How to Pitch a Stock: A Step-by-Step Guide

November 10, 2026November 8, 2026 by Hadrien PUCHE

What is a stock pitch?

A stock pitch is a structured presentation of an investment idea, regarding the shares of a publicly traded company. Its purpose is to convince an audience, such as a portfolio manager or an investment committee, to buy, hold, or sell a stock based on a well-supported financial analysis.

A stock pitch typically combines an analysis of the company and its industry, an assessment of its financial performance and prospects, a valuation of its shares, and an evaluation of the main risks and opportunities. The analysis leads to an investment recommendation (Buy, Hold, or Sell), supported by a clear investment thesis and a target price reflecting the estimated value of the stock over a specified investment horizon.

The objective of this article is to put yourself in the position of a financial analyst and prepare your own stock pitch. You will select a publicly traded company, analyze its business and financial performance, assess its stock valuation, and formulate a reasoned investment recommendation.

A successful company is not necessarily an attractive investment. Whatever its true value may be, what really matters is whether or not its price is above or under this value. As Warren Buffett once said, price is what you pay, value is what you get. Therefore, the true question lies in understanding if the current price correctly reflects the company’s value.

Through this exercise, you will develop your skills in financial analysis, equity valuation, critical thinking, and investment decision-making.

Why is mastering stock pitching useful for your career ?

In finance, you may be asked to prepare slide decks, and pitch any type of investment idea. Moreover, being able to structure and organize a presentation is a key skill that will be useful in whatever field you want to work in.

1. An important tool in finance

Whether you aspire to work in Equity Research, Asset Management, Hedge Funds, or Private Equity, pitching stocks is something you may be asked to do:

Buy-Side (Mutual Funds, Family Offices, Hedge Funds): Long-only portfolio manager evaluates investment ideas pitches to deploy capital over the long term. Pitches are also useful in long/short or market-neutral hedge funds, to construct relative-value ideas or pair-trades (going long on an undervalued company while being short on an overvalued peer to isolate company-specific alpha from market-wide beta).
Sell-Side (Investment Banks): Equity Research Analysts pitch research views to institutional clients to generate trade ideas and commission.
Corporate Finance & M&A: The same fundamental skill set—evaluating business drivers, competitive moats, and financial models—underpins transaction advisory and strategic planning.

2. From Passive Learning to Active Valuation

Studying financial statements in a textbook is static. Building a stock pitch forces you to bridge theory and practice. You move from reading financial ratios to predicting operational drivers, learn to defend your assumptions, test sensitivities, and quantify risk versus reward.

3. Developing a “Variant View”

The market is generally efficient, meaning public information is already reflected in the stock price. The true relevance of a stock pitch lies in training your mind to spot market inefficiencies, situations where public perception, short-term panic, or over-optimism obscures a business’s true long-term earning potential.

How to pick your stock?

The first thing you will have to do is choose a stock to analyze and pitch. Those are the key factors to consider when choosing a firm to analyze:

Size (market capitalization): choosing a “large cap” (over a billion euros/dollars) will let you have access to much more content and research than when choosing a “small cap”, making your work easier.
Geography: European or American firms are usually easier to analyze because of more detailed reporting, a more developed financial ecosystem, and ?. You may also find it easier to analyze a firm from your home country.
Sector / industry: a utility company will be easier to forecast than a biotech one. This ultimately comes down to how you can model the company’s revenue and earnings: for Netflix, you can use number of subscribers X average subscription price, for Amundi you can use Asset under management X average management fee, but forecasting Société Générale’s earnings require you to have a much better understanding of the banking industry.
Number of operating segments: some firms may hold different business units that will have to be analyzed separately, making them more time-consuming to understand and analyze.

We can summarize this choice under two axis: predictability and complexity.

Predictability: can you reasonably make assumptions about this firm’s future revenue and profits, or is it too unpredictable to do so comfortably ?
Complexity: can you easily understand and model how the company makes its money or will it take some time to do so ?

Predictability versus Complexity graph

A graph of predictability vs complexity when it comes to picking a stock

Firms that are predictable and not complex will be easier to analyze. Firms that are both complex and unpredictable will be much more difficult to deal with.

A stock pitch does not have to be a BUY recommendation, but can also be a HOLD or a SELL. Therefore, simply pick a firm you want to work on and you’ll see in the end what your recommendation will be.

Structure of a Stock Pitch

Objective of this case study and roadmap

The goal of this guide is to provide a clear, step-by-step roadmap for building your first stock pitch.

Because you may come from different backgrounds and have more or less experience or expectations for this exercise, we outline what is strictly needed at a basics level for each section, along with how you can take your analysis further (advanced) to get closer to institutional equity research standards.

Step-by-Step Pitch Breakdown

Your pitch should cover the standard structure outlined below. The templates provided further in this article follow this order and structure, and you are welcome to follow it strictly, or feel free to change the order and add any additional section that you believe is relevant to your particular stock pitch.

For each section, start with Basic, and then look at Advanced to explore how to take it further depending on the depth of analysis you are targeting.

1 – Company Overview

What is needed (Basics): Provide a simple overview of what the firm does. Is it a single business or do they have multiple operating segments? In which countries does it operate? What is the current share price, market capitalization, turnover, profits, dividends?
How to take it further: Provide a detailed overview of the firm, including but not limited to detailed key data, shareholder analysis, ESG analysis, and corporate governance structure.

2 – Industry Overview

What is needed (Basics): What business does this firm operate in? Can you find a few key figures on this industry and how the markets believe it will evolve in the coming years? List a few competitors for your company.
How to take it further: Go deeper into the industry analysis by looking at research notes or papers. Try to find all relevant competitors and analyze market share dynamics, entry barriers, and structural shifts.

3 – Investment Thesis

What is needed (Basics): Try to formulate in a few key sentences why you believe this stock is, by itself, a good buy or sell (or neutral) opportunity. What do you think makes this a good or a bad business?
How to take it further: In addition to the basic arguments, try to understand and explain why this company may be undervalued or overvalued by the market. Is there an irrational fear or something the market may be wrong about (or at least irrational)? Is there an assumption embedded in the current share price that may be wrong in your opinion? In other words, how could it be possible that you are right and that the current market price is not?

4 – Valuation

Historical Share Price Analysis

What is needed (Basics): How has the stock evolved in the past 5 years? Can you identify specific events linked to sharp increases or drops in the share price? This is easily done by adding an image of the stock price that you can get from the internet.
How to take it further: Provide a graph of the historical share price versus the relevant index and key competitors, in a base 100 over a few years. Add a list of all relevant key events on the same graph. Build this graph yourself in Excel or Python.

Discounted Cash Flow (DCF)

What is needed (Basics): Build a simplified Discounted Cash Flow (DCF) model by forecasting the shareholders’ free cash flows and discounting them using the Weighted Average Cost of Capital (WACC). Estimate the terminal value using a perpetual growth model and explain the main assumptions underlying your valuation.
How to take it further: Develop a more detailed financial model based on the company’s fundamental operating drivers, integrating the income statement, balance sheet, and cash flow statement. Refine the WACC calculation, incorporate the bridge from enterprise value to equity value, and perform sensitivity and scenario analyses.

Comparable Company Analysis (CCA)

What is needed (Basics): Gather data on the firm’s main competitors. Look at price-to-earnings (P/E) and analyze your company’s current ratio to try and understand if there is room for increase or decrease in this ratio. Once you have a target ratio, you can compute a target price relative to it too.
How to take it further: Multiple ratios (EV/EBITDA, P/E, EV/Sales, etc.), choose the most relevant ratios, much more thinking on what they mean, and add a confidence interval to this.

5 – Valuation Summary, Investment Recommendation, Trading Implementation

What is needed (Basics): Summarize your DCF and comparable company valuation results in a single table to determine and justify your own target price. Do not average in the current stock price or analyst targets—use the current price to compute your expected upside or downside over an explicit horizon (e.g., 12 months), and use consensus targets only as an external point of comparison. Formulate an unambiguous recommendation (Buy, Hold, or Sell). Finally, define a practical trading setup: state your entry price, a take-profit target, and a stop-loss level based on thesis-invalidation criteria, and calculate the resulting reward-to-risk ratio (targeting at least a 3:1 ratio).
How to take it further (Advanced): Synthesize your valuation ranges using a football field chart across base, bull, and bear scenarios, and apply a weighted average with clear economic justification for the assigned weights. Provide a breakdown of recent, relevant analyst expectations (e.g., via Bloomberg/FactSet) in the appendix to highlight your variant view against consensus. On the trading side, detail your position sizing rules relative to fund portfolio limits and daily trading volume (liquidity constraints), identify the specific operational or macro catalysts that will trigger the repricing, and outline predefined conditions under which you would revise, hedge, or exit the trade.

6 – Appendix

What is needed (Basics): It’s a good habit to put your tables and calculations that are not already in your presentation inside of the appendix, in case you get asked any questions. You can also add graphs, maps, the company’s products, the countries it operates in …
How to take it further: Think about deep technical questions you could be asked during a Q&A session. Add captures of your dynamic Excel tables, debt schedules, sensitivity matrices, and detailed analyst opinion breakdowns.

Your stock pitch should present a coherent investment argument supported by financial data, quantitative analysis, and relevant charts or tables. All sources should be properly referenced, and all data has to be exact, up to date, and relevant.

Templates

To guide you through the process, we provide templates for both the excel file and the powerpoint presentation. You will find here one set of templates for the ‘Basic’ work and one for the ‘Advanced’ version.

Basic:

Excel file
Powerpoint template

Advanced:

Excel file
Powerpoint template

Please find all available documents in this google drive : https://drive.google.com/drive/folders/10j3z13r2aw_YNaIS_tfYCTM6SS9lC_gg?usp=sharing

Examples

Here are a few examples of what a pitch could look like.

Please find all available documents in this google drive.

About the use of AI for investment analysis

I would strongly advise you to avoid using AI for this work. Three key reasons why:

Artificial intelligence (AI) tools can help you understand financial concepts, retrieve and organize information, write code, and improve the presentation of your analysis. However, they should not replace your own financial reasoning and investment judgment.
AI-generated financial data, calculations, and interpretations may contain errors or rely on outdated information. You should therefore verify all figures against reliable sources, understand the assumptions underlying your valuation, and critically assess the conclusions.
The objective of this case study is not simply to produce a stock pitch, but to develop your ability to analyze a company, estimate its value, and defend an investment recommendation. You remain responsible for the accuracy, originality, and quality of your work, but what really matters is the path you took, not the final outcome.

Feel free, however, to ask AI for advice, explanations on financial concepts, rewriting some text, or fixing mistakes.

Related financial concepts

Fundamental analysis
Market efficiency and a variant view
Discounted Cash Flow (DCF)
Comparable Company Analysis (CCA)

Why should you be interested in this post?

