Solana: Ascendancy of the High-Speed Blockchain

May 6, 2024April 17, 2024 by Snehasish CHINARA

In this article, Snehasish CHINARA (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2024) explains the evolution of high performance blockchain powered cryptocurrency, Solana.

Historical context and background

Solana, a relatively new entrant in the cryptocurrency arena, emerged against the backdrop of an increasingly crowded and competitive landscape in the digital currency space. Founded in 2020 by Anatoly Yakovenko, a former engineer at Qualcomm, Solana sought to address some of the inherent scalability and speed limitations plaguing earlier blockchain platforms like Ethereum. The platform is named after the Solana Beach in California, where Yakovenko often surfed, symbolizing the project’s ambition to ride the waves of innovation and technological advancement.

Unlike many other cryptocurrencies that primarily rely on Proof of Work (PoW) or Proof of Stake (PoS) consensus mechanisms, Solana introduced a novel consensus mechanism known as Proof of History (PoH). This mechanism aims to optimize transaction processing speed by organizing transactions into a series of chronological events, enabling parallel transaction processing, and significantly enhancing scalability. Solana’s emphasis on scalability and throughput has positioned it as a promising platform for decentralized applications (dApps) and decentralized finance (DeFi) projects seeking high-performance blockchain infrastructure. Its innovative approach has garnered attention and support from investors and developers alike, propelling Solana into the spotlight as one of the leading contenders in the cryptocurrency space.

Solana Logo

Source: Google.

Figure 1. Key Dates in Solana History.

Source: Yahoo! Finance.

Key features

Scalability

Solana is designed to be highly scalable, capable of processing thousands of transactions per second. Its unique consensus mechanism, Proof of History (PoH), combined with a network of nodes running parallel processing, enables Solana to handle a high throughput of transactions efficiently.

Fast Transaction Speeds

With its focus on scalability, Solana boasts incredibly fast transaction speeds. Transactions can be confirmed in milliseconds, making it suitable for applications requiring rapid transaction processing, such as decentralized finance (DeFi) platforms and high-frequency trading.

Low Transaction Costs

Solana aims to keep transaction costs low, even during periods of high network activity. Its efficient use of resources and high throughput allow for cost-effective transactions, making it accessible to users and developers alike.

Proof of History (PoH)

Solana’s unique consensus mechanism, PoH, serves as a historical record for the ordering and time-stamping of transactions. By leveraging PoH, Solana achieves high throughput without sacrificing decentralization or security.

Support for Smart Contracts

Solana is compatible with smart contracts, allowing developers to build decentralized applications (dApps) and execute programmable transactions on the blockchain. It supports smart contract languages like Rust and Solidity, enabling a wide range of developers to build on the platform.

Ecosystem and Development Tools

Solana boasts a growing ecosystem of projects and development tools to support developers in building decentralized applications. Its developer-friendly environment includes tools such as Solana Studio, a web-based IDE for building and deploying smart contracts, and libraries for interacting with the Solana blockchain.

Interoperability

Solana is designed to be interoperable with other blockchains and protocols, facilitating seamless communication and asset transfer between different networks. This interoperability opens up possibilities for cross-chain decentralized applications and enhances the overall utility of the Solana ecosystem.

Use cases

Non-Fungible Tokens (NFTs)

Solana provides an efficient infrastructure for minting, trading, and storing NFTs. Artists, creators, and collectors are utilizing Solana-based marketplaces like Solanart to buy and sell digital collectibles, artwork, and virtual assets. Solana’s high throughput enables seamless NFT transactions, while its low fees make it appealing for creators seeking an alternative to Ethereum’s congested network.

Gaming and Virtual Worlds

Solana’s high-performance blockchain is well-suited for gaming applications and virtual worlds that require fast transaction processing and scalability. Game developers are leveraging Solana’s infrastructure to create blockchain-based games, in-game assets, and decentralized gaming platforms. Projects like Star Atlas, a space-themed massively multiplayer online game (MMO) built on Solana, demonstrate the platform’s potential to disrupt the gaming industry.

Decentralized Autonomous Organizations (DAOs)

Solana provides a robust framework for building decentralized autonomous organizations (DAOs) that enable community governance and decision-making. DAOs on Solana leverage smart contracts to automate voting mechanisms, distribute governance tokens, and execute proposals transparently and efficiently. These DAOs empower communities to collectively manage and govern decentralized protocols, platforms, and resources.

Tokenization of Real-World Assets

Solana facilitates the tokenization of real-world assets such as real estate, stocks, and commodities, enabling fractional ownership and increased liquidity. Projects are exploring Solana’s blockchain to tokenize and trade various asset classes, unlocking new investment opportunities and reducing barriers to entry for traditional markets.

Technology and underlying blockchain

At the core of Solana’s architecture is the Proof of History (PoH) consensus mechanism, which orders transactions before they are processed into blocks. This deterministic sequencing allows for parallel transaction processing and enhances overall network efficiency. Additionally, Solana utilizes a Byzantine Fault Tolerance (BFT) consensus algorithm called Tower BFT, which further ensures network security and integrity.

Solana’s blockchain implements a novel data structure known as the “Solana Architecture,” which includes a combination of a single global state, a high-speed networking stack, and a high-performance virtual machine (VM). This architecture enables Solana to achieve impressive transaction throughput, with the capability to process thousands of transactions per second (TPS) and sub-second transaction finality. Furthermore, Solana leverages a unique mechanism called “Turbine” to optimize block propagation and reduce network latency, enhancing the overall scalability and performance of the platform.

The Solana ecosystem also features a built-in decentralized exchange (DEX), supporting seamless token swaps and liquidity provision directly on-chain. Smart contracts on Solana are executed using a high-performance VM called Sealevel, which is designed to efficiently process complex computations while maintaining low transaction costs. Overall, Solana’s technology stack, comprising innovative consensus mechanisms, advanced data structures, and optimized networking protocols, positions it as a leading blockchain platform capable of supporting a wide range of decentralized applications (dApps) and use cases at scale.

Supply of coins

Solana (SOL) operates on a fixed supply model, with a maximum supply of 489,026,837 SOL tokens. Unlike traditional fiat currencies, Solana’s tokenomics are governed by the principles of cryptocurrency protocols. The initial distribution of SOL tokens occurred through a combination of token sales, strategic partnerships, ecosystem incentives, and network validators’ rewards. Notably, Solana employs a deflationary economic model, wherein a portion of transaction fees is burned, reducing the overall token supply over time. This deflationary mechanism is designed to counterbalance any potential inflationary pressures as the network expands, ensuring the long-term sustainability and scarcity of SOL tokens. Additionally, SOL tokens are used to facilitate various functions within the Solana ecosystem, including transaction fees, staking rewards, governance participation, and decentralized application interactions. As Solana continues to grow and gain adoption, the controlled and predictable token supply dynamics play a crucial role in maintaining the network’s integrity and value proposition.

