Institutional Brokers’ Estimate System (IBES)

Institutional Brokers’ Estimate System (IBES)

Nithisha CHALLA

In this article, Nithisha CHALLA (ESSEC Business School, Grande Ecole Program – Master in Management (MiM), 2021-2024) presents Institutional Brokers’ Estimate System (IBES), which provides market consensus for the financial community.

Introduction

In the fast-paced world of finance, the Institutional Brokers’ Estimate System, commonly known as IBES (often written as “I/B/E/S.”), stands as a linchpin in providing investors, analysts, and financial professionals with reliable forecasts and estimates.

IBES, with its roots in comprehensive data aggregation, takes center stage in providing a panoramic view of financial analysts’ estimates and forecasts. IBES acts as a centralized repository of earnings estimates, revenue projections, and other key financial metrics, serving as a vital resource for investors seeking actionable insights.

History

IBES was created in 1976 by the investment research firm, Lynch, Jones & Ryan (LJR). It was developed to address the need for a centralized system to collect and distribute earnings estimates from brokerage analysts. The database offers historical data from its inception and extends internationally from 1987. Over the years, IBES went through several ownership changes. In 1988, LJR was acquired by Primark Corporation. Primark Corporation later merged with Thomson Financial Services in 1990, and IBES became part of Thomson Financial. Thomson Financial subsequently merged with Reuters Group in 2008 to form Thomson Reuters. Thus, IBES became a part of Thomson Reuters. Then Thomson Reuters was acquired by private equity firm Blackstone Group and rebranded as Refinitiv in 2018. Refinitiv was later acquired by the London Stock Exchange Group (LSEG) in 2020. Therefore, IBES is currently owned by the London Stock Exchange Group (LSEG) through its subsidiary, Refinitiv.

Key Components of IBES

Earnings Estimates

IBES compiles earnings estimates from a multitude of financial analysts, providing a consensus view on the expected earnings of a company. As of the latest data, it aggregates forecasts from over 16,000 analysts worldwide, providing a robust consensus on expected earnings for companies across the globe.

Revenue Projections

Beyond earnings, IBES includes revenue projections, offering a comprehensive view of a company’s expected top-line growth. Analysts’ forecasts are aggregated to provide a consensus estimate, aiding investors in gauging revenue expectations.

Forecasts for Key Financial Metrics

IBES extends beyond earnings and revenue, encompassing a spectrum of financial metrics. This includes estimates for operating margins, cash flows, and other key indicators, providing a holistic view of analysts’ expectations. The sheer volume of data—covering over 18,000 companies—ensures a comprehensive snapshot of financial expectations.

Coverage

There are certain types of coverage when we are talking about data coverage such as the Number of Companies Covered, geographic Coverage, Market Capitalization Coverage, Industry Coverage, Depth of Coverage, and Historical Coverage.

According to LSEG data analytics, I/B/E/S Estimates data is displayed at the company level, screened with rigorous quality control methods across more than 23,400 active companies in more than 90 countries, and sourced from over 19,000 analysts.

Estimates Data from over 950 firms contribute data to I/B/E/S, from the largest global houses to regional and local brokers, with U.S. data back to 1976 and international data back to 1987.

Period

IBES has evolved since its introduction in 1976 and includes international data from 1987. I/B/E/S Global Aggregates, with over 30 years of history, facilitates top-down analysis to better assess potential growth and risk as well as future value across industry, sector, index, country, and region.

Frequency

Initially focusing on annual earnings estimates, the database subsequently broadened to encompass quarterly earnings forecasts.

Firms and Financial analysts

IBES covers a wide range of firms across different industries, sectors, and regions. This includes companies listed on major stock exchanges in numerous countries and markets around the world. 950+ contributors, across 90+ in developed and emerging markets, totaling over 19,000 individual analysts – the most in the industry. Unmatchable history across more than 60,000 companies back to 1976 for North America, 1987 for other markets.

Pricing

The specific pricing for accessing I/B/E/S data is not explicitly mentioned on the IBES website and you have to make a request through the LSEG website which manages IBES. The cost likely varies based on the package and offerings selected, which can include different data sets, access methods, and service frequencies. For detailed pricing information, it’s recommended to directly contact LSEG or the respective service providers you’re interested in, as they can provide more precise details based on your specific requirements and the scale of your intended use.

For academic and non-commercial research purposes, access might be available through institutional subscriptions with specific terms of use, as seen with Aalto University’s subscription financed by the Aalto University Data Hub for its users. This indicates that the availability and cost of I/B/E/S data may vary significantly based on the type of use and the access platform. For precise pricing and package options, directly contacting the service providers is the best approach.

Use of IBES by the Financial Community

Benchmark for Analysis

IBES serves as a benchmark for investors and analysts, quantifying market reactions, it dives into the numbers behind market reactions. According to Faster Capital, studies reveal that stocks experiencing positive earnings surprises, surpassing IBES estimates, tend to outperform the market. These numerical insights underscore the practical implications of aligning investment decisions with IBES consensus.

Market Expectations

Analysts and fund managers utilize IBES to gauge market expectations for specific companies. Understanding consensus estimates aids in forming investment strategies aligned with prevailing market sentiments.

Earnings Season Preparation

During earnings seasons, IBES becomes a critical tool for investors preparing for companies’ financial releases. It provides a consolidated view of analysts’ forecasts, helping investors assess potential surprises or disappointments.

IBES and Tests of Market Efficiency

Academic works

The data was subsequently used as the basis for articles in academic finance journals attempting to demonstrate that changes in consensus earnings estimates could identify opportunities to capture excess returns in subsequent periods.

Information Dissemination

IBES plays a pivotal role in disseminating timely information. As estimates are constantly updated based on new information, IBES ensures that market participants have access to the latest insights, contributing to market efficiency.

Pros and Cons

Given its history and operations in huge industries and markets, we certainly need to know the pros and cons of the IBES estimates. In terms of accuracy metrics, IBES relies on the accuracy of analysts’ forecasts. Statistical metrics, such as the mean absolute error (MAE), offer a quantitative evaluation of the system’s precision.

Conclusion

IBES, when viewed through a data-driven lens, transforms into more than a system and becomes a useful tool for decision-makers navigating the intricacies of financial markets.

Why should I be interested in this post?

In essence, this article discovers how the global data powerhouse, backed by impactful statistics, empowers investors, providing a data-driven lens into market expectations and offering actionable insights for informed decision-making in the dynamic world of finance.

Related posts on the SimTrade blog

   ▶ Aamey MEHTA Market efficiency: the case study of Yes bank in India

   ▶ Louis DETALLE The importance of data in finance

   ▶ Bijal GANDHI Earnings per share

Useful resources

London Stock Exchange Group (LSEG) I/B/E/S Estimates

Wikipedia Institutional Brokers’ Estimate System

Market consensus What is market consensus?

Faster Capital Navigating Markets: The Power of Market Analysis and Consensus Estimates

About the author

The article was written in March 2024 by Nithisha CHALLA (ESSEC Business School, Grande Ecole Program – Master in Management (MiM), 2021-2024).

USD Coin: Deep Dive into the Role of Stablecoins in Modern Finance

 Snehasish CHINARA

In this article, Snehasish CHINARA (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2024) explains the stable coin USD Coin.

Historical context and background

USD Coin (USDC) is a type of cryptocurrency known as a stablecoin, designed to maintain a stable value relative to the US dollar (USD). It was launched in September 2018 by Centre Consortium, a collaboration between cryptocurrency exchange Coinbase and blockchain technology company Circle. The primary goal of USDC is to provide a digital asset that can be easily transferred between users and used for transactions, while minimizing the volatility typically associated with other cryptocurrencies like Bitcoin or Ethereum.

The need for stablecoins like USDC arose due to the inherent volatility of many cryptocurrencies. While Bitcoin and other digital assets have gained significant attention and adoption, their prices can fluctuate dramatically over short periods, which can make them less practical for everyday transactions and financial contracts. Stablecoins like USDC offer a solution to this problem by pegging their value to a stable asset, such as the US dollar, thereby providing stability and predictability for users.

USDC operates on the Ethereum blockchain as an ERC-20 token, making it compatible with a wide range of decentralized applications (dApps) and enabling seamless integration with the broader cryptocurrency ecosystem. This infrastructure allows users to easily send and receive USDC tokens across various platforms and services, including exchanges, wallets, and payment processors.

Since its launch, USDC has seen significant growth in adoption and usage. It has become one of the most widely used stablecoins in the cryptocurrency market, with billions of dollars worth of USDC tokens in circulation. Its stability and liquidity have made it a popular choice for traders, investors, and businesses looking to transact in digital assets without exposure to the volatility of other cryptocurrencies.

USD Coin Logo

Source: Yahoo! Finance.

Figure 1. Key Dates in USDC History

Source: Yahoo! Finance.

Key features

Stability

USD Coin is a stablecoin, meaning it is pegged to the value of the US dollar on a 1:1 basis. This stability is maintained through regular audits and backing by reserves of US dollars held in custody by regulated financial institutions.

Transparency

USDC operates on blockchain technology, providing transparency and immutability of transactions. Every USDC token is backed by an equivalent number of US dollars held in reserve, which is regularly audited and transparently reported to ensure trust among users.

Speed and Efficiency

Transactions involving USDC can be executed quickly and efficiently on blockchain networks, enabling near-instantaneous settlement compared to traditional banking systems, which may take days for cross-border transactions.

Global Accessibility

USDC enables borderless transactions, allowing users to send and receive payments globally without the need for intermediaries such as banks. This accessibility empowers individuals and businesses, particularly in regions with limited access to traditional financial services.

Interoperability

USDC is compatible with various blockchain platforms and protocols, including Ethereum, Algorand, and Solana, among others. This interoperability facilitates its integration into a wide range of decentralized applications (DApps) and decentralized finance (DeFi) ecosystems.

Use cases

Remittances and Cross-Border Payments:

USDC provides a cost-effective and efficient solution for remittance payments and cross-border transactions, enabling individuals and businesses to transfer value across borders quickly and securely without the need for traditional banking intermediaries.

Stable Value Storage

Due to its stable value pegged to the US dollar, USDC serves as a reliable store of value and a hedge against volatility in the cryptocurrency market. Users can hold USDC as a stable asset to preserve purchasing power and mitigate the risks associated with price fluctuations in other cryptocurrencies.

Decentralized Finance (DeFi) Applications

USDC is widely used as a liquidity provider and collateral asset in various DeFi protocols and applications such as decentralized exchanges (DEXs), lending platforms, yield farming, and liquidity pools. Users can leverage USDC to earn interest, borrow assets, or participate in yield farming strategies within the DeFi ecosystem.

Commerce and Payments

Merchants and businesses can accept USDC as a form of payment for goods and services, leveraging its fast transaction settlement times and low transaction fees compared to traditional payment methods. Integrating USDC payments can streamline cross-border commerce and reduce friction associated with fiat currency conversions.

Financial Inclusion

USDC plays a crucial role in expanding financial inclusion by providing access to digital financial services for individuals and communities underserved by traditional banking infrastructure. By utilizing blockchain technology and stablecoins like USDC, individuals without access to traditional banking services can participate in the global economy and access a wide range of financial products and services.

Technology and underlying blockchain

USD Coin (USDC) operates on a blockchain-based infrastructure, primarily leveraging the Ethereum blockchain as its foundation. Utilizing Ethereum’s smart contract functionality, USDC tokens are issued, transferred, and redeemed in a transparent and trustless manner. The ERC-20 standard, a set of rules and protocols defining interactions between tokens on the Ethereum network, governs the behavior of USDC tokens, ensuring compatibility with a wide range of wallets, exchanges, and decentralized applications (DApps). Moreover, USDC employs a consortium model for governance and operation, with regulated financial institutions serving as members responsible for the issuance, custody, and redemption of USDC tokens. These institutions adhere to strict regulatory compliance measures and conduct regular audits to verify that each USDC token is fully backed by an equivalent reserve of US dollars held in custody. This combination of blockchain technology, smart contracts, and regulatory oversight ensures the integrity, transparency, and stability of USD Coin, making it a trusted and widely adopted stablecoin within the cryptocurrency ecosystem.

ERC-20 Standard of Ethereum for USD Coin

The ERC-20 standard, short for Ethereum Request for Comment 20, is a widely adopted technical specification governing the creation and implementation of fungible tokens on the Ethereum blockchain. Introduced by Fabian Vogelsteller and Vitalik Buterin in 2015, ERC-20 defines a set of rules and functions that enable seamless interoperability between different tokens, ensuring compatibility with various decentralized applications (DApps) and wallets. Tokens adhering to the ERC-20 standard are characterized by a consistent set of methods, including transfer, balance inquiry, and approval mechanisms, facilitating easy integration and widespread adoption across the Ethereum ecosystem. This standardization has played a pivotal role in the proliferation of tokenization, empowering developers to create diverse tokenized assets, conduct crowdfunding campaigns through Initial Coin Offerings (ICOs), and establish decentralized exchanges (DEXs) where ERC-20 tokens are traded autonomously. Additionally, ERC-20 compliance enhances security and interoperability, fostering trust and usability within the Ethereum network.

Supply of coins

The supply dynamics of USD Coin (USDC) are governed by its underlying smart contract protocol and the management of its issuer, Centre Consortium, a collaboration between Circle and Coinbase. USDC operates on a principle of full backing, where each USDC token issued is backed by an equivalent number of US dollars held in reserve. This backing ensures a 1:1 peg to the US dollar, maintaining its stability. The issuance and redemption of USDC are facilitated through regulated financial institutions that hold the corresponding fiat reserves. Moreover, USDC’s supply is transparently audited on a regular basis, with attestations provided by reputable auditing firms to verify the adequacy of reserves. Through these mechanisms, the supply of USDC remains elastic, expanding or contracting based on market demand while preserving its stability and trustworthiness as a stablecoin in the digital asset ecosystem.

Historical data for USDC

How to get the data?

The USDC is popular cryptocurrency on the market, and historical data for the USDC such as prices and volume traded can be easily downloaded from the internet sources such as Yahoo! Finance, Blockchain.com & CoinMarketCap. For example, you can download data for USDC on Yahoo! Finance (the Yahoo! code for USD Coin is USDC-USD).

Figure 2. USD Coin data

Source: Yahoo! Finance.

Historical data for the USD Coin market prices

The historical market price of USD Coin (USDC) has remained relatively stable, as its primary function is to maintain a value pegged to the US dollar at a 1:1 ratio. Since its inception, USDC has consistently traded around the $1 mark, with minor fluctuations typically attributed to market dynamics and liquidity conditions. Investors and traders often utilize USDC as a safe haven asset or a means of temporarily exiting volatile cryptocurrency markets, contributing to its stability. This stability has made USDC a preferred choice for individuals and institutions seeking to hedge against cryptocurrency volatility or facilitate seamless transitions between digital and fiat currencies. Additionally, the transparent backing of USDC by reserves of US dollars held in custody by regulated financial institutions further enhances market confidence and contributes to its stable market price over time.The historical market price of USD Coin (USDC) has remained relatively stable, as its primary function is to maintain a value pegged to the US dollar at a 1:1 ratio. Since its inception, USDC has consistently traded around the $1 mark, with minor fluctuations typically attributed to market dynamics and liquidity conditions. Investors and traders often utilize USDC as a safe haven asset or a means of temporarily exiting volatile cryptocurrency markets, contributing to its stability. This stability has made USDC a preferred choice for individuals and institutions seeking to hedge against cryptocurrency volatility or facilitate seamless transitions between digital and fiat currencies. Additionally, the transparent backing of USDC by reserves of US dollars held in custody by regulated financial institutions further enhances market confidence and contributes to its stable market price over time.

Figure 3 below represents the evolution of the price of USD Coin in US dollar over the period Oct 2018 – Dec 2022. The price corresponds to the “closing” price (observed at 10:00 PM CET at the end of the month).

Figure 3. Evolution of the USD Coin price

Source: Yahoo! Finance.

R program

The R program below written by Shengyu ZHENG allows you to download the data from Yahoo! Finance website and to compute summary statistics and risk measures about the USD Coin.

Download R file

Data file

The R program that you can download above allows you to download the data for the USD Coin from the Yahoo! Finance website. The database starts on Oct, 2018. Table 1 below represents the top of the data file for the USD Coin downloaded from the Yahoo! Finance website with the R program.

Table 1. Top of the data file for the USD Coin

Source: computation by the author (data: Yahoo! Finance website).

Python code

You can download the Python code used to download the data from Yahoo! Finance.

Download the Python code for USD Coin data

Python script to download USD Coin historical data and save it to an Excel sheet::

import yfinance as yf

import pandas as pd

# Define the ticker symbol for USD Coin

usdc_ticker = “USDC-USD”

# Define the date range for historical data

start_date = “2020-01-01”

end_date = “2022-01-01”

# Download historical data using yfinance

usdc_data = yf.download(usdc_ticker, start=start_date, end=end_date)

# Create a Pandas DataFrame from the downloaded data

usdc_df = pd.DataFrame(usdc_data)

# Define the Excel file path

excel_file_path = “USDC_historical_data.xlsx”

# Save the data to an Excel sheet

usdc_df.to_excel(excel_file_path, sheet_name=”USDC Historical Data”)

print(f”Data saved to {excel_file_path}”)

# Make sure you have the required libraries installed and adjust the “start_date” and “end_date” variables to the desired date range for the historical data you want to download.

