Tron: Unveiling the Future of Decentralized Applications

May 29, 2024 by Snehasish CHINARA

In this article, Snehasish CHINARA (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2024) explains the innovative world of Tron cryptocurrency, shedding light on its role in the evolution of decentralized applications and the broader blockchain ecosystem.

Historical context and background

Tron (TRX) was founded by Justin Sun in 2017, aiming to revolutionize the entertainment industry and decentralize the internet. The project’s initial coin offering (ICO) raised approximately $70 million, showcasing significant investor interest. Tron was designed as a decentralized platform to support smart contracts and high-throughput applications, providing an infrastructure that could handle a large volume of transactions efficiently.

Initially, Tron was launched as an ERC-20 token on the Ethereum blockchain. This allowed Tron to leverage Ethereum’s robust development community and infrastructure while developing its own blockchain. On June 25, 2018, Tron completed its mainnet launch, migrating from the Ethereum network to its proprietary blockchain, known as Odyssey. This transition marked Tron’s independence and its capability to support decentralized applications (dApps) natively.

The Tron ecosystem has expanded rapidly, integrating various blockchain-based projects and applications. In July 2018, Tron acquired BitTorrent, a popular peer-to-peer file-sharing protocol, further expanding its reach and application base. This acquisition was significant as it integrated BitTorrent’s extensive user base with Tron’s blockchain technology, exemplifying Tron’s vision of a decentralized internet.

Moreover, Tron’s commitment to fostering a decentralized web extends to its support for decentralized finance (DeFi). By providing a robust platform for creating and deploying decentralized applications, Tron has positioned itself as a key player in the DeFi space, enabling applications such as JustSwap, a decentralized exchange, and various other lending and borrowing platforms.

Tron Logo

Source: Tron.

Figure 1. Key Dates in Tron History

Source: Yahoo! Finance.

Key features

Delegated Proof of Stake (DPoS) Consensus Mechanism

Tron’s blockchain utilizes the DPoS consensus algorithm, which enhances scalability and transaction speed. In this system, TRX holders vote for super representatives (SRs) who are responsible for validating transactions and securing the network. This method is more energy-efficient and faster compared to traditional Proof of Work (PoW) systems.

High Throughput and Scalability

Tron’s network is capable of processing up to 2,000 transactions per second (TPS), significantly higher than Bitcoin and Ethereum. This high throughput makes Tron suitable for a wide range of decentralized applications (dApps) that require rapid and efficient transaction processing.

Low Transaction Fees

Transaction fees on the Tron network are minimal, making it an attractive platform for developers and users alike. This cost-efficiency is crucial for microtransactions and frequent trading activities, which are common in decentralized finance (DeFi) applications.

Smart Contracts and dApps

Tron supports the development and deployment of smart contracts and decentralized applications. The Tron Virtual Machine (TVM) is fully compatible with the Ethereum Virtual Machine (EVM), enabling developers to easily port their Ethereum-based dApps to Tron. This compatibility fosters a rich ecosystem of decentralized applications across various sectors.

TRC-20 and TRC-721 Tokens

Tron offers its own standards for tokens: TRC-20 for fungible tokens and TRC-721 for non-fungible tokens (NFTs). These standards are similar to Ethereum’s ERC-20 and ERC-721, respectively, allowing developers to create and manage a wide range of digital assets on the Tron network.

Decentralized Storage

Tron aims to decentralize the internet, and part of this vision includes decentralized storage solutions. By integrating BitTorrent technology, Tron provides a decentralized file sharing and storage system, enhancing data privacy and security.

Rich Developer Resources

Tron offers extensive resources for developers, including comprehensive documentation, development tools, and support programs. This robust support infrastructure encourages innovation and development within the Tron ecosystem.

Use cases

Decentralized Apps (dApps)

Tron is designed to support dApps across multiple sectors. These applications benefit from Tron’s high throughput and low transaction fees, making them more efficient and cost-effective. Popular dApps on Tron include games, social media platforms, and financial services.

Decentralized Finance (DeFi)

Tron has become a significant player in the DeFi space, providing infrastructure for decentralized exchanges (DEXs), lending platforms, and stablecoins. JustSwap, Tron’s decentralized exchange, allows users to trade TRC-20 tokens without intermediaries. Additionally, platforms like JUST provide decentralized lending and borrowing services.

Digital Content and Entertainment

Tron’s primary mission is to decentralize the internet, particularly the digital content and entertainment industry. Platforms like TRON TV and BitTorrent integrate with Tron to offer decentralized content distribution, allowing content creators to share their work directly with consumers without intermediaries, ensuring fairer revenue distribution.

Supply Chain Management

Tron can be used to enhance transparency and traceability in supply chain management. By recording transactions and product movements on the blockchain, companies can ensure authenticity and reduce fraud. This use case is particularly relevant for industries like pharmaceuticals, food, and luxury goods.

Gaming

Tron’s blockchain supports various blockchain-based games that leverage the benefits of decentralized infrastructure. These games offer players true ownership of in-game assets, transparent reward systems, and the ability to trade items outside the game environment. Titles like WINk and others on the Tron platform showcase the potential for blockchain in gaming.

Voting and Governance

Tron’s DPoS consensus mechanism is an example of blockchain-based voting and governance in action. The Tron community votes for super representatives who validate transactions and make governance decisions. This model can be applied to other voting systems, including corporate governance, municipal voting, and organizational decision-making processes.

Data Storage and Sharing

Integrating BitTorrent with Tron provides a decentralized solution for data storage and sharing. This system allows users to store and share files securely without relying on centralized servers, enhancing privacy and reducing the risk of data breaches.

Technology and underlying blockchain

The Tron blockchain is a high-performance, decentralized platform designed to support a vast array of decentralized applications (dApps) and smart contracts. At its core, Tron utilizes a Delegated Proof of Stake (DPoS) consensus mechanism, which enhances transaction throughput and network scalability. DPoS involves TRX holders voting for super representatives (SRs) who are responsible for validating transactions and maintaining the blockchain, ensuring efficient and democratic governance. Tron’s architecture includes the Tron Virtual Machine (TVM), which is fully compatible with the Ethereum Virtual Machine (EVM). This compatibility enables seamless migration of dApps from Ethereum to Tron, fostering cross-chain interoperability. Tron’s blockchain can process up to 2,000 transactions per second (TPS), significantly outpacing many other blockchain networks, making it suitable for applications requiring high transaction volumes. Additionally, Tron supports the creation of custom tokens through its TRC-20 and TRC-721 standards, analogous to Ethereum’s ERC-20 and ERC-721, facilitating the development of fungible tokens and non-fungible tokens (NFTs). This robust infrastructure, combined with low transaction fees, makes Tron an attractive platform for developers and enterprises looking to build decentralized solutions.

Supply of coins

Tron (TRX) operates with a total maximum supply of 100 billion tokens. At its inception, Tron’s token allocation included 40% for initial token sale, 15% for the Tron Foundation, 35% for private placements, and 10% for the team. Over time, Tron’s supply distribution has undergone changes due to various factors such as token burns, airdrops, and network upgrades. Notably, Tron has implemented periodic token burns to reduce the circulating supply and increase scarcity, thereby potentially driving up the value of TRX. Additionally, the Tron Foundation periodically releases tokens from its allocated supply for ecosystem development, partnerships, and other strategic initiatives aimed at fostering the growth of the Tron network. Overall, Tron’s coin supply dynamics are managed with a focus on maintaining balance between liquidity, scarcity, and ecosystem development to support the long-term sustainability and adoption of the Tron blockchain.

Historical data for TRX (Tron)

How to get the data?

Tron is a popular cryptocurrency on the market, and historical data for the Tron 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 Tron on Yahoo! Finance (the Yahoo! code for Tron is TRX-USD).How to get the data?

Figure 2. Tron data

Source: Yahoo! Finance.

Historical data for the TRX (Tron) market prices

TRX (Tron) has experienced a dynamic evolution in its market prices since its inception in 2017. Initially introduced at a fraction of a cent, TRX quickly gained attention within the cryptocurrency community due to its ambitious vision and promising technology. The early months saw considerable volatility, with TRX experiencing rapid price fluctuations amid speculation and market sentiment. As Tron’s ecosystem matured and adoption grew, its market prices reflected both the broader trends in the cryptocurrency market and Tron-specific developments. Notable milestones, such as mainnet launches, strategic partnerships, and ecosystem expansions, often correlated with significant price movements. Despite periods of volatility, Tron’s market prices have shown resilience and a tendency to recover from downturns, reflecting investor confidence in its long-term potential. Over time, TRX has established itself as one of the prominent cryptocurrencies, with a growing user base and a diverse range of use cases. As the cryptocurrency market continues to evolve, Tron’s market prices are likely to reflect both internal and external factors, shaping its trajectory within the broader blockchain landscape.

Figure 3 below represents the evolution of the price of Tron (TRX) in US dollar over the period November 2017 – May 2024 . The price corresponds to the “closing” price (observed at 10:00 PM CET at the end of the month).

Figure 3. Evolution of Tron 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 Tron (TRX).

Data file

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

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

Table 1. Top of the data file for Tron (TRX)

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 Tron (TRX) historical data and save it to an Excel sheet:

import yfinance as yf

import pandas as pd

# Define the ticker symbol for Iron

Tron_ticker = “TRX -USD”

# Define the date range for historical data

start_date = “2017-05-30”

end_date = “2024-04-30”

# Download historical data using yfinance

Tron_data = yf.download(Tron_ticker, start=start_date, end=end_date)

# Create a Pandas DataFrame from the downloaded data

Tron_df = pd.DataFrame(Tron_data)

# Define the Excel file path

excel_file_path = “Tron_historical_data.xlsx”

# Save the data to an Excel sheet

Tron_df.to_excel(excel_file_path, sheet_name=”Tron 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 TRX (Tron)

Figure 4 below gives the evolution of the TRX (Tron) on a daily basis.

Figure 4. Evolution of the TRX (Tron)

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

Figure 5 below gives the evolution of the TRX (Tron) returns from May 2017 to May 2024 on a daily basis.

Figure 5. Evolution of the TRX (Tron) returns.

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

Summary statistics for the TRX (Tron)

The R program that you can download above also allows you to compute summary statistics about the returns of the TRX (Tron).

Table 2 below presents the following summary statistics estimated for TRX (Tron):

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 TRX (Tron)

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

Statistical distribution of the TRX (Tron) returns

Historical distribution

Figure 6 represents the historical distribution of the TRX (Tron) daily returns for the period from May 2017 to May 2024.

Figure 6. Historical distribution of TRX (Tron) returns.

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

Gaussian distribution

Figure 7 below represents the Gaussian distribution of the TRX (Tron) daily returns with parameters estimated over the period from May 2017 to May 2024.

Figure 7. Gaussian distribution of the TRX (Tron) returns.

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

Risk measures of the TRX (Tron) returns

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

Table 3 below presents the following risk measures estimated for the TRX (Tron):

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 TRX (Tron)

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

Why should I be interested in this post?

This post provides an engaging exploration of Tron cryptocurrencies, tailored to both newcomers and seasoned crypto enthusiasts alike. It delves into Tron’s innovative blockchain technology and its transformative impact across diverse industries, from entertainment to decentralized finance. By delving into Tron’s underlying technology, consensus mechanism, and ecosystem development, readers can grasp the potential of Tron to revolutionize decentralized applications, scalability, and cross-chain interoperability. Furthermore, the post examines Tron’s historical price trends, market dynamics, and community governance structure, offering valuable insights for investors, traders, and ecosystem participants. Whether you’re curious about cutting-edge blockchain solutions or searching for investment opportunities in the crypto market, this post provides comprehensive insights into the significance and potential of Tron in shaping the future of decentralized technologies.