Historical data for Solana

How to get the data?

The Solana (SOL) is a popular cryptocurrency on the market, and historical data for the Solana 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 Solana on Yahoo! Finance (the Yahoo! code for Solana is SOL-USD).

Figure 2. Solana data

Source: Yahoo! Finance.

Historical data for the Solana market prices

The historical data for Solana market prices demonstrates a dynamic evolution marked by significant fluctuations and notable trends since its inception. Initially, SOL experienced modest trading activity and price levels, but as Solana gained recognition for its innovative blockchain architecture and scalability features, its market value began to ascend. Early adopters and investors drove demand for SOL tokens, leading to periods of rapid appreciation interspersed with corrections and consolidation phases. Milestones such as protocol upgrades, partnerships, and successful dApp launches often coincided with significant price movements. Moreover, broader market trends and sentiment towards cryptocurrencies influenced SOL’s price dynamics, contributing to both bullish and bearish cycles over time. Overall, SOL’s price trajectory reflects Solana’s journey from its early stages to becoming a prominent player in the blockchain space, highlighting its potential to revolutionize decentralized applications and digital finance despite the inherent volatility of the cryptocurrency market.

Figure 3 below represents the evolution of the price of Solana (SOL) in US dollar over the period April 2020 – December 2023. The price corresponds to the “closing” price (observed at 10:00 PM CET at the end of the month).

Figure 3. Evolution of the Solana (SOL) 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 Solana (SOL).

The R program that you can download above allows you to download the data for the Solana (SOL) from the Yahoo! Finance website. The database starts on April, 2020.

Table 1 below represents the top of the data file for the Solana (SOL) downloaded from the Yahoo! Finance website with the R program.

Table 1. Top of the data file for the Solana (SOL)

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.

Python script to download Solana (SOL) historical data and save it to an Excel sheet::

import yfinance as yf

import pandas as pd

# Define the ticker symbol for Solana Coin

SOL_ticker = “SOL-USD”

# Define the date range for historical data

start_date = “2020-01-01”

end_date = “2022-01-01”

# Download historical data using yfinance

SOL_data = yf.download(SOL_ticker, start=start_date, end=end_date)

# Create a Pandas DataFrame from the downloaded data

doge_df = pd.DataFrame(SOL_data)

# Define the Excel file path

excel_file_path = “SOL_historical_data.xlsx”

# Save the data to an Excel sheet

SOL_df.to_excel(excel_file_path, sheet_name=”SOL_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 Solana (SOL)

Figure 4 below gives the evolution of the Solana (SOL) returns from April, 2020 to December 31, 2023 on a daily basis.

Figure 4. Evolution of the Solana (SOL) returns.

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

Summary statistics for the Solana (SOL)

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

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 Solana (SOL).

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

Statistical distribution of the Solana (SOL) returns

Historical distribution

Figure 5 represents the historical distribution of the Solana (SOL) daily returns for the period from April, 2020 to December 31, 2023.

Figure 5. Historical Solana (SOL) distribution of the returns.

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

Gaussian distribution

Figure 6 below represents the Gaussian distribution of the Solana (SOL) daily returns with parameters estimated over the period from April, 2020 to December, 2023.

Figure 6. Gaussian distribution of the Solana (SOL) returns.

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

Risk measures of the Solana (SOL) returns

The R program that you can download above also allows you to compute risk measures about the returns of the Solana (SOL).

Table 3 below presents the following risk measures estimated for the Solana (SOL):

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 Solana (SOL).

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 Solana (SOL) while the study of the right tail is relevant for an investor holding a short position in the Solana (SOL).

Why should I be interested in this post?

This blog offers an engaging exploration into a cryptocurrency that transcends traditional finance, appealing to a wide audience due to its cultural relevance, investment potential, and vibrant community. Solana’s reputation for high performance in blockchain technology, boasting the capability to process thousands of transactions per second, makes it an appealing option for developers and users seeking efficient transaction processing. Moreover, staying updated on Solana can offer insights into the growth of its ecosystem, including the development of decentralized applications (dApps) and strategic partnerships. For investors, Solana’s increasing popularity and ecosystem growth may signal investment opportunities, making it worthwhile to track news and discussions surrounding the platform. Additionally, Solana’s innovative technical advancements in scalability, consensus mechanisms, and developer tools are of interest to those intrigued by blockchain technology. Engaging with the Solana community provides opportunities for networking and gaining valuable insights into this rapidly expanding ecosystem.

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 Solana

CoinMarketCap Historical data for Solana

About the author

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

Bitcoin est un rêve, un idéal, un espoir

April 15, 2024April 9, 2024 by Jean-Marie CHOFFRAY

Bitcoin est un rêve, un idéal, un espoir

Dans cet article, Jean-Marie CHOFFRAY (Professeur Ordinaire Honoraire d’Informatique Décisionnelle à l’Université de Liège, PhD-77, Management Science, Massachusetts Institute of Technology) introduit son recent article “Mille quatre cent milliards de dollars”.

Personne ne sait précisément ce qu’est Bitcoin (avec « Bvle réseau). Au plan conceptuel, bitcoin (avec « b le jeton) est une chaîne, aussi indestructible que possible, de blocs d’enregistrements, aussi sécurisés que possible. Cette technologie nouvelle est donc un projet en cours dont personne ne peut préjuger l’avenir, quelle que soit la dimension considérée. Bitcoin deviendra ce que la majorité de ses utilisateurs décidera, et surtout aura le courage, d’en faire. Pour Satochi Nakamoto, c’était un rêve. Pour ceux qui travaillent aujourd’hui sur son code, c’est un idéal. Enfin, pour ceux qui l’utiliseront demain, c’est un espoir. L’Histoire de l’Humanité, une autre chaîne de blocs d’enregistrements – à l’évidence, non sécurisés et falsifiables ! – , s’apparente au reflet d’une montée de la Conscience individuelle et collective (cf. « Le phénomène humain » de Pierre Teilhard de Chardin). Bitcoin, c’est L’espoir maintenant (entretien entre Jean-Paul Sartre et Benny Lévy) : « une tension vers la fin, que l’échec, le tragique ne sauraient annuler… La valeur économique de bitcoin serait-elle le prix de la liberté ?