Evolution of the USD Coin

Figure 4 below gives the evolution of the USDC on a daily basis.

Figure 4. Evolution of the USD Coin.

Source: computation by the author (data: Yahoo! Finance website).

Figure 5 below gives the evolution of the USD Coin returns from Oct, 2018 to December 31, 2022 on a daily basis.

Figure 5. Evolution of the USD Coin returns

Source: computation by the author (data: Yahoo! Finance website).

Summary statistics for the USD Coin

The R program that you can download above also allows you to compute summary statistics about the returns of the USD Coin. Table 2 below presents the following summary statistics estimated for the USD Coin:

  • The mean
  • The standard deviation (the squared root of the variance)
  • The skewness
  • The kurtosis.

The mean, the standard deviation / variance, the skewness, and the kurtosis refer to the first, second, third and fourth moments of statistical distribution of returns respectively.

Table 2. Summary statistics for USDC.

Source: computation by the author (data: Yahoo! Finance website).

Statistical distribution of the USD Coin returns

Historical distribution

Figure 6 represents the historical distribution of the USD Coin daily returns for the period from Oct, 2018 to December 31, 2022.

Figure 6. Historical USDC distribution of the returns.

Source: computation by the author (data: Yahoo! Finance website).

Gaussian distribution

The Gaussian distribution (also called the normal distribution) is a parametric distribution with two parameters: the mean and the standard deviation of returns. We estimated these two parameters over the period from October, 2018 to December 31, 2022.

Figure 7 below represents the Gaussian distribution of the USD Coin daily returns with parameters estimated over the period from October, 2018 to December, 2022.

Figure 7. Gaussian distribution of the USDC returns.

Source: computation by the author (data: Yahoo! Finance website).

Risk measures of the USD Coin returns

The R program that you can download above also allows you to compute risk measures about the returns of the USD Coin.

Table 3 below presents the following risk measures estimated for the USD Coin:

  • The long-term volatility (the unconditional standard deviation estimated over the entire period)
  • The short-term volatility (the standard deviation estimated over the last three months)
  • The Value at Risk (VaR) for the left tail (the 5% quantile of the historical distribution)
  • The Value at Risk (VaR) for the right tail (the 95% quantile of the historical distribution)
  • The Expected Shortfall (ES) for the left tail (the average loss over the 5% quantile of the historical distribution)
  • The Expected Shortfall (ES) for the right tail (the average loss over the 95% quantile of the historical distribution)
  • The Stress Value (SV) for the left tail (the 1% quantile of the tail distribution estimated with a Generalized Pareto distribution)
  • The Stress Value (SV) for the right tail (the 99% quantile of the tail distribution estimated with a Generalized Pareto distribution)

Table 3. Risk measures for the USDC.

Source: computation by the author (data: Yahoo! Finance website).

The volatility is a global measure of risk as it considers all the returns. The Value at Risk (VaR), Expected Shortfall (ES) and Stress Value (SV) are local measures of risk as they focus on the tails of the distribution. The study of the left tail is relevant for an investor holding a long position in the XRP while the study of the right tail is relevant for an investor holding a short position in the XRP.

Why should I be interested in this post?

The post offers an opportunity for both newcomers and seasoned cryptocurrency enthusiasts to delve into the concept of stablecoins, gaining insights into how digital assets maintain stability amidst market volatility. Furthermore, the post highlights USDC’s role in fostering financial inclusion by enabling borderless transactions, appealing to readers passionate about democratizing finance. Additionally, exploring USDC’s significance in the burgeoning realm of decentralized finance (DeFi) could intrigue those interested in innovative financial technologies and investment opportunities. Examining USDC’s historical performance and market dynamics can offer valuable insights for investors and traders, while shedding light on its compliance measures and regulatory landscape can address concerns regarding legal risks, contributing to readers’ understanding and confidence in this digital asset.

Related posts on the SimTrade blog

About cryptocurrencies

   ▶ Snehasish CHINARA Bitcoin: the mother of all cryptocurrencies

   ▶ Snehasish CHINARA How to get crypto data

   ▶ Alexandre VERLET Cryptocurrencies

   ▶ Youssef EL QAMCAOUI Decentralised Financing

   ▶ Hugo MEYER The regulation of cryptocurrencies: what are we talking about?

About statistics

   ▶ Shengyu ZHENG Moments de la distribution

   ▶ Shengyu ZHENG Mesures de risques

   ▶ Jayati WALIA Returns

Useful resources

Academic research about risk

Longin F. (2000) From VaR to stress testing: the extreme value approach Journal of Banking and Finance, N°24, pp 1097-1130.

Longin F. (2016) Extreme events in finance: a handbook of extreme value theory and its applications Wiley Editions.

Data

Yahoo! Finance

Yahoo! Finance Historical data for USDC

CoinMarketCap Historical data for USDC

About the author

The article was written in March 2024 by Snehasish CHINARA (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2024).

Private Banks : Treasuries Departments and proprietary asset allocation

Private Banks : Treasuries Departments and proprietary asset allocation

Quentin CHUZET

In this article, Quentin CHUZET (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2019-2023) explains about Private banks Treasuries Departments and the challenges of proprietary asset allocation.

Introduction

Within the Treasury Department of a Private Bank, the role of its employees is to record incoming and outgoing cash flows, as well as to direct the allocation of assets that comprise its Treasury. Thus, on one hand, we find the amount of cash on the asset side, and on the other hand, on the liability side, the amount of client deposits. In other words, it can be said that the Treasury is primarily constituted by the various deposits made by clients.

These are grouped into 2 categories: sight deposits (money visible in the client’s bank account, available and usable at any time) and term deposits (money placed generally in interest-bearing accounts but only available once the placement has matured).

Private Bank balance sheet
Private Bank balance sheet
Source: La Finance Pour Tous

To address performance and revenue challenges, the Treasury department manages cash by investing it in interest-bearing products, aiming to generate significant margins. This activity, performed by the Middle and Front Office teams, is referred to as proprietary trading or prop asset allocation.

Before the 2008 financial crisis, the regulatory environment was less stringent, granting banks greater freedom in risk management. However, the crisis highlighted the dangers of this approach, leading to a significant strengthening of regulations.

In 2013, Basel III introduced one of the most important regulatory agreements in banking. It aims to enhance the resilience of banks by increasing their capital and liquidity requirements. Basel III notably introduced a short-term liquidity ratio (LCR) and a long-term liquidity ratio (NSFR).

Regarding liquidity, private banks must now hold sufficient liquid assets to withstand mass deposit withdrawals. They must also comply with a liquidity coverage ratio (LCR), requiring them to have enough high-quality liquid assets to cover their net cash outflows over a 30-day period.

In terms of solvency, private banks must now comply with a capital adequacy ratio (Cooke ratio), requiring them to have sufficient capital to absorb potential losses. Basel III also introduced a leverage ratio, limiting banks’ leverage.

To ensure compliance with these new regulatory frameworks while continuing to maximize revenue, banks have implemented treasury policies and control ratios to define limits and mitigate risks. These treasury policies, spanning multiple pages, guide Front Office teams in asset allocation to maintain the most efficient risk/return ratio possible. It is important to note that each bank has its own risk level, hence treasury policies and their respective limits may vary from one bank to another.

In general, a bank tends to favor a very low-risk level by prioritizing assets that can be quickly liquidated while limiting exposure to interest rate fluctuations or certain sectors. In the risk management process, liquidity and solvency ratios are monitored, as well as ratios related to interest rate risk and non-systematic risk.

Treasury Policy

These Treasury policies, spanning multiple pages, guide Front Office teams in asset allocation to maintain the most efficient risk/return ratio possible. It is important to note that each Bank possesses a risk level unique to itself, which is why Treasury Policies and their constituent limits may vary from one to another.

Thus, this document is divided into several limits and control ratios aimed at protecting against incurred risks. Among the main ratios present in the Treasury Policies, we find:

Liquidity and solvability ratios

Among liquidity and solvability ratios, controls are placed on the recovery time of securities held by the Treasury, with constraints notably regarding recoverable assets within 2 days and those recoverable within 30 days. Through these ratios, the average lifespan of the portfolio is controlled as well as the maximum duration of the securities, as well as the portion of assets placed with the Central Bank, ensuring a high rate of return in periods of high rates and near-immediate liquidity, as it is possible to recover from one day to the next.

These ratios aim to protect against the greatest risk a bank may face: that of illiquidity. This risk is heightened during crises and when clients wish to make massive withdrawals. The bank must thus ensure that all liquidity can be returned.

Security portfolio allocation’s ratios

Through these ratios, the Bank adheres to exposure limits by sector, industry, or company outlined in the Treasury policy: the leverage ratio is a significant indicator. This allows for diversification of allocations and investments and thus frees from specific risk. There are also control ratios based on Moody’s, S&P, and Fitch ratings or ESG ratings.

Finally, there are also ratios aimed at calculating the share represented by each asset class. It should be noted that each asset class represents what is called a “position” in the Treasury Sheet. In other words, each different class represents a different line. Among these lines are placements in the Money Market (Bond Portfolio, NEUCP Portfolio, placements in OPC funds), term interbank loans, currency and rate SWAPs, etc.

Sensitivity ratios

Through these ratios, the Treasury department controls the sensitivity to rates faced by Treasury assets. The Treasury Policy indicates threshold limits that should not be exceeded to ensure optimal rate adjustments, in the event of both increases and decreases.

Risk Management and Asset Allocation

Managing liquidity and solvability risk

To manage liquidity risk as effectively as possible, Private Banks can consider various strategies:

  • Purchase securities eligible for ECB refinancing
  • Maintain a high proportion of assets placed at the Central Bank on a daily basis (as they are highly liquid and yield interest at times of high interest rates).
  • Maintain a short maturity of the security portfolio and short-term deposits.

Interest-rate risk management

Interest-rate risk is a major issue for Private Banks, since a change in interest rates would have a major impact on the yield and price of bond holdings. The sensitivity of an asset represents the length of time during which it cannot be subjected to a variation in its interest rate. Thus, depending on the prevailing trend surrounding interest rate movements, Treasury Traders must invest to maintain a balanced sensitivity ratio. For example, in a scenario where the market strongly expects a future rate decrease, a strategy aimed at maximizing the adjustment period to the rate and thus the sensitivity ratio may be the best option. Conversely, in a scenario where the market anticipates a significant rate hike in the upcoming period, reducing the adjustment period to the rate for the portfolio would allow a quick re-indexing to a higher rate and reduce the time during which those assets would be “under-earning”.

Specific risk and diversification

A specific risk is linked to a particular event, affecting a single company, a sector of activity or a specific financial instrument. It differs from systemic risk, which affects the entire financial system. To reduce this risk, diversification is a key element, which is why a Private Bank can specify limits by sector or asset class in its Treasury Policy.

Therefore, the Treasury department of the Bank and its Front Office teams can allocate their assets to government bonds, as well as to corporate bonds in sectors such as retail, energy, or Real Estate.

Conclusion

In conclusion, the challenges of asset allocation within the Treasury of private banks are manifold. Guided by Treasury Policies, limits, and control ratios, it must adapt to the emergence of a new regulatory environment to define low-risk, high-liquidity investment strategies while addressing performance and revenue maximization objectives. Furthermore, proprietary asset allocation drives private banks to enhance their internal resources and develop tailored management tools.

Why should I be interested in this post?

If you’re interested in proprietary asset management, or in the workings of a treasury department within a private bank, you’ll find a first overview of these topics in this article.

If you have any questions about the position or the sector, please don’t hesitate to contact me on my personal Linkedin page, I’ll be delighted to answer them.

Related posts on the SimTrade blog

   ▶ Youssef LOURAOUI Asset Allocation Techniques

   ▶ Akshit GUPTA Asset Allocation

   ▶ Youssef LOURAOUI Equity Market Neutral Strategy

Useful resources

La Trésorerie active d’une entreprise

Banque-Trésorerie

Le Bilan d’une banque

Liquidité, solvabilité et crise bancaire : quelles relations ?

Diversification et gestion des risques

Les placements d’une Trésorerie d’entreprise

Le risque de taux

About the author

The article was written in March 2024 by Quentin CHUZET (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2019-2023).

Venture Capital 101: A Quick Overview

Venture Capital 101: A Quick Overview

Alessandro MARRAS

In this article, Alessandro MARRAS (ESSEC Business School, Global Bachelor in Business Administration (GBBA), Exchange Semester, September 2023-December 2023) gives a quick overview of Venture Capital.

What is Venture Capital?

Venture capital (VC) is a specialized form of financing where investors provide funds to startup or early-stage companies with high growth potential. This funding is crucial for startups to develop and expand their business operations. Venture capitalists not only provide financial support but also offer expertise and guidance to help these companies succeed. The goal of venture capital is to generate significant returns by investing in innovative businesses that have the potential to disrupt markets and achieve substantial growth.

VCs have five main functions:

  • They serve as financial intermediaries, channeling capital from investors into promising portfolio companies.
  • Their investments are directed towards private companies, making them illiquid assets.
  • VCs actively participate in the management and oversight of their portfolio companies, embodying active investors.
  • The primary objective of VCs is to maximize financial returns, typically through strategic exits like acquisitions or IPOs.
  • VCs prioritize investments in entrepreneurial ventures with substantial growth potential, aiming to foster internal growth and increase the likelihood of successful exits. These characteristics highlight the dynamic and strategic nature of venture capital investments, contributing to innovation and economic growth.

VCs vs others:

  • VCs differ from angel investors as they function as financial intermediaries rather than investing personal funds directly.
  • Unlike mutual or hedge funds, VCs invest specifically in private companies, placing them within the category of private equity and alternative investments.
  • While all VCs are private equity funds, the inverse isn’t true; not all private equity funds engage in venture capital.
  • VCs set themselves apart from crowdfunding platforms by actively participating in the companies they invest in, providing ongoing monitoring and management support.

How are VCs organized?

Venture capital firms are typically organized as limited partnerships, structured to facilitate investment activities while providing a degree of protection and incentive for both investors and managers. For investors this protection comes in the form of limited liability, meaning their risk of losing money is confined to their investment amount and they are not personally liable for the debts of the business. This allows them to invest in high-risk ventures with a capped downside. For managers the incentive is often structured as carried interest, a share of the profits of the investments, which aligns their financial interests with the success of the firm’s investments. This ensures that managers are motivated to select and nurture companies that will yield high returns, thereby directly linking their compensation to their performance in managing the venture capital firm’s portfolio.

Limited partnerships in venture capital consist of two main categories of partners. Firstly, there are limited partners, who contribute capital to the fund and bear limited liability. These investors can include wealthy individuals, banks, mutual funds, and other institutional investors. Secondly, there is the general partner, responsible for managing the fund’s operations and investments, and who assumes unlimited liability. Figures like Don Valentine, Ben Horowitz, and Peter Thiel are examples of notable general partners in the venture capital industry.

The lifespan of a typical limited partnership in venture capital is around ten years, during which investors’ capital is committed and cannot be withdrawn. General partners receive compensation in the form of a management fee and a share of the profits generated by successful investments, known as carried interest.

Limited partnerships offer tax efficiency, as they are not subject to corporate taxes, with partners instead paying taxes on their share of the profits. Additionally, distributions of securities to partners incur no immediate tax implications, with taxes only due upon the eventual sale of the securities.

This organizational structure provides a framework that incentivizes efficient investment management and aligns the interests of both limited and general partners in achieving successful outcomes.

Benefits of VC

Venture capital offers a range of benefits to both entrepreneurs and investors, fostering innovation, driving economic growth, and facilitating wealth creation. Firstly, venture capital provides crucial funding to startups and early-stage companies that may otherwise struggle to secure financing from traditional sources such as banks or public markets. This injection of capital enables entrepreneurs to pursue ambitious ideas and develop groundbreaking technologies, driving innovation across various industries. Moreover, venture capitalists often bring valuable expertise, networks, and mentorship to the table, helping startups navigate challenges, refine their business strategies, and accelerate their growth trajectory.

Secondly, venture capital plays a pivotal role in job creation and economic development. By supporting high-growth startups, venture capital investments fuel job creation, as these companies expand their operations, hire new talent, and contribute to local economies. Additionally, successful startups can spawn entire ecosystems of suppliers, service providers, and complementary businesses, further stimulating economic activity and driving regional prosperity.

Furthermore, venture capital investment offers attractive returns for investors willing to accept the inherent risks associated with early-stage ventures. While venture capital investments carry a higher risk of failure compared to traditional investments, they also offer the potential for substantial returns on successful exits, such as acquisitions or initial public offerings (IPOs). As a result, venture capital serves as a vital asset class for investors seeking diversification and opportunities for outsized returns in their investment portfolios.

VC Financing Cycle

The Venture Capital Financing Cycle delineates the sequential stages of funding that startups typically undergo, from inception to exit. This cycle starts with the Seed Stage, where initial capital is raised to prove concepts and build prototypes. As the startup matures, it may progress through various rounds of funding—Angel, Series A, Series B, and beyond—each designed to fuel growth, product development, market expansion, and operational scaling. The Bridge stage serves as a critical juncture for preparing more mature startups for substantial future rounds or positioning for exit strategies. The cycle culminates in the Pre-IPO and IPO Preparation stages, where companies ready themselves for public offering or seek acquisition opportunities, marking the exit phase. This framework not only structures the investment landscape but also maps the growth trajectory of startups. The VC financing cycle is emblematic of the symbiotic relationship between investors seeking to maximize returns and startups in need of capital to fuel their growth ambitions, fostering innovation and economic development within the broader ecosystem.

