The Psychology of Trading

May 29, 2023 by Theo SCHWERTLE

The Psychology of Trading

In this article, Theo SCHWERTLE (Maastricht University, School of Business and Economics, Bachelor in International Business, 2023) explains how behavioral biases can influence trading of market aprticiapnts.

Behavioral biases of investors

In complex decision environments, people use basic judgements and preferences to simplify the scenario rather than adhere to a strictly rational approach. This use of mental shortcuts is called heuristics, which are quick and instinctively appealing but may result in poor outcomes (Tversky and Kahneman, 1974). The traditional financial theory (based on expected utility theory) assumes that people are rational agents. In contrast to traditional financial theory, behavioral theories argue that people are generally risk-averse with a skewed view of probability (Kahneman and Tversky, 1979). Some common behavioral biases that have been identified in the literature on investment decisions include overconfidence, the disposition effect and herding behavior.

Prospect Theory

We start with the two main drivers of irrationality: value perception and probability perception.

Value perception. The value function proposed by Kahneman and Tversky (1979) is characterized by the following features. First, it is determined based on departures from a reference point. Second, it typically has a downward, concave slope for gains and an upward, convex slope for losses. This suggests that individuals perceive losses as more painful gains as shown in Figure 1.

Figure 1. Perceived value function.

Source: Kahneman and Tversky (1979).

Probability perception. Individuals tend to assign a lower probability value to outcomes that are more likely to occur and, a higher probability value to outcomes that are less likely to occur as shown in Figure 2.

Figure 2. Perceived probability.

Source: Kahneman and Tversky (1979).

Overconfidence

Overconfidence manifests as an inclination to have an irrationally excessive level of trust in one’s own abilities and opinions and has been thoroughly investigated across many fields (Fischhoff et al., 1977).

Gervais and Odean (2001) explore how overconfidence develops as a result of a dynamic change in beliefs about one’s ability after observing successes and failures. Successful traders tend to be overconfident due to attributing too much credit to their own ability. They showed that overconfidence is highest among inexperienced traders, as proper self-assessment only develops over time. This leads to suboptimal behavior, such as increased trading volume and volatility, lower expected profits, and poor information utilization (Statman et al., 2006).

Ekholm and Pasternack (2007) investigate the link between overconfidence and investor size.
They show that larger investors are less overconfident than small investors. They also show that larger investors, on average, react more positively to good news and more negatively to bad news than smaller investors. Evidence suggests that smaller, more overconfident investors have worse performance following negative news (Ekholm and Pasternack, 2007).

Grinblatt and Keloharju (2009) argue that sensations seekers (people receiving more speeding tickets) and those who showed more overconfidence as measured by a psychological assessment traded more than the average, even after controlling for other factors that might explain trading activity like age, income and gender. Similarly, individual investors tend to buy stocks that have recently caught their attention, like stocks with high trading volume, extreme one-day returns, or those in the news, whereas institutional investors, especially those who follow a value strategy, do not (Barber and Odean, 2007). These results are confirmed by Barber et al. (2022) as Robinhood users, which are, as evidence suggests, less experienced traders, trade substantially more high-attention stocks.

Additionally, men are more prone to overconfidence than women, particularly in male-dominated industries like finance. Thus, men trade more than women and perform worse in terms of returns. Male investors not only engage in more frequent trading but, compared to female investors, also hold larger and less diversified portfolios (Barber & Odean, 2001; Lepone et al., 2022).

Why should I be interested in this post?

This post explores heuristics and behavioral biases in decision-making, particularly in the context of investment decisions. Overconfidence can lead to poor outcomes. Additionally, it touches on gender differences, with men being more prone to overconfidence and engaging in more frequent trading. By understanding these biases, readers can gain insights into human behavior, make more informed investment decisions, and explore the impact of gender on financial outcomes. Overall, this post offers valuable insights into decision-making processes and their implications.

Useful resources

Barber, B.M. and Odean, T. (2007) All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors Review of Financial Studies 21(2):785–818.

Barber, B.M. and Odean, T. (2001) Boys will be Boys: Gender, Overconfidence, and Common Stock Investment The Quarterly Journal of Economics 116(1):261–292.

Ekholm, A. and Pasternack, D. (2007) Overconfidence and Investor Size European Financial Management.

Fischhoff, B., Slovic, P. and Lichtenstein, S. (1977) Knowing with certainty: The appropriateness of extreme confidence. Journal of Experimental Psychology: Human Perception and Performance 3(4):552–564.

Gervais, S. and Odean, T. (2001) Learning to Be Overconfident Review of Financial Studies 14(1):1–27.

Grinblatt, M. and Keloharju, M. (2009) Sensation Seeking, Overconfidence, and Trading Activity The Journal of Finance 64(2):549–578.

Kahneman, D. and Tversky, A. (1979) Prospect Theory: An Analysis of Decision under Risk Econometrica 47(2): 263.

Lepone, G., Westerholm, J. and Wright, D. (2022) Speculative trading preferences of retail investor birth cohorts Accounting & Finance.

Statman, M., Thorley, S. and Vorkink, K. (2006) Investor Overconfidence and Trading Volume Review of Financial Studies 19(4):1531–1565.

Tversky, A. and Kahneman, D. (1974) Judgment under Uncertainty: Heuristics and Biases Science 185(4157):1124–1131.

About the author

The article was written in May 2023 by Theo SCHWERTLE (Maastricht University, School of Business and Economics, Bachelor in International Business, 2018-2023).

My professional experience as B2B Project assistant manager at Dance

May 29, 2023 by Theo SCHWERTLE

My professional experience as B2B Project assistant manager at Dance

In this article, Theo SCHWERTLE (Maastricht University, School of Business and Economics, Bachelor in International Business, 2023) shares his experience as a B2B Project assistant manager at Dance which is a start-up in urban mobility.

About the company

Dance is a progressive company that is reshaping urban mobility by providing an electric mobility subscription service. The company offers members the freedom to explore their city with an electric bike or moped, with maintenance and repairs included in the membership. Founded by the creators of SoundCloud and Jimdo, Dance is currently operating in Berlin, Hamburg, Munich, Vienna, and Paris, with a focus on making urban commuting more connected, convenient, and environmentally friendly.

Logo of the company.
Logo of Dance
Source: Dance.

My internship

As part of the Dance for Business department, I was privileged to contribute to various crucial aspects of the business, including the development and standardization of Business-to-Business (B2B) playbooks for client outreach, engagement, and account management. I also had the opportunity to manage the company pipeline using our Customer Relationship Management (CRM) tool, conduct competitive market research, and collaborate with cross-functional teams to execute lead generation strategies and client retention initiatives.

My missions

My mission at Dance was multifaceted, encompassing both client relationship management and sales strategy. I was responsible for creating and developing B2B pitch decks, preparing and supporting pitch meetings with new clients, and building long-term relationships with our clients to provide the best service possible. Serve as the first point of contact for all B2B clients, but also to find new strategies to acquire more customers. Furthermore, we were making Partnership deals with other service providers to spread the word about the mobility solution that Dance offers.

Required skills and knowledge

This role required strong interpersonal skills for building and maintaining client relationships, as well as proficiency in using CRM tools to manage the company pipeline. It also called for a solid understanding of sales strategies and market research methodologies. Since we were only a small team, communication and constant prioritization of tasks was paramount. Interpersonal skills have strongly increased during that time since I was constantly pitching to the management of firms like AboutYou or Inditex while also taking care of our current clients.

What I learned

Project Management: In preparing B2B pitch decks and supporting pitch meetings, you would have honed your project management and organization skills.

Communication: Being the first point of contact for all B2B clients and building long-term relationships with them would have strengthened your communication and interpersonal skills.

Strategic Thinking: Conducting competitive market research and collaborating on lead generation strategies likely helped develop your strategic thinking and market analysis abilities.

Problem Solving: Proposing solutions in line with business objectives and incorporating new initiatives shows your problem-solving capabilities.

Financial concepts related my internship

Customer Acquisition Cost (CAC)

Customer Acquisition Cost (CAC) refers to the total expenses a company incurs to convince a potential customer to purchase its product or service. It includes costs related to marketing and sales efforts and is a key metric for determining the return on investment for acquisition strategies.

Contribution Margin

Contribution Margin is a financial metric that calculates the profitability for individual items sold by a company. It is determined by subtracting the variable costs (costs that change with the amount of goods or services produced) associated with a product from the revenue generated by that product.

Customer Lifetime Value

Customer Lifetime Value (LTV) is a projection of the total net profit a company expects to earn from a customer throughout the business relationship. It takes into account the revenue a customer would generate, the costs of acquiring and serving the customer, and the duration of the relationship with the customer.

Why should I be interested in this post?

If you’re looking to gain insights into the world of business operations or contemplating a career in a similar industry, this post should be of high interest to you. The financial concepts discussed here form the backbone of many successful businesses. Understanding these concepts can help you view business operations from a new perspective, providing you with a solid base for making informed decisions.

Furthermore, sharing my experience at Dance provides an insider’s perspective into how the start-up operates and how different roles contribute to its success.

My experience at Dance was nothing short of enriching. With the right blend of motivation, attention to detail, and focus on business objectives, I was able to contribute effectively to the company’s success. I hope my insights will inspire and guide those looking to embark on a similar professional journey.

Useful resources

Dance

About the author

The article was written in May 2023 by Theo SCHWERTLE (Maastricht University, School of Business and Economics, Bachelor in International Business, 2018-2023).

The Collapse of Silicon Valley Bank (2023)

May 19, 2023 by Mirabelle DING

The Collapse of Silicon Valley Bank (2023)

In this article, Mirabelle DING (Telfer School of Management, Bachelor in Finance, 2015-2019) analyzes the collapse of Silicon Valley Bank (SVB).

On March 10^th 2023, Silicon Valley Bank, the primary financial institution for the US technology sector, was shut down by California and Federal regulators due to illiquidity and insolvency concerns after depositors withdrew $42 billion within a single day, marking the second largest bank failure in the United States history.

Background of SVB

Silicon Valley Bank (SVB) was founded in 1983 in the Bay Area with its mission to provide banking services to venture capital-backed startups that would have been considered high risk by traditional banks. As a result of its pioneering vision, SVB had established a notable reputation among the tech community, and was providing financing services to nearly half of the venture-backed technology and life science companies in the United States. SVB was ranked the 16^th largest bank in the United States with total assets of $209 billion and was recognized as one of America’s Best Banks by Forbes for five consecutive years before its defunction.

Logo of Silicon Valley Bank.

Source: Silicon Valley Bank.

The Solvency-Liquidity Problem

In 2020, the Federal Reserve cut the Federal Funds rate down to a range of 0% to 0.25% and implemented an unlimited quantitative easing policy in response to the impact of the Covid-19 pandemic, which led to a substantial increase in the financial market’s liquidity and the price of financial assets. The deposit base of SVB also experienced a skyrocket from $60 billion to an impressive $190 billion by the end of 2021. With little demand for loans from its clients, SVB allocated almost three quarters of the incremental deposits in long-maturity US Treasury bonds and mortgage-back securities purchases in order to gain capitalize on the interest rate spread. As a result, SVB exposed itself to greater interest rate and market risks.

Starting from March 2022, the Federal Reserve started to raise the Funds rate to counter inflation. The benchmark rate hiked to 4.5%-4.75% within 12 months, causing a plunge in the financial market liquidity and a severe inverted yield curve of long-term bonds and securities.
As interest rates rose, SVB started suffering deep unrealized losses on much of its securities portfolio, amounting to more than $2 billion by the end of 2022.

Furthermore, due to the declining inflow of venture capital funding, many tech start-ups resorted to withdrawing from SVB to support their daily operations. From March to December, the deposits of SVB shrank rapidly from $200 billion to $175 billion. Since SVB did not protect their liabilities with short term investments for quick liquidations, they had to start selling their bonds at a significant loss and relied heavily on short term loans from Federal Home Loan Banks to accommodate these large withdrawals, totaling $15 billion by the end of 2022.