▶ Lire l’article Bitcoin est un rêve, un idéal, un espoir

Autres articles sur le blog

▶ Snehasish CHINARA Bitcoin: the mother of all cryptocurrencies

▶ Jean-Marie CHOFFRAY Mille quatre cent milliards de dollars

A propos de l’auteur

L’article a été rédigé en mars 2024 par Jean-Marie CHOFFRAY (Professeur Ordinaire Honoraire d’Informatique Décisionnelle à l’Université de Liège, PhD-77, Management Science, Massachusetts Institute of Technology).

Types of Market Consensus

April 15, 2024April 9, 2024 by Nithisha CHALLA

Types of Market Consensus

In this article, Nithisha CHALLA (ESSEC Business School, Grande Ecole Program – Master in Management (MiM), 2021-2024) explains what are the market consensus and different forms of into financial sentiments.

Introduction

Market consensus often talked about in financial circles, goes beyond just a collective opinion; it reflects how investors feel and what’s happening in the financial world. This article looks into different types of market consensus: the agreement on prices between buyers and sellers, the insights provided by financial analysts who study companies’ basics and economic indicators, and the views of technical analysts who analyze stock prices. Several things affect market consensus, like how people generally feel about the market, expectations for market changes, and the actions of important players like central banks.

Understanding the various forms of market consensus is essential for investors and traders navigating the complex world of finance. These collective opinions shape market trends and impact investment decisions.

Price Consensus

One of the most visible forms of market consensus is reflected in asset prices. The principle of supply and demand drives prices, with consensus emerging as traders and investors assess the value of an asset based on various factors such as economic indicators, company performance, and geopolitical events. As these factors change and influence perceptions, they contribute to the evolving consensus reflected in asset prices.

Example: In 2020, Tesla’s stock experienced a tremendous surge as market consensus shifted positively around electric vehicles and renewable energy. Investors believed in the company’s potential for growth, leading to a significant increase in Tesla’s stock price.

Market consensus by financial analysts

Consensus on revenues and earnings

Earnings consensus revolves around the expected future earnings of a company. Analysts and financial experts often provide earnings estimates, and market consensus forms around these forecasts. Deviations from these expectations can lead to significant price movements as investors reassess the company’s performance and prospects. This dynamic interaction between earnings forecasts and market reactions underscores the importance of consensus expectations in shaping investor sentiment and market behavior.

Example: Apple’s quarterly earnings announcements are highly anticipated by the market. If Apple reports earnings that exceed analysts’ consensus estimates, it can lead to a surge in its stock price, reflecting the positive earnings consensus.

Consensus on economic indicators

Economic indicators, such as GDP growth, inflation rates, and unemployment figures, shape the macroeconomic consensus. Governments, central banks, and international organizations routinely release data that analysts and investors leverage to assess the broader economic landscape. Market responses frequently track deviations from consensus forecasts related to these indicators. When actual economic data diverges from expectations, it can trigger significant market movements as investors adjust their outlook on economic conditions and potential policy responses.

Example: During the global financial crisis of 2008, there was a consensus among economists that the world economy was entering a recession. This consensus influenced investor behavior, leading to widespread selling in equity markets and a shift towards safer assets.

Market consensus by technical analysts

Market consensus by technical analysts revolves around interpreting stock price movements and patterns to forecast future market trends. Technical analysts use charts and indicators to identify potential buying or selling opportunities based on historical price data. By analyzing patterns such as support and resistance levels, moving averages, and trading volumes, technical analysts contribute insights into market sentiment and potential price directions. This approach to market consensus complements fundamental analysis and provides a different perspective on investor behavior and sentiment. Breaking through a significant resistance level might lead to a bullish consensus; similarly, breaking through a significant support level might lead to a bearish consensus.

Factors influencing the market consensus

Size of the company and coverage by financial analysts

The size of a company and how many analysts are covering it also influence the stock market consensus. If only a handful of analysts are assigned to the stock, the market consensus estimates are more likely to vary from actual results.

Market sentiment

The sentiment of market participants plays a crucial role in shaping market consensus. Investor sentiment can be bullish or bearish, influenced by factors like news, social media, and market events. Contrarian investors often excel by taking positions opposite to prevailing sentiment, capitalizing on opportunities arising from market overreactions and sentiment shifts. Understanding and analyzing market sentiment is essential for gauging potential market directions and identifying contrarian investment opportunities.

Example: The GameStop saga in early 2021 is a notable example of sentiment consensus. Retail investors on social media forums collectively drove up the stock price of GameStop, challenging traditional market dynamics and catching institutional investors off guard.

Volatility expectations

Volatility expectations, measured by metrics like the VIX (Volatility Index), represent a form of consensus about future market stability. Traders and investors use volatility as an indicator of market risk, and consensus around heightened volatility can lead to defensive strategies, such as increased hedging or reduced risk exposure. Understanding and interpreting volatility consensus is essential for adapting investment strategies to prevailing market conditions and risk perceptions.

Example: The COVID-19 pandemic in 2020 led to a consensus on increased market volatility. The VIX (Volatility Index) surged as investors anticipated heightened uncertainty, prompting shifts in investment strategies to account for the expected market fluctuations.

Policy Consensus

Central bank policies, government regulations, and fiscal measures contribute to policy consensus. Market participants closely monitor statements and decisions made by central banks and governments, forming expectations about interest rates, monetary policy, and regulatory changes. Any surprises in these areas can lead to market volatility.

Example: The announcement of the U.S. Federal Reserve’s quantitative easing policies during the 2008 financial crisis influenced market consensus. The expectation of increased liquidity and lower interest rates contributed to a rally in stock markets.

Technological Consensus

Technological consensus refers to the collective agreement or perception within the technology sector regarding emerging trends, innovations, or the adoption of specific technologies. Industry experts, analysts, and stakeholders contribute to this consensus through assessments of technology developments, market dynamics, and consumer behavior. Consensus within technology influences investment decisions, product development strategies, and market forecasts. Understanding technological consensus is crucial for businesses and investors seeking to navigate the rapidly evolving landscape of technology-driven industries.

Example: The rise of FAANG stocks (Facebook, Apple, Amazon, Netflix, Google) in the last decade reflects a technological consensus. Investors collectively believed in the transformative power of these technology giants, contributing to their substantial market capitalizations.

Conclusion

Market consensus, when explored through the lens of data and statistics, transforms from a theoretical concept to a tangible and actionable tool. Whether you’re an investor, a business leader, or an analyst, integrating statistical insights into your understanding of market consensus adds a layer of precision to decision-making. In the dynamic world of finance, where every percentage point matters, harnessing the power of market consensus with a data-driven approach ensures a more informed and strategic navigation of financial waters.

Why should I be interested in this post?

In essence, this article provides a holistic and data-driven perspective on market consensus, catering to the interests of investors, business professionals, and anyone seeking a nuanced understanding of how collective sentiments shape the financial landscape. Whether you’re actively involved in financial decision-making or simply intrigued by the dynamics of the market, this article offers valuable insights that bridge theory and real-world applications.