Whether you are preparing for interviews in Equity Research, Asset Management, Private Equity, or Corporate Finance, mastering a stock pitch moves you from passive theoretical learning to active investment decision-making.

By engaging with this case study, you will bridge the gap between financial statements and practical business valuation, practice finding market inefficiencies, and build hands-on skills in financial modeling and presentation.

Useful resources

Graham, B. (2003). The Intelligent Investor: The Definitive Book on Value Investing. Revised edition, with commentary by J. Zweig. HarperBusiness Essentials.

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

Rosenbaum, J., and J. Pearl (2022). Investment Banking: Valuation, LBOs, M&A, and IPOs. 3rd edition. Wiley.

Lasse Heje Pedersen (2015): Efficiently Inefficient: How Smart Money Invests and Market Prices Are Determined

Damodaran Online (NYU Stern valuation resources)

SimTrade course

Financial valuation

My experience as an Investment Research Analyst intern at Regarde Familia, a US single-family office

November 7, 2026 by Aarichsun BATLISH

In this article, Aarichsun BATLISH (Indian Institute of Technology Kanpur, B.Tech in Civil Engineering, 2024-2028) shares his experience as an Investment Research Analyst intern at Regarde Familia, a single-family office based in Miami. During the internship, he researched venture capital funds and emerging technology sectors from the point of view of an investor who allocates capital to funds rather than directly to companies.

About the company

Regarde Familia is a single-family office (SFO) founded in 1980 to manage the wealth of the Empire Electric and Regarde family. It is based in Miami, Florida. A single-family office serves one family only: it has no outside clients and does not raise external capital. Its purpose is to preserve and grow the family’s wealth across generations, and Regarde Familia organizes its mission around four pillars: wealth preservation, education of the next generation, philanthropy and longevity.

Like many family offices of its size, Regarde Familia invests a large part of its capital indirectly, by committing money to private equity and venture capital funds run by specialized managers. Industry databases estimate its assets at around US$500 million, with a focus on buyout, growth and venture capital funds. Buyout funds acquire controlling stakes in mature companies, often financed with debt; growth funds back companies that are already expanding; venture capital funds invest in young start-ups. In this structure, the family office is a limited partner (LP): it does not pick start-ups itself but selects the fund managers who do.

Logo of Regarde Familia.

Source: the company.

I worked in Regarde Familia (RF) Research, a team that the office was building in 2026 to give it a forward-looking view of the sectors in which it wanted to allocate capital. Analysts were assigned to categories such as artificial intelligence and deep tech, climate and energy, fintech and cybersecurity, and a second track produced research on technology in higher education. The team worked remotely across India and the United States.

My professional experience

I joined the team after a competitive selection process based on a written assignment. Candidates had five days to produce two in-depth research pieces on sectors of their choice, covering both India and the rest of the world, and to go from the sector to the funds making the strongest bets in it, to a clear recommendation on one fund. I chose climate and energy, and artificial intelligence and deep tech. Around 15 people were selected out of several hundred applicants, based on the quality of their research reports and a demanding review of their analysis. I was selected for both the investment research and the higher-education research tracks and worked remotely from April to July 2026.

My missions

Sector and fund research. My main task was to build what the team called a “decade-cycle view” of my categories: which technologies are likely to gain market traction over the next ten years, which funds are positioned to benefit, and what the investment landscape could look like by the end of the decade. The approach is easiest to explain with the selection assignment I prepared before joining, which relied only on public information. In climate and energy, I built my thesis around electrification and grid-scale storage and then looked for early-stage funds whose portfolios matched it, in India as well as abroad. Congruent Ventures is a good illustration: an early-stage climate fund whose public portfolio includes Fervo Energy (next-generation geothermal power) and SPAN (smart electrical panels), two companies positioned exactly where electrification creates new demand. In India, funds such as Avaana Capital and Infuse Ventures back companies in electric mobility, distributed solar and energy management. In artificial intelligence, I studied how foundation models are moving into the physical world (robotics, autonomous systems and defense) and looked at funds such as Lux Capital, whose public portfolio spans several layers of that stack, from Hugging Face to Applied Intuition and Anduril. I quickly found that the most useful information was not in news articles but in fund letters, portfolio announcements and founder interviews.

Figure 1. How I approached sector and fund research.
Sector and fund research process
Source: the author.

Understanding how fund managers are evaluated. When a manager presents a new fund, an LP has to decide whether past returns came from a repeatable process or from a few lucky deals. During the internship, I learned the kind of questions allocators ask before committing capital: how gross and net returns were generated, how position sizes were decided, how much of the reported value has already been returned in cash, and how the manager’s edge translates into better entry prices. These questions are standard across the industry and many of them appear in the public due diligence questionnaire of the Institutional Limited Partners Association (ILPA).

Deal screening. I also took part in the early screening of investment opportunities. This meant preparing a founder call: requesting the company’s materials in advance, building a questionnaire and a conversation flow mapped to its pitch deck, and then summarizing the discussion in a preliminary investment memo. Such a memo follows a standard investment committee structure: an executive summary, the business model, the market and competition, the fundraising terms, key strengths and risks, and the points that would require due diligence before any decision. I learned to ask two or three questions per slide, to keep the most important question for the end, and not to discuss ticket sizes (the amount the investor would put in) before the investor has decided whether it is interested. I also joined calls where founders pitched their business to the team. Our questions always came back to the fundamentals: the financial projections and the assumptions behind them, what IRR (internal rate of return, the annual return implied by the timing and size of all the cash flows) and investment multiple (total value returned per dollar invested) an investor could expect under those assumptions, and whether the business could sustain itself over the long term given its historical run rate (current revenue extrapolated over a full year). Details of the opportunities I worked on are confidential, but the exercise taught me how an investor turns a one-hour conversation into a decision document.

Research for the higher-education track. On the higher-education track, I contributed to a series of buyer’s guides that help universities select technology solutions: what problem the solution addresses, which data it relies on, how to evaluate vendors objectively, and which questions to ask before signing a contract. Each guide went through several rounds of feedback on content, grammar and formatting before publication.

Required skills and knowledge

On the technical side, the work required reading fund documents (presentations, track records and portfolio pages), mastering the vocabulary of private markets (commitments (the amounts investors promise to a fund), capital calls (when the fund draws on these commitments), IRR, MOIC (multiple on invested capital) and DPI (distributions to paid-in capital), all explained below), and finding reliable information quickly in free sources such as Crunchbase, Tracxn, fund websites and letters, SEC EDGAR filings (the public database of company filings of the US Securities and Exchange Commission) and industry reports. It also required structured writing: an investment committee reads the conclusion first, so every note had to open with a clear view and then present the evidence.

On the personal side, the most important skill was autonomy. The team was remote and spread across time zones, so I had to manage my own priorities, follow up proactively and take the initiative when an opportunity appeared. Clear written communication over Microsoft Teams and email mattered even more than it would in an office.

What I learned

This internship was something very new to me. Working with a US-based family office was an interesting and unique experience, and at the beginning I was not familiar with much of the terminology: commitments, capital calls, carried interest (the share of profits paid to the fund manager), DPI (distributions to paid-in multiple), TVPI (total value to paid-in capital) and many other terms that the team used every day. I learned them by doing the work, asking questions and reading as much as I could.

First, I learned that investing through funds is a different job from investing in companies. A family office does not only ask “Is this start-up good?” but also “Will this manager keep finding good start-ups, and is the fund structured in my interest?”

Second, I learned to stress the fundamentals. When founders pitched their business, the discussion was never about the story alone: we asked for their financial projections, tested them with IRR and multiples, and compared them with the historical run rate to judge whether the business could last. Working on live opportunities showed me how much a careful background check and a few precise questions can change an investment view.

Third, I learned from the people around me. I worked alongside experienced and knowledgeable colleagues, reviewing opportunities together and sharing research and analysis on each company. I also had great mentors who guided me, gave me detailed feedback and inspired me to raise the quality of my work.

Finally, I learned how much a junior analyst’s contribution depends on initiative. In a remote team, the people who learn the most are those who propose ideas, create their own work and communicate it clearly.

Financial concepts related to my professional experience

I present below three financial concepts related to my professional experience: the limited partner and general partner structure, the performance measures of private funds, and the selection of fund managers.

Limited partners (LPs) and general partners (GPs)

Private equity and venture capital funds are usually organized as limited partnerships. The general partner (GP), or an affiliated management entity, is responsible for managing the fund: it raises the fund, selects and manages the investments, and earns a management fee (often around 2% of commitments per year) and a share of the profits called carried interest (often 20% above a minimum return). The limited partners (LPs), such as pension funds, endowments and family offices, provide most of the capital. An LP typically contributes its committed capital progressively through capital calls: it makes a commitment, which the GP draws through capital calls during the first years of the fund as it finds deals. Cash flows back as distributions when portfolio companies are sold or listed, typically after five to ten years. This commitment structure matters for investors: Gourier, Phalippou and Westerfield (2024) show that the obligation to meet capital calls on demand substantially affects an LP’s optimal portfolio and welfare, and that investors require a premium for bearing this commitment risk. My work at Regarde Familia sat on the LP side of this relationship.

Measuring the performance of a private fund: IRR, TVPI and DPI

Because cash goes in and comes out at different dates, a private fund cannot be judged with a simple return. Three measures are standard.

The internal rate of return (IRR) is the discount rate that sets the net present value of all the LP’s cash flows to zero. It takes timing into account, but it can be flattered by returning cash early.

The total value to paid-in multiple (TVPI), close to the multiple on invested capital (MOIC) reported by many managers, equals distributions plus the remaining value of the portfolio, divided by the capital paid in. It shows how much value has been created per dollar invested, whether realized or not.

The distributions to paid-in multiple (DPI) equals distributions divided by the capital paid in. It only counts cash actually returned, which is why LPs watch it closely: a high TVPI combined with a low DPI means that most of the value is still on paper.

Figure 2 shows an illustrative example that I built. An LP commits 100 million dollars to a fund. The GP calls 85 million over the first four years and returns 190 million between years 6 and 9. The net IRR is 15.9% and the final TVPI is 2.24x. The cumulative cash flow falls to minus 85 million before turning positive in year 8: this is the “J-curve” that every private-market investor has to finance. At the end of year 6, if the remaining portfolio were valued at 95 million, the fund would show a TVPI of 1.47x but a DPI of only 0.35x. All the calculations behind Figure 2 are in the Excel file below, which also shows how DPI, RVPI and TVPI change each year.

Figure 2. Cash flows of an illustrative fund commitment and the J-curve.
J-curve of an illustrative fund commitment
Source: computation by the author (illustrative figures), Excel file below (sheets ‘Parameters’, ‘Computations’ and ‘Figure J-curve’).

This is why an allocator’s questions focus on how returns are split between realized and unrealized value, and between a few outlier deals and the rest of the portfolio.