“The Social Media Bank Run”

On March 8^th 2023, SVB announced a $1.8 billion loss on its investment portfolio, alongside a plan to raise $2.25 billion. Consequently, Moody’s downgraded the bank’s credit rating, and the stock price of SVB’s parent company, SVB Financial Group, crashed at the next market opening. Prominent entrepreneurs raised concerns about SVB’s financial situation on social media, which went viral and amplified the panic among the bank’s clients. Depositors rushed to withdraw from their SVB account, culminating a total amount of $42 billion in attempted withdraws within 24 hours. SVB was on the verge of collapse as they could not generate enough cash to meet the escalating need for withdrawals.

On March 10^th 2023, the Federal Deposit Insurance Corporation, which protects the stability of the financial system, took over Silicon Valley Bank in an effort to protect depositors. Unlike personal banking, most clients held more the $250,000 FDIC insured limit in their accounts, putting them at the risk of losing a portion or all of their deposits that exceeded the threshold. To restrain the fear of financial contagion, the Federal Reserve later implemented emergency measures, ensuring that all deposits at SVB will be guaranteed, even for the amount above the $250,000 limit.

Later, the Federal government announced an emergency lending programing to allow distressed banks to borrow from the Federal Reserve as a contingency liquidity plan to cover their withdrawal needs and to restore public confidence in the financial system.

Conclusion

The collapse of SVB reflected an inadequacy in its risk management and strategy, which could have been avoided through regular review and valuation of their investment portfolio, avoidance of concentrating assets in long-term maturities, possession of sufficient liquid assets, and hedging strategies against rising interest rate. This demonstrates the importance for businesses and organizations to properly and promptly manage their financial risk to prevent or mitigate situations that may lead to financial distress.

Useful resources

Apricitas Economics The Death of Silicon Valley Bank

The Federal Reserve Re: Review of the Federal Reserve’s Supervision and Regulation of Silicon Valley Bank

About the author

The article was written in May 2023 by Mirabelle DING (Telfer School of Management, Bachelor in Finance, 2015-2019).

My experience as City Manager at HungryPanda

May 19, 2023 by Mirabelle DING

My experience as City Manager at HungryPanda

In this article, Mirabelle DING (Telfer School of Management, Bachelor in Finance, 2015-2019) shares her professional experience as City Manager at HungryPanda Tech.

About the company

HungryPanda Tech is a global platform focused on overseas Asian community, covering food delivery, online grocery, retail, and lifestyle services. Founded in 2017 in Nottingham, the United Kingdom (UK), HungryPanda has expanded its operations to more than 80 cities in 10 countries, with 3.5 million registered users and over 60,000 merchant partners.

Logo of HungryPanda.

Source: HungryPanda.

The operation team is typically composed of three segments: business development, marketing, and delivery operations. The business development team manages accounts for our existing merchant partnerships and reaches out to new business opportunities. The marketing team is responsible for the promotion of the platform and customer acquisition, as well as negotiating sponsorship with local events. The delivery team ensures the efficiency of the delivery dispatch, quality of service, and recruitment of new carriers. The city manager oversees the workflow and coordinates the three departments to ensure seamless teamwork and achievement of the company’s goal.

My job

I worked as City Manager at HungryPanda for the Toronto Area, which is equivalent to Business Manager.

My missions

As City Manager at HungryPanda, my primary mission was to expand market share and enhance profitability.

Asian food delivery is a niche but competitive market in Toronto. To reinforce the competitive advantage of the company, my team and I had to regularly conduct market research, including industry trends, consumer behaviour analysis, and competitor analysis, to develop strategies and stay on top of the game. For example, we initiated a virtual kitchen program with selective partner merchants, where we researched and identified marketable dishes that were popular in areas with a similar demographic as our customer base. We collaborated with the merchants to design the menu and build exclusive virtual brands that were innovative and appealing to the consumers, which helped the merchants boost their revenue while mitigating the risk of modification on their original menus.

Another important duty of the city manager is to analyze the operational and financial data. The financial analysis includes breaking down the contribution margin of each of our merchant partners and evaluating the return on investment (ROI) of each project and market campaign, which is crucial in understanding our financial performance. The operational data analysis, on the other hand, entails app traffic flow, conversion rate, customer retention rate, redemption rate of discount coupons, etc., which facilitates identifying areas of improvement and optimizing the allocation of online resources. For example, if we launch a promotional discount on selective merchants alongside in-app advertising and text message marketing, analyzing the contribution margin and the customer retention rate of each merchant can help us determine the merchants that will continue to generate growth even after the discount period ends. This approach allowed us to maximize the return on our budget spending and ensure efficient utilization of marketing resources.

Knowledge and skills

During my time at HungryPanda, I have come to recognize several important skills that are essential for business operations:

Effective communication and coordination among different departments
Financial analysis and forecasting to support sustainable growth
Strategic planning to identify opportunities and challenges
Adaptability to react and adjust strategies in a dynamic business environment

Financial concepts related my job

I describe below the following financial concepts related my job: contribution margin, Gross merchandise volume, and the lifetime value (LTV) to customer acquisition cost (CAC) ratio.

Contribution margin

The contribution margin is calculated by sales revenue less the variable costs, and it represents the available revenue to cover the fixed costs (rent, salaries, market spending, etc.). I used contribution margin analysis to identify the profitability of each project and market campaign, and thereby determined which project or market campaign to continue and to invest in.

Gross merchandise volume

Gross merchandise volume (GMV) is the total money value of transactions on the platform. We used GMV as a key performance indicator to assess the scale and growth of our business and to track the overall performance of our long-term operational strategies.

LTV to CAC ratio

The lifetime value (LTV) to customer acquisition cost (CAC) ratio is the expected revenue from new customers relative to the cost of acquiring them. To encourage potential customers to try out the products and services offered on our platform, we frequently launched campaigns targeted at new registers, including offline promotional giveaways, new user discounts, referral rewards, etc. It is essential to analyze the customer acquisition cost and Lifetime value to evaluate the effectiveness and sustainability of each acquisition channel.

Why should I be interested in this post?

The experience at HungryPanda has instilled in me the importance of financial analysis and forecasting in making informed decisions for business operations. I hope this post shares some perspectives on how the application of financial concepts is used in driving business growth and improving profitability.

Useful resources

HungryPanda

About the author

The article was written in May 2023 by Mirabelle DING (Telfer School of Management, Bachelor in Finance, 2015-2019).

Key participants in the Private Equity ecosystem

May 18, 2023 by Matisse FOY

Key participants in the Private Equity ecosystem

In this article, Matisse FOY (ESSEC Business School, Bachelor in Business Administration (BBA), 2019-2023) explains who the key participants in Private Equity (PE) are, and what are their role in the PE ecosystem.

Private Equity is an increasingly important model of financing for companies at different scales. Whether you’re simply interested in the subject or want to find a professional experience, here is a list of the main participants in the PE ecosystem and their function.

Key participants in the Private Equity ecosystem

Source: production by the author

A glossary of the participants

Private Equity funds

PE funds are the central actors in the private equity ecosystem, pooling capital from various sources (mainly from Limited Partners and Investment Banks) and invest this money in private companies, meaning companies whose shares cannot be freely bought and sold on the stock market.

The employees of PE funds are responsible for sourcing, evaluating, and managing investments in “Portfolio Companies”.

Their objective is to enhance the performance of those Portfolio Companies. By doing so, they aim to sell these firms later and generate profit. This profit is primarily derived from the investment capital provided by their investors, from which they take a percentage as their fee.

General Partners (GPs)

These are the managers of the PE fund who make the investment decisions. They have a fiduciary duty to act in the best interest of the LPs.

GPs are typically compensated through a management fee, which is a fixed annual fee for the fund’s operation, and a performance fee (also known as “carry”), which is a percentage of the profits of the fund.

Limited Partners (LP)

Limited Partners are the investors in a PE fund. They include institutional investors like pension funds, university endowments (like Harvard University endowment), insurance companies (e.g., AXA, Allianz), and sovereign wealth funds, as well as high net worth individuals.

Limited Partners provide the capital that the PE funds invest and expect a return on their investment.

Portfolio Companies

Portfolio Companies are the companies in which PE funds invest. They are often in need of capital for growth, restructuring, or as part of a strategy to transition the company from public to private.

The goal of PE funds is to take a share in these companies, improve their performance and sell them for a profit.

Investment Banks

Investment Banks often play a crucial role in the PE ecosystem, especially with regards to the acquisition and sale of portfolio companies by PE funds. They can help PE funds identify potential investment opportunities, facilitate transactions, and provide financing by leveraging Limited Partners’ equity. Moreover, they can help portfolio companies go public when they are sold.

Law Firms and Consultants

These professional service providers support PE funds throughout the investment process:

Law firms help with legal aspects of transactions, including drafting and reviewing contracts, to ensure compliance with relevant laws and regulations, and advising on the structure of deals to minimize legal risks and tax liabilities.
Consultants, on the other hand, assist with due diligence and the development of strategies for improving the performance of portfolio companies. They might also be delegated the sourcing and contact with portfolio companies by PE funds.

Regulators

Regulators oversee and govern the operations of PE funds. They aim to protect the interests of investors and the integrity of the financial markets, in order for the local environment to be as attractive to invest in as possible.

Why should I be interested in this post?

Private Equity is a wide ecosystem. Knowing about its different participants is very important when deciding to work in one of them, in order to understand their importance (who knows, maybe you will be asked questions about these actors will be asked to you in your next interview).

Useful resources

The Financial Times Private Equity

Wall Street Journal Private Equity

Coursera’s MOOC Private Equity and Venture Capital

About the author

The article was written in May 2023 by Matisse FOY (ESSEC Business School, Bachelor in Business Administration (BBA), 2019-2023).

My professional experience as an Assistant to the CFO at Association Science Ouverte

May 18, 2023 by Matisse FOY

My professional experience as an Assistant to the CFO at Association Science Ouverte

In this article, Matisse FOY (ESSEC Business School, Bachelor in Business Administration (BBA), 2019-2023) shares his professional experience as an Assistant to the CFO at Association Science Ouverte.

About the structure

The missions of the Science Ouverte Association are “to open young people to science and science to young people, to fight against a feeling of powerlessness and confinement often too present”. It aims to create a visible and effective structure in Seine-Saint-Denis, capable of arousing scientific vocations and helping young people who are committed to this path.

It offers various activities, especially to high school students: tutoring, science and technology courses, various workshops on 3D graphics, programming, etc.

Logo of Science Ouverte Association
Logo of Association Science Ouverte
Source: Association Science Ouverte

I was a part of the Finance Department, a critical unit within the organization that was responsible for managing the association’s financial resources. As a district association, our department was made of only two people: the CFO and me.

The Finance Department oversees a wide range of functions, including budgeting, accounting, and financial reporting. It also plays a strategic role in decision-making processes by providing financial analysis to guide the association’s decisions.

Furthermore, the Department works with external stakeholders, such as auditors, as well as private and public funders.

My internship

My missions

Throughout my internship, I was tasked with various missions to operate and enhance the accounting and financial tools of the Association. Those missions had both short-term, operational objectives, and long-term objectives. Here is what they mainly consisted in:

Preparation of files for the audit of the accounts
Improving timesheet maintenance (e.g., adding indicators and summary tables for them to be as ergonomic and easy to use as possible)
Verification and updating of financial statements, individual funds and association budgets.

Required skills and knowledge

To work in a corporate finance position and be efficient at your job, you will need to acquire many skills:

Knowledge of financial concepts: During my internship, I was accompanied by the CFO in my learning of specific financial notions related to an association. My previous knowledge of finance and accounting helped understand and assimilate those notions faster.
Basic knowledge of a spreadsheet like Excel: In most structures, Excel will play a key role in your everyday job. Don’t forget to learn basic Excel skills and shortcuts to save time and make your tasks easier.
An understanding of the organization’s industry: Each structure has its financial specificities in terms of business model, objectives and regulatory environment. Learning about them as soon as possible will help shape your decision to be most effective.