Useful resources

CNN Fear & Greed Index: What emotion is driving the market now?

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 April 2024 by Nithisha CHALLA (ESSEC Business School, Grande Ecole – Master in Management (MiM), 2021-2024).

Job description – Financial analysts

April 9, 2024 by Nithisha CHALLA

Job description – Financial analysts

In this article, Nithisha CHALLA (ESSEC Business School, Grande Ecole Program – Master in Management (MiM), 2021-2024) explains the job of financial analysts and their contributions to the investment community.

Introduction

Financial analysts serve as the backbone of the finance industry, providing critical insights and analysis to support investment decisions. Did you know that the Bureau of Labor Statistics in the US projected a 6% growth in employment of financial analysts from 2020 to 2030, faster than the average for all occupations? According to a survey by the CFA Institute (November 2021), most financial analysts believe that their role is becoming increasingly important in today’s complex financial landscape.

This article explores the multifaceted role of financial analysts, with a particular focus on their responsibilities and contributions within both the buy-side and sell-side sectors of the financial industry. Buy-side analysts work for entities that have money to invest, such as mutual funds, hedge funds, pension funds, and high-net-worth individuals. Sell-side analysts work for brokerage firms, investment banks, and other entities that sell investment services. These analysts conduct research and analysis on companies and industries to publish investment recommendations for the firm’s clients and the general investing public.

What Does a Financial Analyst Do?

Financial analysts analyze a firm’s past financial data to spot trends and assess risks, which helps them predict outcomes for business decisions, identify sale or purchase opportunities, and make investment recommendations. To that end, a financial analyst may need to work with different types of data such as company financial statements, the performance of investments such as stocks and bonds, industry research, macroeconomic data, and more, depending upon the specific role they play within the organization.

Buy-Side Financial Analysts: Crafting Investment Strategies

Buy-side analysts work for entities that have money to invest, such as mutual funds, hedge funds, pension funds, and high-net-worth individuals. The job of a financial analyst is important as these financial institutions manage a lot of money. For example, the global assets under management (AUM) of hedge funds amounted to approximately $3.6 trillion in 2020 (Gitnux, December 16, 2023). These analysts perform research and analysis to make direct investment decisions on behalf of their firm’s money. Their primary objective is to identify and capitalize on lucrative investment opportunities that align with their client’s objectives and risk profiles.

Role and responsibilities

Buy-side financial analysts are instrumental in evaluating and recommending alternative investment opportunities to their clients. More and more, they take into consideration the environmental, social, and governance (ESG) factors that are important in their investment decision-making process.

Examples of companies employing Buy-Side Financial Analysts

An investment manager at BlackRock specializes in infrastructure investments and identifies Brookfield Infrastructure Partners L.P. (NYSE: BIP) as a promising opportunity for long-term growth and income generation. BlackRock’s investment in Brookfield Infrastructure Partners helps diversify its clients’ portfolios and provides stable returns over time.

Sell-Side Financial Analysts: Providing Market Insights and Advisory Services

Did you know that the global investment banking revenue reached $124.5 billion in 2020, driven by strong performance in equity underwriting and mergers and acquisitions (M&A) advisory? Sell-side financial analysts work for brokerage firms, investment banks, and financial advisory companies. Their role revolves around providing research and advisory services to institutional and retail investors, as well as corporate clients.

Role and responsibilities

A survey by the Brunswick group found that institutional investors believe that the quality of sell-side research reports has improved over the past five years. Sell-side financial analysts play a crucial role in providing timely and insightful research reports to help clients make informed investment decisions.

Examples of companies employing Sell-Side Financial Analysts:

A sell-side analyst at Goldman Sachs publishes a research report on Amazon.com Inc. (NASDAQ: AMZN), recommending a “buy” rating based on its dominant position in e-commerce and cloud computing, as well as its consistent revenue growth. This recommendation attracts significant investor interest and contributes to a surge in Amazon’s stock price.
An equity research analyst at J.P. Morgan advises Alphabet Inc. (NASDAQ: GOOGL) on strategic options for expanding its autonomous driving technology division, conducting industry analysis and financial modeling to support the recommendations. Alphabet Inc. leverages J.P. Morgan’s expertise to develop a comprehensive growth strategy for its autonomous driving business.
A sell-side analyst at Vanguard conducts extensive research on emerging technology companies and recommends investing in Tesla Inc. (NASDAQ: TSLA), recognizing its leadership in electric vehicles and sustainable energy solutions. This recommendation leads to a significant increase in Tesla’s stock price, generating substantial returns for Vanguard’s clients.

Financial analysts play a pivotal role in shaping investment strategies, providing market insights, and facilitating financial transactions. Whether operating on the buy-side or sell-side, these professionals leverage their analytical expertise and industry knowledge to navigate the complexities of the financial markets. By offering investment recommendations, conducting research, and providing advisory services, financial analysts contribute significantly to the pursuit of financial prosperity and wealth creation.

Skills and Qualifications

Now given the job and the daily environment they have to deal with there are certain skills a financial analyst would need to have. Soft skills such as Analytical Thinking, Communication Skills, Attention to Detail, and Time Management hard skills such as Technology Skills, Quantitative Skills, Financial Analysis, and Industry Knowledge are demanded.

When it comes to the career path of an analyst, requires them to stay updated on industry developments, regulations, and best practices. From various analyses and surveys, we could say that many financial analysts hold a bachelor’s degree in finance, accounting, economics, or a related field. Pursuing a master’s degree in finance, business administration (MBA), or a specialized finance program can provide additional knowledge and credentials.

Obtaining certifications such as the Chartered Financial Analyst (CFA) designation is common in the financial industry. The CFA program covers a broad range of topics including investment analysis, portfolio management, and ethics, and is highly regarded in the field. Apart from the theoretical knowledge gaining practical experience through internships or entry-level positions at financial institutions, investment firms, or corporate finance departments is essential for building foundational skills and industry knowledge.

The Chartered Financial Analyst (CFA) designation is highly valued in the buy-side industry. Buy-side financial analysts need to possess strong data analysis skills to extract actionable insights from large datasets and alternative data sources. They play a crucial role in developing and implementing these customized investment strategies.
Sell-side financial analysts need to possess strong academic credentials and technical skills to excel in their roles. They believe that soft skills such as communication and relationship-building are essential for success in their roles. Sell-side financial analysts need to effectively communicate their research findings and build rapport with clients to gain their trust and confidence.

Remuneration

According to a report by Bloomberg, the average compensation for equity research analysts at investment banks in the United States ranged from $200,000 to $600,000 in 2020, depending on their level of experience and performance. Sell-side financial analysts are well-compensated for their expertise in analyzing and recommending investment opportunities to clients.