VCs financial performance in 2023

In 2023, the venture capital (VC) market experienced significant shifts, reflecting broader economic challenges and evolving investment trends. The year saw a considerable downturn in VC investments, dropping to the lowest levels in four years, with a year-over-year decrease of 35% from the already declining levels of 2022. The total amount raised by VC-backed startups barely surpassed $140 billion, influenced notably by a few mega-deals in the artificial intelligence (AI) sector. The decline was not just in the amount raised but also in deal volume across nearly all fund classes, reaching the lowest point in a decade. Later-stage investments saw the most significant reduction in dollar volume quarter-over-quarter, while Series A investments showed some resilience with a 9% increase.

The backdrop of economic headwinds, valuation concerns, and an overhang of more than 50,000 existing VC-backed startups created a challenging environment for new investments. VC fund formation also experienced a sharp decline, dropping 62% from the record year in 2022, although there was a slight uptick in the last quarter of the year. This situation has led to increased caution among venture capitalists, with a notable reluctance to engage in mega-round financing. Only 50 mega deals were recorded in the last quarter of 2023, marking the lowest total since 2017.

Despite these challenges, AI continued to garner significant attention and investment, driving many of the largest deals in the U.S. during the last quarter of 2023. This trend suggests that while the overall VC investment has declined, specific sectors, particularly those related to technological innovation and AI, continue to attract substantial interest and funding.

Why should I be interested in this post?

As an ESSEC students interested in finance, this post can be a useful resource due to its relevance in the financial sector. Understanding venture capital offers insights into alternative investments, career opportunities in private equity, and the dynamics of financing innovative startups, enriching your knowledge and potential career paths within the finance industry.

Related posts on the SimTrade blog

   ▶ Louis DETALLE A quick presentation of the Private Equity field…

   ▶ Louis DETALLE A quick review of the Venture Capitalist’s job…

   ▶ Marie POFF Film analysis: The Wolf of Wall Street

Useful resources

Zider B. (1998) How Venture Capital Works Harvard Business Review.

Jeffrey Grabow (29/01/2024) Will venture capital market rebound in 2024 or seek new floor? EY

KPMG Venture Pulse Q4 2023

Deloitte 2024 trends in venture capital

About the author

The article was written in March 2024 by Alessandro MARRAS (ESSEC Business School, Global Bachelor in Business Administration (GBBA), Exchange Semester, September 2023-December 2023).

Multiples valuation method for stocks

Multiples valuation method for stocks

Jorge Karam Dib

In this article, Jorge KARAM DIB (ESSEC Business School, Master in Strategy and Management of International Business (SMIB), 2024-2025) introduces the method to evaluate stocks called “multiples valuation”.

How much should a stock be worth?

Valuing stocks is a complex yet essential endeavor for investors seeking to make informed decisions in the financial markets. Knowing if you are paying a fair price or if you are over or under paying for a stock is every investors question. Of course, a decision on whether investing or not in a company comes after a long due diligence of valuating the financials of the company, following their reports, and many more actions to ensure you are buying something of value. Some investors call this method “value investing”, when the investor value a stock above the actual price, it is said that the type of investing is “value”, trying to take advantage of the differential between the actual price and the theoretical price. And when they buy a stock for a competitive advantage that is not expected in the near future to concede, it is usually called “growth investing”. Although some famous investors disagree in these definitions, they are used frequently in the financial language.

While there is no one-size-fits-all approach to valuing stocks, investors often turn to a range of methodologies, one of which is multiples analysis. By examining multiples such as price-to-earnings (P/E), price-to-book (P/B), and price-to-sales (P/S), and many more, investors gain insights into how the market values a company relative to its earnings, assets, or revenue. In this article we will try to shine a light on this valuating method and walk through the benefits and limitations the method has.

Multiples valuation method

Multiples valuation method involves analyzing one or more multiples, such as the price-to-earnings (P/E), price-to-book (P/B), and price-to-sales (P/S) ratios, among others. Each multiple provides a unique perspective on how the market values a company relative to its earnings, assets, or revenue.

The price-to-earnings (P/E) ratio, for instance, compares a company’s stock price to its earnings per share (EPS), indicating how much investors are willing to pay for each dollar of earnings generated by the company. A low P/E ratio may suggest that the stock is undervalued, while a high P/E ratio could indicate overvaluation.

One key aspect of this method is that it is usually compared against the ratios of companies within the same industry. It is from the investor’s criteria to decide which filters to apply for deciding against what companies the objective should be compared with, but usually it should be on the same market, region can also be an important factor, and also the size.

There are many sources of information to consult the information from, many of them free. This platform simplifies the valuation by providing not only the information, but in some cases, the ability to connect through an API and do the consulting automatically through a query. In the next image is shown an example from Capital IQ, one of the most prestigious information sources in the financial world, about Walmart and some recommendations from the platform of similar companies in the market to facilitate the calculation.

Walmart’s comparison.
Walmart
Source: Capital IQ.

Calculations

Calculating multiples involves straightforward mathematical formulas that utilize key financial metrics derived from a company’s financial statements. If we use the case for P/E, then we would need to divide the “Market price per share” with the “Earnings per share”. After getting the ratio, let’s say of Walmart, the next step is to get the median P/E for the “similar” companies that the investor decided to compare the company with. With these two values the investor can start seeing a trend on where the company is positioned versus their competitors. The next step is to calculate the price per share of Walmart using the P/E of the similar companies, and see the comparison between the price with the original P/E versus the “mean” of similar companies.

Example: Walmart

This section will be dedicated to illustrate the method explained earlier using Walmart as an example and Capital IQ as the source of information. The first step is to consult the key financials as shown in the next picture.

Walmart’s key financials.
Walmart financials
Source: Capital IQ.

Next, compute the information with the comparable companies suggested by Capital IQ. Important to note that the companies can be added or withdrawn according to everyone’s own criteria.

Comparable analysis.
Comparable companies
Source: Capital IQ.

After retrieving all the information, the next step is to do the calculations explained earlier in the document. The mean EV/EBITDA ratio for the comparable companies and then recalculate the price of Walmart’s stock using the mean EV/EBITDA ratio.

Mean EV/EBITDA ratio.
EV/EBITDA ratio
Source: Capital IQ.

Valuation of Walmart’s stock.
Valuation
Source: Capital IQ.

In this case, we see an underappreciation of Walmart’s stock, this doesn’t mean that anyone shouldn’t buy stocks of the company, is just the perspective of the valuation method, and also it is not an investing advice.

Conclusions

The multiples valuation method provides investors with a valuable framework for assessing the worth of a stock by comparing it to similar companies within the same industry. By using this method, investors gain insights into how the market values a company relative to its earnings, assets, or revenue.

However, it’s important to recognize the limitations of this approach. Multiples analysis relies heavily on historical financial data and may not fully capture future growth prospects or qualitative factors such as industry dynamics and competitive positioning. Additionally, multiples are subject to fluctuations in market sentiment and may not always reflect the intrinsic value of a company accurately.

Despite these limitations, multiples analysis remains a widely used and valuable tool in the arsenal of investors. When applied judiciously and supplemented with thorough research and analysis, multiples can provide valuable insights into a company’s valuation and help investors identify potential investment opportunities.

Why should I be interested in this post?

This post is with the only intention for educational purposes. Targeting people who are interested in knowing more about valuation methods for stocks. In any way this article pretends to be an investment advice and/or suggestion. Any decision should be taken under personal responsibility and with their respective due diligence previous to the decision.

Related posts on the SimTrade blog

   ▶ Ghali EL KOUHENE Asset valuation in the Real Estate sector

   ▶ Isaac ALLIALI Understanding the Gordon-Shapiro Dividend Discount Model: A Key Tool in Valuation

Useful resources

McKinsey & Company The right role for multiples in valuation

Chastenet E. and A. Marion (2015) Valuation Using Industry Multiples: How to Choose the Most Relevant Multiples, Business Valuation Review, 34(4): 173-183.

Schueler A. (2020) Valuation with Multiples: A Conceptual Analysis, Journal of Business Valuation and Economic Loss Analysis, 15(1) pp. 20190020.

About the author

The article was written in March 2024 by Jorge KARAM DIB (ESSEC Business School, Master in Strategy and Management of International Business (SMIB), 2024-2025).

Contracts for Difference

Contracts for Difference

Isaac ALLIALI

In this article, Isaac ALLIALI (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2019-2023) explains the Contracts for Difference (CFD).

Understanding CFDs

Although banned in the US and for US citizens, the Contract for Difference (CFD) is a relatively new contract, introduced in the 1990s in Great Britain, to allow operators without permission to sell shares short, to speculate on the market’s decline. CFDs were originally based on equity swaps, which allow the exchange of the performance of a stock or stock index for an interest rate. Initially reserved for institutional investors, CFDs were later made available to individual investors and recognized by a directive from the European Union in 2004. Soon after, they gained popularity and rapidly developed in the OTC market. In contrast to centralized exchanges, the “Over The Counter” market facilitates trades directly between participants, offering flexibility for unique financial instruments and fostering niche investment opportunities. While lacking the structure of exchanges, OTC markets play a crucial role in diverse financing needs.

The contract for difference (CFD) is a very popular derivative, which essentially establishes a bilateral agreement between an investor and a broker. This contract does not grant the investor ownership of the underlying asset, but rather the right to receive the difference between the contract’s opening and closing price. Unlike contracts with an expiry date, a CFD is effectively renewed at the closing of each trading day and can be extended forward if desired; you can keep your position open indefinitely as long as there is enough margin in your account to maintain it.

In essence, contracts for differences are very similar to futures with no expiration date. While the contract remains open, your account with the provider will be debited or credited to reflect the interest and dividend alterations. The broker may offer leverage of up to 400 which serves as a powerful tool for amplifying possible financial gains (and losses). Through leveraging, the investor can gain control over a larger position than their initial capital would allow. For instance, if you have €100 in your account and a leverage of 10, you take a position of €1,000 (10*100).CFDs offer great flexibility to investors, CFDs provide investors with great flexibility, offering the ability to:

  • Speculate on a wide range of assets, including stocks, indices, currencies, commodities, etc.
  • Take advantage of market movements, whether upwards or downwards: enabling both buying and selling positions
  • Hedge an investment portfolio, using CFDs, which serve as a safeguard against price declines.
  • Execute advanced trading strategies.

Mechanics of CFD Trading

Opening a CFD position involves several steps. Firstly, traders choose an asset they wish to trade, such as stocks, commodities, or currencies. Secondly, they decide whether to go long (buy) or short (sell) on the asset, basing their decision on their market analysis and expectations for future price movements. Traders also determine the quantity of the asset they want to trade, which can be specified in terms of contracts or units, depending on the CFD provider. CFDs are not standardized products and every CFD broker has their own terms and conditions. Once the initial decisions have been made, the CFD provider calculates the price at which the trade will be executed. This price is typically derived from the market value of the underlying asset, taking into consideration various factors such as supply and demand dynamics, economic data, and market sentiment. The CFD provider may also incorporate a spread into the market price, which represents their profit margin. Margin requirements play a crucial role in opening and maintaining a CFD position. Traders are required to deposit a percentage of the total trade value as collateral, referred to as a margin. This ensures that traders can cover potential losses stemming from adverse price fluctuations. Margin requirements vary depending on factors such as the volatility of the underlying asset and regulatory requirements. Upon opening a position, the value of the CFD fluctuates in accordance to the underlying asset’s price movements. When traders decide to close their position, they execute an opposing trade to offset their initial position. The profit or loss from the trade is calculated based on the price difference between the opening and closing trades, adjusted for any transaction costs.

CFD trading involves various costs and fees that traders should be aware of. Spreads, representing the difference between buy and sell prices, are the primary revenue source for CFD providers and affect trading costs. Narrower spreads typically mean lower costs. Additionally, traders may face overnight financing charges for holding positions, which are influenced by factors such as the asset class and prevailing interest rates. Furthermore, some providers charge commissions on trades, based on elements such as the trading volume. Taking these costs into consideration is crucial for assessing overall profitability and developing effective trading strategies.

Application in the energy industry

NEOEN and BNRG have finalized the financing of three solar power plants in the Republic of Ireland: Hilltown, Hortland, and Millvale, located respectively in the counties of Meath, Kildare, and Wicklow. These plants, with a total capacity of 58 MWc, are among the first large-scale solar projects in the country. As the winners of the first governmental tender for solar power plants in 2020, they benefit from financial support from the Irish government through a Contract for Difference (CFD) mechanism until 2037, ensuring a stable price for the electricity generated. The financing, estimated at 39 million euros excluding financing costs, is provided by NEOEN and BNRG’s equity, alongside non-recourse senior debt from Société Générale. Construction works, awarded to Omexom, are expected to be completed in the first half of 2022,with plant commissioning set/scheduled for the same period. Once operational, these plants will supply the equivalent of 12,700 Irish households with clean electricity, thereby contributing significantly to the country’s energy transition.

Hilltown Solar farm
 Hilltown Solar Farm
Source: BNRG

Millwale Solar farm
Millwale Solar farm
Source: BNRG

Related posts on the SimTrade blog

   ▶ Akshit GUPTA Short selling

   ▶ Alexandre VERLET Understanding financial derivatives: swaps

Useful resources

AMF Les CFD (contracts for difference)

BNRG

Neoen

Neoen (12/08/2021) Neoen et BNRG clôturent le financement de trois centrales solaires (58 MWc) en République d’Irlande

Dupuy, P., Fontaine, P. & Hamet, J. (2018). Chapitre 6. Les marchés dérivés et la gestion du risque. Dans : , P. Dupuy, P. Fontaine & J. Hamet (Dir), Les marchés de capitaux français (pp. 153-204). Caen: EMS Editions.

About the author

The article was written in February 2024 by Isaac ALLIALI (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2019-2023).

CumEx files

CumEx files

Matthieu MENAGER

In this article, Matthieu MENAGER (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2017-2021) we take a look at the CumEx files scandal. The total amount of the fraud, according to the 19 media that revealed the affair (including Le Monde, Die Zeit and La Repubblica) amounts €140 billion.

CumCum & CumEx : What is the difference

CumEx and CumCum are two tax avoidance schemes that came to public attention in 2018. CumCum is considered a legal form of tax optimization, while CumEx is illegal. CumCum involves artificially transferring ownership of dividend-paying shares or securities, when dividends are paid out, to avoid tax. Foreign investors sell their shares back to domestic banks for what is often a very short time before receiving their dividends. These investors thus escape any levy and then recover their dividends, in return for a commission paid to the bank that temporarily housed their financial securities. The operation is so quick that the tax authorities are unable to identify the true owner of the securities. CumEx, on the other hand, consists of obtaining a tax refund on dividends that have never been paid. This practice is illegal and has cost European countries billions of euros.

The damage done by CumEx Files

Dividend fraud, known as “CumEx”, is a form of tax plunder estimated at €140 billion, last amount dating from 2022 (€33.3 billion for France; €28.5 billion for Germany; €27 billion for the Netherlands; €18.8 billion for Spain), as reported by FranceInfo (2021). Revealed in Germany, this fraud affected 11 countries (Germany, France, Spain, Italy, the Netherlands, Denmark, Belgium, Austria, Finland, Norway and Switzerland). Hanno Berger, a renowned lawyer, has devised a manipulation that involves buying and selling shares around the time of the dividend payment, so quickly that the tax authorities can no longer identify the true owner. The manipulation, which requires the agreement of several investors, enables the same dividend tax refund to be claimed several times over, thereby prejudicing the tax authorities.

The scam can be illustrated as follows:

CumCum

Tim from Frankfurt transfers his shares in a French company to Hugo, who lives in Dubai, where dividends are taxed at 0% under an agreement between Paris and Dubai, a few days before the dividend payment (Figure 1). The dividends are then paid by the French company to Hugo (Figure 2) and therefore has no dividend taxes to pay to the French state (Figure 3). The shares are then returned to Tim, who didn’t have to pay any taxes (a tax saving of 15 to 30%). The tax system in France for dividends is a flat tax of 30% and consists of a single flat tax of 12.8% and 17.2% social tax. Tim and Hugo may agree to share the tax saving between them (Figure 4).

Figure1. CumCum mechanism – First step:
 CumCum mechanism - First step:
Source: The author.

Figure 2. CumCum mechanism – Second step:
 CumCum mechanism - Second step:
Source: The author.

Figure 3. CumCum mechanism – Third step:
 CumCum mechanism – Third step:
Source: The author.

Figure 4. CumCum mechanism – Fourth step:
 CumCum mechanism – Fourth step:
Source: The author.

CumEx

Tim, Lea and Hugo will trade hundreds of thousands of shares over a very short period around the dividend payment date. The tax authorities will have no way of knowing who the real owner of the shares is and they’ll pay back taxes they haven’t even collected.

Figure 5. CumEx mechanism
 Figure 5. CumEx mechanism
Source: The author.

Who is implicated?

BNP Paribas, Société Générale, Natixis and Crédit Agricole (Cacib) are suspected of helping their customers to avoid paying tax on dividends. The resale and repurchase of shares, even for a short period, is in fact legal. On the other hand, the industrialisation of this practice to evade tax can be punished. In 2018, BNP Paribas, Crédit Agricole and Société Générale were among the French banks singled out by the “CumEx Files” investigation group for this type of practice.

