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

The Gaussian distribution (also called the normal distribution) is a parametric distribution with two parameters: the mean and the standard deviation of returns. We estimated these two parameters over the period from November, 2017 to May, 2024.

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.

Related posts on the SimTrade blog

About cryptocurrencies

   ▶ Snehasish CHINARA Bitcoin: the mother of all cryptocurrencies

   ▶ Snehasish CHINARA How to get crypto data

   ▶ Alexandre VERLET Cryptocurrencies

About statistics

   ▶ Shengyu ZHENG Moments de la distribution

   ▶ Shengyu ZHENG Mesures de risques

   ▶ Jayati WALIA Returns

Useful resources

Academic research about risk

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

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

Data

Yahoo! Finance

Yahoo! Finance Historical data for 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

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.

Related posts on the SimTrade blog

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

   ▶ Louis DETALLE The importance of data in finance

   ▶ Bijal GANDHI Earnings per share

Useful resources

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

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.

Related posts on the SimTrade blog

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   ▶ Louis DETALLE The importance of data in finance

   ▶ Bijal GANDHI Earnings per share

Useful resources

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).