Why should I be interested in this post?

In essence, this article provides a perspective on the job of financial analysts. For a student who would like to work in finance, it is important to know about the job of a financial analyst as it relates to both the corporate world and financial markets.

Useful resources

Forbes Financial Analyst Job Description

Gitnux Must-Know Hedge Fund Statistics

CFA Survey of CFA Institute members on latest ESG matters

Brunswickgroup About the Brunswick Digital Investor Survey

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 April 2024 by Nithisha CHALLA (ESSEC Business School, Grande Ecole Program – Master in Management (MiM), 2021-2024).

Institutional Brokers’ Estimate System (IBES)

April 9, 2024 by Nithisha CHALLA

Institutional Brokers’ Estimate System (IBES)

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.

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).

My Experience as an Investment Intern at Eurazeo

April 6, 2024 by Dante MARRAMIERO

My Experience as an Investment Intern at Eurazeo

In this article, Dante MARRAMIERO (ESSEC Business School, Master in Strategy and Management of International Business (SMIB), 2020-2023) presents its professional experience in Euazeo, a European leading Private Equity based in Paris.

About Eurazeo

Eurazeo stands as a prominent European firm within the world of alternative investments, boasting a diversified portfolio within various investment strategies, including private debt, real Estate, venture, growth, small-mid buyout, and mid-large buyouts. Eurazeo was initially the family office of the Lazard Freres family, but in 2018 decided to merge with Idinvest in order to start fundraising capital from third parties. Following 2018, Eurazeo’s strategy has always been to reduce the balance sheet investments and to increase the third-party capital investments.

Logo of the company.
Logo of Eurazeo
Source: the company.

Internship Overview

During my time as an Investment Intern at Eurazeo from January 2023 to June 2023, I had the privilege to immerse myself in the intricacies of private equity and alternative investments. My internship included a range of responsibilities aimed at supporting Eurazeo’s investment initiatives. My department was “Direct Transactions” and during my internship, I participated actively in three different activities:

Syndication of Co-Investment Opportunities

I actively participated in the syndication process of four co-investment opportunities across various investment strategies including private debt, growth, and mid-large buyout. This involved conducting comprehensive due diligence, financial analysis, and market research to assess the viability and potential returns of each opportunity. Together, these co-investment opportunities accounted for approximately €750 million in total investment value, underscoring Eurazeo’s commitment to strategic partnerships and collaborative investment initiatives. Co-investments, theoretically speaking can be cataloged under direct transactions as SPV (Special Purpose Vehicle) are created specifically for one single transaction and you are not making the investment for the limited partners but you are making it with the LPs (Limited Partners).

Strategic SPV Structures Analysis

I was tasked with examining strategic Special Purpose Vehicle (SPV) structures solutions for potential investment opportunities. This entailed analyzing, comparing, and developing alternative fundraising structures such as Collateral Fund Obligation and Rated Feeder Fund, focusing on optimizing capital deployment and mitigating risk. The main reason why we were evaluating new financial structures was to attract a category investor that, at the time, was not willing to invest in our funds: American insurance companies. 2023 has been generically speaking a rough year for fundraising capital and for this reason, we decided to implement this kind of solution. A collateral fund obligation is a structure composed by certified debt and equity; this structure will invest in different funds (all managed by Eurazeo) and will have the advantage of using the leverage raised as certified debt to enhance the return on the investment and the Cash on Cash. Therefore, by evaluating various SPV structures, we aimed to enhance our flexibility in structuring investments and optimizing returns for our investors, by using the right amount of leverage.

Evaluation of Secondary Transactions Advisors

I had the opportunity to participate in two competitive selection processes for secondary transaction advisors, tasked with choosing the financial advisor to support us in executing a single asset continuation vehicle. The evaluation process included analyzing and comparing proposed solutions, assessing current market conditions, and evaluating alignment with Eurazeo’s investment strategy and objectives.

Furthermore, this project included evaluation and due diligence, intending to identify strategic partners capable of delivering value-added solutions and maximizing returns for our investors. Single asset continuation vehicles are specialized structures tailored for investments held within the portfolio of a current fund of the firm. These investments require divestment as limited partners seek liquidity. However, recognizing the potential upside, the firm decides to establish these vehicles.

What did I learn during this experience?

My internship at Eurazeo provided invaluable opportunities for skills and knowledge development across various areas:

Financial Analysis: I honed my skills in financial modeling, valuation techniques, and investment analysis through hands-on experience with real-world investment opportunities.
Due Diligence: I gained practical insights into the due diligence process, including a thorough examination of financial statements, market trends, and competitive landscapes.
Strategic Thinking: I developed a strategic mindset by evaluating investment opportunities within the broader context of Eurazeo’s investment thesis and long-term objectives.
Communication and Collaboration: I enhanced my communication and collaboration skills through interaction with cross-functional teams and external stakeholders, fostering effective teamwork and decision-making.

This internship therefore offered a unique opportunity to gain firsthand experience in the dynamic and fast-paced world of private equity and alternative investments. As an aspiring finance professional, this experience has equipped me with the skills, knowledge, and insights necessary to thrive in the competitive landscape of the investment industry. Moreover, it has reaffirmed my passion for finance and deepened my understanding of the critical role played by alternative investment firms in driving economic growth and value creation.

As a newcomer to the finance industry, I had not anticipated the level of intricacy and competition inherent within the environment of Eurazeo. The depth of analysis, the meticulous attention to detail, and the relentless pursuit of excellence underscored the caliber of professionals operating within the firm. Despite the initial surprise, I found myself invigorated by the intellectual rigor and spirited competition that permeated every facet of Eurazeo’s operations.

Central to my experience at Eurazeo was the discovery of a challenging yet remarkably cohesive team—a team that demanded nothing short of excellence yet fostered an environment of camaraderie and mutual support. The intensity of our collaborative efforts forged bonds that transcended professional boundaries, culminating in a shared sense of purpose and accomplishment. Indeed, within the crucible of challenging assignments and tight deadlines, I discovered that the true measure of an internship lies not merely in the tasks accomplished but, in the relationships, forged and the personal growth attained.

Long But Fulfilling Working Hours

While the demands of the internship necessitated long hours and unwavering dedication, I found solace in the gratifying pursuit of knowledge and skill refinement. On average, my workday extended until around 10:30 in the evening, with occasional instances requiring weekend office visits. Despite the rigors of the schedule, the sense of fulfilment derived from contributing to meaningful projects and engaging with industry experts mitigated the challenges posed by extended working hours.