In Germany, dozens of people have been charged (traders, bankers, lawyers, advisers). The Warburg bank in Hamburg is one of the banks accused and should have reimbursed 47 million euros to the German port city. The municipality waived this obligation in 2016. The investigators are therefore looking into whether political leaders – including Olaf Scholz, the city’s mayor at the time – put pressure on the municipal tax authorities to waive recovery of these taxes. The decision to waive repayment of the sums owed by the Warburg bank was apparently taken shortly after a conversation between Olaf Scholz and Christian Olearius, then head of the bank. Investigators discovered more than €200,000 in cash in a safe at the home of another SPD member of parliament, who may have played a role in the bank’s repayment, fuelling suspicions of possible secret financial arrangements.

At the end of March 2023, the French Parquet national financier (PNF – a national prosecutor’s office specializing in major economic and financial crime) launched a wave of searches. On May 2, before the French Senate, Gabriel Attal, Minister Delegate for Public Accounts, made public the total amount of reassessments notified to date by the tax authorities: €2.5 billion, according to L’Express. This bill, which includes penalties in addition to the amounts reassessed, concerns in particular the five banks targeted by the PNF searches (Société Générale, BNP Paribas and its subsidiary Exane, Natixis and HSBC), but also Crédit Agricole (which managed to avoid the search).

Financial concepts

Shares

Shares are part of a company’s equity when it is incorporated as a public limited company. It is therefore a source of financing for the company, in the same way as debt securities, from which, however, it differs clearly. It has an unlimited lifespan (it can only be disposed of by selling the share, and there is no contractual repayment), and its holder bears the full risk of the company (he or she receives no income if the company goes badly, and in the event of liquidation the shareholder takes second place to the creditor in the distribution of the proceeds from the sale of assets – in other words, most of the time he or she can recover nothing). In return, the share gives the holder the right to share in the company’s profits and management via voting rights.

Dividends

By definition, the dividend is defined as the shareholder’s income. This is the amount that a shareholder (owner of shares in a company) receives as a result of the profits generated by the company over a given period. The choice of dividend payment is made at the general meeting. At that time, distributable profits and available reserves are recorded.

Shareholders receive a dividend in two cases:

  • When there is a distributable net profit
  • But also when the company does not make a profit but wishes to draw on its reserves of cash to remunerate its shareholders.

Why should I be interested in this post?

CumEx Files is also a very recent scandal. What I find most shocking is that some very well-known banking institutions are also implicated in the scandal. Politicians (including the German Chancellor) are even suspected of involvement. So it’s worth taking a closer look at these affairs to understand how they worked.

Related posts on the SimTrade blog

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

   ▶ Louis DETALLE The incredible story of Nick Leeson & the Barings Bank

   ▶ Louis DETALLE The 3 biggest corporate frauds of the 21st century

Useful resources

Press articles

Le Parisien (21/10/2021) CumEx Files : la France a perdu 33 milliards d’euros de recettes fiscales en vingt ans

Le Monde (18/10/2018) « CumCum », « CumEx » : le scandale des dividendes expliqué simplement

Le Monde (15/05/2023) Scandale « CumCum » : le fisc réclame plus de 2,5 milliards d’arriérés fiscaux aux banques

L’Express (19/08/2022) Allemagne : tout comprendre au scandale “Cum-ex Files” qui éclabousse Olaf Scholz

France Info (21/10/2021) “CumEx Files” : ce que l’on sait du “pillage fiscal à 140 milliards d’euros” révélé par plusieurs médias

France 24 (21/10/2021) “CumEx Files” : Hanno Berger, le cerveau présumé du scandale aux 140 milliards d’euros

Le Monde (21/10/2021) « CumEx Files » : un pillage fiscal à 140 milliards d’euros, quatre banques françaises dans le viseur du fisc

Videos

Le Monde (18/10/2018) CumEx Files : comment arnaquer le fisc avec la Bourse

Zonebourse (31/03/2023) Comment les banques fraudent le fisc grâce aux dividendes (CumCum, CumEx)

The Dark Side of Money (15/02/2022) CumEx Files: Biggest TAX FRAUD in Europe

Explainitychannel (04/03/2020) Cum-ex deals explained

About the author

The article was written in February 2024 by Matthieu MENAGER (ESSEC Business School, Bachelor in Business Administration (BBA), 2017-2021).

Creating a portfolio of Conviction

Creating a portfolio of Conviction

Chloé ANIFRANI

In this article, Chloé ANIFRANI (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2019-2024) explains what it means to create a portfolio of conviction.

Conviction as a filter for your investment universe

In their article on Asset Allocation, Akshit GUPTA, an ESSEC student, defines the basics criteria considered by investors when building a portfolio. He defines them as the profile of the investor (risk profile, objective, time horizon), characteristics of the chosen assets (expected returns, risk, correlation), and chosen strategy of investing (strategic or tactical allocation).

Although those characteristics are what any good investor should consider in the first place, some other criteria might come into account. This is the case for portfolio used in thematic and conviction funds, which use particular quality filters to reduce their investment universe before taking a closer look at other characteristics in the highlighted assets.

In this article, we will only talk about equity assets (no bonds, structured products…).

For example, these filters might be geographic (global, European, American…), by capitalization (small, mid and large caps) or sector (technology, luxury, energy…).

Another type of filter seen in stock picking might be a conviction: after analyzing a company, the investor strongly believes that it shows a particular quality that makes it eligible for their portfolio.

The filter we will talk about today is one of those: Pricing Power.

Pricing Power is defined as the ability of a company to raise their prices without affecting the demand for their products, mostly thanks to specific technological invocation or patent, brand image and/or high barriers to entry for competitors.

This quality is often observed in sectors like luxury (LVMH, Hermès, Ferrari, Mercedes-Benz…), health (Cooper, Zoetis, medication for rare disease…) or aeronautics (Airbus Dassault…), for example. On the contrary, it is harder to find in sectors like energy, insurance or telecom, where many actors offer the same type of products and services.

Pricing Power in asset allocation: how can an analyst recognize Pricing Power in companies

Although Pricing Power is greatly influenced by sectors, as explained above, it also is company specific. Firms might possess technologies and innovations that allow them to showcase Pricing Power in a sector that isn’t known for it, while others may not be able to utilize their sector’s strength in a way that would lead to gaining Pricing Power.

Therefore, here are some characteristics that help recognize Pricing Power in companies (Louis Vuitton, Ferrari, Hermès, Atoss Software, Capgemini, Airbus, ASML, Safran, EssilorLuxottica, Disney, Netflix, L’oréal):

Brand strength

Companies with strong and well-established brands often have better Pricing Power. Consumers may be willing to pay a premium for products associated with a trusted and recognized brand.

Examples: Louis Vuitton, Ferrari, Hermès

Unique products or services, technological innovation

If a company offers unique or differentiated products or services that are not easily replicated by competitors, it may have greater control over pricing. This uniqueness can create a competitive advantage.

Examples: Atoss Software, Capgemini

High barriers to entry

If a company is established in a sector with high barriers to entry, like high cost of development or strict regulatory environment, it may showcase Pricing Power, as switching from this company to competitors might be difficult for customers in case of price increase.

Examples: Airbus, ASML, Safran

Market position

A leading market position or a dominant market share can provide Pricing Power. Market leaders often have more control over pricing, since customers may view their products as industry standards.

Examples: LVMH, EssilorLuxottica

Customer loyalty

High customer loyalty can enable a company to maintain Pricing Power. However, since customer loyalty derives from a feeling of trust between the client and brand, it shouldn’t be considered as a primary element to examine for investors, as too many raises in prices might negatively impact the relationship.

Examples: Disney, Netflix

Track record of price increases

Examining a company’s historical ability to implement price increases successfully can provide insights into its Pricing Power. Consistent or periodic price increases without significant negative effects on sales would be a positive indicator.

Examples: L’oréal, Louis Vuitton, Hermès

It is important to note that elements like cost advantages (economies of scale, economies of scope, relationship with manufacturers…) do not lead to pricing power, but to margin control, which would be another type of filter.

The case of luxury: intangibles in Pricing Power

To better understand how Pricing Power evolves for brands, let’s talk about the case of luxury today.

In 2022 and 2023, the market has been shaken by the new hawkish monetary policy declared by central banks, with interest rates raising at a rapid pace and to a level that had not been seen for many years.

This new reality led to difficulties for firms which relied heavily on debt to finance their activities.

In the case of the luxury sector, our new situation of “Higher for longer” rates lead to a strengthening of the already existing barriers to entries. Firms that have been major actors in this field for decades (LVMH, Kering, Hemes, Richemont…) should see fewer young brands emerging to their levels for the years to come.

It is interesting to note that those actors’ Pricing Power comes primarily from their brand image. This component helps them to sustain an “asset light” growth, which doesn’t require much investment in new technologies or patents on their parts.

However, an investor might worry that the current inflation and reduction of houses’ purchasing power might affect demand on luxury products, that are often not primary necessities.

Indeed, if 2022 was a particularly good year for the sector (9-11% annual growth), its trajectory has slowed down in 2023, coming back to average historical levels (7-8%). The major actors, who raised their prices significantly in 2022 and 2023, already plan more moderate raises for the years to come.

Those elements should be considered by an investor interested in Pricing Power. However, they do not invalidate it for the sector. We notice two elements that are in its favor for the upcoming years: the consumption of luxury goods is becoming more and more concentrated on the most “iconic” brands (Hermès, Chanel, Louis Vuitton…) and the number of clients is increasing steadily every year. In 2023, there were 400 million of luxury consumers. 50 million of them where millionaires, which is interesting to note, knowing how polarized this sector consumption is: 1% of the customers equals 20% of the total sales. This means that the main luxury consumers are the least affected by the current drop in purchasing power.

With this information and the previously stated higher barriers to entry, we can consider that the current state of the market might actually be beneficial to the luxury sector’s main actors’ Pricing Power.

Funds and ETFs with Pricing Power at their core

To conclude this article, we will cite some funds that have been basing their investment strategies around Pricing Power.

We selected three of these funds, all primarily invested in Eurozone. The funds are Delubac Pricing Power I (FR0011304229), Pictet Premium brands I (LU0217138485, also invested in the US) and Amplegest Pricing Power (FR0010889857).

Top 10 Delubac Pricing Power.
Delubac PP top 10
Source: Morningstar.

Top 10 Pictet Premium Brands.
Pictet PB top 10
Source: Morningstar.

Top 10 Amplegest Pricing Power.
Amplegest PP top 10
Source: Morningstar.

While examining their top 10 stocks, it is interesting to note that some brands, well-established as having Pricing Power, can be found in all three allocations (LVMH, L’Oréal, Linde).

For your information, here are the funds’ performances over the last five years, compared to their zone of investment’s.

Performances over 5 years
PP funds 5y track
Source: Quantalys.

Performances in 2023
PP funds 2023
Source: Quantalys.

Annual Performances from 2017
PP funds perf from 2017
Source: Quantalys.

And yes, investing with conviction can be rewarding and a great way to differentiate your product, but it doesn’t always beat the market!

Why should I be interested in this post?

If you wish to work in Asset Management, as an analyst or funds manager, or as a customer, this post will help you understand what kind of criteria might be used to do so. Asset Managers sell a product, not just a track record, and it is important to know how to build a portfolio around a concept in order to differentiate yourself on a very saturated market!

Related posts on the SimTrade blog

   ▶ Youssef LOURAOUI Asset Allocation Techniques

   ▶ Louis DETALLE A quick interview with an Asset Manager at Vontobel

   ▶ Akshit GUPTA Asset Allocation

Useful resources

Pricing Power is the magic ingredient for equity investors

JP Moorgan Combating inflation with pricing power

Morgan Stanley Combating inflation with pricing power

About the author

The article was written in February 2024 by Chloé ANIFRANI (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2019-2024).

Discovering Private Equity: Behind the Scenes of Fund Strategies

Discovering Private Equity: Behind the Scenes of Fund Strategies

Lilian BALLOIS

In this article, Lilian BALLOIS (ESSEC Business School, Bachelor in Business Administration (BBA), 2019-2023) explains about Private Equity fund strategies.

Reminder: What is Private Equity?

Private Equity entails investors directing capital into privately held enterprises that are not publicly traded on stock exchanges. Private Equity firms manage investors’ funds, which are utilized to secure ownership stakes in these companies, fostering their growth, innovation, or resolution of financial challenges. In exchange, investors anticipate yielding profits upon exiting the investment, typically within a span of 5 to 8 years.

Private equity thus offers a way for companies to receive strategic financing and for investors to earn returns on their investments, in an alternative way to traditional investments.

But how do you know which funds to invest in?

Decoding Success: How to choose the perfect Private Equity Investment Strategy

Aligning Investments with the Company Lifecycle

Private equity investments are aligned with various stages of a company’s lifecycle (Cf. chart below). In the early stages, venture capital provides funding for startups to assist in innovation and growth. As companies mature, growth equity offers expansion capital to fuel further development and market penetration. In the maturity stage, private equity often engages in leveraged buyouts (LBOs) to acquire established companies, implementing operational enhancements and strategic changes to boost efficiency and profitability. Finally, distressed capital may be deployed to support struggling businesses, offering resources and expertise to facilitate turnaround efforts.

Company life cycle.
Company Life Cycle
Source: The author

Venture Capital: at the Introduction Phase

Venture Capital is a private equity and financing approach focused on supporting early-stage startups and high-potential businesses. Investors, including affluent individuals, investment banks, and angel investors, contribute funds to fuel the growth of these companies. Apart from monetary contributions, investors may also offer technical or managerial expertise. An illustrative example of Venture Capital at work is Uber, which in 2010 received its initial major funding of $1.3 million led by First Round Capital. Shortly after, in early 2011, it raised $11 million in a Series A funding round led by Benchmark. With these funds, Uber expanded its operations to various cities in the United States and abroad, including Paris, where the concept originated. By December of 2011, Uber secured $37 million in Series B financing from Menlo Ventures, Jeff Bezos, and Goldman Sachs, further fuelling its global expansion and technological advancements.

Growth Equity: at the Growth Phase

Growth equity is a less speculative form of financing, aids companies in their expansion phase. Unlike venture capital, growth equity is directed at already profitable and mature businesses with minimal debt. This type of funding, commonly involving minority ownership through preferred shares, facilitates strategic business growth, such as entering new markets or acquiring other companies, with a balanced risk-return profile. Adyen, a prime example, initially self-funded, but experienced exponential growth after securing $250M in Series B funding led by General Atlantic in 2014. This injection of capital significantly accelerated Adyen’s trajectory, leading to its successful IPO on Euronext in June 2018, with a market capitalization of €7.1B. Adyen’s subsequent revenue surge to €721.7 million in 2022 further underscores the potency of growth equity in fuelling sustained business growth.

Leveraged Buyouts & Management Buyouts: during the Maturity Phase

Leveraged Buyouts

Leveraged Buyouts (LBOs) funds combine investment funds with borrowed capital to acquire companies, aiming to enhance profitability. By leveraging creditors’ and investors’ money, the fund manager has more capital to purchase larger companies, either outright or by securing a majority stake for strategic control. The term “leveraged buyout” reflects the use of borrowed funds to afford larger acquisitions, potentially resulting in substantial returns if the strategies pay off. An instance of an LBO is Elon Musk’s acquisition of Twitter, Inc. Despite initial resistance from Twitter’s board, who employed a “poison pill” strategy to deter hostile takeovers, Musk’s persistent pursuit led to the acceptance of his buyout offer of $44 billion on April 25.

Management Buyouts

Management Buyouts (MBOs) are transactions in which the existing management team of a company acquires a significant ownership stake or complete ownership of a business. In a MBO, the current managers collaborate with a private equity firm to purchase the business from its existing owners. This transaction is common when a company’s management team believes they can run the business more effectively or exploit growth opportunities better than the current ownership structure allows. The MBO of Dell Inc. in 2013 stands out as one of the largest and most significant in history. With a valuation reaching approximately $24.9 billion. The company’s founder, Michael Dell, partnered with Silver Lake Partners to reclaim control of the company he had founded. The move allowed Dell to implement long-term strategies and make pivotal decisions without the immediate pressures of quarterly earnings reports, facilitating a more nimble and adaptable approach to the rapidly evolving tech landscape.

Distressed Capital: at the Decline Phase

Distressed capital consists in lending to companies facing financial crises and to take control of businesses during bankruptcy or restructuring processes. The strategy involves purchasing distressed companies at a lower price, turn them around, and eventually sell them. Distressed capital carries inherent risk due to investing in financially challenged companies. For example, in May 2020, Hertz Global Holdings, filed for Chapter 11 bankruptcy due to the impact of the COVID-19 pandemic on its business, which saw a significant decline in travel demand. During its bankruptcy proceedings, Hertz secured funding from distressed debt investors to support their operations and restructuring efforts. This financing came from a consortium of lenders and institutional investors, providing Hertz with the liquidity needed to continue operations, pay essential expenses, and navigate the bankruptcy process.

Timeless Investing: Optimizing Portfolios through Vintage Year Diversity

What are “Vintage Years”?

“Vintage years” refer to specific time periods during which a fund was raised or initiated. Each vintage year represents a cohort of funds that were raised and deployed within a similar timeframe. These vintage years are often used by investors and analysts to track the performance of funds over time, as funds raised in the same vintage year may encounter similar market conditions and economic environments, which can affect their overall performance and returns.