What I learned

This experience brought me key valuable lessons about professional environment and work ethic. Here are three of them:

Attention to details: my time in the Finance department taught me how every piece of documentation, and every penny is important. The margin for error is low, and it allowed me to become meticulous is my work.
Effective communication: clear, concise, and timely communication was vital when communicating with my colleagues and superior to accomplished task I was assigned to. When confronted with a new problem, I did not hesitate to contact relevant persons if I couldn’t find the solution myself.
Proactivity: I tried to show initiative, anticipate needs, and propose solutions to existing problems that weren’t directly asked by my manager. This helps to create a positive impression and demonstrate your commitment.

Financial concepts related my internship

Financial forecasting

Financial forecasting refers to the process of estimating the future financial performance of an organization. These forecasts played a crucial role in strategic planning, helping the organization know what they could be able to invest in or not in the months and years to come.

Budgeting

Budgeting allows to estimate revenues and expenditures over a future period. During my internship, I saw how a well-structured budget serves as a roadmap, guiding the association’s financial decisions, and keeping the organization on track financially.

Financial Reporting

Financial reporting involves the process of producing statements that disclose an organization’s financial status to funders and the government. As part of my role, I helped in the preparation of the 2021 and had to work on financial reports. These reports were critical in understanding the financial health of the association, making informed decisions, and ensuring regulatory compliance.

Why should I be interested in this post?

An experience in the financial department of an association helps apply your theoretical knowledge about finance while taking a step back about its role: concretize the most impactful project by allocating resources, reporting, and optimizing them, to get the most of every euro you inject in the structure’s activities.

As I was working next to the association’s activity rooms, it was really gratifying to see that my work has a concrete influence on the young people the association is helping.

Useful resources

Association Science Ouverte

About the author

The article was written in May 2023 by Matisse FOY (ESSEC Business School, Bachelor in Business Administration (BBA), 2019-2023).

My professional experience as a Strategy and Innovation Consultant at Planet Impact Advisory

May 18, 2023 by Matisse FOY

My professional experience as a Strategy and Innovation Consultant at Planet Impact Advisory

In this article, Matisse FOY (ESSEC Business School, Bachelor in Business Administration (BBA), 2019-2023) shares his professional experience as a Strategy and Innovation Consultant at the firm Planet Impact Advisory.

About the company

Planet Impact Advisory is a consulting firm located in Paris and providing strategy consulting services for corporates and venture funds.

The firm uses methodologies mixing strategy consulting, design thinking, and a strong entrepreneurial approach to solve challenges and build impactful projects.

Its missions range from accompanying a mid-size company in the establishment of an investment strategy in the health sector, to the construction of a European program and two innovative platforms to solve the talent crunch in the health sector.

Logo of Planet Impact.

Source: Planet Impact.

My internship experience

My missions

My assignments within the firm were extremely diverse. I was assigned in the mobility, human resources (HR) and even healthcare sectors. These missions mainly consisted in:

Sourcing, production of one-pagers, presentation, and matchmaking with startups for a Swiss-German CVC (Corporate Venture Capital Fund)
Construction of the strategic documentation and support for a fundraising for a startup in the HR Tech / Future of Work.
Conceptualization and competitive analysis of two innovative platforms for an organization in the health sector
Participation in the construction of a database of 2,500 startups in the mobility sector.

Required skills and knowledge

Many soft skills are required to perform in the consulting world:

Strong analytical skills: much the work involves interpreting complex data through dense literature and translating it into actionable strategies.
Communication: whether when facing your superiors or clients, you need to be able to communicate idea in a concise and effective way.
Business fundamentals: you don’t need to be an expert with 10 years of experience in each and every business sector you will be working in, but you should at least know about the core aspects of marketing, finance, and project management.

What I learned

This experience brought me key valuable lessons about professional environment and work ethic. Here are three of them:

Be honest about what you can do and not to do in a timely manner. Being a people pleaser is not a good thing if you get buried under the workload that you will have accepted.
Learn to accept criticisms: there is always room for improvement, especially when starting an experience: don’t take criticism from your superiors personally, and show that you apply it.
Keep an eye for the details: the work you’re sending to the clients must be of excellent quality. One of the witnesses of this quality is the documentation that will be sent to them: check and double-check your work to avoid grammar or spelling errors.

Financial concepts related my internship

Return on Investment (ROI)

The ROI helps determine the profitability of an investment or compare the efficiency of different investments by measuring the gain or loss made on an investment relative to the amount of money invested. In consulting, you need to help clients make informed decisions about where they should be investing their money to get the most out of it.

Financial leverage

Financial leverage refers to the borrowed money used to finance the purchase of assets. In consulting, understanding a client’s industry and risk aversion with regards to how much financial leverage it is willing to take is crucial before taking any financial decision.

Profit margin

Profit margin is a profitability ratio calculated as net income divided by revenue, or net profits by sales. It measures how much percentage of sales a company keeps in earnings. Using profit margin analysis helps understand a company’s pricing strategy and cost structure, providing insights into the company’s operational efficiency.

Why should I be interested in this post?

Sometimes, thinking narrowly about your dream career can cut you off from excellent professional opportunities.

During my search for an internship, I was primarily interested in finance, but my position at the firm was not exclusively dedicated to this area. However, this opportunity broadened my horizons and allowed me to approach financial topics in a different context than finance-oriented position. This experience was thus unique compared to most people who wish to pursue a career in finance.

So, the next time you are looking for a professional experience, don’t hesitate to think broader about what you want to learn.

Useful resources

Planet Impact

About the author

The article was written in May 2023 by Matisse FOY (ESSEC Business School, Bachelor in Business Administration (BBA), 2019-2023).

The DAX 30 index

May 14, 2023 by Nithisha CHALLA

The DAX 30 index

In this article, Nithisha CHALLA (ESSEC Business School, Grande Ecole Program – Master in Management, 2021-2023) presents the DAX 30 index and details its characteristics.

The DAX 30 index

The largest and most liquid 30 publicly traded German companies are represented by the DAX 30 index. This index was established by the Frankfurt Stock Exchange on July 1, 1988. “Deutscher Aktienindex” or the German stock index in English, is abbreviated as DAX. Deutsche Boerse AG, which also runs the Frankfurt Stock Exchange, is in charge of managing the DAX 30.

The choice of the companies for the DAX index is based on a number of variables, such as trading volume, market capitalization, and liquidity. The Deutsche Boerse Index Commission regularly modifies and reviews the index’s composition, ensuring that DAX 30 accurately captures the overall performance of the German stock market.

The DAX 30 is a free float market capitalization-weighted index, which means that each company’s weight in the index is based on the calculation of its market capitalization. The performance of the German stock market is measured against the DAX 30, which is closely monitored by traders and investors worldwide. Investors and traders wishing to follow the performance of the German stock market can easily access the index as it is published and distributed in real-time by several financial news sources.

The ticker symbol “DAX” is used in trading platforms and financial websites to identify the DAX 30.

Table 1 below gives the Top 10 stocks in the DAX 30 index in terms of market capitalization as of January 31, 2023.

Table 1. Top 10 stocks in the DAX 30 index.

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

Calculation of the DAX 30 index value

The performance of the 30 largest and busiest German companies listed on Frankfurt Stock Exchange is reflected in the DAX 30, a blue-chip stock market index. A free-float market-capitalization-weighted methodology is utilized to calculate the index, which means that each company’s weight in the index is determined by its market capitalization adjusted for the shares that are actually traded in the secondary market (float).

The formula to compute the DAX 30 index is given by

Float Adjusted Market Capitalization Index value

where I is the index value, k a given asset, K the number of assets in the index, P^k the market price of asset k, N^k the number of issued shares for asset k, F^k the float factor of asset k, and t the time of calculation of the index.

In a float-adjusted market-capitalization-weighted index, the weight of asset k is given by formula

Float Adjusted Market Capitalization Weighted Index Weight

Use of the DAX 30 index in asset management

Investors can examine the sector weightings and geographic exposure of the index to gain insights into performance of the German economy to identify potential opportunities and risks in particular industries or regions. Asset managers compare performance of their equity portfolios to the performance of the complete market using the DAX 30 as the benchmark. Multiple investment products, including exchange-traded funds (ETFs), options, and futures contracts, all have the index as the starting point.

Benchmark for equity funds

One of the highly significant indices in Europe, the DAX 30 serves as standard for the overall performance of German stock market. The businesses represent numerous industries, including those in the automotive, financial, healthcare, technology, and retail sectors. Asset managers and investors use the DAX 30 as the benchmark to compare performance of their portfolios to that of the market as a whole. It is used as gauge of investor sentiment toward the nation’s businesses and financial markets as well as a barometer for the health of the German economy.

Financial products around the DAX 30 index

There are various financial products available that allow investors to gain exposure to German equity market through the DAX 30 index.

ETFs are investment funds traded on stock exchanges which are designed to track the performance of an index. Some of the ETFs that track the DAX 30 index include the iShares DAX UCITS and the X Trackers DAX UCITS.
Index funds are designed to track the performance of the index. Examples of the index funds based on the DAX 30 index include the DWS Deutschland Index Fund and the Allianz DAX Index Fund.
Futures and options contracts based on the DAX 30 index provide investors with ability to speculate on the future performance of the index. Eurex offers futures and options contracts based on the DAX 30 index.
Certificates are investment products allowing investors to gain exposure to the DAX 30 index. Commerzbank offers a range of certificates linked to the DAX 30 index, such as the ComStage DAX UCITS ETF.

Overall, these financial products offer investors the ability to diversify their portfolios and gain exposure to German equity market, as well as potentially benefit from the performance of the DAX 30 index.

Historical data for the DAX 30 index

How to get the data?

The DAX 30 index is the most common index used in finance, and historical data for the DAX 30 index can be easily downloaded from the internet.

For example, you can download data for the DAX 30 index from December 30, 1987 on Yahoo! Finance (the Yahoo! code for DAX 30 index is ^GDAXI).

Source: Yahoo! Finance.

You can also download the same data from a Bloomberg terminal.

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 DAX 30 index.

Data file

The R program that you can download above allows you to download the data for the DAX 30 index from the Yahoo! Finance website. The database starts on December 30, 1987. It also computes the returns (logarithmic returns) from closing prices.

Table 3 below represents the top of the data file for the DAX 30 index downloaded from the Yahoo! Finance website with the R program.

Table 3. Top of the data file for the DAX 30 index.
Top of the file for the DAX 30 index data
Source: computation by the author (data: Yahoo! Finance website).

Evolution of the DAX 30 index

Figure 1 below gives the evolution of the DAX 30 index from December 30, 1987 to December 30, 2022 on a daily basis.

Figure 1. Evolution of the DAX 30 index.

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

Figure 2 below gives the evolution of the DAX 30 index returns from December 30, 1987 to December 30, 2022 on a daily basis.

Figure 2. Evolution of the DAX 30 index returns.

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

Summary statistics for the DAX 30 index

The R program that you can download above also allows you to compute summary statistics about the returns of the DAX 30 index.

Table 4 below presents the following summary statistics estimated for the DAX 30 index:

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

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

Table 4. Summary statistics for the DAX 30 index.

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

Statistical distribution of the DAX 30 index returns

Historical distribution

Figure 3 represents the historical distribution of the DAX 30 index daily returns for the period from December 30, 1987 to December 30, 2022.

Figure 3. Historical distribution of the DAX 30 index 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 December 30, 1987 to December 30, 2022. The mean of daily returns is equal to 0.02% and the standard deviation of daily returns is equal to 1.37% (or equivalently 3.94% for the annual mean and 28.02% for the annual standard deviation as shown in Table 3 above).

Figure 4 below represents the Gaussian distribution of the DAX 30 index daily returns with parameters estimated over the period from v to December 30, 2022.

Figure 4. Gaussian distribution of the DAX 30 index returns.

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

Risk measures of the DAX 30 index returns

The R program that you can download above also allows you to compute risk measures about the returns of the DAX 30 index.

Table 5 below presents the following risk measures estimated for the DAX 30 index:

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

Table 5. Risk measures for the DAX 30 index.

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 DAX 30 index while the study of the right tail is relevant for an investor holding a short position in the DAX 30 index.

Why should I be interested in this post?

