. Talis, P. 2019. Sex, Drugs, and Bitcoin: How much illegal activity is financed through cryptocurrencies?” The Review of Financial Studies. Vol. 32, p. 1798-1853.

Business Analysis

L, S. 2016. Who is Satoshi Nakamoto, The Economist explains.

Thiemann, A. 2021. Cryptocurrencies: An empirical View from a Tax Perspective, JRC Working Papers on Taxation and Structural Reforms. No 12/2021, European Commission, Joint Research Centre, Seville, JRC126109.

Global Legal Research Directorate. 2018. Regulation of Cryptocurrency Around the World. LL File No. 2018-016036 LRA-D-PUB-002438.

Ryan, H. 2021. U.S. Officials send mixed messages on crypto regulation. Here’s what it all means for investors. NextAdvisor.

American Overseas, 2021. Washington Monthly: Catching Bitcoin tax evaders.

Alexis, G. 2021. Crypto doesn’t have to enable tax cheats Bloomberg Opinion.

Douma, S. 2016. Bitcoin: The pros and cons of regulation. s1453297.

About the author

The article was written in March 2022 by Hugo MEYER (ESSEC Business School, Master in Strategy & Management of International Business (SMIB), 2020-2021).

Black-Scholes-Merton option pricing model

February 4, 2024March 3, 2022 by Jayati WALIA

In this article, Jayati WALIA (ESSEC Business School, Grande Ecole Program – Master in Management, 2019-2022) explains the Black-Scholes-Merton model to price options.

The Black-Scholes-Merton model (or the BSM model) is the world’s most popular option pricing model. Developed in the beginning of the 1970s, this model introduced to the world, a mathematical way of pricing options. Its success was essentially a starting point for new forms of financial derivatives in the knowledge that they could be priced accurately using the ideas and analyses pioneered by Black, Scholes and Merton and it set the foundation for the flourishing of modern quantitative finance. Myron Scholes and Robert Merton were awarded the Nobel Prize for their work on option pricing in 1997. Unfortunately, Fischer Black had died several years earlier but would certainly have been included in the prize had he been alive, and he was also listed as a contributor by Scholes and Merton.

Today, the Black-Scholes-Merton formula is widely used by traders in investment banks to price and hedge option contracts. Options are used by investors to hedge their portfolios to manage their risks.

Assumptions of the BSM Model

As any model, the BSM model relies on a set of assumptions:

The model considers European options, which we can only be exercised at their expiration date.
The price of the underlying asset follows a geometric Brownian motion (corresponding to log-normal distribution for the price at a given point in time).
The risk-free rate remains constant over time until the expiration date.
The volatility of the underlying asset price remains constant over time until the expiration date.
There are no dividend payments on the underlying asset.
There are no transaction costs on the underlying asset.
There are no arbitrage opportunities.

The BSM equation

The value of an option is a function of the price of the underlying stock and its statistical behavior over the life of the option.

A commonly used model is Geometric Brownian Motion (GBM). GBM assumes that future asset price differences are uncorrelated over time and the probability distribution function of the future prices is a log-normal distribution (or equivalently the probability distribution function of the future returns is a normal distribution). The price movements in a GBM process can be expressed as:

GBM equation

with dS being the change in the underlying asset price in continuous time dt and dX the random variable from the normal distribution (N(0, 1) or Wiener process). σ is the volatility of the underlying asset price (it is assumed to be constant). μdt represents the deterministic return within the time interval with μ representing the growth rate of asset price or the ‘drift’.

Therefore, option price is determined by these parameters that describe the process followed by the asset price over a period of time. The Black-Scholes-Merton equation governs the price evolution of European stock options in financial markets. It is a linear parabolic partial differential equation (PDE) and is expressed as:

BSM model equation

Where V is the value of the option (as a function of two variables: the price of the underlying asset S and time t), r is the risk-free interest rate (think of it as the interest rate which you would receive from a government debt or similar debt securities) and σ is the volatility of the log returns of the underlying security (say stocks).

The key idea behind the equation is to hedge the option and limit exposure to market risk posed by the asset. This is achieved by a strategy known as ‘delta hedging’ and it involves replicating the option through an equivalent portfolio with positions in the underlying asset and a risk-free asset in the right way so as to eliminate risk.

Thus, from the BSM equation we can derive the BSM formulae that describe the price of call and put options over their life time.

The BSM formulae

Note that the type of option we are valuing (call or put), the strike price and the maturity date do not appear in the above BSM equation. These elements only appear in the ‘final condition’ i.e., the option value at maturity, called the payoff function.

For a call option, the payoff C is given by:

C_T = max⁡(S_T – K; 0)

For a put option, the payoff is given by:

P_T = max⁡(K – S_T; 0)

The BSM formula is a solution to the BSM equation, given the boundary conditions (given by the payoff equations above). It calculates the price at time t for both a call and a put option.