Manager selection and the persistence of returns

For an LP, the most important decision is which manager to back, and academic research explains why. Kaplan and Schoar (2005) documented persistence in private equity fund performance in their sample: GPs whose previous funds outperformed tend to outperform again, which is unusual in asset management. Harris, Jenkinson and Kaplan (2014) found that buyout funds have, on average, outperformed public markets, with large differences between managers. In venture capital, returns are concentrated in a small number of very large successes, so the gap between top and average funds is wide. Gompers, Gornall, Kaplan and Strebulaev (2020) surveyed venture capitalists and found that the quality of the management team is the factor they rank first when selecting deals. In my work, this translated into one simple rule: look for evidence that a manager’s process, not luck, produced the past results, through portfolio construction, sector conviction and access to the best founders.

Why should I be interested in this post?

If you are interested in private equity or venture capital, most of what you read describes the job of the fund manager. This post describes the other side of the table: the investor who chooses the fund managers. Family offices, endowments and pension funds hire analysts to do exactly this work, and the skills involved (building sector theses, assessing funds and measuring performance) are equally valuable if you later want to join a fund. The post also shows that a student can gain meaningful exposure to private markets through a remote internship, provided that the selection work is strong and that you take the initiative once you join.

Useful resources

Academic research

Gompers, P. A., Gornall, W., Kaplan, S. N., Strebulaev, I. A. (2020). How do venture capitalists make decisions? Journal of Financial Economics, 135(1), 169-190.

Gourier, E., Phalippou, L., Westerfield, M. M. (2024). Capital Commitment. Journal of Finance, 79(5), 3407-3457.

Harris, R. S., Jenkinson, T., Kaplan, S. N. (2014). Private Equity Performance: What Do We Know? Journal of Finance, 69(5), 1851-1882.

Kaplan, S. N., Schoar, A. (2005). Private Equity Performance: Returns, Persistence, and Capital Flows, Journal of Finance, 60(4), 1791-1823.

Business resources

Regarde Familia

Institutional Limited Partners Association (ILPA), Performance Template Guidance (2025)

Institutional Limited Partners Association (ILPA), Due Diligence Questionnaire

Congruent Ventures, Fund III announcement

About the author

The article was written in November 2026 by Aarichsun BATLISH (Indian Institute of Technology Kanpur, B.Tech in Civil Engineering, 2024-2028).

Aarichsun is interested in quantitative finance, financial economics and private markets. After his studies at IIT Kanpur, he plans to pursue a master’s degree, and possibly a PhD, in quantitative finance.

▶ Discover all posts by Aarichsun BATLISH.

My experience as an independent portfolio manager of a family-funded equity and derivatives portfolio

November 7, 2026 by Romil SOJITRA

In this article, Romil SOJITRA (Indian Institute of Technology Kanpur, BS in Economics, 2024-2028) shares his experience as an independent portfolio manager of a family-funded equity and derivatives portfolio in the Indian market, which he started with equities in May 2025 and extended to options in May 2026.

About the market: Indian equity derivatives

My portfolio was invested in Indian listed equities and in equity derivatives (futures and options) traded on the National Stock Exchange of India (NSE). NSE was founded in 1992 and introduced fully electronic, screen-based trading in India. It launched futures on the Nifty 50 index in 2000 and options in 2001. Since then, the Indian equity derivatives market has grown enormously. According to the Futures Industry Association (FIA), NSE was the largest derivatives exchange in the world by number of contracts traded for five consecutive years (2019 to 2023), mainly because of very high volumes in options on the Nifty 50 and Nifty Bank indexes with weekly expiries, that is, contracts that expire within seven days or less and that are heavily traded in the last days before expiry.

Logo of the National Stock Exchange of India (NSE).
Logo of NSE
Source: the company.

This growth came with risks for individual investors. In September 2024, the Securities and Exchange Board of India (SEBI), the market regulator, published a study showing that 93% of individual traders in equity futures and options made losses between FY22 and FY24 (in India, the fiscal year runs from April to March, so the study covers April 2021 to March 2024). Their aggregate net losses reached ₹1.81 lakh crore over the three years (1 lakh = 100,000 and 1 crore = 10 million, so 1 lakh crore = ₹1 trillion), that is about ₹1.8 trillion or roughly US$21 billion, and most of these losses came from options.

SEBI reacted to these losses. Because most losses were concentrated among small traders buying and selling short-dated index options, its circular of October 1, 2024 (applied from November 20, 2024) made this type of speculation harder: the minimum value of an index derivative contract was raised from ₹5-10 lakh to ₹15 lakh, so that each trade requires more capital, and each exchange may now offer weekly expiries on only one benchmark index (the Nifty 50 for NSE), which reduces the number of very short-dated contracts. These rules also mattered for my own sizing: one lot of a Nifty option now represents a notional amount of at least ₹15 lakh, so I could not size positions as finely as I wanted. I therefore started trading derivatives knowing that most individual participants lose money. My objective was not to “beat the market” quickly, but to build a process that controls risk first.

The portfolio was funded by my family. They gave me capital to manage after I had shown consistent results on a smaller amount. This made me accountable to real “clients”, even though they were family members: I reported my performance to them regularly and explained my decisions and my mistakes.

My experience

I started in May 2025 with Indian equities, investing in stocks and learning how markets work alongside my studies at IIT Kanpur. In May 2026, I moved to a more structured and quantitative approach: I added options trading and began applying the concepts presented in this article (the Black-Scholes-Merton model and the Greeks, the Kelly Criterion, Monte Carlo simulations) to a leveraged equity and derivatives portfolio. This post focuses mainly on this second phase, from May to August 2026 (44 trading sessions), and on the drawdown of March-April 2026 that came just before it. I am responsible for the full investment process: generating ideas, building positions, sizing them, hedging the portfolio, and measuring and reporting performance.

My missions

Since May 2026, when I added options to the portfolio, my work has covered the five areas below. The first one (relative-value equity positions) continued the equity book I had run since May 2025; the four others were new.

Building relative-value positions. I constructed long/short equity pairs: buying one stock and selling a related stock (for example, two companies in the same sector) to be exposed to their relative performance rather than to the direction of the overall market. When a pair is well built, a large part of the market risk cancels out, and the return depends on the spread between the two stocks.

Running delta-neutral option positions. My option positions were mainly short volatility: I sold Nifty index options (for example a call and a put together, as in a short strangle) and kept the delta of the whole position close to zero by adjusting it with Nifty futures or by moving one of the option legs. Each position was therefore a combination of options and of the underlying (through futures), not options alone. Small moves of the index then had little impact, and the profit or loss came from two other sources. The first is time decay (theta): an option loses a little value every day as expiry approaches, and as a seller I earned this decay. The second is volatility. As a seller, I lost money when the volatility implied by option prices rose (negative vega), and above all I earned money when the index actually moved less than the volatility implied by the prices at which I sold the options, and lost money when it moved more (negative gamma). I explain this mechanism with an equation in the section on the Greeks below. I used Sensibull, an Indian options analytics platform, to analyze option chains, Greeks and the payoff of each strategy before entering a position. Figure 1 illustrates this analysis on an example: a short iron condor on the Nifty 50 (selling the 24,650 put and the 25,300 call, and buying the 24,350 put and the 25,600 call as protection), whose maximum loss is capped at the width of a wing (300 points) minus the net premium received (78.85 points).

Figure 1. Illustration: payoff of a short iron condor on the Nifty 50 in the Sensibull strategy builder (expiry of November 9, 2026).
Analysis of an option position on Sensibull
Source: Sensibull, screenshot by the author (illustration, November 6, 2026).

Hedging tail risk. Short-volatility and leveraged positions can lose a lot of money in a market crash. To protect the portfolio against extreme events, I held a long-dated index put option as a tail-risk hedge. This hedge costs a small premium in normal times but gains value when the market falls sharply. A bought put has positive gamma: its delta becomes more and more negative as the market falls, so its gains accelerate in a crash. It therefore offsets part of the negative gamma of the options I sold.

Sizing positions and modelling risk. I used the Kelly Criterion to decide how much capital to allocate to each trade, the Black-Scholes-Merton model to price options and compute their Greeks, and correlation and information-ratio analysis to build the portfolio. I also built Monte Carlo simulations. For position sizing, each session is a random draw: a win with probability p = 59.1% that pays b = 1.45 times the average loss, or a loss otherwise. I simulated 10,000 paths of 100 sessions for different bet sizes (this model is detailed in my article on the Kelly Criterion). For option positions, I simulated the Nifty 50 with a geometric Brownian motion, the model behind Black-Scholes-Merton, for several levels of annual volatility (for example 10%, 15% and 25%), and revalued the position on each simulated path.

Measuring and reporting performance. I kept a structured trading journal and a performance report. I tracked the win rate, the profit factor (gross profits divided by gross losses), the Sharpe, Sortino and Calmar ratios, and the drawdown, for each account and each strategy.

Required skills and knowledge

Hard skills. I needed a solid understanding of derivatives: how futures and options are priced (a futures price follows the cost-of-carry relation F = S × e^{(r − q)T}, where S is the spot price, r the interest rate, q the dividend yield and T the maturity, while options are priced with the Black-Scholes-Merton model presented below), what the Greeks measure, and how a combination of options behaves when the price, volatility or time to maturity changes. I also needed statistics and econometrics to measure correlations and evaluate whether a strategy has a real edge or is just lucky. The courses I took at IIT Kanpur in econometrics, financial econometrics and optimization, and my preparation for the CFA Level I exam (which I passed in August 2026), were very useful here.

Soft skills. The most important skills were discipline and emotional control. It is easy to follow rules when trades are winning; it is much harder during a drawdown, when the temptation is to increase risk to recover losses quickly. Behavioral finance explains why. In prospect theory, Kahneman and Tversky (1979) show that people feel a loss more strongly than a gain of the same size (roughly twice as strongly) and become risk-seeking when they are below their reference point: after a loss, a trader is tempted to take more risk to get back to break-even. Kahneman (2011) adds that under stress, fast and intuitive thinking takes over from slow and deliberate thinking. This is why I relied on a written process and on my trading journal, not on my feelings, during the drawdown. I also needed honesty and communication skills to report bad periods to my family as clearly as good ones.

What I learned

The most important lesson came from a difficult period. During the market sell-off of March-April 2026, linked to the US-Iran conflict, my portfolio suffered a heavy drawdown, mainly on high-beta stocks in my equity book (Figure 2). I had underestimated how much my positions were exposed to the same market factor, that is, the general movement of the Indian equity market measured by the Nifty 50. High-beta stocks amplify the moves of the index (a stock with a beta of 1.5 tends to fall by about 1.5% when the index falls by 1%), so in a sell-off they all fall together and their correlations rise: positions that looked different were in fact the same bet on the market. I did not try to win everything back at once. I reduced risk, kept following my process, and the portfolio recovered and became profitable over the following two months.

Figure 2. Evolution of the Nifty 50 index in 2026 (daily prices, January to early November).