Mitigating market cycles

Private equity has demonstrated superior performance compared to public equity throughout market cycles. However, returns are subject to fluctuations based on the phase of the business cycle. For instance, if a fund initiates investments during a downturn, it is likely to encounter a broader array of distressed and undervalued assets, with the potential for profitable exits when the market peaks. Conversely, a fund entering the market at its highpoint may face challenges as assets are likely to be expensive and may risk undervaluation upon entering public markets during the exit phase.

Given the unpredictability of market timing, diversification across vintages serves as a strategic approach to dampen this cyclical risk. This approach aims to create a more stable return profile that mirrors the overall characteristics of the asset class.

Establishing a self-sustaining portfolio

Company life cycle.
Self-funding Portfolio
Source: The author

As written above, funds can diversify through various vintages. This strategy allows to generate returns from an earlier vintage, which are reinvested as commitments for a subsequent vintage. In doing so, a self-funding portfolio is cultivated, steadily appreciating in value over time.

Exploring Sectors of Private Equity Investments

In 2023, technology continued to lead private equity investments, capturing a significant 31% share of total investments. Cloud-related ventures, especially enterprise Software as a Service (SaaS), remained appealing, fueled by expectations of sustained growth. Additionally, the rapid adoption of machine learning, driven by global enterprises integrating generative Artificial Intelligence (GenAI) into operations, signaled a broader trend towards innovation.

Consumer-focused investments, accounting for 14% of total investments, saw a focus on low-risk ventures in the food and agribusiness sector. Sustainable farming, combined agriculture, and timber ventures stood out, driven by increasing emphasis on environmental sustainability and responsible resource management.

In addition, financial services (11%) and health sectors (9%) saw significant private equity activity. In finance, investments spanned various subsectors, reflecting a pursuit of diverse opportunities. Meanwhile, health sector niches like enterprise imaging solutions and voice-based diagnostics attracted attention, driven by innovation in medical technology platforms, highlighting the sector’s transformative potential.

PE deals by sector.
Sectoral Share in Private Equity Deal Values
Source: Moonfare

The private equity landscape in 2023 featured a diverse range of investment opportunities, with technology dominating while consumer, financial services, and health sectors also drew significant interest, providing distinct pathways for growth and value generation.

Why should I be interested in this post?

This post offers a comprehensive overview of private equity investing. It defines private equity and explores various investment strategies such as venture capital, growth equity, leveraged buyouts, management buyouts, and distressed capital, providing practical insights into their roles at different stages of a company’s lifecycle. Additionally, the post discusses the concept of vintage years and their significance in tracking fund performance over time, highlighting the importance of portfolio diversification and risk management.

   ▶ Louis DETALLE A quick presentation of the Private Equity field…

   ▶ Louis DETALLE A quick review of the Growth Capital…

   ▶ Louis DETALLE A quick review of the Venture Capitalist’s job…

   ▶ Matisse FOY Key participants in the Private Equity ecosystem

   ▶ Marie POFF Film analysis: The Wolf of Wall Street

Useful resources

Academic References

Martin, J. and R. Manac (2022) Varieties of funds and performance: the case of private equity, The European Journal of Finance, 28(18) 1819–1866.

EVCA (2007) Guide on Private Equity and Venture Capital for Entrepreneurs

Caselli, S. and M. Zava (2022) Private Equity and Venture Capital Markets in Europe

Specialized Press

Investment Strategies in private equity

Barber F. and M. Goold (2023) The strategic secret of private equity Harvard Business Review

Private Equity Pulse: key takeaways from Q4 2023

Financial Times Private Equity

Wall Street Journal Private Equity

About the author

The article was written in February 2024 by Lilian BALLOIS (ESSEC Business School, Bachelor in Business Administration (BBA), 2019-2023).

Extreme correlation

Extreme correlation

Shengyu ZHENG

In this article, Shengyu ZHENG (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2024) explains the concept of extreme correlation.

Background

In financial risk management, there is a concept that is often overlooked, the extreme correlation also known as tail dependence. Tail dependence reveals how extreme events in two variables are linked. The oversight could leave portfolios exposed to amplified risks during market turbulence. In this post, we will get to see the definition and implications of this concept.

Linear correlation and copula

As presented in the post on copula, using linear correlation to model the dependence structure between random variables poses many limitations, and copula is a more generalized tool that allows to capture a fuller picture of the dependence structure.

Let’s recall the definition of copula. A copula, denoted typically as C∶[0,1]d→[0,1] , is a multivariate distribution function whose marginals are uniformly distributed on the unit interval. The parameter d is the number of variables. For a set of random variables U1, …, Ud with cumulative distribution functions F1, …, Fd, the copula function C satisfies:

C(F1(u1),…,Fd(ud)) = ℙ(U1≤u1,…,Ud≤ud)

Here we introduce Student t-copula as an example, which will also be used as an illustration in the part of extreme correlation.

Tail dependence coefficient

The tail dependence coefficient captures the dependence level of a bivariate distribution at its tails. Let’s denote X and Y as two continuous random variables with continuous distribution F and G respectively. The (upper) tail dependence coefficient between X and Y is defined as:

with the limit of λU∈[0,1]

We can conclude that the tail dependence coefficient between two continuous random variables is a copula property, and it remains invariant with strict increasing transformations of the two random variables.

If λU∈(0,1], X and Y are considered asymptotically dependent in their (upper) tail. If λU=0, X and Y are considered asymptotically independent in their (upper) tail.

It is important to note that the independent of X and Y implies that λU=0, but the converse is not necessarily true. λU describes only the dependence level at the tails.

Examples of extreme correlation

Longin and Solnik (2001) and Gkillas and Longin (2019) employ the logistic model for the dependence function of the Gumbel copula (also called the Gumbel-Hougaard copula) for Fréchet margins, as follows:

This model contains the special cases of asymptotic independence and total dependence. It is parsimonious, as we only need one parameter to model the bivariate dependence structure of exceedances, i.e., the dependence parameter α with 0<α≤1. The correlation of exceedances ρ (also called extreme correlation) can be computed from the dependence parameter α of the logistic model as follows: ρ= 1-α^2. The special cases where α is equal to 1 and α converges towards 0 correspond to asymptotic independence, in which ρ is equal to 0, and total dependence, in which ρ is equal to 1, respectively (Tiago de Oliveira, 1973).

Related posts on the SimTrade blog

About extreme value theory

   ▶ Shengyu ZHENG Extreme Value Theory: the Block-Maxima approach and the Peak-Over-Threshold approach

   ▶ Shengyu ZHENG Optimal threshold selection for the peak-over-threshold approach of extreme value theory

   ▶ Gabriel FILJA Application de la théorie des valeurs extrêmes en finance de marchés

Useful resources

Academic resources

Gkillas K. and F. Longin (2018) Is Bitcoin the new digital Gold?, Working paper, ESSEC Business School.

Longin F. (2016) Extreme events in finance: a handbook of extreme value theory and its applications Wiley Editions.

Longin F. and B. Solnik (2001) Extreme Correlation of International Equity Markets, The Journal of Finance, 56, 649-676.

Zeevi A. and R. Mashal (2002) Beyond Correlation: Extreme Co-Movements between Financial Assets. Available at SSRN: https://ssrn.com/abstract=317122

Other resources

Extreme Events in Finance

Rieder H. E. (2014) Extreme Value Theory: A primer (slides).

About the author

The article was written in January 2024 by Shengyu ZHENG (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2024).

Securities and Exchange Board of India (SEBI)

Securities and Exchange Board of India (SEBI)

Nithisha CHALLA

In this article, Nithisha CHALLA (ESSEC Business School, Grande Ecole – Master in Management (MiM), 2021-2024) presents the Securities and Exchange Board of India (SEBI) which is empowering markets and ensuring integrity.

Introduction to SEBI

The Securities and Exchange Board of India (SEBI) serves as a regulator over the country’s financial markets and has a significant impact on how the economy of the country is shaped. Established in 1988, SEBI’s regulatory authority is responsible for a broad range of activities, including promoting open and honest market processes and protecting investors’ rights and interests. Protecting investors’ rights and interests is SEBI’s main goal. Market manipulation, insider trading, and other fraudulent activities are also in the scope of the regulatory authority. Investors receive reliable and timely information to help them make informed decisions thanks to SEBI’s strict standards and requirements for listed companies on Indian exchanges. This emphasis on openness and disclosure encourages investor trust, which increases market activity.

Logo of Securities and Exchange Board of India.  Logo of Securities and Exchange Board of India
Source: SEBI.

Market development and innovation

The purpose of SEBI goes beyond simple regulation; it also actively promotes market expansion and innovation. SEBI has broadened the investment options available to both institutional and individual investors by introducing mutual funds, derivatives, and alternative investment vehicles. These cutting-edge financial products have expanded the investment landscape and drawn institutional investors from abroad, helping India integrate into the world financial markets.

A barrier to malpractices is SEBI’s effective market surveillance systems. To identify and stop market manipulation, SEBI uses an integrated surveillance system to track trade patterns, price changes, and unusual activity. Its ability to punish offenders shows how committed it is to upholding market integrity.

Global Integration and Investor Confidence

Market-friendly policies and international acclaim have been won by SEBI’s regulatory initiatives. Increased foreign direct investment, portfolio investment, and institutional investor activity in Indian markets are the results of this. India’s reputation as a desirable investment location is greatly influenced by SEBI’s role in establishing a favorable investment climate.

While SEBI’s achievements are noteworthy, it faces challenges such as the rapid pace of technological advancements, ensuring effective implementation of regulations, and maintaining a balance between innovation and investor protection. Moreover, as the financial markets evolve, SEBI’s role in regulating emerging areas like cryptocurrencies and digital assets becomes increasingly critical.

Conclusion

The distinctiveness of SEBI rests not only in its ability to regulate, but also in its innovative projects that go beyond conventional regulatory functions. The SEBI stands as a testament to India’s regulatory foresight, from empowering investors through cutting-edge processes to stimulating innovation while safeguarding investor protection. Its dedication to sustainability, education, and technology-driven surveillance distinguishes it as a regulatory pathfinder that keeps up with changes in the financial world.

Why should I be interested in this post?

For a Master in Management student like me, delving into SEBI’s operations provides a real-world context to the theories we study. Understanding SEBI’s unique initiatives, such as the Regulatory Sandbox (a framework that allows businesses, especially in the financial technology sector, to test innovative products, services, business models in a controlled environment) and its emphasis on sustainability, offers insights into modern regulatory challenges and innovative solutions. Exploring SEBI’s role in investor protection and market integrity enhances my grasp of ethical governance and responsible business practices. SEBI’s dynamic approach aligns with the multidisciplinary nature of my studies, allowing me to connect theoretical knowledge with practical implications in the financial world.

Related posts on the SimTrade blog

   ▶ All posts about financial techniques

   ▶ Akshit GUPTA Securities and Exchange Commission (SEC)

   ▶ Akshit GUPTA Autorité des Marchés Financiers (AMF)

Useful resources

SEBI What’s new in SEBI?

About the author

The article was written in January 2024 by Nithisha CHALLA (ESSEC Business School, Grande Ecole – Master in Management, 2021-2024).

Trading strategies based on market profiles and volume profiles

Trading strategies based on market profiles and volume profiles

Michel Henry VERHASSELT

In this third article on a series on market profiles, Michel Henry VERHASSELT (ESSEC Business School – Master in Finance, 2023-2025) explains trading strategies based on market profiles and volume profiles.

Introduction

We have defined and seen illustrations of all the key concepts related to both market profiles and volume profiles. Let us now look at their practical applications and trading strategies that may be applied.

These techniques apply to both market profiles and volume profiles.

Mean reversion

A mean reversion strategy is a trading approach based on the idea that prices tend to revert to their historical average or mean over time. Traders employing this strategy look for opportunities to enter trades when prices deviate significantly from their historical average, anticipating a return to the mean.

Market profiles naturally fit this kind of strategy, as their whole point is to show where participants have deemed the price to be fair. For example, a trader could consider that when the price is trading below a high-volume area, that area will act as a magnet to pull the price up. The prices in that region were indeed considered fairer, and the current low price would be an anomaly to be corrected by market participants. Therefore, the trader would buy at the current price and sell around the POC or at least within the value area.

Resistance and support

Conversely, a different interpretation within the same framework involves viewing these highly-traded areas as potential resistance or support zones. Support is a crucial level preventing an asset from further decline, often due to an upsurge in buying interest. In contrast, resistance is a pivotal level inhibiting an asset from rising higher, typically caused by intensified selling activity.

For a trader emphasizing resistance and support concepts, consider a rising price nearing a heavily traded zone encountering resistance, similar to reaching a ceiling. The outcome may lead to either a breakout to new highs or a reversal downward. In this context, the value area is not seen as a magnetic force drawing prices toward fair value; instead, it functions as a testing ground. The result hinges on whether the attempt to breach resistance is rejected, leading to a lower price, or successful, resulting in an upward move past this pivotal point. This dynamic interaction adds layers of complexity to mean reversion and support/resistance strategies within the realm of market profiles.

Entries and exits

More generally, traders employ various tools to make well-informed decisions about when to enter or exit market positions. One such powerful tool is the market profile. Even if a trader’s primary strategy relies on other triggers to look at a trade, say for example macro events, they can still leverage market profiles. These profiles help determine optimal entry or exit points, considering factors like obtaining liquidity with minimal market impact and identifying levels for stop losses and target profits based on perceived resistance and support.

Breakouts

As mentioned above, breakout trading is a strategy employed in financial markets where traders capitalize on significant price movements beyond established levels of support or resistance. In a breakout, the price surpasses a predefined range or pattern, triggering potential buying or selling signals. Traders often interpret breakouts as indicators of strong momentum, with the expectation that the price will continue moving in the breakout direction. The aim of breakout trading is to enter positions early in a new trend and ride the momentum for profitable gains.

Market profile can help identify breakout opportunities. For example, when a market exhibits confined trading within a narrow range and the profile reveals an accumulation of TPOs (Time Price Opportunities) near the boundaries of this range, a breakout surpassing these levels could indicate a potential trading opportunity.

False breakout strategy

The false breakout trading strategy relies on discerning instances where the price briefly moves beyond a trading range but subsequently retraces, indicating potential weaknesses in the current trend. In a false bullish breakout, signaling buyers’ weakness, traders might opt for short positions. Conversely, in retraced bearish breakouts, suggesting sellers’ uncertainty, opportunities for long positions may emerge. The effectiveness of this strategy lies in recognizing imbalances in supply and demand, a task facilitated by market profiles.

Market profiles offer a nuanced visual representation of price movements over time, highlighting areas of significant trading activity and the distribution of volume at different price levels. This information aids traders in identifying potential entry and exit points more precisely. By integrating market profiles into the false breakout strategy, traders gain insights into the dynamics of supply and demand within specific price ranges. This, in turn, enhances their ability to navigate market sentiment shifts and make informed decisions, contributing to the overall effectiveness of the false breakout trading strategy.

Single prints

The Market Profile Single Print strategy is a dynamic approach leveraging the unique concept of single prints within the Market Profile chart to identify potential breakout opportunities.

The strategy’s foundation lies in identifying single prints—instances where a price level remains untouched throughout the trading session, creating a gap in the Market Profile chart. Price can often revisit these areas to test these inefficiencies. These single prints therefore act as crucial markers, indicating potential areas of support or resistance. The significance of this lies in the ability to pinpoint breakout levels: a break above a single print suggests a bullish breakout, while a break below indicates a bearish breakout.

Crucially, market profiles assist in managing risk effectively by providing a visual representation of potential areas of support or resistance. Continual monitoring of the trade is emphasized, with adjustments made based on evolving market conditions. Trailing stop-loss orders are recommended to protect profits as the trade progresses favorably.

Related posts on the SimTrade blog

   ▶ Michel VERHASSELT Market profiles

   ▶ Michel VERHASSELT Difference between market profiles and volume profiles

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

   ▶ Theo SCHWERTLE The Psychology of Trading

   ▶ Clara PINTO Strategy and Tactics: From military to trading

Useful resources

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

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

About the author

The article was written in December 2023 by Michel Henry VERHASSELT (ESSEC Business School – Master in Finance, 2023-2025).

Difference between market profiles and volume profiles

Difference between market profiles and volume profiles

Michel Henry VERHASSELT

In this second article on a series on market profiles, Michel Henry VERHASSELT (ESSEC Business School – Master in Finance, 2023-2025) explains the difference between market profiles and volume profiles.

Comparison

Both Market Profiles and Volume Profiles follow the auction theory of markets. According to this theory, price, time and volume are the three processes through which trading takes place.

More exactly:

  • Price advertises all opportunities. It lets the participants know that they can buy or sell an asset at a given price; it tells them what their opportunities are.
  • Time regulates all opportunities. Indeed, the opportunities given by price are limited in time; they are ephemeral and depend on the liquidity and volatility of an asset, in other words, how much time it takes for the price to change and the opportunity to vanish.
  • Volume measures the success or failure of advertised opportunities. Volume reflects the degree of market participation and validates the relevance of the opportunities presented. If an opportunity is advertised and becomes successful that means many participants agree on the fairness of this opportunity and a relatively significant amount of trading activity (volume) takes place at this price. A price that is not accepted over time is, in fact, rejected: the advertisement has failed.