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 Tron

CoinMarketCap Historical data for Tron

About the author

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

My Professional Experience as a Quantitative Analyst Intern at Findoc Financial Services

December 4, 2024May 24, 2024 by Praduman Agrawal

My Professional Experience as a Quantitative Analyst Intern at Findoc Financial Services

In this article, Praduman AGRAWAL (ESSEC Business School, Visiting Scholar, Summer 2024) shares his professional experience as a Quantitative Analyst Intern at Findoc Financial Services.

About the Company

Findoc Group offers both institutional and retail clients quality products and services that cover equity trading, derivative trading, commodity trading, currency trading, IPOs, and mutual fund investments. Findoc Group also offers depository services through NSDL to create a seamless transaction platform. Trades executed through the Findoc Group companies are settled through the Findoc Group Depository Participant. Findoc Group is also involved in business through Findoc Finvest Private Limited (NBFC) for activities such as loan against shares, loan against property, and loan against gold, among other loan services.

Findoc team focuses on providing long-term value addition to its clients while maintaining the highest standards of excellence and professionalism. With a vision to earn a name that represents trust, growth, and passion, Findoc Group practices transparent business operations and prioritizes client satisfaction. Their professional approach emphasizes long-term relationships with clients, constantly generating value-added features without passing the cost burden to the clients.

Logo of the company.
Logo of Findoc Financial Services
Source: Findoc Financial Services

About the Department

At Findoc Financial Services, I was part of the Quantitative Analysis department, specifically within the quantitative desk. Our team was dedicated to developing and implementing trading strategies focused on the Forex pair. My role involved designing a portfolio of trading strategies for USDINR aimed at reducing unsystematic risk through diversification. Using Easy Language on TradeStation, I coded these strategies incorporating a range of indicators such as Bollinger Bands, MACD, VWAP, and RSI, which I customized to fit our specific needs. In addition to the technical aspects, my work also delved into economic analysis and trading psychology, ensuring a comprehensive approach to strategy development. Utilizing 20 years of historical data, I meticulously conducted backtesting and forward testing, rigorously evaluating the effectiveness of our strategies and refining them for optimal performance.

My Internship

During my internship at Findoc Financial Services, I was entrusted with the development and implementation of trading strategies for the Forex market, specifically focusing on the USDINR currency pair. My primary responsibilities included coding trading strategies using Easy Language on TradeStation, backtesting these strategies using historical data, and performing forward testing to validate their effectiveness in real-time market conditions.

My Missions

My main mission was to develop a diversified portfolio of trading strategies aimed at reducing non-systematic risk. This involved extensive coding, data analysis, and testing to ensure the strategies were robust and effective. I also conducted economic analysis and considered trading psychology to enhance the strategies further.

Required Skills and Knowledge

During my internship, I honed a blend of soft and hard skills essential for success in quantitative analysis and trading strategy development. Effective communication was crucial for collaboration and presenting findings to stakeholders. Critical thinking and problem-solving skills were indispensable when encountering challenges in data analysis or strategy development. Adaptability was key as market conditions and requirements often changed, requiring flexibility in approach and strategy. Attention to detail was essential for ensuring accuracy in data analysis and coding of trading algorithms.

On the hard skills front, proficiency in programming languages such as Easy Language for TradeStation was fundamental for coding and testing trading strategies. A strong understanding of statistical methods and financial mathematics was necessary for designing robust strategies and interpreting results accurately. Expertise in data analysis tools and techniques facilitated thorough backtesting and forward testing of strategies. Additionally, a deep understanding of financial markets, including factors influencing currency movements, was essential for developing effective trading strategies.

What I Learned

Throughout my internship, I gained a deeper understanding of trading strategies and the technical and economic factors influencing currency markets. I learned the importance of rigorous testing and validation of strategies to ensure their effectiveness in real-time trading. Additionally, I developed valuable skills in programming, data analysis, and financial market analysis, which are crucial for a career in quantitative finance.

Financial Concepts Related to My Internship

Diversification

Diversification is the practice of spreading investments across different assets to reduce risk. In my role, I developed a portfolio of trading strategies for the USDINR currency pair. Each strategy had its unique approach and risk profile. By employing multiple strategies simultaneously, we aimed to diversify the risk associated with any single strategy. This concept directly links to my job as I focused on building a diversified portfolio of trading strategies to mitigate unsystematic risk.

Technical Indicators

Technical indicators are mathematical calculations based on historical price, volume, or open interest data. These indicators are used by traders to predict future price movements. In my role, I utilized various technical indicators such as Bollinger Bands, MACD, VWAP, and RSI to develop trading strategies for the USDINR pair. These indicators provided insights into market trends, momentum, volatility, and overbought/oversold conditions, which informed our trading decisions. Understanding and effectively using these indicators was crucial for building successful trading strategies.

Backtesting and Forward Testing

Backtesting involves testing a trading strategy using historical data to assess its performance. Forward testing, on the other hand, involves testing the strategy in real-time with live market data. In my role, I conducted both backtesting and forward testing of the trading strategies I developed. By analyzing past market data spanning 20 years, I assessed how well the strategies would have performed historically. Forward testing then allowed me to validate the strategies in real-time market conditions before implementation. The ability to conduct rigorous backtesting and forward testing was essential for evaluating the viability and effectiveness of our trading strategies.

Why Should I Be Interested in This Post?

If you love mathematics and finance, then a role as a Quantitative Analyst is a great fit for you. It broadens your horizon by offering different perspectives on financial markets. You will learn how small events can lead to significant changes in stock prices and how gap-up and gap-down movements can impact trading strategies. You will also gain skills in coding strategies (Algo-trading) that can be applied in various areas.

Useful Resources

Findoc Group

AlgoTrading with Kavin Davey

About the Author

The article was written in May 2024 by Praduman AGRAWAL (ESSEC Business School, Visiting Scholar, Summer 2024).

Structured Debt in Private Equity: Rated Feeder Funds and Collateral Fund Obligations

April 17, 2025May 22, 2024 by Dante MARRAMIERO

Structured Debt in Private Equity: Rated Feeder Funds and Collateral Fund Obligations

In this article, Dante MARRAMIERO (ESSEC Business School, Master in Strategy and Management of International Business (SMIB), 2022-2023) explains structured debt in Private Equity: Rated Feeder Funds and Collateral Fund Obligations. Such debt products open the market for insurance companies.

Structured Financing for Investment

In the intricate world of finance, innovative structures continually emerge to meet investor demands under the current regulatory framework. Among these structures are Rated Feeder Funds (RNFs) and Collateralized Fund Obligations (CFOs), which facilitate investments for investors with specific investment criteria. This happens particularly with insurance companies and asset managers, to access private equity or alternative strategy funds such as growth funds or private credit funds through rated debt instruments. However, structures like RNFs and CFOs are not without their complexities and regulatory scrutiny, especially in light of historical parallels to Collateralized Debt Obligations (CDOs) and the financial crisis of 2008. CFOs and CDOs have exactly the same structure but have different underlying assets: CDOs are based on debt, specifically mortgages while CFOs are based on Private Equity funds.

The two figures below represent the financial structure of RNFs and CFOs.

Figure 1. Financial structure of an RNF.

Source: the author.

Figure 2. Financial structure of a CFO.

Source: the author.

Rated Feeder Funds (RNFs) and Collateral Fund Obligations (CFOs) thus represent innovative approaches to structured financing, aiming to bridge the gap between investors and private equity or alternative strategy funds.

Why are CFOs and RNFs attracting insurance companies?

CFOs and RNFs are both characterized by a blend of debt and equity components. These vehicles raise capital by issuing debt securities, which are then used to invest in a diversified portfolio of assets across multiple funds. The presence of both debt and equity elements not only provides investors with exposure to various underlying assets (in which the various funds have invested) but also introduces a diversification effect that can attract capital seeking lower risk profiles. The debt issued by CFOs typically undergoes scrutiny by rating agencies to determine their creditworthiness, thereby providing assurance to investors regarding the quality of the investment.

On the other hand, RNFs operate through a structure where investors’ capital is channeled through a special purpose vehicle (SPV) or feeder fund. This feeder fund then invests in a master fund managed by the sponsor. This setup allows investors to gain exposure to the underlying assets of the master fund without directly participating in its operations. RNFs, similar to CFOs, offer rated debt instruments to investors, providing them with a regulated and transparent avenue to access private fund investments.

Regulatory Considerations and Risk Mitigation

One of the key attractions of RNFs and CFOs is the regulatory capital treatment they offer to institutional investors. By investing through rated debt instruments, regulated institutions such as insurance companies can benefit from reduced capital requirements compared to direct equity investments in underlying funds.

However, regulatory scrutiny is a critical aspect of these structures, particularly in jurisdictions like the United States and Europe. In the US, concerns have been raised regarding the applicability of risk retention rules, especially in CFO transactions where repayment primarily depends on limited partnership interests. Similarly, European regulations such as the UK and EU Securitization Regulations impose stringent requirements.

In the US, currently, the NAIC – National Association of Insurance Commissioners has blocked investments from insurance companies in CFOs but is working on possible ways to regulate the market and open the investment again in the future.

Conclusion: Balancing Innovation with Regulatory Compliance

Rated Feeder Funds (RNFs) and Collateral Fund Obligations (CFOs) represent innovative solutions for investors seeking exposure to private funds while optimizing regulatory capital requirements. However, their structural complexities and regulatory scrutiny, particularly in the aftermath of the 2008 financial crisis, underscore the importance of due diligence and compliance.

As financial markets evolve, the challenge lies in striking a balance between innovation and regulatory compliance. While RNFs and CFOs offer opportunities for capital efficiency and investment diversification, they must navigate a complex regulatory landscape to ensure stability and mitigate systemic risks. Only through careful consideration of lessons from history and adherence to regulatory guidelines can these structured financing solutions fulfill their promise in the modern financial ecosystem.

Example of precedent transaction: Tikehau raise a $300 million CFO

Tikehau Capital has raised $300 million collateralized fund obligation backed by cashflows from commitments to its direct lending and private debt secondary strategies. The CFO’s assets consisted of interests in Tikehau’s own debt funds as well as third-party managed private debt funds originated by the firm’s private debt secondaries strategy, according to a statement from Jefferies, which advised on the transaction.

Specifically, the CFO assets were largely Tikehau-managed funds: the direct lending and private debt secondaries which were held on the firm’s balance sheet. This transaction allowed Tikehau to raise capital from the big American insurance companies that otherwise would have considered investing in Tikehau’s funds as out of scope.

Why should I be interested in this post?

This post could be particularly intriguing for business students because it highlights diverse methods of fundraising within the private equity sector. This knowledge could benefit students aiming for careers in finance and those seeking to secure funding for their own ventures. Moreover, it provides valuable insights into the pivotal role that debt plays in financing strategies. Furthermore, it could be a good competitive advantage during Private Equity interviews to know about structured finance as it is an emerging topic in the Private Equity industry and not everyone is up to date with it!

Useful resources

Hanson R. (08/11/2023) Collateralised fund obligations and rated note feeders: options for structuring investment into private funds Morgan Lewis

Cadwalader Brief Primer on CFOs and Rated Feeder Funds

About the author

The article was written in May 2024 by Dante MARRAMIERO (ESSEC Business School, Master in Strategy and Management of International Business (SMIB), 2022-2023).