Source: TradingView (data: NSE).

Table 1 summarizes the performance of the portfolio over May-August 2026. A session is a trading day on which I had open positions and recorded a profit or a loss in my journal: there were 44 such sessions over the period. The portfolio delivered a return of 5.79% on deployed capital and 4.05% on total capital (part of the capital stayed in cash as a buffer). Of the 44 sessions, 26 were winners and 18 were losers, which gives a win rate of 59.1% (26/44). The profit factor (gross profits divided by gross losses) was 2.10, so the average winning session earned about 1.45 times what the average losing session lost (2.10 × 18/26). Figure 3 shows the evolution of the portfolio and its drawdown.

Table 1. Performance of the portfolio (May-August 2026).

Measure	Value	How it is computed
Trading sessions	44	Days with open positions
Winning / losing sessions	26 / 18	From the trading journal
Win rate (p)	59.1%	26 / 44
Profit factor	2.10	Gross profits / gross losses
Payoff ratio (b)	1.45	Average win / average loss = 2.10 × 18/26
Return on deployed capital	5.79%	Profit / capital in positions
Return on total capital	4.05%	Profit / total capital
Average share of capital deployed	about 70%	4.05% / 5.79%

Source: trading journal of the author.

Figure 3. Evolution of the portfolio and drawdown (May-August 2026).

Source: computation by the author (data: trading journal).

In July-August, a second drawdown appeared. My journal showed that my edge was decaying: the same strategies were producing weaker results. I decided to de-risk rather than force trades. I learned that knowing when to reduce exposure is as important as knowing when to take risk, and that a strategy’s edge is not permanent.

Financial concepts related to my experience

I present below three financial concepts related to my experience: the Black-Scholes-Merton model and the option Greeks (with an equation that shows how my option positions made or lost money, and alternative ways to bet on volatility), the Kelly Criterion for position sizing, and risk-adjusted performance measures.

The Black-Scholes-Merton model and the option Greeks

The Black-Scholes-Merton model (Black and Scholes, 1973; Merton, 1973) gives a theoretical price for a European option as a function of the price of the underlying asset, the strike price, the time to maturity, the risk-free interest rate and the volatility of the underlying. Its partial derivatives, called the Greeks, measure how the option price reacts to each of these inputs: delta (price of the underlying), gamma (change in delta), vega (implied volatility), and theta (time). In my delta-neutral strategies, I kept the delta of the whole position close to zero, that is, the sum of the deltas of all the options and futures in the position, each multiplied by its quantity: Δ_position = Σ n_i × Δ_i (a future has a delta of 1 per unit of the index). Small market moves then had little impact. My profit or loss came mainly from theta and gamma, that is, from the passage of time and from the difference between implied and realized volatility, as the equation below shows. Without the model and its Greeks, I could not have measured or controlled these exposures.

The profit and loss of an option position: a second-order approximation

For a small change dS in the price of the underlying asset over a short period dt, a second-order Taylor expansion of the value V of an option position gives:

dV ≈ Δ × dS + ½ × Γ × (dS)² + Θ × dt + ν × dσ

where Δ is the delta, Γ the gamma, Θ the theta and ν the vega of the position. The first term is linear: it is the first guess given by the delta. The second term is the convexity. It does not depend on the direction of the move because (dS)² is always positive: for a bought option (Γ > 0, for example the purchase of a put) it is always a gain, and for a sold option (Γ < 0, for example the sale of a call) it is always a loss.

For a delta-neutral position (Δ = 0) with constant implied volatility, only gamma and theta remain. In the Black-Scholes-Merton model, theta and gamma are linked: when the interest rate is ignored, Θ = −½ × Γ × S² × σ_implied². The profit and loss over one day can then be written as:

dV ≈ ½ × Γ × S² × [ (dS/S)² − σ_implied² × dt ]

The term (dS/S)² is the squared return actually realized over the day, while σ_implied² × dt is the squared return implied by option prices. For a seller of options (Γ < 0), the day is profitable when the market moves less than implied volatility predicts, and costly when it moves more. Over many days, the seller earns money when realized volatility is below implied volatility. This is the precise meaning of “betting on volatility”, and it explains the returns of my delta-neutral positions. Empirically, implied volatility is on average higher than realized volatility on equity indexes (the volatility risk premium documented by Bakshi and Kapadia, 2003), which is what option sellers try to earn, at the cost of large losses in sudden market moves.

Figure 4 illustrates the convexity with two positions on an index at 25,000 points: the purchase of an at-the-money put (positive gamma) and the sale of an at-the-money call (negative gamma), both with 30 days to expiry, an implied volatility of 15% and an interest rate of 6% (illustrative parameters). The gamma of each option is 0.000368 per point. For a fall of 2,000 points (−8%), the bought put gains 1,522 points: the delta alone predicts 892 points, and adding the gamma term (+735 points) gives 1,627 points, much closer to the exact value. For moves of ±500 points (±2%), the second-order approximation is almost exact (269 points for the bought put after a fall of 500 points, against 269 points with the full model). The curve of the bought put bends upward (convex), while the curve of the sold call bends downward (concave): for a rise of 2,000 points, the sold call loses 1,643 points against 1,108 points predicted by the delta alone.

Figure 4. Change in value of a bought put and of a sold call as a function of the price of the underlying asset.

Source: computation by the author (Black-Scholes-Merton model).

Figure 5 applies the second equation to a delta-hedged short at-the-money straddle (one call and one put sold with the same strike) with the same parameters. The position earns about 14.5 points of theta per day but loses ½ × Γ × (dS)² when the index moves. The break-even daily move is about 198 points, or 0.79% of the index, which is almost exactly the daily move implied by an annual volatility of 15% (15% / √365 = 0.79%).

Figure 5. Profit and loss over one day of a delta-hedged short straddle as a function of the daily move of the index.
Daily profit and loss of a delta-hedged short straddle
Source: computation by the author (Black-Scholes-Merton model).

You can download below the Excel file with the tables and figures of this section (option prices and Greeks, the position as a function of the price of the underlying asset for a bought put and a sold call, the gamma-theta trade-off, and the payoffs of volatility strategies).

Alternative strategies to bet on volatility

Selling options is only one way to take a view on volatility. Table 2 lists the main alternatives. Long-volatility strategies (positive gamma) gain when the market moves a lot and pay theta every day; short-volatility strategies (negative gamma) earn theta and lose in large moves.

Table 2. Strategies to bet on volatility.

Strategy	Position	Bet	Gamma	Maximum loss
Long straddle / strangle	Buy a call and a put (same strike, or out-of-the-money strikes)	Large move in either direction	+	Premiums paid
Delta-hedged long options (gamma scalping)	Buy options and re-hedge the delta with futures	Realized volatility above implied volatility	+	Premium paid and hedging costs
Long out-of-the-money put (tail hedge)	Buy a long-dated index put	Market crash	+	Premium paid
Short straddle / strangle	Sell a call and a put	Calm market, realized volatility below implied	−	Unlimited
Iron condor	Short strangle plus a bought put and a bought call further out of the money	Index stays within a range	−	Width of the wings minus premium received
Calendar spread	Sell a short-dated option and buy a longer-dated one with the same strike	Calm market now, stable or rising long-dated implied volatility	−	Net premium paid
Volatility index and variance products	Trade volatility directly (India VIX for NSE, variance swaps)	Rise or fall of volatility itself	n/a	Depends on the product (variance swaps are mainly traded by institutions)

Source: the author.

Figure 6 compares the payoff at expiry of a long straddle, a short strangle and an iron condor with the same parameters as above. The iron condor is a defined-risk version of the short strangle: with strikes at 24,000, 24,500, 25,500 and 26,000 points, it collects a net premium of 245 points and its maximum loss is capped at 255 points (the 500-point width of a wing minus the premium), whereas the loss of the short strangle has no limit. Defined-risk structures are therefore better suited to a small portfolio, where one large move must not wipe out months of theta.

Figure 6. Payoff at expiry of three volatility strategies.

Source: computation by the author (Black-Scholes-Merton model).

The Kelly Criterion

The Kelly Criterion (Kelly, 1956) answers the question: if I have a positive edge, what fraction of my capital should I risk on each trade? It chooses the fraction that maximizes the expected logarithm of wealth, which is the same as maximizing the long-run growth rate of capital. For a trade that wins with probability p and pays b times the amount risked, the optimal fraction is f* = p − (1 − p)/b. Risking more than this lowers long-run growth and greatly increases drawdowns. I used the Kelly Criterion as an upper limit for my position sizes and in practice risked only a fraction of it, because my estimates of p and b were based on a limited number of trades. I explain this concept in detail in a separate article on the SimTrade blog.

Risk-adjusted performance measures

A return figure alone says nothing about the risk taken to obtain it. The Sharpe ratio (Sharpe, 1966) divides the excess return over the risk-free rate by the volatility of returns. The Sortino ratio (Sortino and Price, 1994) only penalizes downside volatility, which is more relevant for strategies with asymmetric returns such as options strategies. The Calmar ratio compares the annual return to the maximum drawdown, the largest fall from a peak to a trough. I tracked all three measures in my journal because they tell different stories: my drawdown in March-April 2026 hurt my Calmar ratio much more than my Sharpe ratio, which reminded me that volatility is not the only dimension of risk.

Why should I be interested in this post?

If you are a student interested in asset management, trading or derivatives, managing a real portfolio, even a small one, is one of the best ways to understand how theory meets practice. Concepts such as the Greeks, position sizing and drawdowns become very concrete when your own money, or your family’s, is at stake. This experience also shows that a good trading process is mostly about risk management, record-keeping and honest evaluation of performance, and that most individual derivatives traders lose money if they ignore these principles.

Useful resources

Academic research

Bakshi, G., Kapadia, N. (2003) Delta-Hedged Gains and the Negative Market Volatility Risk Premium, Review of Financial Studies, 16(2), 527–566.

Black, F., Scholes, M. (1973) The Pricing of Options and Corporate Liabilities, Journal of Political Economy, 81(3), 637–654.

Gatev, E., Goetzmann, W. N., Rouwenhorst, K. G. (2006) Pairs Trading: Performance of a Relative-Value Arbitrage Rule, Review of Financial Studies, 19(3), 797–827.

Hull, J. C. (2021) Options, Futures, and Other Derivatives, Eleventh Edition, Harlow, Pearson.

Kahneman, D. (2011) Thinking, Fast and Slow, New York, Farrar, Straus and Giroux.

Kahneman, D., Tversky, A. (1979) Prospect Theory: An Analysis of Decision under Risk, Econometrica, 47(2), 263–291.

Kelly, J. L. (1956) A New Interpretation of Information Rate, Bell System Technical Journal, 35(4), 917–926.

Merton, R. C. (1973) Theory of Rational Option Pricing, Bell Journal of Economics and Management Science, 4(1), 141–183.