All traders feel the pressure of time ticking away during a trade. When a trade stalls and doesn’t go as expected, it can create doubts, especially the longer it remains stagnant. The constant tick of the clock forces traders to ponder what might be going wrong. For instance, the late liquidation or short-covering rally in the pit session may be due to day traders running out of time rather than a lack of trading volume. In that sense, volume must take place within a given time range to validate the price advertisement.

Now when it comes to Volume Profiles, the chart shows the distribution of volume at different price levels, kind of like a visual map of where the action is happening. It uses a vertical histogram to make it easy for traders to see where the most trading activity is concentrated. This charting tool is all about giving traders a closer look at how much trading is going on at different price points over time.

Comparing Volume Profile to Market Profile, we find three key areas of differences: analytical focus, representation of data, and time and price dynamics.

Analytical Focus

Volume Profile: As the name suggests, Volume Profile places a paramount emphasis on volume, aiming to dissect the distribution of trading activity at different price levels over a designated timeframe.

Market Profile: In contrast, Market Profile combines time and price to create a graphical representation of market behavior. It divides price movements into designated time segments, typically 30-minute intervals, offering a nuanced perspective on the interplay between time and price.

Representation of Data

Volume Profile: The chart generated by Volume Profile provides a clear visualization of how volume is distributed across various price levels, offering insights into where significant buying or selling activity is concentrated.

Market Profile: While also representing volume, Market Profile charts use letters (TPOs) to signify the time spent at specific price levels, creating a distinctive visual pattern resembling a probability distribution.

Time and Price Dynamics

Volume Profile: Its primary concern is the interrelation of volume and price, with a focus on understanding the significance of different price levels based on the amount of trading activity.

Market Profile: Integrates time as a crucial factor, providing traders with a holistic view of market behavior over specific time intervals. This temporal dimension aids in identifying periods of heightened activity and potential areas of interest.

Let’s now look at Market and Volume profiles graphs.

Illustration

The figure below is taken from Steidlmayer’s main work: “Steidlmayer on Markets, Trading with Market Profile”. Each letter (A, B, C, D, etc.) corresponds to a single timeframe of 30 minutes. The condensed triangle-shaped figure shows where price has moved throughout the entire time period according to the trading activity.

Market profile.
Market profile
Source: Steidlmayer’s book “Steidlmayer on Markets, Trading with Market Profile”.

If we rotate the figure, we get a bell-shaped pattern that looks like a normal distribution.

Market profile (reversed presentation).
Market profile
Source: Steidlmayer’s book “Steidlmayer on Markets, Trading with Market Profile”.

The price distribution in a Market Profile tends to exhibit a bell-shaped pattern due to the nature of market dynamics and participant behavior. In a well-functioning and liquid market, prices are subject to constant fluctuations driven by the interplay of buying and selling activities and the bell-shaped distribution is simply a reflection of the statistical tendency of prices to cluster around a central point. The majority of trading activity should in theory occur around a fair or equilibrium price. As you move away from this central point, the occurrences of extreme price levels decrease, forming the characteristic bell curve. It is a visual representation of the market’s natural inclination to spend more time around prices that are deemed fair.

The figure below represents the volume profiles of the BTC/USDT pair on Binance’s futures market from December 8 until December 15, 2023.

Volume profile.
Volume profile
Source: exocharts.com.

We see the point of control (POC) that corresponds to the most traded price as a red line extending through the volume profile of each day. The value area is marked both by a whiter grey and dotted lines. The current price is a green line on the far left. On the far right, we find the volume profile for the whole timeframe displayed on the screen, with its own value area and point of control.

While the two profiles are very similar, however instead of looking at price and time as in a market profile, the volume profile focuses on volume. First, the volume profile is indifferent to when exactly a given trade took place within the same timeframe, here a day. Second, the volume profile uses true volume data rather than simply whether or not a trade took place. The length of each bar within a volume profile is directly proportionate to the volume of the trades at that price. In contrast, the market profile does not show the size of the trades but simply shows whether or not a price was traded during a 30-minute period, and then aggregates (or “collapses”) the data to form one profile, as we saw in the bell-shaped curve above.

Why should I be interested in this post?

Students of finance interested in financial markets and trading would be the target audience of this post. I believe this technique to be relatively obscure despite its long history. We rarely see asset charts displayed as histograms as an effort to understand market behavior and participant psychology. I believe it is fundamental to consider that the market is made up of human actors, that these actors have their biases on price and value, and in turn that these biases’ success is represented as a function of volume. Even if a student does not subscribe to this understanding of markets, it would broaden his/her perspective and allow him/her to understand trading more generally.

Related posts on the SimTrade blog

   ▶ Michel VERHASSELT Market profiles

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

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

   ▶ Theo SCHWERTLE The Psychology of Trading

   ▶ Clara PINTO Strategy and Tactics: From military to trading

Useful resources

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

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

TPO versus Volume Profiles

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

About the author

The article was written in December 2023 by Michel Henry VERHASSELT (ESSEC Business School – Master in Finance, 2023-2025).

Market profiles

Market profiles

Michel Henry VERHASSELT

In this first article on a series on market profiles, Michel Henry VERHASSELT (ESSEC Business School – Master in Finance, 2023-2025) explains the history behind this concept and defines its central themes.

Introduction

The concept of Market Profiles emerged as a response to the dynamic nature of financial markets, where prices are in constant flux due to the continuous flow of information. Peter Steidlmayer, a trader at the Chicago Board of Trade during the 1960s and 1970s, sought to develop a charting method that could capture the interplay between price and volume, reflecting the idea that, despite the constant price changes, there should be a fair value around which prices revolve at any given time.

In traditional charting methods like bar charts and candle charts, the emphasis is typically on plotting price against time. Steidlmayer, however, wanted to make volume immediately apparent on the chart. This emphasis on volume is crucial because it provides insights into the level of participation and conviction among market participants.

The development of Market Profile was influenced by various theories and disciplines. In particular, it drew inspiration from the concept of value investing articulated by Benjamin Graham and David Dodd, the statistical bell curve, and John Schultz’s work on minimum trend. By combining these influences, Steidlmayer aimed to create a charting technique that would not only reveal price movements but also offer a visual representation of the market’s perception of value.

Market Profile, as a charting technique, differs significantly from traditional methods. Instead of using standard bar charts with prices plotted against time, Market Profile organizes data in a way that reflects the distribution of prices at different levels. Each time period is represented by a separate column, with prices displayed in ascending order on the vertical axis. This organization provides a visual representation of how much time the market spent at different price levels, creating a histogram-like structure.

The resulting chart, with letters (A, B, C, D, etc.) representing Time Price Opportunities (TPO), helps traders identify key areas such as the Value Area (where the majority of trading activity occurred), the Point of Control (the most traded price level), and Single Prints (indicating areas of price discovery). These elements collectively contribute to a comprehensive understanding of market dynamics and help traders make more informed decisions.

Definitions

We define below the key terms to understand Market Profile: Volume, Value Area, and Point of Control.

Volume

Volume in the context of financial markets refers to the number of contracts or shares traded at during a specific time period. Volume is a crucial component in Market Profile analysis because it provides insights into the level of participation and conviction among market participants. High volume at a particular price level suggests a significant level of interest or agreement on the value of the asset at that point.

Volume helps us shape the Time Price Opportunities. A TPO represents a unit of time and price on a Market Profile chart. Each 30-minute period (or another specified time frame) is represented by a letter, forming a vertical histogram on the price axis. TPOs help visualize the distribution of trading activity at different price levels over time. By organizing price data into these time brackets, traders can identify patterns, trends, and areas of importance, contributing to a better understanding of market behavior.

Value Area

The Value Area represents the range of price levels that contain a specific percentage of the total traded volume (usually 70% of the day’s trading activity). Traders also use the Upper Value Area (where 15% of the volume is located above) and the Lower Value Area (where 15% of the volume is below), with the area in between considered the “fair value” zone. It helps traders identify the price levels that are deemed fair by the market. It provides insights into where the majority of trading activity occurred, offering potential support and resistance zones for future price movements.

Point of Control

Within the value area, we find the Point of Control. The Point of Control is the price level at which the most TPOs occurred during a specific time period. It is considered a point of balance and represents the price where the market found the most acceptance. It indicates the price level that had the most trading activity, suggesting a level of equilibrium where buyers and sellers found agreement. Traders often monitor the POC for potential shifts in market sentiment.

By understanding the interplay between these elements, traders can gain valuable insights into market dynamics, identify key support and resistance zones, and make more informed decisions in their trading strategies.

With this background and definitions, we can look further into the practice of market profiles and its closely related concept, volume profiles.

Why should I be interested in this post?

Students of finance interested in financial markets and trading would be the target audience of this post. I believe this technique to be relatively obscure despite its long history. We rarely see asset charts displayed as histograms as an effort to understand market behavior and participant psychology. I believe it is fundamental to consider that the market is made up of human actors, that these actors have their biases on price and value, and in turn that these biases’ success is represented as a function of volume. Even if a student does not subscribe to this understanding of markets, it would broaden his/her perspective and allow him/her to understand trading more generally.

Related posts on the SimTrade blog

   ▶ Michel VERHASSELT Difference between market profiles and volume profiles

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

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

   ▶ Theo SCHWERTLE The Psychology of Trading

   ▶ Clara PINTO Strategy and Tactics: From military to trading

Useful resources

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

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

Letian Wang (2020) Using Python for Market Profiles

About the author

The article was written in December 2023 by Michel Henry VERHASSELT (ESSEC Business School – Master in Finance, 2023-2025).

Impact du contrôle de gestion sur l’entreprise

Impact du contrôle de gestion sur l’entreprise

Medine ACAR

Dans cet article, Medine ACAR (ESSEC Business School, Programme Bachelor in Business Administration (BBA), 2020-2024) analyse l’impact du contrôle de gestion dans l’entreprise.

Introduction

Le contrôle de gestion est une fonction clé en entreprise, axée sur la performance et l’efficacité. Il implique la planification, la mesure et l’analyse des activités pour aligner les performances avec les objectifs stratégiques de l’entreprise. Ce processus inclut la budgétisation, la prévision financière, et l’analyse des écarts entre les résultats réels et les prévisions. Le contrôle de gestion aide également à identifier les opportunités d’amélioration et à mettre en œuvre des stratégies correctives pour optimiser les opérations et les coûts. Entre autres, le contrôle de gestion assure la santé et la viabilité des entreprises. Allons plus loin.

Amélioration de la Performance et de la Prise de Décision

Le contrôle de gestion, au cœur des stratégies d’entreprise, joue un rôle déterminant dans l’analyse et l’amélioration des performances financières. Il offre une perspective claire sur les forces et faiblesses de l’organisation, permettant ainsi une prise de décision plus stratégique et éclairée. Des études de cas dans divers secteurs, telles que celles menées sur des entreprises comme IBM ou General Electric, illustrent comment l’application rigoureuse du contrôle de gestion peut entraîner une transformation significative dans la performance et la gestion des ressources. Par exemple, l’implémentation par GE des pratiques « Six Sigma » et de gestion Lean sous la direction de Jack Welch a conduit à des améliorations substantielles de l’efficacité opérationnelle et de la réduction des coûts. (Etude de cas: General Electric’s Two-Decade Transformation Under the Leadership of Jack Welch).

Gestion des Risques et Assise de la Durabilité

Au-delà de la simple surveillance financière, le contrôle de gestion est essentiel pour la gestion des risques et la durabilité à long terme de l’entreprise. Il permet d’identifier les risques potentiels, tant financiers qu’opérationnels, et de mettre en place des stratégies pour les atténuer. Des recherches menées dans le domaine bancaire, par exemple, mettent en lumière l’importance de cette fonction pour prévenir les crises financières et assurer une stabilité continue.

L’étude “Management controls and crisis: evidence from the banking sector” menée par Pall Rikhardsson, Carsten Rohde, Leif Christensen, Catherine E. Batt en 2021, sur l’utilisation des contrôles de gestion lors de la crise financière de 2008 dans six banques a révélé que l’emploi à la fois de contrôles de gestion organiques et mécanistes était essentiel pour gérer le changement.

Ces contrôles jouent trois rôles principaux :

  • Guider et contrôler le comportement
  • Changer les perceptions internes et externes
  • Assurer la responsabilité.

Résumé

Le contrôle de gestion n’est pas seulement un outil de surveillance financière ; c’est un levier stratégique qui influence profondément la performance, la prise de décision, la gestion des risques et, en fin de compte, la durabilité de l’entreprise. Les études dans ce domaine confirment son rôle inestimable dans le succès et la pérennité des entreprises à travers le monde.

Autres articles sur le blog

   ▶ Jessica BAOUNON Enjeux de la pratique de la pleine conscience et de l’intelligence émotionnelle dans la fonction de contrôle de gestion

   ▶ Chloé POUZOL Contrôle de gestion chez Edgar suites

   ▶ Emma LAFARGUE Contrôle de gestion chez Chanel

Ressources utiles

Robert Obert et Marie-Pierre Mairesse (2008) “Le Contrôle de Gestion: Organisation et Mise en Œuvre”, Dunod.

Case Study: General Electric’s Two-Decade Transformation Under the Leadership of Jack Welch

6 sigma (2017) General Electric (GE) et Six Sigma

Henderson, K.M. and Evans, J.R. (2000) “Successful implementation of Six Sigma: benchmarking General Electric Company”, Benchmarking: An International Journal, 7(4): 260-282.

Karim Saïd and Soufiane Kherrazi (2021) Du contrôle de gestion à l’innovation dans le contrôle HBR France

Rikhardsson, P., Rohde, C., Christensen, L. et Batt, C.E. (2021) “Management controls and crisis: evidence from the banking sector” Accounting, Auditing & Accountability Journal, 34(4): 757-785.

A propos de l’auteure

L’article a été rédigé en décembre 2023 par Medine ACAR (ESSEC Business School, Programme Bachelor in Business Administration (BBA), 2020-2024).

Volume-Weighted Average Price (VWAP)

Volume-Weighted Average Price (VWAP)

Raphael TRAEN

In this article , Raphael TRAEN (ESSEC Business School, Global BBA, 2023-2024) explains about the Volume-Weighted Average Price (abbreviated as VWAP), a statistic used by traders to determine the average trading taking into account transaction volume.

Definition

The volume-weighted average price (VWAP) is a measurement that shows the average price of a security, adjusted for its volume. It is calculated during a specific trading session by taking the total dollar value of trading in the security (sum of the products of the price by the quantity of each trade during the trading session) and dividing it by the total volume of trades (sum of the quantities of each trade during the trading session). The formula for calculating VWAP is given by

Formula VWAP

Where N is the number of transactions during the trading session (trading day).

VWAP can also be computed for consecutive time intervals during the trading sessions.

Sometimes, the price is replaced by a “typical price” computed as the average of the minimal price, maximal price, and closing price observe over a time interval.

Typical price

Interpreting the VWAP indicator / Key takeaways

Volume-weighted average price (VWAP) is a popular technical indicator used by traders and investors to identify trends, support and resistance levels, and potential entry and exit points. It can also be used for example to assess the liquidity and market depth of a security. If the VWAP is closely clustered around the current price, it suggests that there is a lot of liquidity and that the market is well-balanced. If the VWAP is spread out over a wide range of prices, it suggests that the market is less liquid and that there is a higher risk of wide price swings.

Breakout above the VWAP line suggests a bullish trend

A breakout above VWAP suggests that the price has momentum and is moving upwards. This could be due to increased buying pressure from investors, indicating a shift in sentiment towards the security. Once the price breaks above VWAP, it can act as a support level, making it more difficult for the price to fall below that level.

This could be an opportunity to enter a long position, anticipating the price to continue rising.

Breakdown below the VWAP line suggests a bearish trend

If the price of a security breaks below the VWAP line, it may signal a potential bearish trend. This could be an opportunity to enter a short position, anticipating the price to continue falling.

VWAP line can act as support or resistance level

The VWAP line can also function as a support or resistance level, representing a price range where the price of the security may tend to bounce off.

VWAP to identify trends

If the VWAP line is trending upwards, it suggests an overall upward trend in the price of the security. This could indicate favorable conditions for long-term investments. Conversely, if the VWAP line is trending downwards, it suggests an overall downward trend in the price of the security. This could indicate caution for long-term investments.

Conclusion

It is important to note that VWAP is just one indicator, and it should not be used in isolation. It is always a good idea to consider other technical indicators, such as the moving average convergence divergence (MACD) and the relative strength index (RSI), before making any trading decisions.

Often, multiple interpretations are possible and because of this, it is important to use the VWAP in combination with other indicators.

As I said, a breakdown below the VWAP may suggest a bearish trend. But it can also be interpreted as the following: Stocks with prices below the VWAP are considered as undervalued and those with prices above it, overvalued.

So while some institutions may prefer to buy when the price of the security is below the VWAP or sell when it is above, VWAP is not the only factor to consider. In strong uptrends, the price may continue to move higher for many days without dropping below the VWAP at all. Therefore, waiting for the price to fall below the VWAP could mean a missed opportunity if prices are rising quickly.

Why should I be interested in this post?

This article will provide students interested in business and finance a comprehensive overview of VWAP and how it is used by traders and investors. By understanding this fundamental concept in technical analysis, students will gain a valuable tool for making informed investment decisions.