For a number of reasons, management students (as future managers and individual investors) should learn about the DAX 30 index. The index includes wide range of industries, including energy, finance, telecommunications, and consumer goods, and it covers the biggest and most liquid German companies. Understanding how the index is constructed, how it performs, and the companies that make up the index is important for anyone studying finance or business in Russia or interested in investing in German equities.

Individual investors can assess the performance of their own investments in the German equity market with the DAX 30 index. Last but not least, a lot of asset management firms base their mutual funds and exchange-traded funds (ETFs) on the DAX 30 index which can considered as interesting assets to diversify a portfolio. Learning about these products and their portfolio and risk management applications can be valuable for management students.

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.

Business

CFI DAX Stock Index Explained

Wikipedia An introduction to the DAX 30 index

Avatrade Trade the DAX index

Data

Yahoo! Finance

Yahoo! Finance Historical data for the DAX 30 index

About the author

The article was written in May 2023 by Nithisha CHALLA (ESSEC Business School, Grande Ecole Program – Master in Management, 2021-2023).

The MOEX Russia index

May 14, 2023 by Nithisha CHALLA

The MOEX Russia index

In this article, Nithisha CHALLA (ESSEC Business School, Grande Ecole Program – Master in Management, 2021-2023) presents the MOEX Russia index and details its characteristics.

The MOEX Russia index

The Moscow Exchange Russia Index (MOEX Russia Index) is market-capitalization-weighted index of the 50 biggest and most liquid companies listed on the Moscow Exchange. It was first presented in 1997 and serves as the benchmark index for the Russian stock market.

A wide range of sectors are covered by the MOEX Russia Index, including consumer goods, energy, finance, and telecommunications. By market capitalization, Gazprom, Sberbank, Lukoil, Novatek, and Tatneft were the top five index members as of September 2021.

The MOEX Russia Index is a market-capitalization-weighted index, which means that rather than using share price to determine a company’s weight in the index, it utilizes market capitalization. This enables it to depict the overall performance of the Russian equity market with greater accuracy.

Investors and asset managers frequently use the MOEX Russia Index as a benchmark to monitor the performance of the Russian equity market. ETFs and index funds are examples of financial products that are made to track the MOEX Russia Index.

The MOEX Russia Index has the ticker “IMOEX” in the financial sector.

Table 1 below gives the Top 10 stocks in the MOEX Russia index in terms of market capitalization as of January 31, 2023.

Table 1. Top 10 stocks in the MOEX Russia index.

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

Calculation of the MOEX Russia index value

As per the free-float methodology, which is used to calculate the MOEX Russia Index, each company’s weight in the index is determined by the percentage of its shares that are available for public trading rather than by its overall market capitalization. The goal of this methodology is to present a more accurate picture of the market value of each company.

The formula to compute the MOEX Russia is given by

Float Adjusted Market Capitalization Index value

Where I is the index value, k a given asset, K the number of assets in the index, P^k the market price of asset k, N^k the number of issued shares for asset k, F^k the float factor of asset k, and t the time of calculation of the index.

In a float-adjusted market-capitalization-weighted index, the weight of asset k is given by formula can be rewritten as

Float Adjusted Market Capitalization Weighted Index Weight

Use of the MOEX Russia index in asset management

For asset managers who make investments in the Russian equity market, the MOEX Russia index serves as a crucial benchmark. It is used as an exchange-traded fund (ETF) and Russian equity fund performance benchmark. The index can be used by investors to assess the performance of their portfolios and compare it to the performance of the complete market.

Benchmark for equity funds

Equity funds that invest in Russian companies use the MOEX Russia Index as a benchmark. The MOEX Russia index can also serve as the foundation for the investment products that track indices, like index funds and ETFs. These goods are made to follow the index’s performance and give buyers access to Russian equity market. Investors can gain broad market exposure through the purchase of these products without picking individual stocks.

Financial products around the MOEX Russia index

There are several financial products tracking the performance of the MOEX Russia Index, allowing investors to gain exposure to the Russian stock market.

ETFs are investment funds traded on the stock exchanges, designed to track performance of an index. There are several ETFs that track the MOEX Russia Index, such as the Xtrackers Russia UCITS and the VanEck Vectors Russia
Index funds are designed to track performance of an index. Index funds based on the MOEX Russia Index include the Sberbank Asset Management MOEX Russia Index Fund and the Raiffeisen Russia Equity Fund.
Futures and options contracts based on the MOEX Russia Index provide investors with the ability to speculate on the future performance of the index. For example, the Moscow Exchange offers futures contracts based on the MOEX Russia Index.
Certificates are investment products that allow investors to get exposure to the MOEX Russia Index. Société Générale offers a range of certificates linked to the MOEX Russia Index, such as the MOEX Russia Index Tracker Certificate.

Historical data for the MOEX Russia index

How to get the data?

The MOEX Russia index is the most common index used in finance, and historical data for the MOEX Russia index can be easily downloaded from the internet.

For example, you can download data for the MOEX Russia index from January 3, 1984 on Yahoo! Finance (the Yahoo! code for MOEX Russia index is IMOEX.ME).

Source: Yahoo! Finance.

You can also download the same data from a Bloomberg terminal.

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 MOEX Russia index.

Data file

The R program that you can download above allows you to download the data for the MOEX Russia index from the Yahoo! Finance website. The database starts on January 3, 1984. It also computes the returns (logarithmic returns) from closing prices.

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

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

Evolution of the MOEX Russia index

Figure 1 below gives the evolution of the MOEX Russia index from January 3, 1984 to December 30, 2022 on a daily basis.

Figure 1. Evolution of the MOEX Russia index.

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

Figure 2 below gives the evolution of the MOEX Russia index returns from January 3, 1984 to December 30, 2022 on a daily basis.

Figure 2. Evolution of the MOEX Russia index returns.

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

Summary statistics for the MOEX Russia index

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

Table 4 below presents the following summary statistics estimated for the MOEX Russia index:

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

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

Table 4. Summary statistics for the MOEX Russia index.

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

Statistical distribution of the MOEX Russia index returns

Historical distribution

Figure 3 represents the historical distribution of the MOEX Russia index daily returns for the period from January 3, 1984 to December 30, 2022.

Figure 3. Historical distribution of the MOEX Russia index 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 January 3, 1984 to December 30, 2022. The mean of daily returns is equal to 0.02% and the standard deviation of daily returns is equal to 1.37% (or equivalently 3.94% for the annual mean and 28.02% for the annual standard deviation as shown in Table 3 above).

Figure 4 below represents the Gaussian distribution of the MOEX Russia index daily returns with parameters estimated over the period from January 3, 1984 to December 30, 2022.

Figure 4. Gaussian distribution of the MOEX Russia index returns.
Gaussian distribution of the daily MOEX Russia index returns
Source: computation by the author (data: Yahoo! Finance website).

Risk measures of the MOEX Russia index returns

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

Table 5 below presents the following risk measures estimated for the MOEX Russia index:

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

Table 5. Risk measures for the MOEX Russia index.

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 MOEX Russia index while the study of the right tail is relevant for an investor holding a short position in the MOEX Russia index.

Why should I be interested in this post?

For a number of reasons, management students (as future managers and individual investors) should learn about the MOEX Russia index. The index includes wide range of industries, including energy, finance, telecommunications, and consumer goods, and it covers the biggest and most liquid companies listed on the Moscow Exchange. Understanding how the index is constructed, how it performs, and the companies that make up the index is important for anyone studying finance or business in Russia or interested in investing in Russian equities.

Individual investors can assess the performance of their own investments in the Russian equity market with the MOEX Russia index. Last but not least, a lot of asset management firms base their mutual funds and exchange-traded funds (ETFs) on the MOEX Russia index which can considered as interesting assets to diversify a portfolio. Learning about these products and their portfolio and risk management applications can be valuable for management students.

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.

Business

wikipedia What is the MOEX Russia index?

Moex Everything about MOEX

Data

Yahoo! Finance

Yahoo! Finance MOEX Russia index

About the author

The article was written in May 2023 by Nithisha CHALLA (ESSEC Business School, Grande Ecole Program – Master in Management, 2021-2023).

The BOVESPA index

May 14, 2023 by Nithisha CHALLA

The BOVESPA index

In this article, Nithisha CHALLA (ESSEC Business School, Grande Ecole Program – Master in Management, 2021-2023) presents the BOVESPA index and details its characteristics.

The BOVESPA index

The BOVESPA Index, or IBOVESPA, is the benchmark stock market index of the São Paulo Stock Exchange (B3) in Brazil. The index was launched on January 2, 1968, and tracks the performance of the 80 most traded stocks on the exchange.

As of 2021, the top 10 constituents of the BOVESPA Index included companies from a range of sectors such as finance, energy, materials, and consumer goods. Some of the largest companies in the index include Petrobras, Vale, Itau Unibanco, and Banco Bradesco.

The BOVESPA Index is considered a crucial indicator of the Brazilian stock market’s overall health and serves as a benchmark for Brazilian equity mutual funds and exchange-traded funds (ETFs). The index is weighted by free float market capitalization, which means that the more valuable a company is, the more significant its impact on the index’s movements.

The BOVESPA Index has experienced significant fluctuations in the past due to factors such as political instability, economic crises, and shifts in global commodity prices. Trading platforms and financial websites represent the BOVESPA Index using the ticker symbol “IBOV”.

Table 1 below gives the Top 10 stocks in the BOVESPA index in terms of market capitalization as of January 31, 2023.

Table 1. Top 10 stocks in the BOVESPA index.

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

Calculation of the BOVESPA index value

The index is a market-capitalization-weighted index, which means that the weight of each company in the index is determined by its market capitalization, calculated by multiplying the number of outstanding shares by the current market price per share. It tracks the performance of the largest and most actively traded companies listed on the Sao Paulo Stock Exchange (B3).

The formula to compute the BOVESPA index is given by

Market Capitalization Index value

Where I is the index value, k a given asset, K the number of assets in the index, P^k the market price of asset k, N^k the number of issued shares for asset k, and t the time of calculation of the index.

In a market capitalization-weighted index, the weight of asset k is given by formula can be rewritten as

Market Capitalization Weighted Index Weight

Which clearly shows that the weight of each asset in the index is its market capitalization of the asset divided by the sum of the market capitalizations of all assets.

Note that the divisor, whose calculation is based on the number of shares, is typically adjusted for events such as stock splits and dividends. The divisor is used to ensure that the value of the index remains consistent over time despite changes in the number of outstanding shares.

Use of the BOVESPA index in asset management

The BOVESPA Index is frequently used by investors, analysts, and financial institutions to track the overall trend of the Brazilian stock market and to make investment decisions. It is also used as a basis for the creation of financial products such as exchange-traded funds (ETFs) and index futures contracts.

Benchmark for equity funds

The BOVESPA index is widely considered as the benchmark index for the Brazilian stock market and is used as a measure of the performance of the Brazilian economy. It includes a diverse range of companies from various sectors such as finance, mining, energy, and consumer goods. Some of the largest companies listed on the BOVESPA Index include Petrobras, Vale, Itau Unibanco, and Banco Bradesco.

Financial products around the BOVESPA index

There are various financial instruments available to investors seeking to track the performance of the BOVESPA index.

ETFs are popular investment products that allow investors to gain exposure to the BOVESPA index. These include the iShares MSCI Brazil ETF and the BMO MSCI Brazil Index ETF.
Index funds are also designed to track the performance of an index. The BlackRock Brazil Equity Index Fund and the Bradesco FIA BOVESPA Index Fund are examples of index funds that track the BOVESPA index.
Futures and options contracts based on the BOVESPA index provide investors with the ability to speculate on the future performance of the index. BM&FBOVESPA, the Brazilian futures and options exchange, offers futures contracts based on the BOVESPA index.
Certificates are investment products that allow investors to gain exposure to the BOVESPA index. Credit Suisse and Itau Unibanco offer certificates linked to the BOVESPA index, such as the Brazil Index Tracker Certificate.

Historical data for the BOVESPA index

How to get the data?

The BOVESPA index is the most common index used in finance, and historical data for the BOVESPA index can be easily downloaded from the internet.