Sharpe, W. F. (1966) Mutual Fund Performance, Journal of Business, 39(1), 119–138.

Sortino, F. A., Price, L. N. (1994) Performance Measurement in a Downside Risk Framework, Journal of Investing, 3(3), 59–64.

Business resources

National Stock Exchange of India (NSE)

SEBI (2024) Updated SEBI study reveals 93% of individual traders incurred losses in equity F&O between FY22 and FY24

SEBI (2024) Circular of October 1, 2024: Measures to strengthen Equity Index Derivatives framework for increased investor protection and market stability.

Futures Industry Association (FIA)

CFA Institute

Sensibull

About the author

The article was written in October 2026 and revised in November 2026 by Romil SOJITRA (Indian Institute of Technology Kanpur, BS in Economics, 2024-2028).

▶ Discover all posts by Romil SOJITRA.

My professional experience at Tea Ideas: redesigning a social enterprise café in Singapore

November 4, 2026 by Arjun KODAPPULLY

In this article, Arjun KODAPPULLY (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2024-2028) shares his professional experience as an operations and business development intern at Tea Ideas, a Singapore social enterprise that runs the Sippy Mug cafés. He explains what he did during the internship and uses it to present three business concepts: unit economics, break-even analysis and the financing of mission-driven organizations.

About the company

Tea Ideas Pte Ltd is a social enterprise based in Singapore and founded in 2012. It sells tea-based food and beverage products and creates jobs for people who face barriers to work, such as seniors, caregivers, people with disabilities (PWDs) and people living with debilitating health conditions. Its mission statement says that “for some, it’s a first job, for others, a step toward entrepreneurship”. The company has been a member of raiSE, the Singapore Centre for Social Enterprise, since 2017.

The product range includes teas, cookies, cold brew coffee and customized corporate gifting. Tea Ideas also operates the Sippy Mug cafés and presents its model as Singapore’s first food and beverage social franchising model, in which trainees receive hands-on training and a pathway towards co-ownership. raiSE classifies the company as an “Emerging Social Enterprise”, a category for businesses that have tracked at least three months of social impact and generated market revenue. It received the Brands of Good Award in 2019 and 2020 and was a Steward Leadership 25 Honouree in 2023. The supporters listed in its profile include the National Kidney Foundation, the Ministry of Home Affairs, Prudential Corporation Asia and the TungLok Group.

According to the impact summary published in its raiSE directory profile, seven beneficiaries are employed and 52% of the manpower is dedicated to social impact creation. These figures are self-disclosed by the company, so they should be read as its own report and not as audited data. Tea Ideas is a small private company: it has no market capitalization or stock chart.

The company operates in a dense ecosystem. raiSE alone supports more than 350 social enterprise members. In food and beverages, Tea Ideas shares the market with other inclusive businesses such as Dignity Kitchen and with social enterprise cafés listed in the raiSE directory, such as Foreword Coffee.

Logo of Tea Ideas.

Source: the company.

I worked at a Sippy Mug café located inside the Ministry of Home Affairs. The café is small, with one counter and a compact kitchen area. Its team combines colleagues on the autism spectrum, who take orders and prepare drinks, with older mentors who coach them, and the founders are closely involved in the daily running of the business. Our group of three interns joined this team for one month.

My professional experience

The internship lasted one month in 2026. Looking back, it had four phases: learning the system, fixing it, rethinking the business model and testing it in public.

My missions

Learning the system. During the first week we served customers and prepared drinks while documenting how everything was done. We created visual guides for every item on the menu, to be displayed at each workstation. Each guide gives the sequence of steps, the quantities, the timings and a photo of each step, so that a team member can follow a recipe without guessing. We then expanded this work into a guidebook covering the opening routine, mise en place, taking orders on the point-of-sale system, and the closing and cleaning procedure.

Fixing the system. After the first week, simultaneous orders made the small work area chaotic. I proposed a station-based workflow: every person owns a station, every order is written on a ticket placed on a slider, the person taking the order reads it aloud, and the kitchen starts with the items that take the longest so that everything reaches the plate warm at the same time. By the end of the second week, I was coordinating the kitchen. In parallel, a guest mixologist ran a session with the team, and we added new drinks to the menu, such as an Oreo frappe, a Biscoff frappe and a cold brew tea.

Rethinking the model. In the second half of the internship we helped prepare the application for a S$50,000 social enterprise grant offered through raiSE. This meant presenting the business model, building financial projections and explaining how the money would be used. We spent several days on it, including a full day of strategy discussions with the founders in Johor Bahru, Malaysia. The central idea was to use the café as a training ground for a workplace integration model: older mentors train PWDs in the café, and the trainees are then placed in regular corporate workplaces where they can earn a living.

Testing in public. At the end of the internship we ran a roadshow in a corporate office, with a stall to sell Tea Ideas tea products, hand out samples and talk to prospective customers.

Required skills and knowledge

The hard skills were process mapping and the writing of operating procedures, building a simple financial model in a spreadsheet with revenue lines, cost lines and scenarios, using a point-of-sale system and applying food hygiene standards. The soft skills mattered at least as much: patience and clear communication with colleagues who need precise and unambiguous instructions, leading without formal authority, adapting quickly when a plan fails in the middle of service, and presenting a business case to the founders and, indirectly, to a grant reviewer.

What I learned

The most important lesson came from a colleague on the autism spectrum. He followed every instruction with more precision than most people would, but when something went wrong, for example when too much salt went into a dish, the only recovery he knew was to start again. I would simply add water. It showed me that the limit was in the design of the instructions and not in the person. A good operating procedure needs recovery steps, not only the normal path. This is also the logic of the workplace integration model: train people not only to execute but to adapt.

On the business side, I learned how a funding proposal is read. A reviewer wants to see a story of pilot, validation, scale and sustainability, supported by explicit assumptions. I also learned how much operations drive financial results: a ticket system or a clearer recipe card looks like a small change, but it changes how many orders a small team can serve in an hour.

Economic, financial and business concepts related to my professional experience

I present below three concepts related to my professional experience: unit economics, break-even analysis and blended financing. The numbers in the first two sections are stylized illustrations that I built for this article. They are not Tea Ideas’ figures.

Unit economics and contribution margin

Unit economics measures what one unit of a business earns after the costs that vary with that unit. In this example, the unit of analysis is a billable session. The contribution margin (CM) is the price (P) minus the variable cost per unit (VC), and the contribution margin ratio is this amount divided by the price. The contribution margin measures the amount available to cover fixed costs and, once these costs are covered, to generate operating profit.

CM = P − VC and CM ratio = (P − VC) / P

Suppose a four-hour support session is billed S$200. Delivering it costs S$90 for the person who delivers it, S$30 for supervision and S$20 for administration. All costs in this example are assumed to vary with the number of sessions delivered. The contribution is S$60, or 30% of the price (Figure 1).

Figure 1: Stylized unit economics of a four-hour support session.
Stylized unit economics of a support session
Source: author’s own illustration, stylized numbers.

This was the starting point of the financial projections we prepared. Before forecasting a single year, we had to ask what one unit of service earns after the people who deliver it are paid. The framework also shows where management attention matters most. With these numbers, a 10% price increase (from S$200 to S$220) lifts the contribution from S$60 to S$80, an increase of 33.33%, while a 10% reduction in all variable costs (S$14 out of S$140) increases the contribution margin by 23.33%. In this example, increasing the price has a greater impact on the contribution margin than reducing variable costs by the same percentage. However, the effectiveness of a pricing strategy also depends on how customers respond to price changes.

Break-even analysis and operating leverage

Fixed costs, such as rent, core salaries, and insurance, remain constant within a relevant range of activity and over a given time horizon. The break-even volume is the number of units at which total contribution equals fixed costs. The operating profit (EBIT) is equal to the total contribution margin minus fixed costs (FC). The break-even volume is reached when EBIT equals zero.

EBIT = Q × CM – FC

Q* = FC / CM

where Q is the number of sessions sold, CM is the contribution margin per session, and FC represents fixed costs.

With stylized fixed costs (FC) of S$120,000 per year and a contribution of S$60 per session, break even is reached at 2,000 sessions (Figure 2). A pilot year has few clients and sits to the left of this point, so it makes a loss. A grant can help finance the operating costs of the pilot phase, provided these expenditures are eligible under the grant conditions. Initial losses may be economically justified if the business plan demonstrates a credible path toward financial sustainability.

Figure 2: Stylized break-even analysis.

Source: author’s own illustration, stylized numbers.

Beyond the break-even point, each additional session adds its full contribution of S$60 to operating profit. Operating leverage measures the sensitivity of operating profit to changes in sales volume. For a given contribution margin per unit and sales volume, higher fixed costs increase the relative sensitivity of operating profit to changes in sales volume, provided operating profit remains positive. It explains why scaling a service business is attractive, but also why a pilot must prove demand before fixed costs are committed.

Blended financing and mission drift

A social enterprise sells products or services like a business, but its goal is a social outcome. It therefore often combines earned revenue with grants and donations. A grant does not dilute ownership and is usually tied to a purpose, but it is temporary. Earned revenue is renewable, but it creates a risk that researchers call mission drift: the organization moves toward the customers who pay best and away from the people it wants to serve (Battilana and Lee, 2014). Yunus, Moingeon and Lehmann-Ortega (2010) argue that a social business must be designed so that its economic engine covers its costs while serving the social objective.

In our case, the objective was to use the grant to finance the pilot phase while developing a sustainable revenue stream from companies paying for workplace integration programs. These revenues could eventually cover the program’s operating costs. Funders also look at what is measured. Ebrahim and Rangan (2014) distinguish outputs (for example, the number of people trained), outcomes (for example, the number who keep a job) and impact, defined as lasting change in the lives of people. A proposal that is explicit about this ladder is easier to evaluate and harder to criticize.

Why should I be interested in this post?

If you are a business or finance student, this post shows how finance tools apply outside a bank. Unit economics, break-even analysis, and financing decisions are also central to the financial analysis of startups and investment projects. A grant application is also close to an investment memo: it requires assumptions, scenarios and a clear use of proceeds. Impact investing and sustainability reporting are areas where understanding how a mission-driven organization balances revenue and impact is directly useful. Finally, an operations role in a very small organization gives responsibility early, and it teaches you what drives margins in a way that a valuation model captures only indirectly.

Useful resources

Academic research

Battilana, J., Lee, M., (2014) Advancing Research on Hybrid Organizing: Insights from the Study of Social Enterprises, Academy of Management Annals, 8(1), 397–441.

Ebrahim, A., Rangan, V. K., (2014) What Impact? A Framework for Measuring the Scale and Scope of Social Performance, California Management Review, 56(3), 118–141.

Yunus, M., Moingeon, B., Lehmann-Ortega, L., (2010) Building Social Business Models: Lessons from the Grameen Experience, Long Range Planning, 43(2–3), 308–325.