Related posts on the SimTrade blog

   ▶ Shruti CHAND Technical analysis

   ▶ Shruti CHAND Technical Analysis, Moving Averages

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

   ▶ Giovanni PAGLIARDI Tail relation between return and volume

Useful resources

Academic articles

Menkhoff, L. (2010) The use of technical analysis by fund managers: International evidence, Journal of Banking & Finance 34(11): 2573-2586.

Kirkpatrick II, C. D., and J.R. Dahlquist (2010) Technical Analysis: The Complete Resource for Financial Market Technicians. FT press.

Videos

Humbled Trader VWAP Trading Strategy Crash Course (YouTube video)

MHFIN VWAP Explained For Beginners In Under 5 Minutes (YouTube video)

About the author

The article was written in December 2023 by Raphael TRAEN (ESSEC Business School, Global BBA, 2023-2024).

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

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

Raphael TRAEN

In this article, Raphael TRAEN (ESSEC Business School, Global BBA, 2023-2024) delves into the fascinating world of correlation and its profound impact on diversification strategies in the financial realm. Understanding correlation is crucial for crafting well-diversified investment portfolios that can effectively mitigate risk and enhance overall performance (the famous trade-off between risk and expected return).

Statistical correlation

Definition

Statistical correlation is a quantitative measure of the strength and direction of the linear relationship between two variables. It describes how two variables are related to each other and how one variable changes in response to the other (but remember that correlation is not causality!).

Mathematically (or more precisely statistically), correlation is defined by the following formula:

Correlation formula

where ρ1,2 is the correlation coefficient between the two random variables (say X1 and X2), 𝜎1,2 the covariance between the two random variables, and 𝜎1 and 𝜎2 are the standard deviation of each random variable.

Correlation is measured on a scale from -1 to +1, with -1 representing a perfect negative correlation, +1 representing a perfect positive correlation, and 0 representing no correlation.

Correlation vs Independence

Correlation and independence are two statistical measures that describe the relationship between two variables. As already mentioned, correlation quantifies the strength and direction of the relationship, ranging from perfect negative (one variable decreases as the other increases) to perfect positive (both variables increase or decrease together). Independence on the other hand indicates the absence of any consistent relationship between the variables.

If two random variables are independent, their correlation is equal to zero. But if the correlation between two random variables is equal to zero, it does not necessarily mean that they are independent. This can be illustrated with an example. Let us consider two random variables, X and Y, defined as follows: X is a random variable that takes discrete values from the set {-1, 0, 1} with equal probability (1/3) and Y is defined as Y = X2.

E(X) = 0, as the expected value of X is (1 + 0 + (-1))/3 = 0
E(Y) = E(X2) = (12 + 02 + (-1)2)/3 = 2/3
E(XY) = (-1 * 1 + 0 * 0 + 1 * 1)/3 = 0

Cov(X, Y) = E(XY) – E(X)E(Y) = 0 – 0 * (2/3) = 0

As Corr(X, Y) is equal to Cov(X, Y) / (sqrt(Var(X)) * sqrt(Var(Y))), we find that Corr(X, Y) = 0.

Application in finance

We now consider a financial application : the construction of portfolios. We show that correlation is a key input when building portfolios.

If the concept of portfolios is completely new to you, I recommend first reading through the article by Youssef LOURAOUI about Portfolio.

Portfolio with two assets

In the world of investments, understanding the expected return and variance of a portfolio is crucial for informed decision-making. These two statistical measures provide valuable insights into the potential performance and risk of a collection of assets held together. In what follows, we first focus on a portfolio consisting of two assets.

Return and expected return of a portfolio

The return of a two-asset portfolio P is computed as

Return two assets

where w1 and w2 are the weights of the two assets in the portfolio and R1 and R2 are the returns of the two assets.

The expected return of the two-asset portfolio P is computed as

Expected return two assets

where w1 and w2 are the weights of the two assets in the portfolio and μ1 and μ2 are the expected returns of the two assets.

Risk of a portfolio

The standard deviation (squared root of the variance) of a two-asset portfolio is computed as

Standard deviation of the return of a two-asset portfolio

or

Standard deviation of the return of a two-asset portfolio

where w1 and w2 are the weights of the two assets in the portfolio, 𝜎1 and 𝜎2 are the standard deviations of the returns of the two assets, and 𝜎1,2 and ρ1,2 are the covariance and correlation coefficient between the two assets returns.

The first expression uses the covariance 𝜎1,2 and the second expression the correlation ρ1,2.

Impact of correlation on diversification (the case of two assets)

From the above formulas follows a very interesting theorem called the “Diversification effect” which says the following: with two assets, suppose the weights of both securities are positive. As long as the correlation coefficient is less than 1, the standard deviation of a portfolio of two securities is less than the weighted average of the standard deviation deviations of the individual securities. Investors can obtain the same level of expected return with lower risk.

The figures below illustrate the impact of the correlation between the two assets on portfolio diversification and the efficient portfolio frontier. For a given level of portfolio risk, the lower the correlation, the higher the expected return of the portfolio.

Impact of the correlation on portfolio diversification

Impact of the correlation on portfolio diversification

Impact of the correlation on portfolio diversification

Impact of the correlation on portfolio diversification

Impact of the correlation on portfolio diversification

You can download below an Excel file (from Prof. Longin’s course) that illustrates the impact of correlation on portfolio diversification.

Excel file on impact of correlation

Diversification effect (extension to several assets)

With many assets, suppose the weights of all securities are positive. As long as the correlations between pairs of securities are less than 1, the standard deviation of a portfolio of many assets is less than the weighted average of the standard deviations of the individual securities.

Why should I be interested in this post?

Understanding correlation is an essential skill for any investor seeking to build a well-diversified portfolio that can withstand market volatility and achieve long-term growth. By carefully analyzing correlation dynamics and incorporating correlation analysis into their investment strategies, investors can effectively manage risk exposure and build resilient portfolios that can weather market storms and emerge stronger on the other side.

Related posts on the SimTrade blog

   ▶ Youssef LOURAOUI Portfolio

   ▶ Jayati WALIA Standard deviation

   ▶ Youssef LOURAOUI Hedge fund diversification

   ▶ Lou PERRONE Navigating the Balance Between Risk and Reward in Finance

Useful resources

Prof. Longin’s ESSEC Master in Management “Fundamentals of finance” course.

William Pouder’s ESSEC BBA “Finance” course.

About the author

The article was written in December 2023 by Raphael TRAEN (ESSEC Business School, Global BBA, 2023-2024).

Ethereum – Unleashing Blockchain Innovation

Ethereum – Unleashing Blockchain Innovation

 Snehasish CHINARA

In this article, Snehasish CHINARA (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2024) explains Bitcoin which is considered as the mother of all cryptocurrencies.

Historical context and background

Ethereum is a groundbreaking blockchain platform that emerged in the wake of Bitcoin’s success in 2015. While Bitcoin introduced and popularized the blockchain concept, Ethereum has leveraged this technology more effectively than any other digital currency. Promoters of new projects tend to rely on Ethereum’s tools rather than embark on the lengthy and expensive process of developing a new blockchain. Ethereum was conceived by a young Canadian programmer, Vitalik Buterin, who saw limitations in Bitcoin’s functionality and envisioned a decentralized platform capable of executing smart contracts. Buterin’s idea gained traction in the cryptocurrency community, and he, along with a team of developers, published the Ethereum whitepaper in late 2013. The platform’s official development began in 2014, with a crowdfunding campaign that raised over $18 million in Bitcoin, making it one of the most successful initial coin offerings (ICOs) of its time. Ethereum’s genesis block was mined on July 30, 2015, marking the official launch of the network.

Ethereum’s innovative concept of smart contracts and decentralized applications (DApps) quickly garnered attention within the blockchain and cryptocurrency space. The platform introduced a Turing-complete programming language, enabling developers to create a wide array of decentralized applications. Ethereum’s native cryptocurrency, Ether (ETH), serves as both a digital currency and a utility token within the ecosystem. Over the years, Ethereum has undergone several network upgrades to improve scalability and security, most notably the transition from a proof-of-work (PoW) to a proof-of-stake (PoS) consensus mechanism with the Ethereum 2.0 upgrade. This transition aims to address the network’s scalability issues and reduce its energy consumption, positioning Ethereum as a sustainable and versatile blockchain platform for the future. Today, Ethereum continues to play a pivotal role in the blockchain and decentralized finance (DeFi) space, powering a vast array of projects, including NFT platforms, decentralized exchanges, and decentralized applications that have reshaped the way we think about finance and technology.

Ethereum Logo
Ethereum Logo
Source: Yahoo! Finance .

Figure 1. Key Dates in Ethereum History
 Key Dates in Ethereum History
Source: Yahoo! Finance .

Key Features of Ethereum

Smart Contracts

Ethereum is renowned for its pioneering smart contract functionality. Smart contracts are self-executing agreements with predefined rules and conditions, enabling automated and trustless transactions. This feature has broad applications in various industries, including finance, supply chain management, and legal services.

Decentralization

Ethereum operates on a decentralized network of nodes, making it resistant to censorship and single points of failure. This decentralization ensures the security and integrity of the blockchain, with no single entity having control over the network.

Ether (ETH)

Ethereum’s native cryptocurrency, Ether, serves as both a digital currency and a utility token. It’s used to pay for transaction fees, secure the network through staking in Ethereum 2.0, and as a medium of exchange within the ecosystem.

Interoperability

Ethereum is designed to interact with other blockchains and networks, fostering compatibility and collaboration across the blockchain ecosystem. Projects like Polkadot and Cosmos aim to enhance this interoperability.

EVM (Ethereum Virtual Machine)

The Ethereum Virtual Machine is a runtime environment for executing smart contracts. It’s a critical component that ensures the same execution of smart contracts across all Ethereum nodes, making Ethereum’s ecosystem reliable and consistent.

EIPs (Ethereum Improvement Proposals)

Ethereum has a robust governance model for protocol upgrades and improvements, with EIPs serving as the mechanism for proposing and implementing changes. This allows for community-driven innovation and adaptation.

Use Cases of Ethereum

Decentralized Finance (DeFi)

Ethereum is at the heart of the DeFi movement, offering lending, borrowing, trading, and yield farming services through DApps like Compound, Aave, and Uniswap. DeFi has disrupted traditional finance, providing open and inclusive access to financial services.

Non-Fungible Tokens (NFTs)

Ethereum’s ERC-721 and ERC-1155 token standards have fueled the NFT boom. NFTs enable the ownership and trade of unique digital assets, from art and music to virtual real estate and collectibles, all recorded on the blockchain.

Supply Chain Management

Ethereum’s transparent and tamper-proof ledger is used to track and verify the authenticity and provenance of products. This enhances supply chain efficiency and trust, reducing fraud and counterfeiting.

Gaming and Virtual Worlds

Ethereum is the platform of choice for blockchain-based gaming and virtual reality experiences. DApps like Decentraland and Axie Infinity allow users to trade in-game assets and participate in virtual economies.

Tokenization of Assets

Real-world assets, such as real estate, stocks, and commodities, can be tokenized on the Ethereum blockchain, making them more accessible for investment and trading.

Identity Verification

Ethereum can be used to secure and manage digital identities, enhancing privacy and reducing the risk of identity theft.

Social Impact

Ethereum is leveraged for social impact projects, including humanitarian aid distribution, voting systems, and tracking philanthropic donations, ensuring transparency and accountability.

Content Distribution

Ethereum-based projects are exploring decentralized content platforms, enabling creators to have more control over their intellectual property and revenue.

Ethereum’s versatility and ongoing development make it a crucial platform for a wide range of applications, from financial innovation to social change and beyond, driving the evolution of the blockchain and cryptocurrency space.

Technology and underlying blockchain

Ethereum’s underlying technology is rooted in blockchain, a distributed ledger system known for its security, transparency, and decentralization. Ethereum, like Bitcoin, employs a blockchain to record and verify transactions, but it offers a distinct set of features and capabilities that set it apart. At the core of Ethereum’s technology is the Ethereum Virtual Machine (EVM), a decentralized computing environment that executes smart contracts. Smart contracts are self-executing agreements with predefined rules and conditions that automate processes without the need for intermediaries.

Ethereum uses a consensus mechanism known as Proof of Stake (PoS), which is a significant departure from Bitcoin’s Proof of Work (PoW). PoS allows network participants, known as validators, to create new blocks and secure the network by locking up a certain amount of Ether as collateral. This approach is more energy-efficient and scalable compared to PoW, addressing some of the limitations that Bitcoin faces. Ethereum’s blockchain is a public and permissionless network, meaning that anyone can participate, transact, and develop decentralized applications (DApps) on the platform without needing approval.

The Ethereum ecosystem also employs a variety of token standards, with ERC-20 and ERC-721 being the most well-known. ERC-20 tokens are fungible and often used for cryptocurrencies, while ERC-721 tokens are non-fungible and have powered the explosion of NFTs (Non-Fungible Tokens). These standards have facilitated the creation and interoperability of a vast array of digital assets and DApps on the platform. Ethereum’s robust governance model, through Ethereum Improvement Proposals (EIPs), allows the community to suggest and implement changes, ensuring that the platform remains adaptable and responsive to evolving needs and challenges. Ethereum’s groundbreaking technology and active development community have positioned it as a leader in the blockchain space, with far-reaching implications for industries beyond just cryptocurrencies.

Supply of coins

Ethereum initially used a proof-of-work (PoW) consensus algorithm for coin mining, similar to Bitcoin. The process involved miners solving complex mathematical puzzles to validate transactions and add new blocks to the blockchain. Miners competed to solve these puzzles, and the first one to succeed was rewarded with newly minted Ethereum coins (ETH). This process was resource-intensive and required significant computational power.

However, Ethereum has been undergoing a transition to a proof-of-stake (PoS) consensus mechanism as part of its Ethereum 2.0 upgrade. The PoS model doesn’t rely on miners solving computational puzzles but instead relies on validators who lock up a certain amount of cryptocurrency as collateral to propose and validate new blocks. Validators are chosen to create new blocks based on the amount of cryptocurrency they hold and are willing to “stake” as collateral.

This transition to PoS is occurring in multiple phases. The Beacon Chain, which is the PoS blockchain that runs parallel to the existing PoW chain, was launched in December 2020. The full transition to Ethereum 2.0, including the complete shift to PoS, is expected to occur in multiple subsequent phases.

As of Q1 2023, there are approximately 121,826,163.06 Ethereum (ETH) coins in circulation, a key distinction from Bitcoin, which has a capped supply of 21 million. Ethereum, created by Vitalik Buterin, was designed without a specific supply limit, allowing for an unlimited number of coins if mining continues. Despite this, there is a cap of 18 million ETH coins that can be mined annually, equating to around 2 ETH per block. Ethereum Classic (ETC), a separate blockchain resulting from a community dispute, also exists with 135.3 billion coins. The Ethereum blockchain’s size was 175 GB in 2021, considerably smaller than Bitcoin’s 412 GB. Approximately 5750 Ethereum blocks are mined daily, with mining difficulty increasing and around 2,151 active nodes globally, primarily in the USA. Ethereum’s potential to become deflationary is acknowledged, contingent on mining costs exceeding rewards, as stated in a GitHub disclaimer.

Figure 2. Number of Ethereum Transaction per Day
Number of bitcoins in circulation
Source: BitInfoCharts (Ethereum Transactions historical chart).

Historical data for Ethereum

How to get the data?

The Ethereum is the most popular cryptocurrency on the market, and historical data for the Ethereum such as prices and volume traded can be easily downloaded from the internet sources such as Yahoo! Finance, Blockchain.com & CoinMarketCap. For example, you can download data for Ethereum on Yahoo! Finance (the Yahoo! code for Ethereum is ETH-USD).

Figure 4. Ethereum data
 Ethereum data
Source: Yahoo! Finance.

Historical data for the Ethereum market prices

Historical data on the price of Ethereum holds paramount significance in understanding the cryptocurrency’s market trends, investor behavior, and overall performance over time. Analyzing historical price data allows investors, analysts, and researchers to identify patterns, cycles, and potential indicators that may influence future price movements. It provides valuable insights into market sentiment, periods of volatility, and the impact of significant events or developments within the Ethereum ecosystem. Traders use historical data to formulate strategies, assess risk, and make informed decisions. Furthermore, the data aids in evaluating the success of protocol upgrades, regulatory changes, and shifts in broader economic conditions, offering a comprehensive view of Ethereum’s evolution. The historical price data of Ethereum serves as a crucial tool for market participants seeking to navigate the dynamic and sometimes unpredictable nature of the cryptocurrency market.

With the number of coins in circulation, the information on the price of coins for a given currency is also important to compute Ethereum’s market capitalization.

Figure 5 below represents the evolution of the price of Ethereum in US dollar over the period Nov 2017 – Dec 2023. The price corresponds to the “closing” price (observed at 10:00 PM CET at the end of the month).

Figure 5. Evolution of the Ethereum price

Source: Yahoo! Finance.

R program

The R program below written by Shengyu ZHENG allows you to download the data from Yahoo! Finance website and to compute summary statistics and risk measures about the Ethereum.

Download R file

Data file

The R program that you can download above allows you to download the data for the Ethereum from the Yahoo! Finance website. The database starts on December, 2017.

Table 3 below represents the top of the data file for the Ethereum downloaded from the Yahoo! Finance website with the R program.

Table 3. Top of the data file for the Ethereum.
Top of the file for the Ethereum data
Source: computation by the author (data: Yahoo! Finance website).

Python code

You can download the Python code used to download the data from Yahoo! Finance.