For example, you can download data for the BOVESPA index from January 3, 1984 on Yahoo! Finance (the Yahoo! code for BOVESPA index is ^NSEI).

Source: Yahoo! Finance.

You can also download the same data from a Bloomberg terminal.

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 BOVESPA index.

Data file

The R program that you can download above allows you to download the data for the BOVESPA index from the Yahoo! Finance website. The database starts on January 3, 1984. It also computes the returns (logarithmic returns) from closing prices.

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

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

Evolution of the BOVESPA index

Figure 1 below gives the evolution of the BOVESPA index from January 3, 1984 to December 30, 2022 on a daily basis.

Figure 1. Evolution of the BOVESPA index.

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

Figure 2 below gives the evolution of the BOVESPA index returns from January 3, 1984 to December 30, 2022 on a daily basis.

Figure 2. Evolution of the BOVESPA index returns.

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

Summary statistics for the BOVESPA index

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

Table 4 below presents the following summary statistics estimated for the BOVESPA index:

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

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

Table 4. Summary statistics for the BOVESPA index.

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

Statistical distribution of the BOVESPA index returns

Historical distribution

Figure 3 represents the historical distribution of the BOVESPA index daily returns for the period from January 3, 1984 to December 30, 2022.

Figure 3. Historical distribution of the BOVESPA index 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 January 3, 1984 to December 30, 2022. The mean of daily returns is equal to 0.02% and the standard deviation of daily returns is equal to 1.37% (or equivalently 3.94% for the annual mean and 28.02% for the annual standard deviation as shown in Table 3 above).

Figure 4 below represents the Gaussian distribution of the BOVESPA index daily returns with parameters estimated over the period from January 3, 1984 to December 30, 2022.

Figure 4. Gaussian distribution of the BOVESPA index returns.
Gaussian distribution of the daily BOVESPA index returns
Source: computation by the author (data: Yahoo! Finance website).

Risk measures of the BOVESPA index returns

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

Table 5 below presents the following risk measures estimated for the BOVESPA index:

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

Table 5. Risk measures for the BOVESPA index.

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 BOVESPA index while the study of the right tail is relevant for an investor holding a short position in the BOVESPA index.

Why should I be interested in this post?

For a number of reasons, management students (as future managers and individual investors) should learn about the BOVESPA index. The BOVESPA index is a key benchmark for the Indian equity market, which is a fast developing market. Understanding how the index is constructed, how it performs, and the companies that make up the index is important for anyone studying finance or business in India or interested in investing in Indian equities.

Individual investors can assess the performance of their own investments in the Japanese equity market with the BOVESPA index. Last but not least, a lot of asset management firms base their mutual funds and exchange-traded funds (ETFs) on the BOVESPA index which can considered as interesting assets to diversify a portfolio. Learning about these products and their portfolio and risk management applications can be valuable for management students.

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.

Business

Capital What is the Bovespa index?

Wikipedia An introduction to the Bovespa

International Finance Corporation Everything about Bovespa

Data

Yahoo! Finance

Yahoo! Finance BOVESPA index

About the author

The article was written in May 2023 by Nithisha CHALLA (ESSEC Business School, Grande Ecole Program – Master in Management, 2021-2023).

The Nifty 50 index

May 13, 2023 by Nithisha CHALLA

The Nifty 50 index

In this article, Nithisha CHALLA (ESSEC Business School, Grande Ecole Program – Master in Management, 2021-2023) presents the Nifty 50 index and details its characteristics.

The Nifty 50 index

One of the important stock market indices in India is the Nifty 50 index, also referred to as the NSE Nifty. The National Stock Exchange (NSE) of India first introduced this index in 1996, and it currently measures the performance of the top 50 companies listed on the exchange.

Market capitalization, liquidity, and trading volumes are just a few of the criteria that are used to choose the companies that will be included in the Nifty 50 index. The index’s companies come from a variety of industries, including, among others, banking, IT, healthcare, and energy.

The Nifty50 is a free float market capitalization-weighted index, which means that the market capitalization of each stock determines how much of that stock is included in the index. In comparison to a price-weighted index, the Nifty 50 is a better representation of the Indian stock market as a whole because of this.

Indian mutual funds, exchange-traded funds, and other financial products frequently use the Nifty 50 index as a benchmark. Since it offers insightful information about how the Indian economy and stock market are performing, it is also closely watched by investors and traders worldwide.

The ticker symbol used for the Nifty 50 index is “NIFTY”.

Table 1 below gives the Top 10 stocks in the Nifty 50 index in terms of market capitalization as of January 31, 2023.

Table 1. Top 10 stocks in the Nifty 50 index.

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

Calculation of the Nifty 50 index value

The top 50 companies listed on the National Stock Exchange (NSE) of India are tracked by the Nifty 50 stock market index in India. It is frequently used as the benchmark index for the Indian equity market and as a gauge of the state of the Indian economy as a whole. Companies from a variety of industries, including financial services, information technology, energy, and consumer goods, make up the Nifty50 index.

A free-float market-capitalization-weighted methodology is utilized to calculate the Nifty 50 index, which means that each company’s weight in the index is determined by its market capitalization adjusted for the shares that are actually traded in the secondary market (float).

The formula to compute the Nifty 50 index is given by

Float Adjusted Market Capitalization Index value

In a float-adjusted market-capitalization-weighted index, the weight of asset k is given by formula can be rewritten as

Float Adjusted Market Capitalization Weighted Index Weight

Use of the Nifty 50 index in asset management

The Nifty 50 serves as a benchmark for asset managers to assess the performance of their Indian equity portfolios. Asset managers can determine whether their investments are producing alpha, or outperforming the market, by comparing the returns of their portfolios to the performance of the index. If their portfolios underperform the index, they might need to adjust their stock selection or investment strategies to boost returns.

Benchmark for equity funds

In India, the Nifty 50 is frequently used as a benchmark for equity funds. By reflecting the performance of the top 50 companies listed on the National Stock Exchange of India, the index offers a snapshot of the performance of the Indian stock market. Investors can learn how well their investment is doing relative to the market by comparing the performance of a fund to the Nifty 50. If a fund consistently outperforms the index, the asset manager likely has a sound investment strategy and is adept at stock selection and market timing.

Financial products around the Nifty 50 index

There are several financial products that track the performance of the Nifty 50 index, allowing investors to gain exposure to the Indian stock market.

ETFs are investment funds traded on stock exchanges, designed to track the performance of an index. There are several ETFs that track the Nifty 50 index, such as the ICICI Prudential Nifty ETF and the Kotak Nifty ETF.
Index funds are also designed to track the performance of an index. Index funds based on the Nifty50 index include the HDFC Index Fund-Nifty 50 Plan and the UTI Nifty Index Fund.
Futures and options contracts based on the Nifty 50 index provide investors with the ability to speculate on the future performance of the index. For example, the National Stock Exchange of India (NSE) offers futures contracts based on the Nifty 50 index.
Certificates are investment products that allow investors to gain exposure to the Nifty50 index. Some banks in India offer certificates linked to the Nifty 50 index, such as the SBI Magnum Nifty Next 50 Index Fund.

With the help of these financial products, investors can invest in a diversified portfolio of 50 large-cap Indian companies from a range of industries and get exposure to the performance of the Nifty 50 index. Investors can gain a deeper understanding of industry trends, market competition, and the elements that contribute to business success by examining the performance of companies within these sectors. Asset managers can use these financial products as a benchmark to compare the performance of their equity portfolios to the performance of the entire market.

Historical data for the Nifty 50 index

How to get the data?

The Nifty 50 index is the most common index used in finance, and historical data for the Nifty 50 index can be easily downloaded from the internet.

For example, you can download data for the Nifty 50 index from January 3, 1984 on Yahoo! Finance (the Yahoo! code for Nifty 50 index is ^NSEI).

Source: Yahoo! Finance.

You can also download the same data from a Bloomberg terminal.

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 Nifty 50 index.

Data file

The R program that you can download above allows you to download the data for the Nifty 50 index from the Yahoo! Finance website. The database starts on January 3, 1984. It also computes the returns (logarithmic returns) from closing prices.

Table 3 below represents the top of the data file for the Nifty 50 index downloaded from the Yahoo! Finance website with the R program.

Table 3. Top of the data file for the Nifty 50 index.
Top of the file for the Nifty 50 index data
Source: computation by the author (data: Yahoo! Finance website).

Evolution of the Nifty 50 index

Figure 1 below gives the evolution of the Nifty 50 index from January 3, 1984 to December 30, 2022 on a daily basis.

Figure 1. Evolution of the Nifty 50 index.

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

Figure 2 below gives the evolution of the Nifty 50 index returns from January 3, 1984 to December 30, 2022 on a daily basis.

Figure 2. Evolution of the Nifty 50 index returns.

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

Summary statistics for the Nifty 50 index

The R program that you can download above also allows you to compute summary statistics about the returns of the Nifty 50 index.

Table 4 below presents the following summary statistics estimated for the Nifty 50 index:

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

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

Table 4. Summary statistics for the Nifty 50 index.

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

Statistical distribution of the Nifty 50 index returns

Historical distribution

Figure 3 represents the historical distribution of the Nifty 50 index daily returns for the period from January 3, 1984 to December 30, 2022.

Figure 3. Historical distribution of the Nifty 50 index 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 January 3, 1984 to December 30, 2022. The mean of daily returns is equal to 0.02% and the standard deviation of daily returns is equal to 1.37% (or equivalently 3.94% for the annual mean and 28.02% for the annual standard deviation as shown in Table 3 above).

Figure 4 below represents the Gaussian distribution of the Nifty 50 index daily returns with parameters estimated over the period from January 3, 1984 to December 30, 2022.

Figure 4. Gaussian distribution of the Nifty 50 index returns.
Gaussian distribution of the daily Nifty 50 index returns
Source: computation by the author (data: Yahoo! Finance website).

Risk measures of the Nifty 50 index returns

The R program that you can download above also allows you to compute risk measures about the returns of the Nifty 50 index.

Table 5 below presents the following risk measures estimated for the Nifty 50 index:

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

Table 5. Risk measures for the Nifty 50 index.

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 Nifty 50 index while the study of the right tail is relevant for an investor holding a short position in the Nifty 50 index.

Why should I be interested in this post?

For a number of reasons, management students (as future managers and individual investors) should learn about the Nifty 50 index. The Nifty 50 index is a key benchmark for the Indian equity market, which is a fast developing market. Understanding how the index is constructed, how it performs, and the companies that make up the index is important for anyone studying finance or business in India or interested in investing in Indian equities.

Individual investors can assess the performance of their own investments in the Japanese equity market with the Nifty 50 index. Last but not least, a lot of asset management firms base their mutual funds and exchange-traded funds (ETFs) on the Nifty 50 index which can considered as interesting assets to diversify a portfolio. Learning about these products and their portfolio and risk management applications can be valuable for management students.

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.

Business

CFI What is the NIFTY 50 Index?

Wikipedia An introduction to the NIFTY 50

NSE India 25 years journey of NSE

Data

Yahoo! Finance

Yahoo! Finance Nifty 50 index

About the author

The article was written in May 2023 by Nithisha CHALLA (ESSEC Business School, Grande Ecole Program – Master in Management, 2021-2023).

The CSI 300 index

May 13, 2023 by Nithisha CHALLA

The CSI 300 index

In this article, Nithisha CHALLA (ESSEC Business School, Grande Ecole Program – Master in Management, 2021-2023) presents the CSI 300 index and details its characteristics.

The CSI 300 index

The performance of 300 large-cap stocks traded on the Shanghai and Shenzhen stock exchanges in China is tracked by the capitalization-weighted stock market index known as the CSI 300 (China Securities Index 300). The China Securities Index Company, a joint venture between the Shanghai Stock Exchange and the Shenzhen Stock Exchange, introduced it in April 2005.

The CSI 300’s members are chosen based on their free float market capitalization, liquidity, as well as other aspects like profitability, potential for growth, and financial soundness. Companies from a wide range of industries, including finance, consumer goods, energy, and technology are included in the index.