Business resources

Tea Ideas profile in the raiSE member directory

raiSE, Singapore Centre for Social Enterprise

Tea Ideas

About the author

The article was written in November 2026 by Arjun KODAPPULLY (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2024-2028).

▶ Discover all posts by Arjun KODAPPULLY.

My experience as a Cross-Asset Structurer in a Structured Products Broker

November 4, 2026 by Jules HERNANDEZ

In this article, Jules HERNANDEZ (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2021-2025) shares his professional experience as Cross-Asset Structurer in a structured products brokerage firm.

What is this article about?

In this article, I will explore the role of a structured products brokerage firm and the four key services it provides: structuring, best execution, financial analysis and investment ideas, and product monitoring and lifecycle management. I will then share my experience as an intern, describe my main responsibilities, from product pricing and marketing presentations to client and issuer relationships, and explain the knowledge and skills required to succeed in this field, including how to prepare for an interview.

However, having knowledge about these investment solutions will help you to fully understand this article and the description of an internship in a structured product broker. Therefore, if you have limited knowledge about structured products, I highly recommend reading this article first where you’ll find an explanation of this financial instrument and many details related to it: Structured products: what’s behind them?

What is a structured product broker?

A structured product broker acts as an intermediary between professional investors and issuing banks such as JPMorgan, Goldman Sachs, UBS, BBVA, Citi, Barclays, and many others. Its clients include wealth management advisers, known in France as Conseillers en Gestion de Patrimoine (CGPs), single-family offices and multi-family offices, as well as private banks. Although these clients may have different business models and customer bases, they share the same objective when approaching a broker: finding a structured product that reflects their market views and investment objectives, while obtaining the most competitive pricing possible. To achieve this, brokers leverage their network of banking partners to source and compare proposals, helping clients identify the most attractive investment opportunities available on the market.

You will find below a chart clarifying the structured products actor and the distribution chain :

Structured products actors and the distribution chain

How is a Structured Products Brokerage Firm Organized?

From my experience, a structured products brokerage firm can be divided into two main teams: Sales and Structuring. Both teams must work closely together to ensure that each transaction runs smoothly, from the initial client discussion to the final execution of the order.

The Sales Team

The Sales team is responsible for developing the firm’s client base and maintaining long-term relationships with existing clients. This involves prospecting new clients, contacting wealth management advisers, family offices and private banks, and demonstrating the value a broker can bring to their investment activities.

Beyond acquiring new clients, Sales professionals act as the main point of contact throughout the investment process. They identify clients’ needs, understand their market views, advise their clients about the market, present investment opportunities, and communicate proposals prepared with the Structuring team. For example, a client may have a positive outlook on equity markets but may not want broad exposure to the entire market or to every sector. In this case, the broker can help refine the client’s investment view by identifying sectors with strong growth potential, such as electrification, and highlighting companies that could benefit from this trend. Examples include Schneider Electric, Bloom Energy or Prysmian, which could be suitable underlyings for a structured product. The objective is to help clients translate their market views into more targeted investment opportunities.

They also explain product mechanics, discuss potential risks and returns, and can even teach client about structured products. Maintaining strong relationships is essential, as clients rely on their Sales contacts not only to access investment products but also to receive relevant market insights and timely investment ideas.

The Structuring Team

The Structuring team provides the technical and operational support required to turn client requests into executable investment solutions. Working closely with Sales, its responsibilities include designing and pricing structured products, obtaining quotes from issuing banks, comparing available structures, and preparing marketing presentations for clients. Its role also extends beyond the initial design and pricing stages. The team monitors product events, coordinates with issuers, and helps ensure that financial orders are processed correctly. Depending on the firm’s organization, these responsibilities can cover activities associated with both the front office, such as structuring and pricing, and the back office, such as transaction processing and operational follow-up.

The two teams are complementary: Sales’ objectives are to understand the client’s needs and communicate the investment opportunity, while the Structuring Team aims to provide the technical expertise and operational support needed to deliver it.

The Four Key Services of a Structured Products Broker

Structuring Services: Turning Market Views into Investment Solutions

One of the broker’s core responsibilities is to translate a client’s market expectations into a structured investment product. Clients may have a specific view on an underlying asset, an expectation about future market volatility, or a target return subject to defined risk constraints. The broker’s role is to convert these expectations into a product with clearly defined payoff conditions. For example, imagine a wealth manager who expects the Euro Stoxx 50 to remain relatively stable or rise moderately over the next three years. Rather than investing directly in the index, the wealth manager may want to offer clients a product that generates periodic coupons as long as the index remains above a predefined barrier, with the possibility of early redemption if certain conditions are met. The broker works with the Sales team to understand the client’s objectives, including the desired maturity, coupon level, capital protection conditions, and acceptable risk. The Structuring team then uses pricing tools provided by different issuing banks to model and compare possible structures. By adjusting parameters such as coupon barriers, capital barriers, observation dates, and maturity, it can assess different combinations and identify solutions that best match the client’s requirements.

This process requires a solid understanding of financial instruments, derivatives, market parameters, and the mechanics of structured products. It also involves balancing several constraints: a more attractive coupon may require the client to accept a higher level of risk, while stronger capital protection may reduce the potential return. The objective is not simply to design a product with an attractive headline yield, but to find a structure whose payoff, risk profile, and pricing are consistent with the client’s investment objectives.

Best Execution: Finding the Most Competitive Offer

Best execution is another fundamental service provided by structured products brokers. Because multiple banks can issue similar structured products, the broker can request competing proposals and compare the terms offered by different issuers. The comparison goes beyond the headline coupon. Depending on the product, relevant parameters may include the issue price, coupon, observation schedule, barriers, maturity, capital protection features, and the issuer’s credit quality. The broker also considers the consistency of each proposal with the client’s investment objectives and the practical conditions required to execute the transaction. For example, suppose a client wants to invest €1 million in a three-year autocallable product linked to a basket of European equities. The broker may approach several issuers with the same product specifications. One bank might offer a 7.5% annual coupon, another 7.8%, and a third 8.0%. The broker would then give the best price to its customer. The diagram below illustrates how our best execution service works.

Best Execution service

This ability to compare multiple issuers is a key source of value. An individual wealth manager may have access to only a limited number of banking partners, whereas a broker can leverage a broader network of issuers and centralise the comparison process. The objective is to obtain the best available terms for the client, while ensuring that the product remains appropriate for their needs. However, this should not be confused with simply choosing the highest coupon: the overall terms, risks, issuer quality, and execution conditions all matter.

Financial Analysis and Investment Ideas

A structured products broker also provides financial analysis to help clients and Sales teams identify investment opportunities. This research can cover macroeconomic developments, equity markets, interest rates, currencies, commodities, corporate fundamentals, volatility, and relative-value opportunities.

The purpose is to connect market developments with actionable investment ideas. For example, if a broker’s analysis suggests that a particular equity index offers an attractive risk-return profile, the team may investigate whether a structured product linked to that index could be relevant. Similarly, changes in implied volatility or interest-rate expectations may create opportunities to design products with different coupon levels or payoff profiles. Research therefore supports both the development of investment ideas and the structuring process. The analysis team helps identify potential underlying assets and market themes, while Sales professionals use these insights to discuss opportunities with clients and explain the rationale behind a proposed investment. This service is particularly valuable for wealth managers and private banks that may not have dedicated research teams covering every asset class. By providing accessible market commentary, investment scenarios, and product ideas, a broker helps clients make more informed decisions and gives Sales teams relevant material for their client discussions. Importantly, financial analysis should also consider the downside scenario. An investment idea must be assessed in terms of its potential return, the risks that could undermine the underlying market view, and the conditions under which the product could incur losses.

Product Monitoring and Lifecycle Management

A structured product is not static once it has been issued. Its market value fluctuates throughout its life in response to several factors, including the performance of its underlying assets, interest rates, dividends, volatility, and the issuer’s credit spread. Depending on its structure, changes in these parameters can materially affect the product’s valuation and potential redemption value.

A broker helps clients access this information easily by providing valuations and facilitating communication with issuers. This allows wealth managers and other professional investors to monitor their positions and better understand how market developments affect their investments. In addition to valuation, structured products can generate several contractual events during their lifetime. These may include coupon payments, the observation of coupon or capital barriers, early redemption through autocall features, and maturity. Monitoring these events is essential to ensure that clients understand what has happened to their investments and what may happen next.

Many brokers therefore use dedicated digital platforms that allow clients to view their portfolios, monitor product valuations, and track upcoming or completed events. Some platforms also include integrated pricing tools, enabling users to simulate different product structures, adjust parameters, and explore potential payoffs before submitting a request to the broker. These tools improve transparency and simplify portfolio monitoring. They also allow clients to become more autonomous when exploring investment opportunities, while retaining access to the broker’s expertise for product design, pricing comparisons, and execution. Overall, product monitoring extends the broker’s role beyond the initial transaction. It helps maintain the relationship with clients throughout the product’s lifecycle and provides them with the information needed to follow their investments over time.

My internship

I spent six months working alongside the Structuring team, informally referred to in French as “La Structu”. Interns are entrusted with real responsibilities and quickly exposed to both sides of the business: clients and banking partners (issuers). This level of exposure requires rigor, attention to detail, and a strong sense of responsibility, since the intern’s tasks can have a significant impact on transactions, client relationships, and ultimately, the firm’s business.

My missions

One of the most interesting aspects of my internship was the variety of responsibilities I had. Working within the Structuring team gave me exposure to almost every stage of a structured product transaction, from its initial pricing to its execution and monitoring. Pricing was one of my main responsibilities. Using the dedicated platforms provided by our banking partners like Goldman Sachs, BNP Paribas and Société Générale, I could simulate different structured products and obtain indicative pricing. For instance, I could enter the parameters of an autocallable product, such as its coupon barrier, capital protection barrier and maturity, to determine the coupon each bank was prepared to offer. I would then compare the quotes received from different issuers to identify the most competitive proposal and share the results with the Sales team. The aim was to provide clients with the best available terms for their desired structure. This task demanded precision and a good understanding of product mechanics, as even small differences in pricing could influence the final terms offered to a client.

Below, you’ll find an exemple of a pricing platform. The following pricer is provided by Bloomberg. On this pricer, we can find all the relevant paramters used to price a structured product :

Bloomberg structured product pricer

My responsibilities did not end once a client decided to invest. I also took part in the operational follow-up of transactions, helping ensure that orders were processed and settled correctly. This involved coordinating with issuing banks, their back-office teams and custodians, checking transaction details and following up whenever an issue arose. Although less visible than pricing, this work was essential to ensure that transactions were completed smoothly and that all parties had the correct information.

Another part of my role involved document verification and product monitoring. Before products could be properly recorded and monitored, I had to check the documentation supplied by issuers, including term sheets, Key Information Documents (KIDs), target market documents and brochures used for life insurance contracts. I also entered newly issued products into a dedicated monitoring platform, enabling the firm and its clients to follow their valuations and key lifecycle events. This required particular attention to detail, since the information recorded needed to remain consistent with the product’s official documentation.