Private Equity and Italy, is it a nice combination?

May 22, 2024May 19, 2024 by Dante MARRAMIERO

Private Equity and Italy, is it a nice combination?

In this article, Dante MARRAMIERO (ESSEC Business School, Master in Strategy and Management of International Business (SMIB), 2022-2024) explains about the peculiar situation of the Italian Private Equity market.

Italian conservative market is now opening up

Italy’s family-owned enterprises, steeped in tradition yet poised for transformation, stand at a crossroads of opportunity. For decades, these businesses have been the backbone of the country’s economy, rooted in principles of heritage and resilience. Yet, as the global marketplace evolves, so too must they. Enter private equity – once a distant concept, now a beacon of possibility, offering two distinct paths for growth: co-investment and external capital infusion.

As previously mentioned, family businesses dominate the Italian economy, with around 784,000 companies (nearly 85%) being family-owned according to AIDAF, the Italian Association of Family Businesses. These small and medium-sized enterprises are mainly active in sectors like fashion, mechanical engineering, and food, producing high-quality “Made in Italy” products.

One driver for ownership changes in Italian family businesses is succession planning, as many seek external capital to finance growth, international expansion, technology investments or innovations due to being undercapitalized. Private equity funds have stepped in to provide this capital, with Italy being one of Europe’s most mature private equity markets. As a confirmation of this, annual private equity investments in Italy correspond to around 0.36% of GDP, on par with Germany but lower than the UK, Netherlands, and France.

Key target sectors for Italian private equity include IT/communications and industrial goods. Around 14% of Italian family office assets are invested in private equity, with surveys suggesting most intend to increase these allocations. What could make the difference for the future is that younger generations of Italian wealth holders are expected to further drive family office investments into private equity and venture capital, being more interested in backing early-stage businesses, especially in technology. Their international outlook and experience abroad are also influencing asset allocation decisions.

Co-Investment: Fostering Collaboration for Shared Success

Traditionally, family businesses have been hesitant to engage with private equity, wary of relinquishing control and diluting their legacy. However, a paradigm shift is underway as a new wave of private equity firms sets its sights on these familial enterprises, offering tailored solutions to suit their diverse needs.

Co-investment, as mentioned earlier, represents one avenue for growth. Through co-investment, family businesses can partner with private equity firms to pursue inorganic growth opportunities while retaining operational control. This collaborative approach allows for shared risk and rewards, leveraging the strengths of both parties to unlock new synergies and market opportunities.

External Capital Infusion: Embracing Change for Accelerated Growth

Yet, co-investment is not the only path forward. Another emerging trend in Italy’s business landscape is the willingness of family businesses to open their doors to external capital funding, including partial divestment to private equity firms. While this option may involve ceding a degree of ownership and autonomy, it also presents an opportunity to access significant capital infusion and strategic guidance.

The decision between co-investment and external capital infusion is not a one-size-fits-all proposition. Each option carries its own set of benefits and considerations, depending on the unique circumstances and aspirations of the family business in question. Some may find co-investment to be a more palatable approach, allowing them to maintain a greater degree of control over their operations. Others may see external capital infusion as a means to accelerate growth and access new markets.

The Role of Family Offices

Integral to navigating these choices is the role of the family office – a trusted advisor tasked with safeguarding the financial interests of affluent families. Whether pursuing co-investment or external capital infusion, family offices play a crucial role in guiding decision-making and ensuring alignment with long-term goals.

As Italy’s family businesses chart a course toward the future, the convergence of private equity and family offices offers a wealth of opportunities for growth and revitalization. By embracing both co-investment and external capital infusion, these enterprises can leverage the strengths of private equity partnerships while preserving their unique identities and legacies.

Conclusion: Two Paths, One Destination

In conclusion, the dual paths of co-investment and external capital infusion represent two sides of the same coin for Italy’s family businesses. By carefully weighing the options and leveraging the expertise of family offices, these enterprises can navigate the complexities of modern business and chart a course towards sustainable success in an ever-changing world.

Why should I be interested in this post?

This post presents a valuable opportunity to better understand the unique characteristics of the Italian market, predominantly driven by family businesses, and to explore its evolving landscape as it embraces a new business paradigm: Private Equity. Studying this transition offers insights not only for academic enrichment but also for future career prospects in Private Equity. Understanding how Italy’s traditional family business-dominated market is adapting to and integrating Private Equity opens doors for both educational exploration and potential professional paths in this sector.

Useful resources

Bocconi Student Private Equity Club (2024) Overview: The Private Equity Market in Italy over the last 20 years

Deutsche Bateiligungus AG (2023) Private equity in Italy – an undervalued market

FamilyCapital (2020) The big private equity groups backed by families

AltiGlobal (2023) Italy’s next generation of wealth holders step up to grow family wealth while they wait for senior leadership roles

About the author

The article was written in May 2024 by Dante MARRAMIERO (ESSEC Business School, Master in Strategy and Management of International Business (SMIB), 2022-2024)

BNB’s Journey through the Digital Economy’s Cryptocurrency Landscape

May 15, 2024May 15, 2024 by Snehasish CHINARA

In this article, Snehasish CHINARA (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2024) explains the intricate workings and transformative potential of Binance Coin (BNB), exploring its origins, utility, market dynamics, and future prospects of this cornerstone of the digital economy.

Historical context and background

Binance Coin (BNB) is one of the most prominent cryptocurrencies in the market, known for its unique utility and its connection to the world’s largest cryptocurrency exchange, Binance. Binance, founded by Changpeng Zhao (CZ) in 2017, quickly rose to prominence as one of the leading cryptocurrency exchanges globally. Its rapid growth can be attributed to several factors, including its user-friendly interface, wide range of supported cryptocurrencies, and a robust security system. Binance gained significant traction during the bull run of 2017, becoming the go-to platform for traders and investors seeking to capitalize on the burgeoning cryptocurrency market.

In July 2017, Binance conducted an Initial Coin Offering (ICO) to raise funds for the development of its platform and ecosystem. During the ICO, the exchange issued its native cryptocurrency, Binance Coin (BNB), which operates on the Ethereum blockchain as an ERC-20 token. The ICO was a massive success, raising $15 million within just a few weeks.

BNB was initially designed as a utility token to facilitate transactions on the Binance platform, offering users various benefits such as discounted trading fees and participation in token sales hosted on the exchange. Binance committed to using a portion of its profits to buy back and burn BNB tokens periodically, reducing the overall supply over time and potentially increasing its value.

As Binance continued to expand its services and offerings, the utility of BNB expanded beyond just a means of reducing trading fees. BNB became an integral part of the Binance ecosystem, serving as the native currency for various applications and services, including Binance Launchpad, Binance Smart Chain, Binance DEX (decentralized exchange), and Binance NFT marketplace, among others.

The introduction of Binance Smart Chain (BSC) in 2020 further elevated the importance of BNB in the cryptocurrency space. BSC, a blockchain platform compatible with Ethereum Virtual Machine (EVM), was developed to provide an alternative to Ethereum for decentralized finance (DeFi) applications and decentralized applications (DApps). BNB serves as the native currency of BSC, powering transactions and fueling the ecosystem’s growth.

BNB Logo

Source: Binance.

Figure 1. Key Dates in BNB History

Source: Yahoo! Finance.

Key features

Utility Token

BNB was initially designed as a utility token to facilitate transactions on the Binance platform. Users can utilize BNB to pay for trading fees, exchange fees, and various other services offered by Binance.

Discounted Fees

One of the primary benefits of holding BNB on the Binance exchange is the opportunity to receive discounts on trading fees. Binance offers users who pay their trading fees with BNB a significant discount, incentivizing its use within the platform.

Token Burn

Binance commits to using a portion of its profits to buy back and burn BNB tokens regularly. This token burning mechanism reduces the total supply of BNB over time, potentially increasing its scarcity and value.

Multiple Use Cases

BNB has expanded beyond its original utility and is now used for various purposes within the Binance ecosystem. It serves as the native currency for token sales on Binance Launchpad, transaction fees on Binance DEX, and participation in Binance NFT marketplace, among other applications.

Binance Smart Chain (BSC)

BNB is the native cryptocurrency of Binance Smart Chain, a blockchain platform compatible with Ethereum Virtual Machine (EVM). BSC offers low transaction fees and high throughput, making it an attractive option for decentralized applications (DApps) and decentralized finance (DeFi) projects.

Governance

BNB holders have the opportunity to participate in the governance of the Binance ecosystem. They can vote on proposals and decisions related to the development and direction of Binance, providing a degree of decentralization and community involvement.

Staking and Yield Farming

BNB holders can participate in staking and yield farming programs, allowing them to earn rewards or yield by locking up their BNB tokens and contributing to the security and operation of the network.

Use cases

Participation in Token Sales

BNB holders often have exclusive access to token sales hosted on the Binance Launchpad platform. These sales allow users to invest in promising blockchain projects at an early stage, with BNB serving as the primary currency for participation.

Payment Method

BNB can be used as a payment method for various goods and services, both online and offline. Several merchants and businesses accept BNB as a form of payment, enabling users to utilize their cryptocurrency holdings for everyday transactions.

Binance Ecosystem Services

BNB is utilized within various services and applications offered by the Binance ecosystem. This includes decentralized finance (DeFi) protocols, decentralized exchanges (DEXs), non-fungible token (NFT) marketplaces, and more. BNB serves as the native currency for transactions, governance, and incentives within these platforms.

Cross-Chain Compatibility

With the introduction of Binance Smart Chain (BSC), BNB gained cross-chain compatibility, enabling it to be used in decentralized applications (DApps) and DeFi protocols across different blockchain networks. This expansion of utility enhances the interoperability and flexibility of BNB as a cryptocurrency.

Technology and underlying blockchain

Binance Coin (BNB) operates on two primary blockchain networks: the Ethereum blockchain and the Binance Smart Chain (BSC). Initially launched as an ERC-20 token on the Ethereum blockchain, BNB has since undergone a significant expansion with the introduction of the Binance Smart Chain. The Ethereum-based BNB tokens are utilized for various functions within the Binance ecosystem, including trading fee discounts, participation in token sales, and payments. However, to address scalability and transaction cost concerns, Binance developed the Binance Smart Chain, a parallel blockchain compatible with Ethereum Virtual Machine (EVM). BSC combines high performance with low transaction fees, offering a viable alternative for decentralized applications (DApps) and decentralized finance (DeFi) protocols. BNB serves as the native currency of BSC, facilitating transactions, powering smart contracts, and providing liquidity across the Binance Smart Chain ecosystem. BSC employs a proof-of-stake (PoS) consensus mechanism, where validators stake BNB to secure the network and validate transactions. This hybrid approach leverages the benefits of both decentralization and efficiency, making BNB and the Binance Smart Chain integral components of the rapidly evolving blockchain landscape.

Supply of coins

The total supply of Binance Coin (BNB) is capped at 200 million tokens. However, BNB’s initial distribution was structured to release a portion of this supply gradually over time. During its initial coin offering (ICO) in July 2017, Binance allocated 50% of the total token supply to the public sale, representing 100 million BNB tokens. The remaining 50% was split among the Binance team, early investors, and strategic partners, with a significant portion earmarked for ecosystem development and marketing efforts. Notably, Binance committed to periodic token burns, where a portion of BNB tokens used to pay for trading fees on the Binance exchange is systematically removed from circulation and permanently destroyed. These token burns serve to reduce the overall supply of BNB over time, potentially increasing its scarcity and value. As of [current date], multiple token burns have occurred, steadily decreasing the circulating supply of BNB and contributing to its deflationary nature. Additionally, BNB’s migration from the Ethereum blockchain to its own blockchain, Binance Chain, introduced a new mechanism for token issuance and governance, further shaping the supply dynamics of BNB within the Binance ecosystem.