The CSI 300 is frequently used by traders and investors as a benchmark for the Chinese stock market to gauge market trends and assess portfolio performance. As a measure of the health of China’s economy and of investor perception of the nation’s companies and financial markets, it is also closely watched by policymakers, economists, and analysts. The performance of the Chinese economy can be closely tracked by both domestic and foreign investors thanks to the CSI 300.

Through a range of financial products, including exchange-traded funds (ETFs), index funds, futures, and options contracts, investors can get exposure to the CSI 300 index.

The CSI 300 index has the ticker symbol “CSI300” in the financial sector.

Table 1 below gives the Top 10 stocks in the CSI 300 index in terms of market capitalization as of January 31, 2023.

Table 1. Top 10 stocks in the CSI 300 index.

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

Table 2 below gives the sector representation of the CSI 300 index in terms of number of stocks and market capitalization as of January 31, 2023.

Table 2. Sector representation in the CSI 300 index.

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

Calculation of the CSI 300 index value

The China Securities Index Company, a joint venture between the Shanghai Stock Exchange and the Shenzhen Stock Exchange, is in charge of managing the index.

A free-float market-capitalization-weighted methodology is utilized to calculate the CSI 300 index, which means that each company’s weight in the index is determined by its market capitalization adjusted for the shares that are actually traded in the secondary market (float).

The formula to compute the CSI 300 index is given by

Float Adjusted Market Capitalization Index value

In a float-adjusted market-capitalization-weighted index, the weight of asset k is given by formula can be rewritten as

Float Adjusted Market Capitalization Weighted Index Weight

Use of the CSI 300 index in asset management

The performance of the biggest and most liquid stocks listed on the Shanghai and Shenzhen stock exchanges is frequently monitored by investors using the CSI 300 index, which serves as a benchmark for the Chinese equity market. Asset managers use the index to compare the returns on their portfolios to market returns and to decide which investments to make. The CSI 300 index, which is focused on China’s domestic A-share market, may not accurately reflect the entire Chinese market, it is important to note. To gain a deeper understanding of the Chinese equity market, investors should also take into account other indexes like the MSCI China index and the FTSE China index.

Benchmark for equity funds

We must take into account the index’s makeup in order to determine whether the CSI 300 index serves as a benchmark for equity funds in China. The top 300 companies listed on the Shanghai and Shenzhen stock exchanges, which together make up about 70% of the total market capitalization of the Chinese equity market, are represented by the CSI 300 index. The index provides a thorough representation of the Chinese economy by including businesses from a wide range of industries, including financial, industrial, consumer goods, and technology.

As a result, equity funds that invest in the Chinese equity market frequently use the CSI 300 index as a benchmark. Fund managers can assess their performance by comparing the returns on their investments to the returns produced by the index.

Financial products around the CSI 300 index

There are various financial products available to investors who wish to gain exposure to the Chinese stock market through the CSI 300 index.

ETFs are investment funds traded on stock exchanges that aim to track the performance of an index. There are several ETFs that track the CSI 300 index, such as the iShares CSI 300 Index ETF and the China AMC CSI 300 Index ETF.
Index funds are similar to ETFs in that they aim to track the performance of an index. Some examples of index funds that track the CSI 300 index include the E Fund CSI 300 Index Fund and the China Southern CSI 300 Index Fund.
Futures and options contracts based on the CSI 300 index allow investors to speculate on the future performance of the index. The China Financial Futures Exchange offers futures contracts based on the CSI 300 index.
Certificates linked to the CSI 300 index are investment products that offer exposure to the index. China Merchants Bank, for example, offers a range of certificates linked to the CSI 300 index.

Historical data for the CSI 300 index

How to get the data?

The CSI 300 index is the most common index used in finance, and historical data for the CSI 300 index can be easily downloaded from the internet.

For example, you can download data for the CSI 300 index from March 11, 2021 on Yahoo! Finance (the Yahoo! code for CSI 300 index is 000300.SS).

Source: Yahoo! Finance.

You can also download the same data from a Bloomberg terminal.

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 CSI 300 index.

Data file

The R program that you can download above allows you to download the data for the CSI 300 index from the Yahoo! Finance website. The database starts on March 11, 2021. It also computes the returns (logarithmic returns) from closing prices.

Table 3 below represents the top of the data file for the CSI 300 index downloaded from the Yahoo! Finance website with the R program.

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

Evolution of the CSI 300 index

Figure 1 below gives the evolution of the CSI 300 index from March 11, 2021 to December 30, 2022 on a daily basis.

Figure 1. Evolution of the CSI 300 index.

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

Figure 2 below gives the evolution of the CSI 300 index returns from March 11, 2021 to December 30, 2022 on a daily basis.

Figure 2. Evolution of the CSI 300 index returns.

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

Summary statistics for the CSI 300 index

The R program that you can download above also allows you to compute summary statistics about the returns of the CSI 300 index.

Table 4 below presents the following summary statistics estimated for the CSI 300 index:

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

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

Table 4. Summary statistics for the CSI 300 index.

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

Statistical distribution of the CSI 300 index returns

Historical distribution

Figure 3 represents the historical distribution of the CSI 300 index daily returns for the period from March 11, 2021 to December 30, 2022.

Figure 3. Historical distribution of the CSI 300 index 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 March 11, 2021 to December 30, 2022. The mean of daily returns is equal to 0.02% and the standard deviation of daily returns is equal to 1.37% (or equivalently 3.94% for the annual mean and 28.02% for the annual standard deviation as shown in Table 3 above).

Figure 4 below represents the Gaussian distribution of the CSI 300 index daily returns with parameters estimated over the period from March 11, 2021 to December 30, 2022.

Figure 4. Gaussian distribution of the CSI 300 index returns.
Gaussian distribution of the daily CSI 300 index returns
Source: computation by the author (data: Yahoo! Finance website).

Risk measures of the CSI 300 index returns

The R program that you can download above also allows you to compute risk measures about the returns of the CSI 300 index.

Table 5 below presents the following risk measures estimated for the CSI 300 index:

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

Table 5. Risk measures for the CSI 300 index.

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 CSI 300 index while the study of the right tail is relevant for an investor holding a short position in the CSI 300 index.

Why should I be interested in this post?

For a number of reasons, management students (as future managers and individual investors) should learn about the CSI 300 index. The CSI 300 index is a key benchmark for the Japanese equity market, which is one of the world’s largest market. Understanding how the index is constructed, how it performs, and the companies that make up the index is important for anyone studying finance or business in Japan or interested in investing in Japanese equities.

Individual investors can assess the performance of their own investments in the Japanese equity market with the CSI 300 index. Last but not least, a lot of asset management firms base their mutual funds and exchange-traded funds (ETFs) on the CSI 300 index which can considered as interesting assets to diversify a portfolio. Learning about these products and their portfolio and risk management applications can be valuable for management students.

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.

Business

Wikipedia CSI 300 Index

Capital What is the CSI 300 Index?

CEI data China Index: CSI 300 Index: Financial

Data

Yahoo! Finance

Yahoo! Finance CSI 300 index

About the author

The article was written in May 2023 by Nithisha CHALLA (ESSEC Business School, Grande Ecole Program – Master in Management, 2021-2023).

The Euro Stoxx 50 index

May 12, 2023 by Nithisha CHALLA

The Euro Stoxx 50 index

In this article, Nithisha CHALLA (ESSEC Business School, Grande Ecole Program – Master in Management, 2021-2023) presents the Euro Stoxx 50 index and details its characteristics.

The Euro Stoxx 50 index

The performance of 50 large-capital companies with headquarters in Eurozone nations is reflected in the Euro Stoxx 50 stock market index. On February 26, 1998, Stoxx Ltd., a partnership between Deutsche Börse AG, Dow Jones & Company, and SIX Group AG, launched it. Companies from a wide range of industries, including the financial, consumer goods, healthcare, and industrial sectors are all included in the index.

Stocks for the Euro Stoxx 50 index are chosen based on market capitalization, liquidity, and sector representation, among other things. Every year in September, the index’s composition is reviewed, and adjustments are made as needed to reflect the state of the market and the performance of the companies.

The free-float market-capitalization-weighted index known as the Euro Stoxx 50. This means that rather than stock price, the index weights each company according to its market capitalization. The index is made available to the investors and traders worldwide and is disseminated in real-time by several financial news outlets.

The Euro Stoxx 50 index’s ticker symbol in the financial sector is “STOXX50E.”

Table 1 below gives the Top 10 stocks in the Euro Stoxx 50 index in terms of market capitalization as of January 31, 2023.

Table 1. Top 10 stocks in the Euro Stoxx 50 index.

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

Table 2 below gives the sector representation of the Euro Stoxx 50 index in terms of number of stocks and market capitalization as of January 31, 2023.

Table 2. Sector representation in the Euro Stoxx 50 index.

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

Calculation of the Euro Stoxx 50 index value

The performance of 50 sizable, blue-chip companies from 12 Eurozone nations, including France, Germany, Italy, and Spain, is tracked by the free-floating market-capitalization-weighted Euro Stoxx 50 index. The index, that includes a wide range of industries including financial services, energy, healthcare, consumer goods, and information technology, is intended to represent the performance of the most liquid and actively traded companies in Eurozone.

A free-float market-capitalization-weighted methodology is utilized to calculate the Euro Stoxx 50 index, which means that each company’s weight in the index is determined by its market capitalization adjusted for the shares that are actually traded in the secondary market (float).

The formula to compute the Euro Stoxx 50 index is given by

Float Adjusted Market Capitalization Index value

where I is index value, k a given asset, K the number of assets in the index, P^k the market price of asset k, N^k the number of issued shares for asset k, F^k the float factor of asset k, and t the time of calculation of the index.

In a float-adjusted market-capitalization-weighted index, the weight of asset k is given by formula can be rewritten as

Float Adjusted Market Capitalization Weighted Index Weight

Use of the Euro Stoxx 50 index in asset management

One of the significant indices in Europe, the Euro Stoxx 50 is quite famous and changes in it can have a big impact on market trends and investor sentiment. Investors and traders worldwide have access to index’s real-time values that are published and distributed by a number of financial news sources. The Euro Stoxx 50 is a crucial resource for investors putting efforts to understand the economic and political climate of the Eurozone and gain access to the equity market there. The index can be used by the asset managers as a benchmark to compare the performance of their portfolio to the overall market and to spot potential risk or opportunity areas.

Benchmark for equity funds

Investors and fund managers frequently use the Euro Stoxx 50 to track the health of the Eurozone economy and assess investment opportunities in the region. It is recognized as the top benchmark for the performance of the Eurozone equity market. It consists of businesses from a range of industries, including consumer goods, technology, and finance. The index is used by asset managers to monitor and assess performance of their portfolios in relation to the overall market.

Financial products around the Euro Stoxx 50 index

There are several financial products tracking performance of the Euro Stoxx 50 index. These products allow investors to get exposure to the European stock market.

ETFs are investment funds traded on stock exchanges that are designed to track the performance of an index. Several ETFs track the Euro Stoxx 50 index, such as the iShares EURO STOXX 50 UCITS and the Amundi ETF EURO STOXX 50 UCITS
Index funds based on the Euro Stoxx 50 index also allow investors to track performance of the index. Examples of index funds tracking Euro Stoxx 50 index include the DWS Invest Euro Stoxx 50 Fund and the BNP Paribas Easy Euro Stoxx 50 UCITS ETF.
Futures and options contracts based on Euro Stoxx 50 index provide investors with the ability to speculate on future performance of the index. For example, Eurex offers futures contracts based on the Euro Stoxx 50 index.
Certificates are investment products that allow investors to gain exposure to Euro Stoxx 50 index. Societe Generale offers range of certificates linked to the Euro Stoxx 50 index, such as the EURO STOXX 50 Tracker Certificate.

Investors and asset managers may use these financial products to gain exposure to the Euro Stoxx 50 index and manage their portfolios’ risk and return.

Historical data for the Euro Stoxx 50 index

How to get the data?

The Euro Stoxx 50 index is the most common index used in finance, and historical data for the Euro Stoxx 50 index can be easily downloaded from the internet.

For example, you can download data for the Euro Stoxx 50 index from January 3, 1984 on Yahoo! Finance (the Yahoo! code for Euro Stoxx 50 index is ^STOXX50E).