Finally, I contributed to the preparation of marketing materials used by the Sales team. These included presentations for client seminars and webinars, as well as product-specific presentations and one-pagers outlining a structure’s main features, payoff mechanics and investment rationale. The challenge was to turn technical financial information into clear, accessible material that Sales professionals could use when presenting investment opportunities to clients.

Alongside these core responsibilities, I also worked on various projects, particularly the automation of operational tasks using Excel and Bloomberg. The aim was to reduce manual work and improve efficiency in the team’s day-to-day activities. I also carried out regular market monitoring. Every week, I prepared and presented a short market update covering the macroeconomic environment and the main developments in economic news. This required keeping track of key indicators, central bank decisions, major market events and companies’ financial announcements that could boost implied volatility and offer investment opportunities to our clients.

Required skills and knowledge

Working in a structured products brokerage firm requires a combination of personal qualities and technical knowledge. While financial expertise can be developed over time, certain qualities are essential to work effectively in such a fast-paced environment.

First of all, teamwork and collaboration are arguably the most important qualities. As explained earlier, the business relies on close cooperation between the Sales and Structuring teams, as well as regular communication with issuing banks and clients. Information must flow smoothly between everyone involved, and the success of a transaction often depends on how well these different parties work together. Being approachable, responsive and willing to help colleagues is therefore just as important as individual performance. No matter how strong your technical skills are, you cannot succeed in this environment by working in isolation.

From my experience, I would then say that curiosity and a genuine interest in financial markets are equally important. Markets evolve constantly, driven by economic data, central bank decisions, geopolitical developments and corporate news. Understanding these movements helps identify investment opportunities, anticipate clients’ needs and assess how changing market conditions affect structured products. Reading financial news regularly, following market trends and developing your own investment views are all good ways to build this curiosity. You do not need to know everything from day one, but you must be willing to learn every day.

Rigour and attention to detail are also essential qualities. Structured products involve complex payoff mechanisms, precise contractual terms and multiple operational steps. An incorrect pricing parameter, an error in a term sheet or a mistake in an order can have financial consequences for both the client and the firm. Being organized, checking your work carefully and remaining focused under pressure are therefore fundamental to the job.

Beyond these personal qualities, working in this field requires a solid technical understanding of financial markets and financial engineering. You need to understand how different asset classes behave, including equities, interest rates, foreign exchange and commodities, as well as the main factors influencing their prices. Knowledge of derivatives, particularly options, is also valuable, since their pricing and risk characteristics underpin many structured products. It is equally important to understand the mechanics of structured products themselves: how coupons are generated, how barriers work, what triggers an early redemption, and under which conditions an investor may incur losses. A basic understanding of volatility, dividends, interest rates and correlation can also help explain why a product’s pricing changes depending on market conditions and its underlying assets.

And finally, it goes without saying, proficiency in Excel is particularly useful. It is used every day for analysing data and automating recurring tasks. Familiarity with Bloomberg can also be an advantage, as it allows you to access market data, monitor financial news and analyse market movements more efficiently.

What I learned during this internship

Of course, this internship allowed me to develop my technical knowledge, particularly in pricing structured products and understanding their mechanics. It also gave me a much clearer picture of how structured products are distributed, from the relationship between brokers and issuing banks to the role of wealth managers, Family Offices and private banks. I discovered an ecosystem involving a wide range of actors, each with their own objectives and constraints, and learned how they work together to bring investment solutions to clients.

But beyond the technical skills, the most important lesson I took away from this experience was the importance of curiosity. Financial markets are constantly evolving, and there is always something new to understand. An economic announcement, a central bank decision, a geopolitical event or a sudden change in volatility can create new opportunities or completely change the way an investment should be approached. Following the markets is therefore not simply about knowing what happened, but about understanding why it happened, how different asset classes interact and what the implications might be for investors. Why does a rise in interest rates affect equity valuations? How can a geopolitical event influence currency markets? Why does implied volatility change the pricing of an option? These are the kinds of questions that make financial markets so interesting to me. During my internship, I realised that curiosity also means going beyond the tasks you are assigned. Whenever I came across an underlying asset or an investment theme I did not know well, I wanted to understand it better, explore the factors that could influence its performance and consider whether it could offer an interesting investment opportunity. This approach helped me gradually move beyond the technical aspects of structuring and develop a broader understanding of the investment rationale behind each product. It also encouraged me to share my market views and ideas with the Sales team, sometimes on topics that were not initially part of my responsibilities.

I believe that curiosity is what allows you to keep learning, improve your judgement and become more relevant in this industry. Technical knowledge can be acquired, and tools can be mastered, but the willingness to ask questions, challenge your assumptions and continuously deepen your understanding is what drives long-term progress. This is what I enjoyed most during my internship, and it is the mindset I want to carry forward throughout my career in financial markets.

What to know for your interview?

The purpose of this section is not to provide an exhaustive explanation of every concept you may encounter during an interview. Instead, it is intended as a checklist of the main topics you should be familiar with before applying for an internship or a position in structured products. For more detailed explanations, I recommend consulting other articles on the SimTrade Blog, particularly Structured Products: What’s Behind Them?, which provides a deeper look at several of the concepts mentioned below.

First, you should have a solid understanding of how financial markets work and be familiar with the main asset classes. In structured products, four asset classes are particularly important: Equity, Rates, Credit and FX.

You should be able to explain the main characteristics of each asset class and understand how they react to main economic events such as central bank decisions and inflation announcements.

The Main Types of Structured Products

You should know the mechanics of the most common structured products, understand how they generate returns and identify the risks faced by investors. In particular, you should be familiar with:

Phoenix Products with Memory: structured products offering conditional coupons when the underlying asset meets a predefined coupon barrier condition on an observation date, with unpaid coupons potentially accumulated and paid later if the condition is subsequently satisfied.
Reverse Convertibles: structured products offering a fixed coupon, generally independent of the underlying asset’s performance, in exchange for exposure to its downside risk, subject to the issuer’s credit risk.
Capital-Protected Interest Rate Products: products designed to provide exposure to interest rate markets while protecting the invested capital at maturity, subject to the terms of the product and the issuer’s credit risk.
Credit-Linked Notes (CLNs): products whose returns and repayment depend on the credit performance of one or more reference entities or credit events.
Participation Products: products designed to provide exposure to the performance of an underlying asset, sometimes with a participation rate greater than or less than 100%.

Derivatives and Options

Options are at the heart of many structured products, so understanding them is essential. You should know the difference between calls and puts, European and American options, and vanilla and exotic options. You should also understand the main factors affecting option prices, including the underlying asset price, volatility, time to maturity, interest rates and dividends.

You should be particularly comfortable with the following option payoffs and strategies:

Upside participation mechanisms: call options, digital options and up-and-out calls.
Downside protection mechanisms: down-and-in puts, put spreads and leveraged puts.

You should be able to sketch their payoff profiles, explain how they behave under different market scenarios and understand how they can be combined to create a structured product.

Another useful concept to master is the replication of a digital option using a tight call spread. By combining a long call (lower strike) and a short call (higher strike) with nearby strike prices, it is possible to approximate the payoff of a digital option, with the accuracy of the approximation depending on the distance between the strikes. This question is almost always asked during an interview. The objective is to assess whether the candidate understands basic trading concepts and replication mechanics.

The Greeks and Risk Sensitivities

Finally, you should understand the main option Greeks, which measure how an option’s value responds to changes in different market parameters:

Delta: sensitivity to changes in the underlying asset price.
Gamma: sensitivity of delta to changes in the underlying asset price.
Vega: sensitivity to changes in implied volatility.
Theta: sensitivity to the passage of time.
Rho: sensitivity to changes in interest rates.

For structured products, it is particularly useful to understand how these sensitivities evolve over the product’s lifetime and how they are affected by barriers, early redemption features and market conditions. You do not necessarily need to derive every formula from memory, but you should be able to explain what each Greek measures and why it matters for pricing and risk management.

Why should you be interested in this post?

If you are interested in financial markets and considering a career in structured products, this article offers an overview of the role of a structured products broker, the main services provided and the responsibilities of a structuring intern. You will also find practical advice on the skills and knowledge required to prepare for an interview, based on my own six-month internship experience in a structured products brokerage firm.

Useful resources

SRP Website – Industry insights This website is very useful to learn about the structured products industry. SRP conducts a lot of research on this matter

Yaël Eljarrat-Ouakni What is a Structured Product? Société Générale Private Banking France.

Pôle commun AMF-ACPR (06/2026) Distribution, frais et performance des produits structurés – Rapport final – Juin 2026

Autorité des Marchés Financiers (AMF) (24/05/2025) 2025 Markets and Risk Outlook

SRP (18/03/2025) Global Market review 2024, Europe Market review 2024

About the author

The article was written in November 2026 by Jules HERNANDEZ (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2021-2025).

▶ Discover all posts by Jules HERNANDEZ.

VIX Futures and Options

October 31, 2026 by Saral BINDAL

In this article, Saral BINDAL (Indian Institute of Technology Kharagpur, Metallurgical and Materials Engineering, 2024–2028; Research Assistant at ESSEC Business School) examines the characteristics, pricing, and settlement of VIX futures and options, along with their term structure and market microstructure.

Introduction

The Cboe Volatility Index (VIX) is a real-time measure of the market’s expectation of S&P 500 volatility over the next 30 days. It is calculated from the prices of S&P 500 Index (SPX) options and is expressed as an annualized percentage. The VIX is widely used as a measure of expected market volatility. For more details, see the previous posts on Implied Volatility and Option Prices and Cboe Volatility Index.

The VIX itself is an index and cannot be directly bought or sold. To allow market participants to trade volatility, Cboe introduced VIX derivatives such as futures and options. VIX futures were introduced in 2004, followed by VIX options in 2006. VIX futures provide linear exposure to changes in the market’s pricing of the future level of the VIX Index, while VIX options provide nonlinear exposure, allowing investors to take positions on changes in the VIX through a range of strike prices and maturities.

VIX Futures

A VIX futures contract is a standardized agreement whose value is linked to the VIX Index at a specified future date (expiration date of the contract). A VIX futures contract reflects the market’s pricing of the VIX Index at a specified future expiration date. Its price incorporates expectations about future volatility as well as risk premia and therefore should not necessarily be interpreted as an unbiased forecast of the future VIX level. VIX futures are traded on the Cboe Futures Exchange (CFE) under the symbol VX.