Historical data for Binance Coin (BNB)

How to get the data?

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

Figure 2. Binance Coin (BNB) data

Source: Yahoo! Finance.

Historical data for Binance Coin (BNB) market prices

Binance Coin (BNB) has experienced significant volatility and price fluctuations since its inception in 2017. Initially launched at a price of around $0.10 during its ICO, BNB quickly gained traction and surged to an all-time high of over $40 in January 2018, fueled by the widespread adoption of the Binance exchange and the overall bullish sentiment in the cryptocurrency market at the time. However, like many other cryptocurrencies, BNB subsequently underwent a prolonged bear market, with its price plummeting to single-digit levels by the end of 2018. The following years saw periods of both growth and consolidation for BNB, with its price closely tied to developments within the Binance ecosystem, regulatory developments, and broader market trends. Notable milestones, such as the launch of Binance Smart Chain (BSC) and the subsequent rise of decentralized finance (DeFi) applications, have often coincided with bullish movements in BNB’s price. As of the latest data, BNB has demonstrated resilience and continued to maintain its position among the top cryptocurrencies by market capitalization, reflecting its enduring relevance and utility within the digital asset landscape.

Figure 5 below represents the evolution of the price of Binance Coin (BNB) in US dollar over the period November 2017 – May 2024. The price corresponds to the “closing” price (observed at 10:00 PM CET at the end of the month).

Figure 3. Evolution of Binance Coin (BNB) 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 Binance Coin (BNB).

Data file

The R program that you can download above allows you to download the data for the Binance Coin (BNB) from the Yahoo! Finance website. The database starts on November, 2017.

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

Table 1. Top of the data file for the Binance Coin (BNB)

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 Binance Coin (BNB) historical data and save it to an Excel sheet::

import yfinance as yf

import pandas as pd

# Define the ticker symbol for Binance Coin (BNB)

Binance_ticker = “BNB-USD”

# Define the date range for historical data

start_date = “2020-01-01”

end_date = “2022-01-01”

# Download historical data using yfinance

Binance_data = yf.download(Binance_ticker, start=start_date, end=end_date)

# Create a Pandas DataFrame from the downloaded data

Binance_df = pd.DataFrame(Binance_data)

# Define the Excel file path

excel_file_path = “Cardano _historical_data.xlsx”

# Save the data to an Excel sheet

Binance_df.to_excel(excel_file_path, sheet_name=” Binance 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 Binance Coin (BNB)

Figure 4 below gives the evolution of the Binance Coin (BNB) on a daily basis.

Figure 4. Evolution of the Binance Coin (BNB)

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

Figure 5 below gives the evolution of the Binance Coin (BNB)returns from November, 2017 to May, 2024 on a daily basis.

Figure 5. Evolution of the BNB returns.

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

Summary statistics for the Binance Coin (BNB)

The R program that you can download above also allows you to compute summary statistics about the returns of the Binance Coin (BNB).

Table 2 below presents the following summary statistics estimated for Binance Coin (BNB):

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 Binance Coin (BNB).

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

Statistical distribution of the Binance Coin (BNB) returns

Historical distribution

Figure 6 represents the historical distribution of the Binance Coin (BNB) daily returns for the period from November, 2017 to May, 2024.

Figure 6. Historical BNB distribution of the returns.

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

Gaussian distribution

Figure 7 below represents the Gaussian distribution of the Binance Coin (BNB)daily returns with parameters estimated over the period from November 2017 to May 2024.

Figure 7. Gaussian distribution of Binance Coin (BNB) returns.

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

Risk measures of the Binance Coin (BNB)returns

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

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 Binance Coin (BNB).

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 Binance Coin (BNB)while the study of the right tail is relevant for an investor holding a short position in the Binance Coin (BNB).

Why should I be interested in this post?

This post offers a captivating exploration of Binance Coin (BNB), tailored for both newcomers to the cryptocurrency realm and seasoned enthusiasts alike. It delves into the innovative utility and multifaceted ecosystem surrounding BNB, shedding light on its pivotal role within the Binance platform and beyond. By dissecting BNB’s diverse applications, including trading fee discounts, participation in token sales, and ecosystem services like decentralized finance (DeFi) and non-fungible tokens (NFTs), readers can grasp the breadth of opportunities presented by this digital asset. Additionally, the post delves into the historical price data and supply dynamics of BNB, providing valuable insights for investors, traders, and stakeholders navigating the volatile cryptocurrency market. Whether you’re intrigued by the potential of utility tokens or seeking to harness the power of the Binance ecosystem, this post offers an insightful journey into the significance and possibilities of Binance Coin in shaping the future of decentralized finance and digital economies.

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 Binance Coin (BNB)

CoinMarketCap Historical data for Binance Coin (BNB)

About the author

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

The selling process of funds

May 11, 2024 by David-Alexandre BLUM

The selling process of funds

In this article, David-Alexandre BLUM (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2019-2023) explains about the selling process of funds.

The process of selling a fund involves several key steps and stakeholders. The fund is built by management teams that develop the strategy, allocation, and follow the macroeconomic scenario of economists. Let’s take the example of Lazard Frères Gestion.

Lazard Frères Gestion is characterized by active management based on fundamental analysis. Financial analysis and knowledge of companies are at the heart of the management process. All institutional management relies on the same macroeconomic scenario. Financial assets are the major source of financing for the real economy. The economy is cyclical. Financial assets are also cyclical. Volatility or bubble effects can cause the market price of an asset to diverge from its fair economic price in the short term, but not in the long term. Over time, it is the ability to capture the different phases of the cycle that creates outperformance.

Lazard Frères Gestion’s investment philosophy is based on both reading the economic cycle and evaluating companies. When products are launched and marketable, the distribution team prospects new clients (through seminars, contacts, or research), responds to tenders, or meets specific client requests.

The client meeting

Once the initial contact has been made, the sales team must prepare a specialized presentation for clients, gathering necessary documents (such as reports, management points, comparative studies, etc.). The preparation of such a presentation is crucial and should not be overlooked. It is important to choose the right arguments to highlight, taking into account the current economic environment and supporting the scenario put forward by the management team.

Selling a fund requires a thorough understanding of the management process, portfolio values, managers’ philosophy, as well as the benchmark performance and various risk and performance indicators of the presented fund.

Once the speech is prepared and the materials finalized, it is up to the seller to showcase the fund’s qualities. If the meeting goes well and negotiations are successful, the transaction can follow quickly or the client may request an entirely different service, such as a dedicated fund (customized fund), if it meets the criteria for accessing the service.

The team then needs to manage the contractual and administrative aspects to finalize the operation. The potential buyer conducts thorough due diligence on the fund, examining financial documents, contracts, internal procedures, and regulatory compliance to ensure there are no hidden issues or unforeseen risks.

Once due diligence is completed and the terms of the transaction are finalized, the parties draft and sign the sales contracts. In addition, they obtain the necessary approvals from the relevant regulatory authorities to transfer the fund’s management to the buyer.

After obtaining regulatory approvals, the client transfers the fund’s assets and liabilities to the buyer. This may include transferring securities, contracts with custodians and fund administrators, as well as communicating with the concerned investors.

Once the transfer is completed, the buyer integrates the fund into their own management structure and takes over the daily management of the fund, ensuring that investment objectives and applicable regulations are met.

Managing the relationship and the customer service

The team’s work does not stop at mere sales. Its role is much more important. It must ensure the proper receipt of financial and legal documents sent to clients periodically. It must also respond to all information requests about the subscribed products and ensure that the services subscribed by the client are performed. It is their duty to do everything possible to justify any underperformance and to nourish the relationship with information and explanations. To do this, they attend various committees and internal meetings to keep abreast of different movements and tactical bets. The sales team can encourage its client to invest more in its product and propose new products that seem to meet the client’s demand. In the event that the client chooses to withdraw from the fund, the relationship does not end there, and it is up to the sales team to work to potentially bring their client back.

From a technical standpoint, the sales team must master the knowledge of its products. The sales team must deeply understand the characteristics of the fund, including investment strategy, objectives, underlying assets, sectoral and geographical allocation, as well as fees and associated costs.

It is essential to know the historical performance of the fund, risk-adjusted returns (such as the Sharpe ratio), and comparisons with benchmark indices or similar funds. The sales team must be able to explain the main risks associated with the fund, such as market, credit, liquidity, and currency risks, as well as the measures taken by the fund manager to mitigate these risks.

Knowledge of regulations applicable to investment funds, such as UCITS or AIFMD directives in Europe, and disclosure and reporting requirements, is crucial to ensure compliance and client trust.

The sales team must be able to identify target investors for the fund, taking into account their risk profile, investment objectives, and liquidity needs.

Team members must master the procedures for subscribing and redeeming fund shares, including deadlines, fees, and specific conditions.

Communication and presentation: Communication and presentation skills are essential for clearly and convincingly explaining the benefits of the fund and addressing potential clients’ questions.

By mastering these technical aspects, the sales team will be able to effectively present the investment fund to potential clients, address their concerns, and assist them in making informed investment decisions.

Why should I be interested in this post?

The sales process of a fund helps to better understand the functioning of the investment fund market and the dynamics between asset management companies, investors, and financial intermediaries.

Investors who understand the sales process of a fund are better equipped to evaluate fund offerings and make informed investment decisions based on their objectives and risk tolerance. Professionals working or considering working in the financial industry, particularly in the areas of asset management, sales, and investment advisory, will benefit from a deep understanding of the fund sales process to enhance their skills and professional performance.

Furthermore, understanding the sales process of a fund can assist investors and financial advisors in comparing different investment products, such as mutual funds, exchange-traded funds (ETFs), and alternative investment funds, to determine the best solution for their specific needs and objectives.

Useful resources

Lazard Frères Gestion

Lazard Frères Gestion Les métiers de la gestion d’actifs (webinaire)

Lazard Frères Gestion Qu’est-ce que la gestion d’actifs ?

Lazard Frères Gestion Quelle allocation d’actifs pour un portefeuille diversifié ?

Hull J., P. Roger (2017) Options futures et autres actifs dérivés Pearson Education.

About the author

The article was written in May 2024 by David-Alexandre BLUM (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2019-2023).

My professional experience as an Institutional Sales Assistant with Lazard Frères Gestion

May 11, 2024 by David-Alexandre BLUM

My professional experience as an Institutional Sales Assistant with Lazard Frères Gestion

In this article, David-Alexandre BLUM (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2019-2023) shares his professional experience as an Institutional Sales Assistant with Lazard Frères Gestion.

About the company

In 1848, Alexandre, Lazare and Simon Lazard, three French brothers from the Alsace region, founded Lazard Frères & Co in New Orleans as a dry goods merchant, having emigrated to the United States in the early 1840s.

Today, Lazard serves investors worldwide with a broad range of global investment solutions and asset management services. Lazard Asset Management operates in 19 countries across North America, Europe and Asia, as well as in Australia. The group focuses on strategies based on rigorous and detailed analysis and dynamic asset management.

Lazard Frères Gestion combines the power of a large global organization with the flexibility of a small entrepreneurial firm and focuses on asset management and advisory services for individual and institutional clients.