Download the Excel file with Ethereum data

Python script to download Ethereum historical data and save it to an Excel sheet::

import yfinance as yf
import pandas as pd

# Define the ticker symbol for Ethereum
eth_ticker = “ETH-USD”

# Define the date range for historical data
start_date = “2020-01-01”
end_date = “2022-01-01”

# Download historical data using yfinance
eth_data = yf.download(eth_ticker, start=start_date, end=end_date)

# Create a Pandas DataFrame from the downloaded data
eth_df = pd.DataFrame(eth_data)

# Define the Excel file path
excel_file_path = “ethereum_historical_data.xlsx”

# Save the data to an Excel sheet
eth_df.to_excel(excel_file_path, sheet_name=”ETH Historical Data”)

print(f”Data saved to {excel_file_path}”)

# Make sure you have the required libraries installed and adjust the “start_date” and “end_date” variables to the desired date range for the historical data you want to download.

Evolution of the Ethereum

Figure 6 below gives the evolution of the Ethereum on a daily basis.

Source: computation by the author (data: Yahoo! Finance website).

Figure 6. Evolution of the Ethereum.

Source: computation by the author (data: Yahoo! Finance website).

Figure 2 below gives the evolution of the Ethereum returns from November 09, 2017 to December 31, 2022 on a daily basis.

Figure 7. Evolution of the Ethereum returns.

Source: computation by the author (data: Yahoo! Finance website).

Summary statistics for the Ethereum

The R program that you can download above also allows you to compute summary statistics about the returns of the Ethereum.

Table 4 below presents the following summary statistics estimated for the Ethereum:

  • The mean
  • The standard deviation (the squared root of the variance)
  • The skewness
  • The kurtosis.

The mean, the standard deviation / variance, the skewness, and the kurtosis refer to the first, second, third and fourth moments of statistical distribution of returns respectively.

Table 4. Summary statistics for the Ethereum.
Summary statistics for the Ethereum
Source: computation by the author (data: Yahoo! Finance website).

Statistical distribution of the Ethereum returns

Historical distribution

Figure 8 represents the historical distribution of the Ethereum daily returns for the period from November 09, 2017 to December 31, 2022.

Figure 8. Historical Ethereum distribution of the returns.

Source: computation by the author (data: Yahoo! Finance website).

Gaussian distribution

The Gaussian distribution (also called the normal distribution) is a parametric distribution with two parameters: the mean and the standard deviation of returns. We estimated these two parameters over the period from November 09, 2017 to December 31, 2022. The annualized mean of daily returns is equal to 30.81% and the annualized standard deviation of daily returns is equal to 62.33%.

Figure 9 below represents the Gaussian distribution of the Ethereum daily returns with parameters estimated over the period from November 09, 2017 to December 31, 2022.

Figure 9. Gaussian distribution of the Ethereum returns.

Source: computation by the author (data: Yahoo! Finance website).

Risk measures of the Ethereum returns

The R program that you can download above also allows you to compute risk measures about the returns of the Ethereum.

Table 5 below presents the following risk measures estimated for the Ethereum:

  • The long-term volatility (the unconditional standard deviation estimated over the entire period)
  • The short-term volatility (the standard deviation estimated over the last three months)
  • The Value at Risk (VaR) for the left tail (the 5% quantile of the historical distribution)
  • The Value at Risk (VaR) for the right tail (the 95% quantile of the historical distribution)
  • The Expected Shortfall (ES) for the left tail (the average loss over the 5% quantile of the historical distribution)
  • The Expected Shortfall (ES) for the right tail (the average loss over the 95% quantile of the historical distribution)
  • The Stress Value (SV) for the left tail (the 1% quantile of the tail distribution estimated with a Generalized Pareto distribution)
  • The Stress Value (SV) for the right tail (the 99% quantile of the tail distribution estimated with a Generalized Pareto distribution)

Table 5. Risk measures for the Ethereum.
Risk measures for the Ethereum
Source: computation by the author (data: Yahoo! Finance website).

The volatility is a global measure of risk as it considers all the returns. The Value at Risk (VaR), Expected Shortfall (ES) and Stress Value (SV) are local measures of risk as they focus on the tails of the distribution. The study of the left tail is relevant for an investor holding a long position in the Ethereum while the study of the right tail is relevant for an investor holding a short position in the Ethereum.

Why should I be interested in this post?

Ethereum, the pioneering blockchain platform, is an essential topic for management students due to its potential to transform industries, create innovative business opportunities, and disrupt traditional financial systems. Understanding Ethereum’s smart contracts, DeFi ecosystem, NFT market, and global impact can provide students with a competitive edge in a rapidly evolving business landscape, enabling them to navigate emerging trends, make informed investment decisions, and explore entrepreneurship in the digital economy.

Related posts on the SimTrade blog

About cryptocurrencies

▶ Snehasish CHINARA Bitcoin: the mother of all cryptocurrencies

▶ Snehasish CHINARA How to get crypto data

▶ Alexandre VERLET Cryptocurrencies

▶ Youssef EL QAMCAOUI Decentralised Financing

▶ Hugo MEYER The regulation of cryptocurrencies: what are we talking about?

About statistics

▶ Shengyu ZHENG Moments de la distribution

▶ Shengyu ZHENG Mesures de risques

▶ Jayati WALIA Returns

Useful resources

Academic research about risk

Longin F. (2000) From VaR to stress testing: the extreme value approach Journal of Banking and Finance, N°24, pp 1097-1130.

Longin F. (2016) Extreme events in finance: a handbook of extreme value theory and its applications Wiley Editions.

Data

Yahoo! Finance

Yahoo! Finance Historical data for Ethereum

CoinMarketCap Historical data for Ethereum

About the author

The article was written in December 2023 by Snehasish CHINARA (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2024).

Free Cash Flow: A Critical Metric in Finance

Free Cash Flow: A Critical Metric in Finance

Lou PERRONE

In this article, Lou PERRONE (ESSEC Business School, Global BBA, 2019-2023) explains the concept of Free Cash Flow and its importance in financial analysis.

What is Free Cash Flow?

Free Cash Flow (FCF) is a critical financial metric used to determine the amount of cash generated by a business after accounting for capital expenditures. It represents the cash available for distribution among all the securities holders of a company (equity holders and debt holders) and provides insight into a company’s financial health and its ability to pursue opportunities without external financing.

How is Free Cash Flow Calculated?

The general formula for calculating Free Cash Flow:

Free Cash Flow = Operating Cash Flow – Capital Expenditures

Operating Cash Flow refers to the total amount of cash generated by a company’s core operating activities. It reflects the cash generated from the actual business operations of selling goods and services.

Capital Expenditures (CapEx) are funds used by the company to purchase, upgrade, and maintain physical assets. This could include expenditures on property, plant, equipment, and technology.

Example of FCF

The Excel document provided as an exemple is a critical financial tool that enables an in-depth analysis of ABC Limited’s Free Cash Flow (FCF). By meticulously tracking the inflows and outflows of cash, the spreadsheet highlights the company’s capacity to generate cash after covering all its capital expenditures. Observing the ‘Net Cash Flow’ row, we can discern the periods where the company has successfully managed its resources to produce a positive FCF, which indicates surplus cash availability that can be used for debt repayment, reinvestment in the business, dividends to shareholders, or as a reserve for future growth opportunities. Conversely, any negative FCF would warrant a closer investigation into the company’s spending on assets or its operational efficiency. The ability to forecast and analyze FCF is crucial for business sustainability and strategic financial planning, as it provides a clearer picture of financial health beyond simple profitability metrics.

Example of computation of free cash flows.
Example of computation of free cash flow
Source: the author.

Why is Free Cash Flow Significant?

FCF is an important indicator of a company’s financial strength. It shows how efficient a company is at generating cash and is often used by analysts and investors to assess whether a company has the financial flexibility to invest in its business, pay down debt, return money to shareholders, or weather economic downturns.

Factors Impacting Free Cash Flow

While many elements can impact the calculation of FCF, some key influencers include:

  • Revenue Growth: Increases in sales can lead to higher operating cash flows.
  • Operating Margins: Efficiency in managing operational costs can lead to better margins and in turn affect FCF positively.
  • Capital Efficiency: Companies that manage their capital expenses efficiently can have higher free cash flows, as they spend less on fixed assets relative to the cash they generate.

The Dual Nature of FCF

A consistently positive FCF indicates a company’s ability to generate surplus cash after meeting all its operational and capital requirements. On the other hand, consistently negative FCF might suggest that the company is investing heavily for future growth or struggling to generate enough cash.

Interpreting Free Cash Flow

It’s crucial to contextualize FCF within the industry and the specific company’s growth stage. High-growth firms might have lower FCF due to heavy investments, while mature companies might generate more consistent free cash flows.

Why should I be interested in this post?

Understanding Free Cash Flow is indispensable for management students. It not only measures a company’s profitability but also its liquidity, solvency, and the overall health of its business model. By assessing FCF in conjunction with other financial metrics, students can gain a comprehensive view of a company’s financial health, aiding them in making informed investment or management decisions. Whether it’s for investment appraisal or corporate financial analysis, understanding the nuances of FCF is fundamental for anyone in the realm of finance and business.

Related posts on the SimTrade blog

   ▶ All posts about Financial Techniques

Useful resources

Damodaran A. (2012) Investment Valuation: Tools and Techniques for Determining the Value of Any Asset 3rd ed. New York: Wiley.

Brealey R.A., Myers S.C., and Allen F. (2011) Principles of Corporate Finance 11th ed. New York: McGraw-Hill/Irwin.

Ross S.A., Westerfield R.W., and Jaffe J. (2016) Corporate Finance 11th ed. New York: McGraw-Hill/Irwin.

About the author

The article was written in December 2023 by Lou PERRONE (ESSEC Business School, Global BBA, 2019-2023).

Extreme returns and tail modelling of the CSI 300 index for the Chinese equity market

Extreme returns and tail modelling of the CSI 300 index for the Chinese equity market

Shengyu ZHENG

In this article, Shengyu ZHENG (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2024) describes the statistical behavior of extreme returns of the CSI 300 index for the Chinese equity market and explains how extreme value theory can be used to model the tails of its distribution.

The CSI 300 index for the Chinese equity market

The CSI 300 Index, or China Securities Index 300, is a comprehensive stock market benchmark that tracks the performance of the top 300 A-share stocks listed on the Shanghai and Shenzhen stock exchanges. Introduced in 2005, the index is designed to represent a broad and diverse spectrum of China’s leading companies across various sectors, including finance, technology, consumer goods, and manufacturing. The CSI 300 is a crucial indicator of the overall health and direction of the Chinese stock market, reflecting the dynamic growth and evolution of China’s economy.

The CSI 300 employs a free-float market capitalization-weighted methodology. This means that the index’s composition and movements are influenced by the market value of the freely tradable shares, providing a more accurate representation of the companies’ actual impact on the market. As China continues to play a significant role in the global economy, the CSI 300 has become a key reference point for investors seeking exposure to the Chinese market and monitoring economic trends in the dynamic economy. With its emphasis on the country’s most influential and traded stocks, the CSI 300 serves as an essential tool for both domestic and international investors navigating the complexities of the Chinese financial landscape.

In this article, we focus on the CSI 300 index of the timeframe from March 11th, 2021, to April 1st, 2023. Here we have a line chart depicting the evolution of the index level of this period.

Figure 1 below gives the evolution of the CSI 300 index from March 11th, 2021, to April 1st, 2023 on a daily basis.

Figure 1. Evolution of the CSI 300 index.
Evolution of the CSI 300 index
Source: computation by the author (data: Yahoo! Finance website).

Figure 2 below gives the evolution of the logarithmic returns of CSI 300 index from March 11th, 2021, to April 1st, 2023 on a daily basis. We observe concentration of volatility reflecting large price fluctuations in both directions (up and down movements). This alternation of periods of low and high volatility is well modeled by ARCH models.

Figure 2. Evolution of the CSI 300 index logarithmic returns.
Evolution of the CSI 300 index return
Source: computation by the author (data: Yahoo! Finance website).

Summary statistics for the CSI 300 index

Table 1 below presents the summary statistics estimated for the CSI 300 index:

Table 1. Summary statistics for the CSI 300 index.
summary statistics of the CSI 300 index returns
Source: computation by the author (data: Yahoo! Finance website).

The mean, the standard deviation / variance, the skewness, and the kurtosis refer to the first, second, third and fourth moments of statistical distribution of returns respectively. We can conclude that during this timeframe, the CSI 300 index takes on a downward trend, with relatively important daily deviation, negative skewness and excess of kurtosis.

Tables 2 and 3 below present the top 10 negative daily returns and top 10 positive daily returns for the index over the period from March 11th, 2021, to April 1st, 2023.

Table 2. Top 10 negative daily returns for the CSI 300 index.
Top 10 negative returns of the CSI 300 index
Source: computation by the author (data: Yahoo! Finance website).

Table 3. Top 10 positive daily returns for the CSI 300 index.
Top 10 positive returns of the CSI 300 index
Source: computation by the author (data: Yahoo! Finance website).

Modelling of the tails

Here the tail modelling is conducted based on the Peak-over-Threshold (POT) approach which corresponds to a Generalized Pareto Distribution (GPD). Let us recall the theoretical background of this approach.

The POT approach takes into account all data entries above a designated high threshold u. The threshold exceedances could be fitted into a generalized Pareto distribution:

 Illustration of the POT approach

An important issue for the POT-GPD approach is the threshold selection. An optimal threshold level can be derived by calibrating the tradeoff between bias and inefficiency. There exist several approaches to address this problematic, including a Monte Carlo simulation method inspired by the work of Jansen and de Vries (1991). In this article, to fit the GPD, we use the 2.5% quantile for the modelling of the negative tail and the 97.5% quantile for that of the positive tail.

Based on the POT-GPD approach with a fixed threshold selection, we arrive at the following modelling results for the GPD for negative extreme returns (Table 4) and positive extreme returns (Table 5) for the CSI 300 index:

Table 4. Estimate of the parameters of the GPD for negative daily returns for the CSI 300 index.
Modelling of negative extreme returns of the CSI 300 index
Source: computation by the author (data: Yahoo! Finance website).

Table 5. Estimate of the parameters of the GPD for positive daily returns for the CSI 300 index.
Modelling of positive extreme returns of the CSI 300 index
Source: computation by the author (data: Yahoo! Finance website).

Figure 3 represents the historical distribution of negative return exceedances and the estimated GPD for the left tail.

Figure 3. GPD for the left tail of the CSI 300 index returns.
GPD for the left tail of the CSI 300 index returns
Source: computation by the author (data: Yahoo! Finance website).

Figures 4 represents the historical distribution of positive return exceedances and the estimated GPD for the right tail.

Figure 4. GPD for the right tail of the CSI 300 index returns.
GPD for the right tail of the CSI 300 index returns
Source: computation by the author (data: Yahoo! Finance website).

Applications in risk management

Extreme Value Theory (EVT) as a statistical approach is used to analyze the tails of a distribution, focusing on extreme events or rare occurrences. EVT can be applied to various risk management techniques, including Value at Risk (VaR), Expected Shortfall (ES), and stress testing, to provide a more comprehensive understanding of extreme risks in financial markets.

Why should I be interested in this post?

Extreme Value Theory is a useful tool to model the tails of the evolution of a financial instrument. In the ever-evolving landscape of financial markets, being able to grasp the concept of EVT presents a unique edge to students who aspire to become an investment or risk manager. It not only provides a deeper insight into the dynamics of equity markets but also equips them with a practical skill set essential for risk analysis. By exploring how EVT refines risk measures like Value at Risk (VaR) and Expected Shortfall (ES) and its role in stress testing, students gain a valuable perspective on how financial institutions navigate during extreme events. In a world where financial crises and market volatility are recurrent, this post opens the door to a powerful analytical framework that contributes to informed decisions and financial stability.

Download R file to model extreme behavior of the index

You can find below an R file (file with txt format) to study extreme returns and model the distribution tails for the CSI 300 index.

Download R file to study extreme returns and model the distribution tails for the CSI 300 index

Related posts on the SimTrade blog

About financial indexes

▶ Nithisha CHALLA Financial indexes

▶ Nithisha CHALLA Calculation of financial indexes

▶ Nithisha CHALLA The CSI 300 index

About portfolio management

▶ Youssef LOURAOUI Portfolio

▶ Jayati WALIA Returns

About statistics

▶ Shengyu ZHENG Moments de la distribution

▶ Shengyu ZHENG Mesures de risques

▶ Shengyu ZHENG Extreme Value Theory: the Block-Maxima approach and the Peak-Over-Threshold approach

▶ Gabriel FILJA Application de la théorie des valeurs extrêmes en finance de marchés

Useful resources

Academic resources

Embrechts P., C. Klüppelberg and T. Mikosch (1997) Modelling Extremal Events for Insurance and Finance Springer-Verlag.

Embrechts P., R. Frey, McNeil A.J. (2022) Quantitative Risk Management Princeton University Press.

Gumbel, E. J. (1958) Statistics of extremes New York: Columbia University Press.

Longin F. (2016) Extreme events in finance: a handbook of extreme value theory and its applications Wiley Editions.

Other resources

Extreme Events in Finance

Chan S. Statistical tools for extreme value analysis

Rieder H. E. (2014) Extreme Value Theory: A primer (slides).

About the author

The article was written in November 2023 by Shengyu ZHENG (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2024).