A game-changing internship

My internship at Eurazeo stands as a transformative chapter in my professional journey, characterized by unexpected challenges, profound growth, and enduring camaraderie. Through immersion in the fast-paced realm of private equity, I have gained invaluable insights, honed essential skills, and cultivated enduring relationships that will undoubtedly shape my future endeavours. As I reflect on my time at Eurazeo, I am reminded that true growth emerges from embracing adversity, fostering meaningful connections, and steadfastly pursuing excellence—lessons that will continue to guide me on the path toward personal and professional fulfillment.

Why should I be interested in this post?

I aspire that this experience might aid other students intrigued by Private Equity in gaining deeper insights into the internal dynamics and the range of exposure one can encounter within a private equity firm. Often, when students hear about private equity, their minds jump straight to financial analysis and modeling, overlooking the broader scope. My aim is for this article to spark curiosity among students about this sector, encouraging them to explore the private equity market further.

Useful resources

Eurazeo

Bain Bain private Equity Report 2023

About the author

The article was written in April 2024 by Dante MARRAMIERO (ESSEC Business School, Master in Strategy and Management of International Business (SMIB), 2020-2023)

Doge Coin: Unraveling the Phenomenon of the Internet’s Favourite Cryptocurrency

May 2, 2024April 5, 2024 by Snehasish CHINARA

In this article, Snehasish CHINARA (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2024) explains the evolution of the most popular memecoin, Dogecoin.

Historical context and background

Dogecoin, a cryptocurrency that started as a joke, quickly evolved into a significant player in the world of digital assets. Created in December 2013 by software engineers Billy Markus and Jackson Palmer, Dogecoin was initially intended to satirize the hype surrounding cryptocurrencies at the time. The coin’s logo features the Shiba Inu dog from the “Doge” meme, which was immensely popular on the internet during that period. Despite its humorous origins, Dogecoin gained traction due to its welcoming and inclusive community, as well as its low transaction fees and fast confirmation times.

The early days of Dogecoin saw rapid adoption and a vibrant online community rallying around the coin. It gained attention for its philanthropic efforts, including fundraising campaigns to sponsor charity events and support causes such as building water wells in developing countries and sponsoring Olympic athletes. These initiatives helped distinguish Dogecoin from other cryptocurrencies and fostered a sense of community among its users.

Over time, Dogecoin’s popularity continued to grow, fueled in part by endorsements from notable figures such as Elon Musk, who frequently tweeted about the coin, further amplifying its visibility. Despite facing occasional security issues and challenges, Dogecoin has persevered, becoming one of the most recognized and traded cryptocurrencies in the market. Its unique blend of humor, community spirit, and accessibility has endeared it to a wide range of users, making it a significant player in the ever-expanding crypto landscape.

Doge Coin Logo

Source: Yahoo! Finance.

Figure 1. Key Dates in Doge Coin History

Source: Yahoo! Finance .

Key features

Decentralization

Like most cryptocurrencies, Dogecoin operates on a decentralized network, meaning it is not controlled by any single entity or organization. Transactions are recorded on a public ledger known as the blockchain, which is maintained by a network of nodes.

Scrypt Algorithm

Dogecoin uses the Scrypt algorithm for its proof-of-work consensus mechanism, which is less energy-intensive compared to Bitcoin’s SHA-256 algorithm. This allows for greater accessibility to mining for individuals with standard computer hardware.

Fast Transactions

Dogecoin boasts relatively fast transaction times, with blocks being mined approximately every minute. This makes it suitable for quick and efficient transfers of value.

Low Transaction Fees

Transaction fees on the Dogecoin network are typically minimal, making it cost-effective for transferring even small amounts of value.

Meme Culture

Dogecoin’s branding and marketing heavily leverage internet meme culture, particularly the “Doge” meme featuring the Shiba Inu dog. This playful and approachable branding sets Dogecoin apart from other cryptocurrencies and contributes to its widespread appeal.

Use cases

Tipping

Dogecoin gained popularity early on for its use as a tipping currency on social media platforms like Reddit and Twitter. Users can easily send small amounts of Dogecoin to content creators or other users as a form of appreciation.

Charitable Donations

The Dogecoin community has a history of supporting charitable causes and disaster relief efforts. Dogecoin has been used to raise funds for various initiatives, including sponsoring athletes, funding clean water projects, and aiding during natural disasters.

E-commerce

Some online merchants and retailers accept Dogecoin as a form of payment for goods and services. This includes businesses ranging from small independent shops to larger e-commerce platforms.

Micropayments

Dogecoin’s low transaction fees and fast confirmation times make it suitable for micropayments, allowing users to easily transfer small amounts of value online.

Community Engagement

Dogecoin continues to serve as a vehicle for community engagement and participation. Its lighthearted and inclusive nature fosters a sense of camaraderie among its users, who often come together for events, fundraisers, and online discussions.

Experimental Projects

Developers and enthusiasts sometimes use Dogecoin for experimental projects or to explore new applications of blockchain technology. These projects can range from art and gaming to decentralized finance (DeFi) experiments.

Technology and underlying blockchain

Dogecoin operates on a blockchain-based technology similar to Bitcoin and many other cryptocurrencies. It employs a decentralized peer-to-peer network that relies on nodes spread across the globe to validate and record transactions. Dogecoin’s blockchain uses the Scrypt hashing algorithm, which was initially designed to facilitate quicker confirmation times compared to Bitcoin’s SHA-256 algorithm. This choice of algorithm allows for a more accessible mining process, enabling individuals with standard computer hardware to participate in securing the network and earning rewards. Transactions on the Dogecoin network are grouped into blocks, which are then added to the blockchain through a process known as mining. Miners compete to solve complex mathematical puzzles, and the first miner to solve a puzzle validates the transactions in a block and adds it to the blockchain. Dogecoin’s block time is approximately one minute, resulting in faster transaction confirmations compared to Bitcoin’s ten-minute block time. Additionally, Dogecoin originally had a limitless supply, with a fixed reward of 10,000 DOGE per block; however, this changed in 2014 to an inflationary model, where a fixed number of coins are added to the supply each year. This combination of technology and economic design contributes to Dogecoin’s unique characteristics and its appeal within the cryptocurrency ecosystem.

Supply of coins

Dogecoin’s supply dynamics are distinctive within the cryptocurrency landscape. Initially launched with no hard cap on its total supply, Dogecoin features an inflationary issuance model designed to maintain a steady influx of coins into the market. Unlike Bitcoin’s fixed supply of 21 million coins, Dogecoin’s issuance rate started at 5 billion coins per year and gradually decreases over time. This inflationary nature ensures a continuous supply of Dogecoin, theoretically allowing for ongoing miner rewards and a sustained incentive for network participation. However, it’s worth noting that while the supply of Dogecoin is technically infinite, the rate of new coin creation diminishes over time, resulting in a decreasing inflation rate and a more stable supply trajectory. This unique supply mechanism distinguishes Dogecoin from many other cryptocurrencies and can influence its long-term economic dynamics and utility as a medium of exchange or store of value.