With offices in Paris, Lyon, Bordeaux, Nantes, Brussels and Luxembourg, Lazard Frères Gestion manages €30 billion of assets on behalf of institutional and retail clients.

Lazard Frères Gestion Logo
Logo of Lazard Frères Gestion
Source: Lazard Frères Gestion

The department I joined at Lazard Frères Gestion is the Distribution France sales department. The team handles customer relations and organizes meetings to sell and inform about the company’s funds. The department works closely with the management and marketing teams. Financial analysis and company knowledge are at the heart of Lazard Frères Gestion’s management processes. The model is characterized by an “analyst-manager” approach: the entire team is involved in investment decisions, and each analyst-manager can contribute his or her own valuation and market expertise. In addition, all institutional management is based on the same macroeconomic scenario. Lazard Frères Gestion’s investment philosophy is based on both business cycle analysis and company valuation.

My internship

As an institutional sales assistant, I worked as part of the Distribution France sales team and was in constant contact with customers. Therefore, I took part in all our management committees, videoconferences and events. I worked with management, client servicing, reporting, risk control, legal and middle office.

My missions

Follow the sales activity of the Distribution team (multi-managers, private banks, insurance networks and independent asset managers)
Participate in specific responses to client requests
Help prepare client meetings
Prepare quarterly (or more frequent) management reports on flagship funds for clients
Summarize videoconferences organized on flagship funds and equity, fixed income, convertible and diversified fund management committees
Follow the performance of flagship funds and defined peer groups
Send specific documents from our tools (performance attribution, inventories, allocations, specific performance comments and fund positioning).

Required skills and knowledge

Firstly, the position I held required a broad knowledge of all asset classes. Lazard Frères Gestion offers a wide range of investment solutions: equities, bonds, mutual funds, structured products… It is therefore essential to know all the relevant vocabulary and the specific features of all the asset classes. I needed to know the different financial indicators and ratios in order to understand the different financial analyses carried out by the analyst teams.

Thoroughness and efficiency were the qualities that my superiors expected most of me. There was no room for error, even in an emergency, and in a sales department the unexpected was commonplace. When writing memos on funds intended for clients, it was essential to transpose the managers’ analysis correctly. It was also my responsibility to respond to clients as quickly and accurately as possible.

During this internship, I required to master some essential skills in order to be successful such as rigor, adaptability or efficiency. Adaptability was key as my role was cross-functional. I was in contact with most of the departments at Lazard Frères Gestion. I worked on projects with marketing in conjunction with the management teams, or I had to provide answers thanks to the reporting departments, which sent me the data I needed to make my calculations in response to clients.

What I learned

This experience was a real springboard for learning about the finance profession. I had the opportunity to apply the theoretical aspects to real projects and to work on various subjects under the guidance of experienced professionals. I also had the opportunity to perfect my knowledge of the financial market environment through daily contact with the Management, Reporting, Risk, Legal and Marketing teams. In particular, I improved my knowledge of asset management, understanding of macroeconomics and financial analysis.

In particular, I was able to learn a lot about the different asset classes, thanks to the discussions I had with professionals and the various presentations I attended. Additionally, I was able to follow a number of fund sales and look after a variety of clients. I worked on various client presentations and financial documents such as reports and prospectuses.

Financial concepts related to my internship

Sale of financial products

I learnt a lot about commercial sales techniques. It is important to know what elements to emphasize when prospecting or selling financial products, especially when dealing with professional and highly technical clients.

I was able to familiarize myself with macro-economic and financial indicators that helped me understand certain economic scenarios and management decisions.

I therefore carefully analyzed the financial markets and improved my understanding of the different types of financial markets, such as the stock, bond, foreign exchange (Forex) and derivatives markets.

Through various client meetings, I was able to familiarize and educate myself on the different financial products. I dealt with a variety of financial products, such as equities, bonds, currencies, derivatives (options, futures, swaps) and mutual funds.

Fixed income management

Before this internship, I was much more comfortable with equities than bonds. However, the various committees, training sessions and discussions I’ve had have taught me a lot about bond management. In particular, I was able to follow how the management teams manipulated the funds’ modified duration to take advantage of unprecedented market conditions following the crises at the US regional banks and Crédit Suisse.

I was also able to follow the launch of a bond fund and understand the entire portfolio construction process by following the strategy implemented by the management teams.

Macro-economics

During my internship I was able to acquire knowledge in macroeconomics by studying economic indicators, monetary and fiscal policies, international trade, business cycles, exchange rates, the relationship between financial markets and the economy, and sovereign debt. These skills enable me to better understand the global economic environment, assess investment opportunities and risks, and contribute to investment decision-making. At Lazard, a team of macro-economists gives analyses and predicts a scenario for the coming months, which are taken into account by all the managers in different proportions in order to respect the management process and the objective of their funds. It is therefore essential for the distribution team to know and master this scenario in order to explain the performance and strategies implemented.

Why should I be interested in this post?

If you are looking for a formative experience in finance with responsibilities and challenges combining financial expertise and sales, this internship is for you. You will be working on a wide range of assets and investment universes. Lazard Frères Gestion will require you to be rigorous and hard-working, but you’ll learn a lot about asset management.

Useful resources

Lazard Frères Gestion

Lazard Frères Gestion Les métiers de la gestion d’actifs (webinaire)

Lazard Frères Gestion Qu’est-ce que la gestion d’actifs ?

Lazard Frères Gestion Quelle allocation d’actifs pour un portefeuille diversifié ?

Hull J. and P. Roger (2017) Options futures et autres actifs dérivés Pearson Education.

About the author

The article was written in May 2024 by David-Alexandre BLUM (ESSEC Business School, Global Bachelor in Business Administration, 2019-2023).

Cardano: Exploring the Future of Blockchain Technology

May 6, 2024May 6, 2024 by Snehasish CHINARA

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

Historical context and background

Cardano is a blockchain platform that was founded in 2017 by Charles Hoskinson, one of the co-founders of Ethereum. The project was initiated by Input Output Hong Kong (IOHK), a technology company focused on blockchain and cryptocurrency. Cardano’s development was guided by a scientific philosophy and peer-reviewed research, aiming to create a more scalable, sustainable, and interoperable blockchain platform. Cardano is named after Gerolamo Cardano, the Italian mathematician, whereas the cryptocurrency associated with the platform is named after Ada Lovelace, the English mathematician.

Cardano distinguishes itself through its layered architecture, which separates the platform’s settlement layer (Cardano Settlement Layer, CSL) from its computation layer (Cardano Computation Layer, CCL). This separation allows for greater flexibility and scalability, as well as easier implementation of updates and improvements.

Another notable feature of Cardano is its consensus mechanism, called Ouroboros, which is based on a proof-of-stake algorithm. Ouroboros aims to achieve both security and scalability by allowing users to participate in the consensus process based on the amount of cryptocurrency they hold, rather than requiring expensive computational resources like Bitcoin’s proof-of-work mechanism.

Cardano’s development has been divided into phases, each focusing on different aspects of the platform’s functionality and features. These phases include Byron (foundation), Shelley (decentralization), Goguen (smart contracts), Basho (scaling), and Voltaire (governance). As of the time of writing, Cardano has successfully completed the Byron and Shelley phases, with ongoing work on the Goguen phase, which will enable the implementation of smart contracts and decentralized applications (dApps) on the platform.

Cardano Logo

Source: Cardano.

Figure 1. Key Dates in Cardano History

Source: Yahoo! Finance.

Key features

Layered Architecture

Cardano’s architecture is divided into two layers – the Cardano Settlement Layer (CSL) and the Cardano Computation Layer (CCL). This separation allows for greater flexibility, scalability, and easier implementation of updates and improvements.

Ouroboros Consensus Protocol:

Cardano uses the Ouroboros proof-of-stake consensus algorithm, which is designed to be secure, scalable, and energy-efficient. It allows users to participate in the consensus process based on the amount of cryptocurrency they hold, rather than requiring expensive computational resources.

Scalability

Cardano is designed to be highly scalable, capable of handling a large number of transactions per second. Through its layered architecture and consensus mechanism, Cardano aims to achieve scalability without sacrificing security or decentralization.

Interoperability

Cardano aims to enable interoperability between different blockchain networks and protocols. This will allow for seamless transfer of assets and data between different platforms, facilitating greater connectivity and usability of decentralized applications (dApps).

Smart Contracts

Cardano is developing support for smart contracts, which are self-executing contracts with the terms of the agreement directly written into code. Smart contracts will enable the creation of decentralized applications (dApps) on the Cardano platform, opening up a wide range of possibilities for developers and users.

Governance

Cardano has a built-in governance mechanism that allows stakeholders to participate in the decision-making process for the future development of the platform. This includes voting on proposals for protocol upgrades, funding development projects, and other governance-related decisions.

Formal Verification

Cardano emphasizes formal methods and peer-reviewed research in its development process. This includes using formal verification techniques to ensure the correctness and security of its protocols and smart contracts, reducing the risk of bugs and vulnerabilities.

Use cases

Decentralized Finance (DeFi)

Cardano’s smart contract capabilities can facilitate various DeFi applications, including decentralized exchanges (DEXs), lending platforms, stablecoins, and automated market makers (AMMs). Smart contracts on Cardano can enable programmable financial transactions without the need for intermediaries, providing users with greater control over their assets and reducing counterparty risk.

Supply Chain Management

Cardano’s blockchain can be utilized to track and authenticate products throughout the supply chain, ensuring transparency and accountability. By recording every step of a product’s journey on the blockchain, stakeholders can verify its origin, quality, and authenticity, thereby reducing fraud, counterfeiting, and logistical inefficiencies.

Identity Management

Cardano’s blockchain can serve as a secure and decentralized platform for identity management, enabling individuals to control and manage their digital identities without relying on centralized authorities. By leveraging cryptographic techniques, users can securely authenticate themselves and access various services, such as voting, healthcare, and financial transactions, while maintaining privacy and security.

Voting and Governance

Cardano’s blockchain can support transparent and tamper-resistant voting and governance systems, enabling communities to make collective decisions and govern decentralized organizations (DAOs). By using blockchain technology, voting processes can be made more secure, efficient, and auditable, ensuring fair and democratic outcomes.

Tokenization of Assets

Cardano’s blockchain can tokenize various real-world assets, such as real estate, stocks, and commodities, making them easily tradable and transferable on a global scale. By representing assets as digital tokens on the blockchain, ownership rights can be easily verified, fractional ownership can be enabled, and liquidity can be increased, unlocking new opportunities for investment and asset management.

Technology and Underlying Blockchain

Cardano is built on a multi-layered architecture designed to provide scalability, interoperability, and sustainability. At its core, Cardano utilizes a proof-of-stake (PoS) consensus mechanism called Ouroboros, which offers a more energy-efficient and secure alternative to traditional proof-of-work (PoW) systems. Ouroboros divides time into epochs and slots, with slot leaders responsible for creating new blocks and validating transactions within each slot. This approach ensures that the blockchain remains secure and decentralized while enabling high transaction throughput and low latency.

Cardano’s blockchain consists of two main layers: the Cardano Settlement Layer (CSL) and the Cardano Computation Layer (CCL). The CSL serves as the foundation for the platform’s native cryptocurrency, ADA, and facilitates secure and efficient peer-to-peer transactions. It employs a UTXO (Unspent Transaction Output) model similar to Bitcoin, where transactions are represented as inputs and outputs, ensuring transparency and immutability.