Source: Yahoo! Finance.

You can also download the same data from a Bloomberg terminal.

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 Euro Stoxx 50 index.

Data file

The R program that you can download above allows you to download the data for the Euro Stoxx 50 index from the Yahoo! Finance website. The database starts on January 3, 1984. It also computes the returns (logarithmic returns) from closing prices.

Table 3 below represents the top of the data file for the Euro Stoxx 50 index downloaded from the Yahoo! Finance website with the R program.

Table 3. Top of the data file for the Euro Stoxx 50 index.
Top of the file for the Euro Stoxx 50 index data
Source: computation by the author (data: Yahoo! Finance website).

Evolution of the Euro Stoxx 50 index

Figure 1 below gives the evolution of the Euro Stoxx 50 index from January 3, 1984 to December 30, 2022 on a daily basis.

Figure 1. Evolution of the Euro Stoxx 50 index.

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

Figure 2 below gives the evolution of the Euro Stoxx 50 index returns from January 3, 1984 to December 30, 2022 on a daily basis.

Figure 2. Evolution of the Euro Stoxx 50 index returns.

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

Summary statistics for the Euro Stoxx 50 index

The R program that you can download above also allows you to compute summary statistics about the returns of the Euro Stoxx 50 index.

Table 4 below presents the following summary statistics estimated for the Euro Stoxx 50 index:

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

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

Table 4. Summary statistics for the Euro Stoxx 50 index.

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

Statistical distribution of the Euro Stoxx 50 index returns

Historical distribution

Figure 3 represents the historical distribution of the Euro Stoxx 50 index daily returns for the period from January 3, 1984 to December 30, 2022.

Figure 3. Historical distribution of the Euro Stoxx 50 index 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 January 3, 1984 to December 30, 2022. The mean of daily returns is equal to 0.02% and the standard deviation of daily returns is equal to 1.37% (or equivalently 3.94% for the annual mean and 28.02% for the annual standard deviation as shown in Table 3 above).

Figure 4 below represents the Gaussian distribution of the Euro Stoxx 50 index daily returns with parameters estimated over the period from January 3, 1984 to December 30, 2022.

Figure 4. Gaussian distribution of the Euro Stoxx 50 index returns.
Gaussian distribution of the daily Euro Stoxx 50 index returns
Source: computation by the author (data: Yahoo! Finance website).

Risk measures of the Euro Stoxx 50 index returns

The R program that you can download above also allows you to compute risk measures about the returns of the Euro Stoxx 50 index.

Table 5 below presents the following risk measures estimated for the Euro Stoxx 50 index:

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

Table 5. Risk measures for the Euro Stoxx 50 index.

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 Euro Stoxx 50 index while the study of the right tail is relevant for an investor holding a short position in the Euro Stoxx 50 index.

Why should I be interested in this post?

For a number of reasons, management students (as future managers and individual investors) should learn about the Euro Stoxx 50 index. It is made up of businesses from 11 different Eurozone nations that operate in a variety of industries, including banking, technology, and healthcare. The Euro Stoxx 50 index is a key benchmark for the European equity market, which is one of the world’s largest market. Understanding how the index is constructed, how it performs, and the companies that make up the index is important for anyone studying finance or business in Europe or interested in investing in European equities. Students interested in careers in investment banking, asset management, or global business may find this information useful.

Individual investors can assess the performance of their own investments in the European equity market with the Euro Stoxx 50 index. Last but not least, a lot of asset management firms base their mutual funds and exchange-traded funds (ETFs) on the Euro Stoxx 50 index which can considered as interesting assets to diversify a portfolio. Learning about these products and their portfolio and risk management applications can be valuable for management students.

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.

Business

Wikipedia History of Euro Stoxx 50

Capital What is the Euro Stoxx Index Definition?

Deutsche Börse Xetra EURO STOXX 50® Index derivatives

Data

Yahoo! Finance

Yahoo Finance Euro Stoxx 50 index

About the author

The article was written in April 2023 by Nithisha CHALLA (ESSEC Business School, Grande Ecole Program – Master in Management, 2021-2023).

The FTSE 100 index

January 17, 2025May 12, 2023 by Nithisha CHALLA

The FTSE 100 index

In this article, Nithisha CHALLA (ESSEC Business School, Grande Ecole Program – Master in Management, 2021-2023) presents the FTSE 100 index and details its characteristics.

The FTSE 100 index

The Financial Times and the London Stock Exchange established the FTSE 100 index in 1984. It is now run by FTSE Group, a partnership between the Financial Times and the London Stock Exchange. The index, which is regarded as the standard index for the UK equity market, includes the 100 largest companies by market capitalization that are listed on the London Stock Exchange.

Larger companies have a greater influence on the index’s movements than smaller ones because the index is market capitalization-weighted. HSBC, Royal Dutch Shell, BP, and Unilever are a few of the biggest companies that make up the FTSE 100 as of 2021. The FTSE 100 is a key metric for gauging the state of the UK economy because it serves as a benchmark for funds and investment portfolios with UK roots. Recent occurrences like Brexit, the COVID-19 pandemic, and adjustments to the global economy have all had an effect on the index.

The sectoral composition of the FTSE 100 is one of its distinctive features. The financial and resource sectors account for a significant portion of the index’s total market capitalization, which heavily favors these industries.

How is the FTSE 100 index represented in trading platforms and financial websites? The ticker symbol used in the financial industry for the FTSE 100 index is “UKX”.

Table 1 below gives the Top 10 stocks in the FTSE 100 index in terms of market capitalization as of January 31, 2023.

Table 1. Top 10 stocks in the FTSE 100 index.

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

Table 2 below gives the sector representation of the FTSE 100 index in terms of number of stocks and market capitalization as of January 31, 2023.

Table 2. Sector representation in the FTSE 100 index.

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

Calculation of the FTSE 100 index value

The FTSE 100 is a market capitalization-weighted index, which means that each company’s weight in the index is determined by its market capitalization, i.e., the total value of all its outstanding shares. The index, which is regarded as the standard index for the UK equity market, includes the 100 largest companies by market capitalization that are listed on the London Stock Exchange.

The formula to compute the FTSE 100 index is given by

Market Capitalization Index value

In a market capitalization-weighted index, the weight of asset k is given by formula can be rewritten as

Market Capitalization Weighted Index Weight

Which clearly shows that the weight of each asset in the index is its market capitalization of the asset divided by the sum of the market capitalizations of all assets.

Use of the FTSE 100 index in asset management

The performance of large-cap companies listed on the London Stock Exchange is frequently measured against the FTSE 100. Investors can gain insight into the overall health of the UK economy and spot potential opportunities or risks in particular industries or regions by examining the sector weightings and geographic exposure of the index. It serves as a benchmark for asset managers to compare the performance of their equity portfolios to the overall market performance.

Benchmark for equity funds

One of the most popular metrics for assessing the performance of the UK stock market is the FTSE 100. It includes businesses from a wide range of sectors, including consumer goods, healthcare, energy, and finance. As a result, it is frequently used by investors and fund managers to monitor the UK economy’s performance and evaluate the country’s investment opportunities.

Financial products around the FTSE 100 index

There are several financial products that track the performance of the FTSE 100 index, allowing investors to gain exposure to the Japanese stock market.

ETFs are investment funds traded on stock exchanges, designed to track the performance of an index. There are several ETFs that track the FTSE 100 index, such as the iShares Core FTSE 100 ETF and the Vanguard FTSE 100 UCITS ETF.
index funds are also designed to track the performance of an index. index funds based on the FTSE 100 index include the HSBC FTSE 100 Index Fund and the Legal & General UK 100 Index Fund.
Futures and options contracts based on the FTSE 100 index provide investors with the ability to speculate on the future performance of the index. For example, the London International Financial Futures and Options Exchange (LIFFE) offers futures contracts based on the FTSE 100 index.
Certificates are investment products that allow investors to gain exposure to the FTSE 100 index. Société Générale offers a range of certificates linked to the FTSE 100 index, such as the FTSE 100 Tracker Certificate.

Historical data for the FTSE 100 index

How to get the data?

The FTSE 100 index is the most common index used in finance, and historical data for the FTSE 100 index can be easily downloaded from the internet.

For example, you can download data for the FTSE 100 index from January 3, 1984 on Yahoo! Finance (the Yahoo! code for FTSE 100 index is ^FTSE).

Source: Yahoo! Finance.

You can also download the same data from a Bloomberg terminal.

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 FTSE 100 index.

Data file

The R program that you can download above allows you to download the data for the FTSE 100 index from the Yahoo! Finance website. The database starts on January 3, 1984. It also computes the returns (logarithmic returns) from closing prices.

Table 3 below represents the top of the data file for the FTSE 100 index downloaded from the Yahoo! Finance website with the R program.

Table 3. Top of the data file for the FTSE 100 index.
Top of the file for the FTSE 100 index data
Source: computation by the author (data: Yahoo! Finance website).

Evolution of the FTSE 100 index

Figure 1 below gives the evolution of the FTSE 100 index from January 3, 1984 to December 30, 2022 on a daily basis.

Figure 1. Evolution of the FTSE 100 index.

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

Figure 2 below gives the evolution of the FTSE 100 index returns from January 3, 1984 to December 30, 2022 on a daily basis.

Figure 2. Evolution of the FTSE 100 index returns.

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

Summary statistics for the FTSE 100 index

The R program that you can download above also allows you to compute summary statistics about the returns of the FTSE 100 index.

Table 4 below presents the following summary statistics estimated for the FTSE 100 index:

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

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

Table 4. Summary statistics for the FTSE 100 index.

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

Statistical distribution of the FTSE 100 index returns

Historical distribution

Figure 3 represents the historical distribution of the FTSE 100 index daily returns for the period from January 3, 1984 to December 30, 2022.

Figure 3. Historical distribution of the FTSE 100 index 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 January 3, 1984 to December 30, 2022. The mean of daily returns is equal to 0.02% and the standard deviation of daily returns is equal to 1.37% (or equivalently 3.94% for the annual mean and 28.02% for the annual standard deviation as shown in Table 3 above).

Figure 4 below represents the Gaussian distribution of the FTSE 100 index daily returns with parameters estimated over the period from January 3, 1984 to December 30, 2022.

Figure 4. Gaussian distribution of the FTSE 100 index returns.
Gaussian distribution of the daily FTSE 100 index returns
Source: computation by the author (data: Yahoo! Finance website).

Risk measures of the FTSE 100 index returns

The R program that you can download above also allows you to compute risk measures about the returns of the FTSE 100 index.

Table 5 below presents the following risk measures estimated for the FTSE 100 index:

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

Table 5. Risk measures for the FTSE 100 index.

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 FTSE 100 index while the study of the right tail is relevant for an investor holding a short position in the FTSE 100 index.

Why should I be interested in this post?

For a number of reasons, management students (as future managers and individual investors) should learn about the FTSE 100 index. The FTSE 100 index is a key benchmark for the Japanese equity market, which is one of the world’s largest market. Understanding how the index is constructed, how it performs, and the companies that make up the index is important for anyone studying finance or business in Japan or interested in investing in Japanese equities.

Individual investors can assess the performance of their own investments in the Japanese equity market with the FTSE 100 index. Last but not least, a lot of asset management firms base their mutual funds and exchange-traded funds (ETFs) on the FTSE 100 index which can considered as interesting assets to diversify a portfolio. Learning about these products and their portfolio and risk management applications can be valuable for management students.

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.

Business

Axi What is the FTSE 100 index and how to trade it?

CMC markets An introduction to the FTSE 100

Nerd Wallet What is the FTSE 100?

Data

Yahoo! Finance

Yahoo Finance FTSE 100 index

About the author

The article was written in April 2023 by Nithisha CHALLA (ESSEC Business School, Grande Ecole Program – Master in Management, 2021-2023).

My experiences as Fixed Income portfolio manager then Asset Liability Manager at Banque de France

May 1, 2023 by William ARRATA

My experiences as Fixed Income portfolio manager then Asset Liability Manager at Banque de France

In this article, William ARRATA (Lecturer in advanced portfolio management at ESSEC Business School Master in Finance and Master in Management since 2014) shares his professional experience as Fixed Income Portfolio Manager then Asset Liability Manager at Banque de France.