The value for a call option at time t is given by:

Call option value equation

The value for a put option at time t is given by:

Put option value equation

where

With the notations:
S_t: Price of the underlying asset at time t
t: Current date
T: Expiry date of the option
K: Strike price of the option
r: Risk-free interest rate
σ: Volatility (the standard deviation of the return on the underlying asset)
N(.): Cumulative distribution function for a normal (Gaussian) distribution. It is the probability that a random variable is less or equal to its input (i.e. d₁ and d₂) for a normal distribution. Thus, 0 ≤ N(.) ≤ 1

Figure 1 gives the graphical representation of the value of a call option at time t as a function of the price of the underlying asset at time t as given by the BSM formula. The strike price for the call option is 50€ with a maturity of 0.25 years and volatility of 50% in the underlying.

Figure 1. Call option value

Source: computation by author.

Figure 2 gives the graphical representation of the value of a put option at time t as a function of the price of the underlying asset at time t as given by the BSM formula. The strike price for the put option is 50€ with a maturity of 0.25 years and volatility of 50% in the underlying.

Figure 2. Put option value
Source: computation by author.

You can download below the Excel file for option pricing with the BSM Model.

Some Criticisms and Limitations

American options

The Black-Scholes-Merton model was initially developed for European options. This is a limitation of the equation for American options which can be exercised at any time before the expiry date. The BSM model would then not accurately determine the option value (an important case when the underlying asset pays a discrete dividend).

Stocks paying dividends

Also, in reality, most stocks pay dividends, and no dividends was an assumption in the initial BSM model, which analysts now eliminated by accommodating the dividend yield in the formula if required.

Constant volatility

Another limitation is the use of constant volatility. Volatility is the measure of risk based on the standard deviation of the return on the underlying asset. In reality the value of an asset will change randomly, not with a specific constant pattern regarding the way it can change.

Finally, the assumption of no transaction cost neglects the liquidity risk in the market since transaction costs are clearly incurred in the real world and there exists a bid-offer spread on most underlying assets. For the most heavily traded stocks, this cost may be low but for others it may lead to an inaccuracy.

Useful resources

Black F. and M. Scholes (1973) The Pricing of Options and Corporate Liabilities The Journal of Political Economy 81, 637-654.

Merton R.C. (1973) Theory of Rational Option Pricing Bell Journal of Economics 4, 141–183.

About the author

The article was written in March 2022 by Jayati WALIA (ESSEC Business School, Grande Ecole Program – Master in Management, 2019-2022).

Quick review on the most famous trading frauds ever…

February 20, 2022 by Louis DETALLE

In this article, Louis DETALLE (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023) reviews on the most famous trading frauds ever…

Jerôme Kerviel and Société Générale

In 2008, the French bank Société Générale announced having been defrauded by one of its traders, Jérôme Kerviel whom you might have heard about. This fraud cost Société Générale € 4,9 billion and Jérôme Kerviel was accused by the bank of having held positions up to € 50 billion on financial markets without permission. Jérôme Kerviel, on the other hand, accused the bank of having known about his practices since the beginning and of confronting him about them only because he had lost a lot of money. As a consequence, Société Générale’s share lost nearly half his value when the issue was brought to light and the trial is still ongoing…

JP Morgan and Bruno Iksil: The whale

Bruno Iksil joined JP Morgan Chase in 2005 after a short time at Natixis. This French trader got his nickname because of his risky multi-million-dollar bets on credit default swaps (CDS), insurance contracts designed to protect against a country or company default.

In April 2012, the Wall Street Journal and Bloomberg were alerted by brokers on “huge” and “very risky” positions taken in the credit market. A trader had bet on the good health of American companies and sold, in very large quantities (several tens of billions of dollars), insurance contracts to cover themselves against their bankruptcy.

His bets were all the riskier since the US economy was showing major signs of slowdown. Other investors and banks, attracted by this opportunity, did not hesitate to take Iksil on, which quickly created an untenable situation for JP Morgan. The losses generated by the “whale” positions amounted to 6.2 billion dollars for JP Morgan.

As a result, the bank’s quarterly results were down by 660 million dollars, while its share price fell by 20% on the New York Stock Exchange.

Nick Leeson & the Barings Bank

Nick Leeson was a 28-year-old trader who had made a name for himself at Barings, England’s oldest investment bank. He became the head of the bank’s Singapore subsidiary by making high-risk, speculative bets. Nick Leeson took advantage of a loophole in the bank’s trading system to conceal his financial activities.

Unfortunately, Nick Leeson’s luck ran out and he suffered huge losses. Nick Leeson took advantage of a loophole in the bank’s trading records to hide his losses. With each trade, Nick Leeson hoped to mop up the previous losses to the point of no return. One evening, Leeson placed a trade betting that the Nikkei exchange rate would remain stable overnight. This seemingly low-risk trade turned out to be a disaster as an earthquake in Kobe caused the Nikkei and all Asian markets to collapse.