Historical data for Doge Coin

How to get the data?

The Doge Coin is popular cryptocurrency on the market, and historical data for the Doge Coin 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 Doge Coin on Yahoo! Finance (the Yahoo! code for Doge Coin is Doge-USD).

Figure 2. Doge Coin data

Source: Yahoo! Finance.

Historical data for the Doge Coin market prices

Since its inception in 2013, Dogecoin has experienced notable price fluctuations, driven by a combination of factors including market speculation, community engagement, and broader trends in the digital asset space. Initially launched as a joke currency, Dogecoin’s price remained relatively stable for several years, trading at fractions of a cent. However, its price surged dramatically in early 2021, fueled by social media hype and celebrity endorsements, reaching all-time highs of over 70 cents per coin. This unprecedented rally brought Dogecoin into the spotlight, attracting widespread attention from investors and media outlets. Despite subsequent price corrections, Dogecoin has maintained a prominent position in the cryptocurrency market, with its price influenced by various factors including Elon Musk’s tweets, meme culture, and broader market sentiment. Overall, the historical price evolution of Dogecoin exemplifies the volatile and dynamic nature of the cryptocurrency market, highlighting the interplay between community enthusiasm, market speculation, and broader industry trends.

Figure 3 below represents the evolution of the price of Doge Coin in US dollar over the period November 2018 – December 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 Doge 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 Doge Coin.

Data file

The R program that you can download above allows you to download the data for the Doge Coin from the Yahoo! Finance website. The database starts on October, 2018.

Table 1 below represents the top of the data file for the Doge Coin downloaded from the Yahoo! Finance website with the R program.

Table 1. Top of the data file for the DOGE 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.

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

import yfinance as yf

import pandas as pd

# Define the ticker symbol for Doge Coin

doge_ticker = “DOGE-USD”

# Define the date range for historical data

start_date = “2020-01-01”

end_date = “2022-01-01”

# Download historical data using yfinance

doge_data = yf.download(doge_ticker, start=start_date, end=end_date)

# Create a Pandas DataFrame from the downloaded data

doge_df = pd.DataFrame(doge_data)

# Define the Excel file path

excel_file_path = “DOGE_historical_data.xlsx”

# Save the data to an Excel sheet

doge_df.to_excel(excel_file_path, sheet_name=”DOGE_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 Doge Coin

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

Figure 4. Evolution of the DOGE Coin Figure 5 below gives the evolution of the Doge Coin returns from November, 2018 to December 31, 2022 on a daily basis.

Figure 5. Evolution of the Doge Coin returns.

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

Summary statistics for the Doge Coin

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

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

Statistical distribution of the Doge Coin returns

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

Figure 6. Historical Doge Coin 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 Ethereum daily returns with parameters estimated over the period from October, 2018 to December, 2022.

Figure 9. Gaussian distribution of the Doge Coin returns.

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

Risk measures of the Doge Coin returns

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

Table 3 below presents the following risk measures estimated for the Doge 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 Doge Coin.

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 Doge Coin.

Why should I be interested in this post?

This blog offers an engaging exploration into a cryptocurrency that transcends traditional finance, appealing to a wide audience due to its cultural relevance, investment potential, and vibrant community. From its origins as a meme coin to its remarkable price movements, understanding Dogecoin’s dynamics provides valuable insights into both the cryptocurrency market and internet culture. Delving into its technological underpinnings, community engagement, and market trends offers a concise yet comprehensive overview of Dogecoin’s significance in the evolving landscape of digital currencies.

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 Doge Coin

CoinMarketCap Historical data for Doge Coin

About the author

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

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

May 29, 2024April 2, 2024 by 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.

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.

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

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.

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

April 1, 2024 by Quentin CHUZET

Private Banks : Treasuries Departments and proprietary asset allocation

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

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.

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).

My experience as Revenue Analyst at Olympique de Marseille

April 1, 2024 by Quentin CHUZET

My experience as Revenue Analyst at Olympique de Marseille

In this article, Quentin CHUZET (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2019-2023) shares his professional experience as Revenue Analyst at Olympique de Marseille.

About the company

Olympique de Marseille is a French football club founded in 1899. It is one of the most emblematic clubs in French and European football history, with an emblematic international reputation. Through decades of performance and media exposure, it has become an emblem of French sport, inscribing itself as a culture and tradition for millions of supporters around the world.

Logo of the Olympique de Marseille
Logo of Olympique de Marseille
Source: Olympique de Marseille.

Its takeover by the Mc Court Group in 2016 initiated a radical change in management and in the harmonization of processes, to continue to broaden its scope and enable grows bigger. Therefore, the club generates more than 200m € for the seasons 2021-2022 and 2022-2023 with 270m € of revenues expected for the season 2023-2024 It bases its growth on the sporting aspect but also on innovative sport business strategies. With a stadium capacity surpassing any of other Ligue 1 club (67.000 seats) and a meticulous ticketing strategy, the club is part of the Top 10 of the most attractive stadiums in Europe and continues to capitalize on its national and European reach.

OM Revenues vs other clubs.
OM Revenues vs others – Deloitte Report
Source: Deloitte Football Benchmark.

My apprenticeship

Joining Olympique de Marseille allowed me to discover and evolve within the soccer ecosystem, but also to take a practical approach to issues relating to football clubs revenues optimization and maximization.

The diversity of revenue sources at a club like Olympique de Marseille opens the door to a wide range of missions and tasks. As I was directly involved in all strategic aspects of revenue management : I was notably involved in defining the pricing of Ticketing & Hospitality (VIP) revenue streams, drawing up business plans for club projects, managing budgets and revenue targets set at the start of the season, analyzing financial opportunities represented by the entry of sponsors or investors, etc.

I therefore occupied a strategic role, acting as a pivot between the sales and finance teams, defining budgets alongside management control and that could be reachable for the commercial team, and while supporting the sales teams in achieving the latter by analyzing revenue opportunities and proposing strategic recommendations.

1/4 French Cup, season 2022-2023 : OM-Annecy. Historical record of attendance for a ¼ in the club’s and competition’s history.
Velodrome OM Annecy
Source: Olympique de Marseille.