On top of the CSL, the CCL enables the execution of smart contracts and decentralized applications (dApps) using Plutus, Cardano’s purpose-built programming language. Plutus is based on Haskell, a functional programming language known for its safety and reliability, and allows developers to write smart contracts with formal verification capabilities, ensuring correctness and security. Additionally, Cardano supports interoperability with other blockchains through sidechains and cross-chain communication protocols, enabling seamless integration with existing infrastructure and networks.

Cardano’s development is guided by a rigorous scientific approach, with ongoing research and peer-reviewed papers driving innovation and advancement. The platform’s roadmap is divided into distinct phases, including Byron (foundation), Shelley (decentralization), Goguen (smart contracts), Basho (scaling), and Voltaire (governance), each focusing on specific features and functionalities. This modular approach allows for continuous improvement and evolution, ensuring that Cardano remains at the forefront of blockchain technology.

Supply of Coins

The supply of coins for Cardano (ADA) is governed by a predetermined protocol established during its initial launch. The total maximum supply of ADA is capped at 45 billion coins. Unlike some cryptocurrencies that have fixed supplies, Cardano’s distribution occurs gradually through a process called “minting.” During the initial phase, ADA tokens were distributed through a public sale and allocated to early supporters, development, and the Cardano treasury. Ongoing minting of ADA occurs through the process of staking, where ADA holders can delegate their coins to stake pools to participate in the network’s consensus and earn rewards. This incentivizes stakeholders to actively participate in the security and governance of the network while also distributing newly minted coins in a decentralized manner. As a result, the circulating supply of ADA gradually increases over time, with new coins being minted and distributed to participants in the Cardano ecosystem.

Historical data for Cardano

How to get the data?

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

Figure 2. Cordano data

Source: Yahoo! Finance.

Historical data for the Cardano (ADA) market prices

Cardano (ADA) has experienced notable fluctuations in its market price since its inception, reflecting both broader trends in the cryptocurrency market and developments specific to the Cardano project. Following its launch in 2017, ADA initially saw rapid growth, fueled by enthusiasm for its innovative technology and ambitious roadmap. However, like many cryptocurrencies, ADA’s price has been subject to volatility, with periods of sharp appreciation followed by corrections and consolidation. Historical data for Cardano’s market prices reveals a series of peaks and troughs, influenced by factors such as market sentiment, regulatory developments, technological milestones, and macroeconomic trends. Despite this volatility, Cardano has maintained its position as one of the top cryptocurrencies by market capitalization, attracting a dedicated community of supporters and investors. As the project continues to evolve and achieve key milestones, its market price remains closely watched by traders, investors, and stakeholders in the cryptocurrency ecosystem.

Figure 3 below represents the evolution of the price of Cardano (ADA) in US dollar over the period November 2017 – May 2024. The price corresponds to the “closing” price (observed at 10:00 PM CET at the end of the month).

Figure 3. Evolution of Cardano 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 Cardano (ADA).

Data file

The R program that you can download above allows you to download the data for the Cardano (ADA) from the Yahoo! Finance website. The database starts on November, 2017.

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

Table 1. Top of the data file for the Cardano

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 Cardano (ADA) historical data and save it to an Excel sheet::

import yfinance as yf

import pandas as pd

# Define the ticker symbol for Cardano “ADA-USD”

Cardano_ticker = “ADA-USD”

# Define the date range for historical data

start_date = “2020-01-01”

end_date = “2022-01-01”

# Download historical data using yfinance

Cardano_data = yf.download(Cardano_ticker, start=start_date, end=end_date)

# Create a Pandas DataFrame from the downloaded data

Cardano_df = pd.DataFrame(Cardano_data)

# Define the Excel file path

excel_file_path = “Cardano _historical_data.xlsx”

# Save the data to an Excel sheet

Cardano_df.to_excel(excel_file_path, sheet_name=”Cardano 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 Cardano (ADA)

Figure 4 below gives the evolution of the Cardano (ADA) on a daily basis.

Figure 4. Evolution of the Cardano (ADA)

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

Figure 5 below gives the evolution of the Cardano (ADA) returns from November, 2017 to May, 2024 on a daily basis.

Figure 5. Evolution of the Cardano returns

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

Summary statistics for the Cardano (ADA)

The R program that you can download above also allows you to compute summary statistics about the returns of the Cardano (ADA).

Table 2 below presents the following summary statistics estimated for the Cardano (ADA):

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 Cardano.

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

Statistical distribution of the Cardano (ADA) returns

Historical distribution

Figure 6 represents the historical distribution of the Cardano (ADA) daily returns for the period from November, 2017 to May, 2024.

Figure 6. Historical Cardano distribution of the returns.

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

Gaussian distribution

Figure 7 below represents the Gaussian distribution of the Ethereum daily returns with parameters estimated over the period from November, 2017 to May, 2024.

Figure 7. Gaussian distribution of the Cardano returns.

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

Risk measures of the Cardano (ADA) returns

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

Table 3 below presents the following risk measures estimated for the Cardano (ADA):

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 Cardano.

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

Why should I be interested in this post?

This post provides a compelling exploration of Cardano, catering to both novices and seasoned cryptocurrency enthusiasts alike. It delves into Cardano’s innovative blockchain technology and its role in revolutionizing various sectors, including finance, governance, and social impact initiatives. By understanding Cardano’s layered architecture, consensus mechanism, and ongoing development phases, readers can gain valuable insights into its potential to address scalability, interoperability, and sustainability challenges in the blockchain space. Moreover, the post examines Cardano’s historical performance, market dynamics, and community-driven governance model, offering invaluable perspectives for investors, traders, and stakeholders. Whether you’re intrigued by cutting-edge blockchain solutions or seeking investment opportunities in the cryptocurrency market, this post provides comprehensive insights into the significance and potential of Cardano in shaping the future of decentralized technologies.

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 Cardano

CoinMarketCap Historical data for Cardano

About the author

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

Morningstar

May 3, 2024 by Nithisha CHALLA

Morningstar

In this article, Nithisha CHALLA (ESSEC Business School, Grande Ecole Program – Master in Management (MiM), 2021-2024) presents Morningstar, which provides financial data, news and analytics for the financial community.

Introduction

Morningstar is a leading provider of independent investment research, data, and analysis, dedicated to helping investors make informed decisions and achieve financial success. Their mission is to provide investors with unbiased insights, trusted research, and actionable data to navigate the complexities of the financial markets. Whether it is for a novice investor seeking guidance or a seasoned professional managing portfolios, Morningstar’s platform offers valuable resources to support their investment journey.

Logo of Morningstar

Source: Morningstar

History

Morningstar, Inc. is an American financial services firm headquartered in Chicago, Illinois, and was founded by Joe Mansueto in 1984. Its initial investment was of US$80,000. In July 1999, Morningstar accepted an investment of US$91 million from SoftBank in return for a 20 percent stake in the company. The two companies had formed a joint venture in Japan the previous year.

Morningstar’s initial public offering occurred on May 3, 2005, with 7,612,500 shares at $18.50 each. The manner in which Morningstar went public is notable. They elected to follow Google’s footsteps and use the OpenIPO method rather than the traditional method. This allowed individual investors to bid on the price of the stock, and allowed all investors equal access.

In 2006, Morningstar acquired Ibbotson Associates, Inc., an investment research firm. In 2007, Morningstar acquired the mutual fund data business of S&P Global. In 2010, Morningstar acquired credit rating agency Realpoint for $52 million and begins offering structured credit ratings and research to institutional investors.

In the same year, Morningstar acquired Old Broad Street Research Ltd. (OBSR), a UK-based provider of fund research, ratings and investment advisory services, for $18.3 million. After the acquisition, renamed as Morningstar OBSR. In 2014, Morningstar acquired ByAllAccounts, Inc., a provider of innovative data aggregation technology for financial applications. In 2016, Morningstar acquires remaining ownership of PitchBook Data for approximately $225 million.

In 2019, Morningstar acquired the world’s fourth largest credit rating agency DBRS for $669 million. After the acquisition, DBRS merged with Morningstar’s credit rating business. In 2020, Morningstar entered into an agreement to acquire Sustainalytics, a research and ratings firm specializing in environmental, social and governance (ESG) insights. In 2021, Morningstar announced it would acquire the UK and international operations of the Australian wealth management platform Praemium.

In 2022, Morningstar announced that it will acquire the S&P Global-owned Leveraged Commentary & Data (LCD), a company specializing in leveraged loan market data, for $600 million up to $650 million. Today, from retirement planning to ESG (Environmental, Social, Governance) investing, Morningstar remains committed to helping individuals and institutions navigate the complexities of the financial markets and achieve their financial goals with confidence.

Key Components of Morningstar

Earnings Estimates

Price/projected earnings for a stock is the ratio of the company’s most recent month-end share price to the company’s estimated earnings per share (EPS) for the current fiscal year. According to Morningstar’s website, if a third-party estimate for the current year EPS is not available, the company will calculate an internal estimate based on the most recently reported EPS and average historical earnings growth rates.

Morningstar generates this figure in-house based on stock statistics from our internal equities databases. For stocks, this figure is calculated monthly. For funds and portfolios, Morningstar updates this figure upon receipt of the most-recent portfolio holdings from the asset manager.

The P/E ratio relates the price of the stock to the per-share earnings of the company. A high P/E generally indicates that the market will pay more to obtain the company because it has confidence in the company’s ability to increase its earnings. Conversely, a low P/E indicates that the market has less confidence that the company’s earnings will increase, and therefore will not pay as much for its stock. In most cases high average P/E ratio indicates a manager has paid a premium for stocks that have a high potential for increased earnings. If the average P/E ratio is low, the manager may believe that the stocks have an overlooked or undervalued potential for appreciation.

Revenue Projections

Morningstar aggregates earnings estimates and consensus revenue forecasts from analysts, providing insights into market expectations for future revenue levels. This data helps investors gauge sentiment and anticipate potential revenue surprises. Morningstar presents revenue projections and growth rates based on proprietary models and industry analysis.

For example, on Feb 22, 2024, Morningstar’s chief executive officer, Kunal Kapoor said that “We finished 2023 on a strong note, crossing $2 billion in revenue for the fiscal year and delivering meaningful increases in organic revenue, margins, and cash flow for the quarter”. This not only implements that the company has been successful but also shows how reliable the company data is for the investors.

Forecasts for Key Financial Metrics

Morningstar calculates and forecasts key profitability ratios such as return on equity (ROE), return on assets (ROA), and operating margins. These metrics indicate a company’s efficiency and financial health, aiding investors in evaluating investment opportunities. It provides cash flow projections, including operating cash flow, free cash flow, and cash flow from investing and financing activities. Investors can analyze cash flow trends and liquidity positions to assess a company’s ability to generate and manage cash.

In the company website, investors can access historical data and perform comparisons of key financial metrics over time, across companies, or within industries. This helps identify trends, benchmark performance, and identify investment opportunities.

Coverage

Morningstar offers investment management services through its investment advisory subsidiaries, with approximately $286 billion in assets under advisement and management as of Dec. 31, 2023. The Company operates through wholly- or majority-owned subsidiaries in 32 countries.

According to Morningstar, the company website maintains coverage oMorningstar.com offers coverage of 950 stocks, 1,150 mutual funds, and 300 ETFs, plus market news, economic analysis, portfolio-planning insights, and investment commentary.

Period

Morningstar offers several periods of windows to access and analyse the data, such as Single Period(certain definite time), Trailing Periods(end date is fixed), Rolling Window(typically used to measure consistency of data), Forward Extending Window (fixes the start date for each time period), Regular Periods from start and Regular Periods from end. These various options give the investors detailed and customised analysis of what they are looking for.