As a result of the massive losses, management realized that Leeson had been hiding a lot of money, and Barings, which had lost more than a billion dollars, more than twice its capital, went bankrupt.

About the author

The article was written in February 2022 by Louis DETALLE (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023).

Quick review of the most famous investments frauds ever…

February 20, 2022 by Louis DETALLE

Quick review of the most famous investments frauds ever…

In this article, Louis DETALLE (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023) explains what a tax specialist works on, on a daily basis.

The most famous amongst all frauds ever: Charles Ponzi

Born in 1882, this Italian man has built himself quite a reputation in the fraud industry due to his invention: the Ponzi Pyramid. That consisted of a financial business plan that promised a 50% interest rate within 45 days to the investors that picked his solution. You may wonder how it was possible to reach such rates, well it was possible because Charles Ponzi reimbursed the old investors their initial investment plus the interests with the money collected from newer investors and so on… In fact, Ponzi developed an idea that he encountered in the “Banca Zarossi” in Montreal, that relied on a similar principle that made it impossible to reimburse all the clients if they came altogether asking for their savings.

At the end of his fraud, in 1919, Charles Ponzi had managed to convince nearly 40 000 investors to commit to his business plan for $15 million which account for several dozens of current billion dollars. To this day, Ponzi is still considered the father of financial fraud and several others drew from his example.

The Great Bernard Madoff

Bernard Madoff was a New York hedge fund manager who promised the most experienced investors his hedge fund would provide a 7% annual growth whatever the economic conjuncture. His fund relied on the same principle as the Ponzi system that Bernard Madoff hid successfully thanks to his fame in the finance sector. In fact, his renown made all this fraud possible and explains how institutions such as HSBC, Santander, BNP Paribas or Nomura got played. In 2008, when the trick was no longer viable, a 65 billion dollar fraud was unveiled…

The unviable mechanism behind this type of fraud: the Ponzi pyramid

A Ponzi pyramid is a fraudulent financial scheme that enables its creator to offer investors unusually high rates for very limited risk. The offer may seduce lots of investors which will only see their money back if newer investors contribute later. The scam is named after its inventor, Charles Ponzi, has been repeated several times.

However, it must be stated that a Ponzi scheme cannot last… Indeed, let’s consider the following example: Investor A invests 10 euros. The fraud promises to pay back twice as much two months later. Two months later, the company approaches new investors with the same promise. Investors B & C invest 10 euros. Their money is used to pay back the 20 euros promised to Customer A and so on…

In the example, for each round of new investors which corresponds to the maturity of the round of investments, the fraud must convince twice as many investors to invest as during the last row, in order to multiply the funds by 2 (so that the previous row of investors be reimbursed). In the following example, after 20 rounds of investors, the fraud will have to gather 10 485 760 € in order to reimburse the 19^th round of investors. As you can see, the scheme had already exceeded its viable size due to an exponential growth which can only cause the loss of the last round of investors and the dreadful financial consequences that comes with it.

About the author

The article was written in February 2022 by Louis DETALLE (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023).

Understand the importance of data providers and how they influence global finance…

What are data providers?

The different types of data that are exchanged for financial purposes

The main dynamics of the Data Providers’ market

Useful resources

Related posts on the SimTrade blog

About the author

The importance of data in finance

Why is data used?

In which finance-related fields is data used?

The importance of data regulation though

Useful resources

Related posts on the SimTrade blog

About the author

Introduction

Calculating VaR using Monte Carlo simulations

Advantages and limitations of Monte Carlo method for VaR

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About the author

Introduction

Mathematical foundation

Implementation of the Black-Litterman asset allocation model in practice

Why should I be interested in this post?

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Academic research

About the author

Introduction

Mathematical foundations

Implementation of the Markowitz asset allocation model in practice

Why should I be interested in this post?

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Academic research

About the author

The incredible story of Nick Leeson & the Barings Bank

History of Barings and Nick Leeson’s background

The great fraud

Conclusion

Useful resources

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What happened between Bruno Iksil & JP Morgan

Bruno Iksil: background of a French trader based in London

Iksil’s activities at JP Morgan

JP Morgan’s losses

Conclusion and aftermath of the affair

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Introduction

How Monte Carlo simulation works

Example: Apple stock

Applications in finance

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My professional experience as financial research assistant in a green finance institute

About the company

What about my internship？

Job missions

Requirements for this internship

What I have learnt from the internship

Financial Concepts

Convertible bonds

Green bonds

Underwriting

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Excel functions for mortgage

Mortgage

PMT

IPMT and PPMT

IPMT

PPMT

RATE

Example with an Excel file

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