About the company

Founded in 1800 by Napoléon Bonaparte, Banque de France began as a private institution for managing state debts and issuing notes. The first Basic Statutes of the Bank were established in 1808, where the Bank’s notes in French Franc became legal tender. In 1936 the Bank was nationalized. In 1993, a reform granted the Bank independence, in order to ensure price stability, regardless of domestic politics. This reform cleared the path for the European monetary union. In 1998, the Bank became a founding member of the European System of Central Banks which groups together the European Central Bank and the National Central Banks of all countries that have adopted the Euro. On 1st January 1999, France adopted the euro. Nowadays Banque de France’s three main missions, as defined by its statuses, are to drive the French monetary strategy, ensure financial stability and provide services to households, small and medium businesses and the French state. In particular, it manages the accounts and the facilitation of payments for the Treasury and some public companies. The Bank is a sui generis public entity governed by the French Monetary and Financial Code. The conditions whereby it conducts its missions on national territory are set out in its Public Service Contract. François Villeroy de Galhau has served as Governor of the Banque de France since 1 November 2015.

Logo of Banque de France.

Source: the company.

Since 2019, I work as Asset Liability Manager at the Financial Directorate of the General Secretariat of Banque de France, having previously worked from 2013 to 2019 as Fixed Income portfolio manager in the Markets Directorate of the Directorate General Financial Stability and Operations.

The Markets Directorate of Banque de France encompasses the management of foreign exchange reserves and gold, foreign exchange operations, and the provision of investment services to foreign central banks and international organizations. The Directorate is fully integrated from front office to back office and custody. It is split into five divisions and totals 120 persons, based in Paris, Poitiers, New York and Singapore.

The Financial Directorate of Banque de France encompasses the accounting of Eurosystem monetary policy operations as well as BdF’s investment operations, the costing and budgeting of expenses, management control, Asset Liability Management modeling of the Balance sheet, as well as the investment and management of BdF’s Capital and pension funds, on which the Socially Responsible Investment strategy of BdF is also enforced. It is split into five divisions and totals around 100 persons.

My jobs

From 2013 to 2019, I was Fixed Income Portfolio Manager in the Reserves Management Division of the Markets Directorate, in charge of managing foreign exchange reserves. In essence, the job consists of managing a fixed income portfolio, with the objective of consistently outperforming its benchmark through time.

Since 2019, I am an Asset Liability Manager (modeling mainly) in the Financial Management Division of the Financial Directorate. It consists of balance sheet modeling and projection through time. It is a quantitative position, which requires knowledge in stochastic calculus and programming languages. In addition, it is also a special job in the sense that the central bank balance sheet is unique in its kind such that asset and liability management (ALM) modeling at the central bank also requires understanding monetary policy operations.

My missions

My position as a Fixed Income portfolio manager in the Reserves Management Division of the Markets Directorate started in 2013. Foreign exchange reserves are held in various currencies, and each currency is actively managed against a benchmark into a specific portfolio, which is daily marked-to-market. I have been responsible for the management of one of those portfolios for 6 years. As for all portfolios, it is invested in money market instruments (reverse repos, repos, deposits, fully hedged swaps, STIR futures) on the one hand, and bonds from different types of issuers on the other hand. It also makes use of derivatives such as bond futures, rates futures, and Interest Rate Swaps. Each portfolio is managed in reference to a benchmark, around which risk limits are defined. Those risk limits give leeway to the portfolio manager to do tactical asset allocation, in order to “beat” the benchmark. Tactical allocation can take many forms.

First, the portfolio manager (p.m.) has the possibility do “time the market”, which is named after “duration position” in the Fixed-Income universe. This translates into an increase or a decrease of the differential duration (duration in excess of the benchmark) of his portfolio. A duration position is implemented when expectations from the p.m. on the interest rate path differ from what is priced in the forward curve (i.e. the p.m. expects indeed rates to “reprice” in the future according to his expectations, e.g., to move up or down). To benefit from this expected variation, the p.m. adjusts the differential duration of his portfolio. For instance, he increases the differential duration if he expects rates to go down. Such positions can also be combined. Combining a long duration position with a short duration position on two different segments of the yield curve can be a relevant investment strategy when the p.m. expects the yield curve to steepen or to flatten. This creates a spread position, referred to as “butterfly position”.

Market timing bets can be set using different techniques. This can stem from a regular central bank watching, which allows to understand the central bank “reaction function“ and to take positions in advance of other market participants. It can also be done using quantitative tools such as rates models.

The benchmark can also be beaten using security selection. This consists in substituting a bond whose price is seen as deviating from its fair value with another bond. The p.m. sells the “richer” bond and buys the “cheaper” bond. Such a strategy should not embed a duration mismatch with the benchmark, i.e., the duration of the bond sold (the bond in the benchmark) should equal the one of the bond bought. For instance, the p.m. can choose to sell a bond whose yield is deemed below its estimated fair yield (whose price is too high) and buy a bond whose yield is deemed either fairly priced or above its estimated fair yield, with identical durations for the two bonds. There are many ways to estimate bonds’ fair yield. One can employ a model such as the Nelson Siegel Svensson (NSS) model. This model proposes a parametric form for the zero coupon rate curve of a given issuer. Observed market yields can then be compared to theoretical yields, to identify “cheap” and “rich” bonds.

Such tactical positions can be held over varying horizons, usually not more than 6 months.

The p.m. can also implement some “arbitrage” strategies, for instance on the repo market, by lending “special” securities against least expensive (“General Collateral”) security (see infra). When the risk framework allows it, he can manage the short-term portion of his portfolio by taking advantage of the basis between money market rates between his currency and another currency (“cross currency basis”), when the interest rate parity is not enforced. He can then build a “synthetic” money market position made out in his portfolio’s currency, by using a FX derivative and a foreign currency money market instrument, to benefit from the higher rate of return provided by the synthetic money market rate versus the “natural” one. At last, he can also substitute the purchase of a bond on a given segment by investing in a risk-free instrument and a future on that bond, to take advantage of a deviation in the “cash and carry relationship” (see infra).

My second experience started in 2019 at the Financial Directorate, as an Asset Liability manager in charge of modeling the balance sheet of Banque de France and proposing strategies for the investment portfolios of Banque de France.
The job starts with the modeling of the different assets and liabilities of a central bank balance sheet. The central bank balance sheet is unique and requires an understanding of the dynamics of monetary policy operations, but also on the drivers of banknotes issuances, target 2 positions, accounts of non-banking clients, etc… In an unconventional monetary policy environment such as the one experienced by the Euro System since 2014, the dynamics of the balance sheet have somewhat become more complex. What is crucial in this step is to provide with a joint modeling of all elements concerned as they interact with each other in specific ways.

Another important task lies on the projection of economic and financial market through time. It relies on modeling over a long-term horizon (usually a 10-year horizon) the evolution of the financial and economic variables to which the central bank is exposed. This requires the usage of stochastic calculus and programming skills, as projections models are implemented with programming languages such as R, Python or Matlab. For instance, one can take advantage of the existence of listed options on assets such as Euribor futures, French sovereign bonds futures, fed funds futures or US Treasuries futures. By making some assumptions about the price process of those assets, it is possible to retrieve their implied distributions at given horizons (so called “risk neutral densities”). Those distributions can then be used to build a large number of scenarios (say 1000) which are applied to the modeled balance sheet, to propose a distribution of future revenues through time.

The fact that the BdF belongs to the Euro System also requires understanding the rules for sharing the monetary revenues of the 20 national central banks of the Euro System. Analytical balance sheets have to be modeled, to compute monetary revenues for each national central bank.

At last, this ALM exercise can also serve as the basis for devising optimal investment strategies for investment portfolios of Banque de France non-monetary balance sheet.

Required skills and knowledge

A fixed income portfolio manager should be skilled in money markets, fixed income securities and derivatives, portfolio management (in particular tactical allocation and performance attribution tools) and fully understand the impact of macroeconomics and monetary policy on rates markets.

An Asset Liability Manager should be skilled in fixed income securities, financial accounting, probabilities and statistics, stochastic calculus, rates models and option pricing, programming languages such as R, Python or Matlab, and monetary policy when it comes to modeling the balance sheet of a central bank.

An Asset Liability Management position in an ideal position after a Fixed Income portfolio management position. Having explored the many facets of Fixed Income and monetary policy are indeed very helpful to start an Asset Liability Management position. It is very satisfactory to develop analytical skills on the aggregate balance sheet after having worked on a specific portfolio.

What I learned

I learned a lot in all the fields I mentioned, but in particular about some topics that are not extensively covered in masters in finance’s curricula, such as money markets and monetary policy. I learned a lot about unconventional monetary policy, as it has been enriched from the recent experiences of the Fed, the ECB or the BoE, which we not in textbooks when I graduated 15 years ago. At last, as time went by I gained capacities in programming languages (especially related to quantitative finance), which is a prerequisite for ALM modeling, and a “nice to have” for fixed income portfolio management.

Financial concepts related my internship

I develop below three financial concepts related to my activities: implied repo and basis, par-par asset swap and specialness on the repo market.

Implied repo and basis

The implied repo rate is the rate of return earned by a market participant who sells a bond future contract and buys the Cheapest to Deliver (CtD) bond in the basket of bonds available for delivery at contract maturity. The implied repo rate should be compared to the effective repo rate of the CtD, and the difference between the two is referred to as the “net basis”. An arbitrage profit can be captured by combining a position on the bond future, the CtD and a reverse repo, depending on the sign of the net basis.

Par-par asset swap

It is a position that consists in purchasing a bond and entering into an interest rate swap such that the combined position is a floating rate bond valued at par. Forcing the value of the bundled position to equal par implicitly requires the fixed rate of the swap to equal the bond’s coupon rate, and as a result, the swap’s initial value will differ from zero. As the obtained synthetic floating rate bond is trading at par, its discount rate is a par rate. As such, it is not distorted anymore by the discrepancy between the bond coupon rate and its current market yield (which is at the origin of the discount/premium). Thus it is a pure measure of the ytm of the issuer on the considered maturity.

Specialness on the repo market

A reverse repurchase agreements (“reverse repo”) is a transaction whereby cash is lent on the market against collateral, usually a bond, to mitigate counterparty default risk. Such an operation falls into the many possible money market instruments available to p.m. to earn a return on the short-term portion of their portfolios.

Conversely, a repo transaction implies lending a bond against cash. The counterparty of a repo trader is a reverse repo trader.

When a reverse repo trade is initiated to lend cash, the cash lender will require from his counterparty that the collateral posted fills some characteristics (for instance, Investment Grade sovereign bonds with a residual maturity below 10 years), but he will not require a particular bond. The collateral posted by his counterparty is referred to as “General Collateral” (GC). This is why the rate of return earned on the trade is named after the GC repo rate.

But in some instances, some bonds in the market are particularly looked after (for instance, newly issued bonds in the days surrounding their auction, or the Cheapest-to-Deliver Bond of a future contract). They are usually in high demand when they have been sold short by market makers or primary dealers, and they must borrowed to be delivered to the bond buyers in due time. As those bonds have to be delivered, they cannot be substituted by another bond as would be the case for GC collateral (the repo transaction is said to be “security driven”). Thus the demand from short sellers on those bonds is inelastic to price, and they will be inclined to pay a lower rate than the GC rate to borrow them on the repo market, as they are at risk of failing to deliver otherwise. Such bonds are referred to as “special” (spec) collateral in the repo market, as opposed to the GC.

The rate on special collateral is lower than on the GC, which means that the cash leg will receive a lower remuneration when the borrowed bond is spec. Thus looking for a special bond entails a cost for the borrower. Specialness on a bond is often measured by computing the spread between the GC repo rate and the special repo rate on that bond.

Useful resources

Banque de France

About the author

The article was written in May 2023 by William ARRATA (Lecturer in Advanced Portfolio Management at ESSEC Business School’s MiF and MiM and Asset Liability Manager at Banque de France).

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