Frequency

The period and frequency of forecasts on Morningstar vary based on user needs and market dynamics. Morningstar Quantitative Rating has the unique advantage of maintaining a monthly update cycle.

Firms and Financial analysts

Morningstar, Inc. is a leading provider of independent investment insights in North America, Europe, Australia, and Asia. The Company offers an extensive line of products and services for individual investors, financial advisors, asset managers and owners, retirement plan providers and sponsors, and institutional investors in the debt and private capital markets. Morningstar has the one of the largest independent manager research teams in the world with more than 100 analysts covering more than 4000 unique funds.

Pricing

The business model of Morningstar is a three tier based pricing namely License-based, Asset-based and Transaction-based.

License-based: The majority of the research, data, and proprietary platforms are accessed via subscriptions or contract-based licensing arrangements that grant access on either a per user or enterprise-basis for a specified period of time. Licensed-based revenue includes Morningstar Data, Morningstar Direct, Morningstar Advisor Workstation, Morningstar Office, PitchBook Data, Premium Memberships on Morningstar.com, and other similar products.

Asset-based: They charge basis points and other fees for assets under management or advisement. Morningstar Investment Management, Workplace Solutions, and Morningstar Indexes products all fall under asset-based revenue.

Transaction-based: Ad sales on Morningstar.com and their Credit Ratings products comprise the majority of the products that are transactional, or one-time, in nature, versus the recurring revenue streams represented by their licensed and asset-based products.

Use of Morningstar by the Financial Community

Benchmark for Analysis

Company is known as a benchmark for analysis in the financial market. Over the years their strategy has delivered excellent diversification and risk-adjusted returns. Since the September 2000 start of its longest-tenured manager, Rolley, the fund’s 7.7% annualized gain through the end of March 2024 beat the average global allocation Morningstar Category fund by more than 3 percentage points with better Sharpe and Sortino ratios, measures of risk-adjusted results.

Market Expectations

Morningstar provides access to economic data and forecasts that shape market expectations. Users can track indicators such as GDP growth, inflation rates, unemployment figures, and interest rate projections to anticipate broader economic trends.

Earnings Season Preparation

During earnings season, Morningstar offers detailed analysis of company earnings reports relative to consensus estimates. Investors can assess whether companies meet, exceed, or fall short of market expectations, influencing stock prices and investor sentiment.

For a deeper understanding, this is an example of how they would state seasons preparation – “I think third-quarter earnings in and of themselves will generally be very strong. Management guidance last quarter probably shouldn’t be all that difficult to beat. And also the U.S. has just remained defiant in the face of tight monetary policy. So, economic growth has been much stronger than we had originally expected. In fact, if you look at our numbers here, we just recently updated our GDP forecast for the third quarter to 3.9%.”

Morningstar and Tests of Market Efficiency

Academic works

Morningstar conducts and publishes academic research on market efficiency, analyzing factors that contribute to market anomalies and deviations from efficient market hypotheses. This research informs investment strategies and challenges conventional market theories. The efficient-market hypothesis, or EMH, implies that the market quickly and accurately incorporates all information regarding a stock’s actual value into its price. They believe that Investors can’t gain an informational advantage and shouldn’t try to beat the market. Instead, they should simply track the market through a broad market index fund.

Information Dissemination

Morningstar facilitates information dissemination and transparency, providing investors with access to comprehensive data and research reports. By empowering investors with timely and accurate information, Morningstar contributes to market efficiency by reducing information asymmetry.

Pros and Cons

Morningstar compares fund performance against relevant benchmarks and market indices, offering insights into fund managers’ ability to generate alpha (excess returns). By evaluating relative performance, Morningstar assesses market efficiency and the value of active management.

Morningstar’s performance attribution analysis helps investors evaluate the effectiveness of investment strategies and identify sources of outperformance or underperformance.

On the other side, Morningstar is primarily a research and analysis platform, and it may not offer direct trading capabilities or execution of investment transactions. Like any investment service, Morningstar’s ratings and recommendations are based on historical data and assumptions, and they may not fully anticipate future market fluctuations or uncertainties.

Conclusion

Morningstar offers valuable resources for investors seeking to research, analyze, and manage their investments effectively. Understanding the pros and cons can help investors leverage Morningstar’s strengths while being mindful of potential limitations or considerations.

Why should I be interested in this post?

Understanding how financial professionals use platforms like Morningstar can contribute to your professional development, especially if you’re pursuing a career in finance, investment management, or financial advising. It demonstrates your interest in industry trends and best practices.

Useful resources

Morningstar Morningstar, Inc. Reports Fourth-Quarter, Full-Year 2023 Financial Results

Wikipedia Morningstar

Morningstar Articles, products and services for professional investors

Morningstar Performance Reporting – Time Periods

Morningstar Earnings Season: What to Expect

About the author

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

Bloomberg

May 20, 2024May 3, 2024 by Nithisha CHALLA

Bloomberg

In this article, Nithisha CHALLA (ESSEC Business School, Grande Ecole Program – Master in Management (MiM), 2021-2024) presents Bloomberg, which provides financial data, news and analytics for the financial community.

Bloomberg

Bloomberg is a company that provides financial data, news, and analytics to people in finance and other industries. The company was created to address the need for better financial data and analytics access in the business world. Bloomberg’s flagship product is the Bloomberg Terminal, a computer software system that provides real-time data on financial markets, economic indicators, and news.

Logo of Bloomberg

Source: Bloomberg

History

Bloomberg LP was founded in 1981 by Michael Bloomberg, a former investment banker who recognized the growing demand for reliable financial information and analytics. The company initially focused on developing and selling computer terminals that provided real-time data on stocks, bonds, and other financial instruments.

Bloomberg’s key innovation, Bloomberg Terminal, was introduced in the early 1980s. This groundbreaking platform revolutionized the way financial professionals accessed market data and news, offering a comprehensive toolset for trading, analysis, and research.

Over the years, Bloomberg expanded its offerings beyond terminals to include data analytics, financial software, and media services. Bloomberg News, launched in 1990, became a prominent source of business and financial journalism, providing timely news coverage and insightful analysis to subscribers worldwide.

In the 21st century, Bloomberg continued to innovate and diversify its business, introducing new products and services tailored to the evolving needs of the financial industry. The company expanded its global presence with offices in major financial centers across North America, Europe, Asia, and beyond.

Today, the company’s commitment to innovation, integrity, and excellence has solidified its position as a trusted partner for businesses, governments, and institutions seeking reliable financial information and insights.

Bloomberg has evolved since its introduction in 1981 and has grown the company from a single location to 176 locations and with 20000 employees. Bloomberg with over 40 years of history, facilitates bottom-up and top-down analysis to better assess potential growth and risk as well as future value across industry, sector, index, country, and region.

Bloomberg covers a wide range of firms across different industries, sectors, and regions. According to Chen (2023), Bloomberg grew to a leading financial company with over 325,000 subscriptions to its professional services, almost 1 million global circulations of Bloomberg Businessweek, and over 150 news bureaus internationally. To support this massive network of financial information, data encryption, messaging, and trading, Bloomberg employs over 4,000 computer engineers around the globe.

Key Components of Bloomberg

Earnings Estimates

Bloomberg provides estimates and forecasts about how companies will perform financially. It collects data from many sources and use it to predict things like earnings and revenue for different businesses.

According to Guttmann from Statista (2022), Bloomberg LP, which operates within the business of information services, news services, broadcasting, streaming, and print, generated an estimated 12.2 billion U.S. dollars. A year earlier, the company’s revenue results stood at 10 billion dollars.

Revenue Projections

Alongside earnings estimates, Bloomberg provides forecasts on how much revenue companies are expected to generate. This helps stakeholders analyze and project the growth and performance of businesses over specific periods.

For example, on June 01, 2023, Bloomberg Intelligence released an expected revenue projection on the AI industry for the next ten years stating “Rising demand for generative AI products could add about $280 billion of new software revenue and the Emerging Industry Could Grow at a CAGR of 42% Over the Next 10 Years”. This helps stakeholders of the respective industry to analyze, project, and invest accordingly.

Forecasts for Key Financial Metrics

Bloomberg’s platform includes forecasts for various important financial metrics beyond earnings and revenue, such as cash flow, profit margins, and growth rates. These forecasts assist in assessing the overall financial health and prospects of companies.

In April 2024, Bloomberg launched a point-in-time data solution that gives quants a competitive edge. By pre-ingesting, mapping, and linking many different data sources together, Bloomberg allows customers to significantly reduce the time needed to generate signals or insights. With this launch, Bloomberg is responding to customers’ need for differentiated, value-adding data with standardized company-level fundamentals, estimates, and deep industry-specific metrics, alongside macro information.

Coverage

Bloomberg covers a wide range of companies across different industries, from large corporations to smaller businesses. This extensive coverage ensures that users have access to comprehensive financial data and insights.

Bloomberg Markets maintains coverage of stocks, bonds, commodities, emerging markets, and forex markets from more than 160 countries. Bloomberg Politics holds the largest news agency in the world in US politics and major global events with more than 1000 reporters and staff. Bloomberg News publishes around 5000 stories daily.

Period

Bloomberg usually provides data with the longest available time period. For example, for Bitcoin data starts in 2010.

Frequency

The period and frequency of forecasts on Bloomberg vary based on user needs and market dynamics. Users can access real-time updates and forecasts as frequently as necessary to stay informed about market changes and company performance.

Bloomberg offers the only cloud-native real-time data delivery option suitable for capital markets use. It delivers B-PIPE through an intra-cloud connection to a managed virtual private cloud on Amazon Web Services (AWS) via AWS Private Link. This low-latency option provides speed, reliability, and security via connectivity that remains solely in AWS.

Pricing

The specific pricing for accessing Bloomberg data is mentioned on the Bloomberg website and it states to have categories like all-in access, limited access, and also has special student access for limited data. The cost likely varies based on the package and offerings selected, which can include different data sets, yearly or monthly subscriptions, and access methods. For detailed pricing information, it is recommended to directly go through the Bloomberg website as it includes very precise information that pretty much caters to all the needs based on the functionality.

Bloomberg charges a fee for its services, usually on a subscription basis. The cost can be high, but many financial professionals find it worth it for the valuable information they receive.

Use of Bloomberg by the Financial Community

Benchmark for Analysis

Professionals rely on Bloomberg’s extensive database of financial data, economic indicators, and market news to conduct in-depth analyses of companies, industries, and markets. The platform provides powerful tools and customizable features that enable users to create detailed financial models, perform comparative analysis, and track key performance metrics.

Market Expectations

Financial professionals use Bloomberg to stay informed about market expectations and sentiment. The platform aggregates market forecasts, including earnings estimates, revenue projections, and economic indicators, allowing users to assess consensus expectations and potential market trends. Bloomberg’s real-time updates and customizable alerts enable users to monitor shifts in market sentiment and adjust investment strategies accordingly.

Earnings Season Preparation

During earnings seasons, Bloomberg becomes an essential tool for financial professionals preparing for corporate earnings releases. The platform’s earnings analysis tools help users interpret financial results, identify underlying trends, and make informed decisions based on earnings reports.

Bloomberg and Tests of Market Efficiency

Academic works

Researchers and scholars leverage Bloomberg’s vast dataset and analytics tools to conduct empirical studies on market behavior, information dissemination, and the efficiency of asset pricing models. By analyzing historical market data and real-time information flow, academics assess the degree to which markets reflect all available information and efficiently incorporate new information into asset prices.