My missions

Strategic recommendations and pricing

The main task assigned to a Revenue Analyst at Olympique de Marseille is to play an active role in defining pricing for the general public and VIPs at each match. To do this, I had to identify the exogenous variables of each match and quantify their impact in order to measure inbound demand as a function of a certain price level. Using historical data from similar matches and cross-referencing it with the maximization opportunities available at any given moment, I was responsible for drawing up the pricing and sales strategy (choosing best commercialization timing, best offer to address etc.) calculating forecast revenue trends and presenting my recommendations to management on several occasions. Thanks to our strategy the club reached the total of 1.550.000 attenders for a unique season and broke other records as the highest attendance ever known for one game (65.984 attendees vs PSG), the highest average attendance for one season (62.065 attendees), the highest number of sold-out games (23 over the season) and the highest attendance ever known for a French Cup game (63.929 attendees). Finally, the strategies leaded by the Revenue Management department leaded to a 4.8M € maximization of the total club revenues.

Participation in club project profitability studies

More generally, as a Revenue Analyst, I was involved in every aspect of the club’s revenue development.

Conducting pricing analysis and manage commercial strategy was part of the Ticketing & Hospitality revenue stream. However, my missions also involved providing my vision on certain high-stakes projects involving additional revenue generation. From the arrival of potential sponsors and investors to the launch of the new “Peuple Bleu&Blanc” loyalty program, I was responsible for measuring the profitability of these projects by calculating the potential income they could generate, while defining and steering a strategy for maximizing it.

Intermediary role between sales and finance teams

The role was multi-functional in terms of strategy, finance, and sales. We had to define ambitious, realistic budgets alongside management control, while supporting the sales teams in achieving them. To do this, we analyzed revenue opportunities and proposed appropriate strategic recommendations for the Ticketing, Hospitality, Retail, Membership and other revenue streams.

Development of steering tools and reporting dashboards

As the analysis are based almost entirely on the measurement of KPIs (Key Performance Indicators), the development of management tools represented a major challenge. So, it was up to me to play an active part, working with the IT (Information Technology) and back-office teams.

Required skills and knowledge

In my role at Olympique de Marseille, it is crucial to understand the financial and commercial issues facing the club and its teams. It’s also important to develop a strategic way of thinking in which everything can be optimized.

As a Revenue Analyst, every recommendation needs to be backed up by data, and every opportunity must be quantified to support each recommendations. That’s why you need to have an excellent mind for analyzing and interpreting data.

Furthermore, the numerous presentations and reports to management require solid written and oral communication skills (PowerPoint presentations, e-mail reports, etc.), as well as adaptability to the person you’re talking to, in order to make the right points and get your recommendations accepted.

In addition, mastery of Excel and the budgeting/revenue forecasting process is key to this position. As each decision has a significant impact on the season’s revenues, numerous budget forecasts are required, particularly around Best/Mid/Worst scenarios. It is also necessary to master the Power BI tool (Business Intelligence by Microsoft) to be able to develop and interpretate every KPI.

Finally, it is essential to be humble and questioning in order to identify areas for improvement in each strategy, and to meet management’s requirements on an ongoing basis and know how to identify the best practices of these recommendations.

OM revenues details for the 2022/2023 season.
OM Revenues - Deloitte Report
Source : Deloitte Report

Financial concepts related to my apprenticeship

Budget Management

Budget management is a key concept in this position, influencing the decisions of sales teams and having a major impact on every strategy implemented. Budget management represents a guideline for costs and revenues, and in particular the preparation of a forecast budget which serves as a basis for input management.

Revenue Management

Revenue Management is a key concept which consists of defining variables considered to have an impact on maximizing revenues in periods of growth and limiting losses in periods of decline. For a club such as Olympique de Marseille, this means capitalizing on positive trends (good sporting dynamics, special matches, etc.) and protecting revenues when the sportive situation is non-favorable : succession of bad games, bad weather on matchday, non-attractive game due to opponent.

Business Plans and Financial modeling

Studying the profitability of projects and simulating the impact of commercial strategies involves drawing up business plans and financial models in order to calculate forecast revenues. To do this, we need to establish different scenarios adapted to different contexts and KPIS and then take into account the action and development plans for these projects, as well as the business models used.

Why should I be interested in this post?

If you’re interested in the field of Finance and Strategy, particularly within a soccer club, then this article will give you a clearer picture of the tasks and skills required, as well as the importance of Revenue Management within a sporting institution. Through this empowering experience, I was able to develop both the hard skills that will serve me well in my future career in Finance, and the soft skills that will enable me to perform in a demanding, high-pressure environment.

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.

Useful resources

Official OM website

Deloitte reporting : Football Money League 2024 edition

Official French Football League website : All DNCG report until 2021-2022 season

Example of a Football club balance sheet : SC Bastia (French 1st division) during the 2015-2016 season

About the author

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

Historical context and background

Key features

Scalability

Fast Transaction Speeds

Low Transaction Costs

Proof of History (PoH)

Support for Smart Contracts

Ecosystem and Development Tools

Interoperability

Use cases

Non-Fungible Tokens (NFTs)

Gaming and Virtual Worlds

Decentralized Autonomous Organizations (DAOs)

Tokenization of Real-World Assets

Technology and underlying blockchain

Supply of coins

Historical data for Solana

How to get the data?

Historical data for the Solana market prices

R program

Python code

Evolution of the Solana (SOL)

Summary statistics for the Solana (SOL)

Statistical distribution of the Solana (SOL) returns

Historical distribution

Gaussian distribution

Risk measures of the Solana (SOL) returns

Why should I be interested in this post?

Related posts on the SimTrade blog

About cryptocurrencies

About statistics

Useful resources

Academic research about risk

Data

About the author

Bitcoin est un rêve, un idéal, un espoir

Autres articles sur le blog

A propos de l’auteur

Types of Market Consensus

Introduction

Price Consensus

Market consensus by financial analysts

Consensus on revenues and earnings

Consensus on economic indicators

Market consensus by technical analysts

Factors influencing the market consensus

Size of the company and coverage by financial analysts

Market sentiment

Volatility expectations

Policy Consensus

Technological Consensus

Conclusion

Why should I be interested in this post?

Related posts on the SimTrade blog

Useful resources

About the author

Job description – Financial analysts

Introduction

What Does a Financial Analyst Do?

Buy-Side Financial Analysts: Crafting Investment Strategies

Role and responsibilities

Examples of companies employing Buy-Side Financial Analysts

Sell-Side Financial Analysts: Providing Market Insights and Advisory Services

Role and responsibilities

Examples of companies employing Sell-Side Financial Analysts:

Skills and Qualifications

Remuneration

Why should I be interested in this post?

Related posts on the SimTrade blog

Useful resources

About the author

Institutional Brokers’ Estimate System (IBES)

Introduction

History

Key Components of IBES

Earnings Estimates

Revenue Projections

Forecasts for Key Financial Metrics

Coverage

Period