Information Dissemination

One key aspect of market efficiency is the speed and accuracy of information dissemination. Bloomberg facilitates the rapid dissemination of market news, economic data, and corporate announcements to a global audience of financial professionals. Researchers use Bloomberg to study how quickly information is incorporated into asset prices and whether markets efficiently reflect public and private information.

Pros and Cons

Given its history and operations in widely known industries and markets, we certainly need to know the pros and cons of Bloomberg.

Bloomberg provides users with access to extensive financial data and analytics, enabling rigorous empirical studies on market efficiency. Bloomberg’s customizable tools and advanced features facilitate complex analyses and modeling for testing various market efficiency hypotheses. The platform offers real-time updates and historical data, allowing researchers to analyze market behavior over different periods and market conditions.

On the other side, Bloomberg’s subscription costs may limit access to users with limited budgets or academic institutions with constrained resources. The complexity of Bloomberg’s interface and data structure may also present a learning curve for users new to the platform.

Conclusion

Bloomberg’s impact extends across the financial community, serving as a trusted resource for investors, traders, analysts, and corporate professionals worldwide.

Why should I be interested in this post?

According to me, mastering Bloomberg can equip management students with valuable skills and knowledge that are directly applicable to careers in finance, business analysis, and strategic management. It offers a practical way to enhance analytical capabilities, stay updated with industry trends, and build a strong foundation for future professional success.

Useful resources

Bloomberg

Wikipedia Bloomberg L.P.

Statista Estimated revenue generated by Bloomberg LP worldwide from 2015 to 2022

Bloomberg Generative AI to Become a $1.3 Trillion Market by 2032, Research Finds

Bloomberg Bloomberg Launches Point-in-Time Data Solution that Gives Quants a Competitive Edge

About the author

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

FactSet

May 20, 2024May 3, 2024 by Nithisha CHALLA

FactSet

In this article, Nithisha CHALLA (ESSEC Business School, Grande Ecole Program – Master in Management (MiM), 2021-2024) presents FactSet, which provides financial data, news and analytics for the financial community.

Introduction

FactSet also known as FactSet Research Systems Inc. is a leading provider of financial data and analytics solutions, catering to professionals in the investment industry. Whether you are a financial analyst, portfolio manager, or student of finance, FactSet offers powerful tools and resources to analyze markets, evaluate companies, and make informed investment decisions.

Logo of Factset

Source: Factset

History

Founded in 1978 by Howard Wille and Charles Snyder, FactSet began as a startup aiming to revolutionize financial data analysis. The company initially focused on providing financial analysis software for investment professionals, aiming to streamline data processing and analysis in the rapidly evolving financial industry.

Throughout the 1980s and 1990s, FactSet expanded its product offerings and market presence, catering to a growing demand for sophisticated financial data and analytics solutions. In its journey of growth and expansion, FactSet has made several strategic acquisitions to enhance its product portfolio and expand its market reach.

In 2007, FactSet acquired Market Metrics, a provider of market research and consulting services for asset managers. In 2015, FactSet acquired Portware LLC, a leading provider of multi-asset trade automation solutions for asset managers and institutional traders. In 2020, FactSet acquired Truvalue Labs, a pioneer in AI-driven environmental, social, and governance (ESG) data and analytics.

From its humble beginnings in the late 1970s to its current status as a global leader in financial data and analytics, FactSet has continually evolved to meet the evolving needs of the investment community.

Key Components of FactSet Research Systems

Earnings Estimates

Earnings estimates refer to predictions made by financial analysts and experts regarding how much profit a company is expected to generate in the future.FactSet gathers and provides these estimates, which are crucial for investors and analysts to assess a company’s potential performance.

According to Factset, 2024, the company’s consensus estimates are aggregated from a wide base of contributors and cover 19,000+ active companies across 90+ countries.

Revenue Projections

Revenue projections indicate how much revenue a company is expected to generate over a specific period. FactSet’s revenue projections are based on industry trends, market conditions, and company-specific factors, providing users with valuable insights into sales performance and revenue drivers.

For example, in June 2024, Factset released an expected revenue projection for S&P 500 companies stating “The blended (companies that reported and the estimation of the companies yet to report)earnings growth rate for the S&P 500 for Q4 2023 is 3.2%. It should be noted that analysts are currently projecting (year-over-year) revenue growth for all four quarters of 2024. For Q1 2024 through Q4 2024, the current estimates for revenue growth are 3.5%, 4.6%, 5.0%, and 5.7%, respectively.”. This helps stakeholders of the respective industry to analyze, project, and invest accordingly.

Forecasts for Key Financial Metrics

In addition to earnings estimates, FactSet offers forecasts for key financial metrics such as revenue growth rates, cash flow projections, and profit margins. These forecasts provide insights into overall financial health and performance metrics, helping users assess business strategies, identify growth opportunities, and make informed investment decisions. By analyzing financial forecasts, users can anticipate market trends and evaluate the potential impact on investment portfolios.

Coverage

FactSet covers a wide range of companies across different industries, from large corporations to smaller businesses. This extensive coverage ensures that users have access to comprehensive financial data and insights.

FactSet provides access to 200+ data items, including 100+ metrics across 18 industries encompassing airlines, banking, insurance, oil/gas, and retail. It leverages estimates that are collected directly from the research reports and flat file feeds of 800+ contributors across 55 countries.

Period

Factset has evolved since its introduction in 1978 and in 2023, Factset has expanded its client base to over 7,900 and increased its user base by 6%, surpassing 189,000 users. Factset with over 45 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

The period and frequency of forecasts on Factset vary based on user needs and market dynamics. FactSet incorporates quarterly and annual financial reports from companies, including earnings releases, revenue figures, balance sheets, and cash flow statements. Users can access real-time updates and forecasts based on two categories that are data frequency and update frequency, where they provide quarterly data frequency and an intraday update frequency.

FactSet also includes on a timely basis event-driven updates such as corporate actions (e.g., mergers, acquisitions, dividends) and regulatory filings (e.g., SEC filings), providing comprehensive coverage of market events impacting investment decisions.

Firms and Financial analysts

According to James Chen (2023à, FactSet is broken down into three business units: one in the United States, one in Europe, and one in Asia-Pacific. The business unit located in the United States provides financial solutions to financial professionals as well as domestic financial institutions. The European and Asia-Pacific business units only service financial professionals in the regions in which each unit operates.

As of 2023, FactSet services over 200,000 users in more than 8,000 companies and organizations. FactSet has 37 offices in 20 countries. The company reports it has had a client retention rate of 95% with 43 years of revenue growth.

Pricing

FactSet’s pricing model varies based on subscription plans and user requirements. Students can explore different pricing tiers to access specific data sets, analytical tools, and premium features tailored to their academic or research needs. Understanding FactSet’s pricing structure is essential for management students evaluating the cost-benefit of utilizing its services for financial analysis and research purposes.

FactSet provides its services for a lower price than some of its competitors because the company uses multiple sources to provide its data, which creates pricing competition between suppliers.

Use of FactSet by the Financial Community

Benchmark for Analysis

FactSet serves as a benchmark for financial analysis, offering a comprehensive suite of tools for analyzing companies, industries, and markets. Management students can use FactSet to perform detailed financial modeling, comparative analysis, and valuation assessments, gaining practical experience in fundamental analysis techniques.

Market Expectations

FactSet provides access to economic data and forecasts that shape market expectations. Users can track indicators such as GDP growth, inflation rates, unemployment figures, and interest rate projections to anticipate broader economic trends.

Earnings Season Preparation

During earnings seasons, by leveraging FactSet’s capabilities, users can navigate earnings announcements with confidence, interpret financial results effectively, and make well-informed investment decisions based on fundamental analysis and market intelligence.

FactSet and Tests of Market Efficiency

Academic works

Academic researchers use FactSet’s extensive database and analytical tools to conduct empirical studies on various topics in finance, economics, and investment management. FactSet’s rich dataset allows researchers to analyze market behavior, asset pricing models, and the impact of economic indicators on financial markets. Researchers use FactSet to assess whether asset prices reflect all available information, conducting event studies and anomaly detection to identify market inefficiencies.

Information Dissemination

Information dissemination refers to the process of distributing financial data, market insights, and analytical reports to users within the investment community using FactSet’s platform. FactSet provides real-time market data on stock prices, indices, commodities, currencies, and other financial instruments. Users can access live updates and monitor market movements as they occur, enabling timely decision-making and risk management.

FactSet disseminates earnings releases, corporate news, and press releases from companies within its coverage universe. Users receive alerts and notifications about important announcements, enabling them to stay informed about company developments and assess potential market impacts.

Pros and Cons

Given its history and operations in so many industries and markets, we certainly need to know the pros and cons of the FactSet.

FactSet provides researchers with access to extensive financial data and analytics, and comprehensive financial data coverage across global markets. FactSet provides a user-friendly interface and intuitive features. It has very well-known powerful tools for financial analysis and investment research.

On the other side, FactSet subscription costs may limit access to users with limited budgets or in academic institutions with constrained resources. The complexity of mastering advanced functionalities may also present a learning curve for users new to the platform.

Conclusion

FactSet impact extends across the financial community, serving as a trusted resource for investors, traders, analysts, and corporate professionals worldwide.

Why should I be interested in this post?

As a management master’s student focusing on finance or strategic analysis, understanding and utilizing financial data platforms like FactSet can greatly enhance your skills and career prospects.

Useful resources

FactSet

Truvalue Labs

Wikipedia FactSet

FactSet FactSet Estimates – Consensus

FactSet FactSet Annual Report 2023

FactSet Earnings Insight

About the author

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

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

January 15, 2025April 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).

Historical context and background

Key features

Delegated Proof of Stake (DPoS) Consensus Mechanism

High Throughput and Scalability

Low Transaction Fees

Smart Contracts and dApps

TRC-20 and TRC-721 Tokens

Decentralized Storage

Rich Developer Resources

Use cases

Decentralized Apps (dApps)

Decentralized Finance (DeFi)

Digital Content and Entertainment

Supply Chain Management

Gaming

Voting and Governance

Data Storage and Sharing

Technology and underlying blockchain

Supply of coins

Historical data for TRX (Tron)

How to get the data?

Historical data for the TRX (Tron) market prices

R program

Data file

Python code

Evolution of the TRX (Tron)

Summary statistics for the TRX (Tron)

Statistical distribution of the TRX (Tron) returns

Historical distribution

Gaussian distribution

Risk measures of the TRX (Tron) 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

My Professional Experience as a Quantitative Analyst Intern at Findoc Financial Services

About the Company

About the Department

My Internship

My Missions

Required Skills and Knowledge

What I Learned

Financial Concepts Related to My Internship

Diversification

Technical Indicators

Backtesting and Forward Testing

Why Should I Be Interested in This Post?

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Useful Resources

About the Author

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Structured Financing for Investment

Why are CFOs and RNFs attracting insurance companies?

Regulatory Considerations and Risk Mitigation

Conclusion: Balancing Innovation with Regulatory Compliance

Example of precedent transaction: Tikehau raise a $300 million CFO

Why should I be interested in this post?

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Co-Investment: Fostering Collaboration for Shared Success

External Capital Infusion: Embracing Change for Accelerated Growth

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Why should I be interested in this post?

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Useful resources

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Historical context and background

Key features

Utility Token

Discounted Fees

Token Burn

Multiple Use Cases