The pricing of VIX futures differs from that of conventional futures on a tradable stock or stock index. For a stock, the futures price can be related to the current spot price through a cash-and-carry arbitrage relationship, typically expressed as F₀ = S₀e^(r-q)T, where r is the interest rate and q is the dividend yield. This relationship relies on the ability to buy and hold the underlying asset. In contrast, the VIX is an index and cannot itself be directly bought or held. Therefore, the VIX futures contract does not simply represent the current VIX value carried forward to a future date. Instead, it is a market price determined by participants’ expectations about future volatility (the expected value of VIX at maturity), together with the supply and demand for volatility exposure and the risk associated with holding the contract.

VIX futures have a $1,000 contract multiplier. Therefore, a one-point change in the price of a VIX futures contract corresponds to a $1,000 change in the value of one contract.

For example, if an investor buys one VIX futures contract at 20 and its price subsequently rises to 23, the change of 3 VIX points corresponds to a gain of:

VIX Futures Calculation Example

before transaction costs and any financing or opportunity costs associated with margin requirements.

Transaction Costs

Trading VIX derivatives involves both explicit (commissions and fees) and implicit (price impact) transaction costs. These affect the cost of entering and exiting positions and, ultimately, strategy performance. These costs can be broken down into the following components:

Brokerage commissions: Fees charged by brokers for executing trades.

Exchange transaction fees: Fees levied by the exchange on executed contracts.

Clearing and regulatory fees: Charges associated with clearing trades and applicable regulatory requirements.

Bid-ask spread: The difference between the best available ask and bid prices. Crossing the spread creates an immediate execution cost, which varies with market liquidity and volatility.

Slippage: The difference between the expected and actual execution prices. It can arise from market movements, order size and limited liquidity, particularly during periods of elevated volatility.

Margin Requirements and Costs

Unlike transaction costs, margin is not a fee paid to execute a trade. It is the collateral required to maintain a derivatives position, tying up capital that could otherwise be deployed elsewhere.

When opening a position, a trader must meet the initial margin requirement. As the position is marked to market, gains and losses affect the account balance. If losses reduce the balance below the maintenance margin requirement, additional funds may be needed to keep the position open.

The cost of margin therefore lies primarily in the capital committed to the trade and its associated opportunity cost. Traders may also incur financing costs if they borrow funds to meet margin requirements. These considerations become particularly important in VIX derivatives, where margin requirements can change with market conditions and increase during periods of elevated volatility.

Settlement of VIX Futures

VIX futures are cash-settled. Unlike futures on physical commodities, there is no delivery of an underlying asset at expiration because the VIX itself is an index and not a traded asset. Instead, the final profit or loss is determined using the final settlement value of the contract.

The final settlement value is determined on the morning of the expiration date, usually a Wednesday, through a Special Opening Quotation (SOQ) of the VIX Index. The SOQ is calculated using the VIX methodology applied to the relevant SPX options and determines the final settlement value of the VIX futures contract. As expiration approaches, the futures price converges toward this settlement value, reflecting the contract’s approaching expiration.

For a long VIX futures position, the profit or loss at settlement can be represented as:

VIX Futures Payoff Formula

For example, suppose an investor purchases one VX futures contract at 20. If the final settlement value is 28, the investor receives:

VIX Futures Calculation Example 2

Thus, the cash-settlement mechanism allows investors to gain or lose based on changes in the market’s pricing of volatility without owning the underlying SPX options used in the VIX calculation.

Figure 1 below illustrates the VIX Futures P&L at Settlement for both long and short positions as a function of the VIX value at maturity, for a VX futures contract bought (long) or sold (short) at a futures price of 20. The contract has a multiplier of $1,000 per index point and is cash-settled at maturity at the Special Opening Quotation (SOQ) of the VIX Index.

Figure 1. VIX Futures P&L at Settlement

Source: computation by the author.

VIX Futures Term Structure

A term structure exists because VIX futures contracts have different expiration dates and therefore reflect market expectations over different future periods. As the time to expiration changes, the information, uncertainty, and expected volatility associated with that future period also change, causing futures contracts with different maturities to trade at different prices.

An upward-sloping term structure indicates that VIX futures with later expiration dates are priced at higher VIX levels than contracts with nearer expirations. This is known as contango. By contrast, a downward-sloping term structure indicates that VIX futures with later expiration dates are priced at lower VIX levels than contracts with nearer expirations. This is known as backwardation.

Figure 2 below illustrates the VIX Futures Term Structure using data obtained from Cboe, covering expiration dates from September 16, 2026, to May 18, 2027. The figure shows an upward-sloping curve, indicating a state of contango. This suggests that VIX futures contracts with later expiration dates are priced at higher levels than those with nearer expirations, indicating that the market assigns higher prices to longer-dated volatility exposure.

Figure 2. VIX Futures Term Structure (September 2026)

Source: computation by the author (Cboe data).

This term structure has implications for investors who maintain long positions in VIX futures. As contracts approach expiration, investors seeking to maintain their exposure must sell the expiring contracts and buy later-dated ones. In a contango market, longer-dated futures contracts trade at higher prices than nearer-dated contracts. Maintaining a long futures exposure therefore involves replacing expiring contracts with higher-priced contracts. Although contango can create a negative roll effect for long futures strategies, the realized return depends on subsequent changes in futures prices and the specific rolling strategy. For long positions, this can create a drag on returns as futures prices evolve toward settlement. However, the outcome depends on how futures prices change over time, rather than on contango alone. The relationship between trading demand, futures pricing and the term structure is discussed further in the market microstructure section below.

VIX Options

VIX options are exchange-traded derivatives that provide nonlinear exposure to the VIX settlement value at expiration. Their pricing is closely related to the corresponding VIX futures contract rather than solely to the current spot level of the VIX Index. VIX options are traded on Cboe Options Exchange and are based on the Cboe Volatility Index (VIX). Unlike individual stocks in stock options, the VIX is itself an index derived from prices of S&P 500 Index (SPX) options.

Cboe offers both monthly and weekly VIX option expirations, providing market participants with different maturities for expressing views on volatility.

Each VIX option contract has a $100 multiplier. Therefore, the dollar value of an option is obtained by multiplying its quoted premium by $100.

For example, if an investor buys one VIX call option with a strike price of 20 and the VIX settlement value is 15 at expiration, the option finishes 5 VIX points out-of-the-money. Consequently, the option expires worthless, with a payoff of zero (before accounting for the premium paid, transaction costs, and other applicable charges).

Settlement of VIX Options

VIX options are cash-settled. This means that there is no physical delivery of an underlying asset when an option is exercised. Instead, the value of an exercised option is determined using the final settlement value of the VIX.

The final settlement value is determined on the morning of the expiration date, usually a Wednesday through a Special Opening Quotation (SOQ) of the VIX Index. The SOQ is calculated using the VIX methodology applied to the relevant SPX options and is used to determine the final settlement value of the VIX option.

For a VIX call option, the settlement value is based on the amount by which the final VIX settlement value exceeds the strike price, and for a VIX put option, the settlement value is based on the amount by which the strike price exceeds the final VIX settlement value.

For a call option, this can be represented as:

VIX Call Option Payoff Formula

For a put option:

VIX Put Option Payoff Formula

For example, suppose an investor holds a VIX call option with a strike price of 20 and the final settlement value is 28. The option’s settlement value is:

VIX Options Calculation Example 2

If the final settlement value is below 20, the call expires without value.

Figure 3 illustrates the P&L at expiration for long and short VIX call positions as a function of the VIX value at maturity. For a strike price of 20, a premium of 2.00 VIX points, and a $100 multiplier, the long call has a maximum loss of $200 when the VIX settles at or below 20 and unlimited gain above the breakeven level of 22. VIX options are cash-settled at expiration based on the VIX SOQ.

Figure 3. VIX Call Option P&L at Settlement

Source: computation by the author.

Figure 4 illustrates the P&L at expiration for long and short VIX put positions as a function of the VIX value at maturity. For a strike price of 20, a premium of 2.00 VIX points, and a $100 multiplier, the long put has a maximum loss of $200 when the VIX settles at or above 20 and becomes profitable below the breakeven level of 18. VIX options are cash-settled at expiration based on the VIX SOQ.

Figure 4. VIX Put Option P&L at Settlement

Source: computation by the author.

The table below summarizes the key characteristics of VIX futures and options contracts discussed above.

Figure 5. Key Characteristics of VIX Futures and VIX Options

Source: computation by the author.

Market Participants

VIX futures and options involve several types of market participants with different objectives.

Institutional investors and asset managers may use VIX derivatives to hedge portfolios against changes in market volatility.

Hedge funds and proprietary trading firms may use VIX futures and options to speculate on future movements in volatility or implement volatility-related strategies.

Market makers provide liquidity by quoting prices at which they are willing to buy and sell VIX derivatives, facilitating trading in these instruments.

Individual investors can also access VIX futures and options through eligible brokers, subject to account approval, margin requirements and regional availability. They may use these instruments to speculate on changes in expected market volatility or to hedge equity-market exposure. However, access to these products does not imply that they are suitable for all investors, given their leverage, complexity and potential for substantial losses.

Other market participants may combine VIX futures, VIX options, SPX options, and other volatility-related instruments to construct different trading and hedging strategies.

Market Microstructure

Prices in financial markets emerge from the interaction between buyers and sellers. In the conventional microeconomic framework, the demand curve represents the quantities buyers are willing to purchase at different prices, while the supply curve represents the quantities sellers are willing to offer. Their intersection determines the market equilibrium, where quantity demanded equals quantity supplied. In practice, trading mechanisms can take different forms like an electronic order book or block trading arrangements for large transactions. Demand and supply are influenced by market participants’ expectations of future prices (volatility for VIX futures and option markets).

VIX futures and options are traded on electronic markets where buyers and sellers submit orders. Market participants can use different order types, particularly market orders and limit orders. In the VIX futures market, limit orders are recorded in an electronic order book, which displays outstanding buy and sell orders along with their prices and quantities. Incoming orders are matched against existing orders according to the exchange’s matching rules. The highest price buyers are willing to pay is the best bid, while the lowest price sellers are willing to accept is the best ask. The difference between these two prices is the bid-ask spread, which represents an important component of transaction costs. Market depth refers to the quantities available for trading at different price levels. Together, the bid-ask spread and market depth provide useful indicators of market liquidity and the potential price impact of executing an order.

VIX options also trade electronically. Market makers provide quotes and may supply liquidity by taking the opposite side of a trade. Multi-leg strategies, such as vertical spreads, present an additional execution challenge because the trader needs to buy and sell options at a suitable combined price. Cboe’s Complex Order Book (COB) allows these orders to be matched as a combined position, while its Complex Order Auction (COA) gives eligible orders an opportunity for price improvement.

For large VIX futures positions, block trades offer an alternative to execution through the displayed order book. Eligible counterparties can negotiate a transaction privately and report it to the exchange under the applicable rules. This can help participants execute large orders without relying entirely on the liquidity available in the electronic market.

Trading prices also differ from the value used to settle contracts at expiration. VIX futures and options are cash-settled using the Special Opening Quotation (SOQ) of the VIX Index on the final settlement date. Unlike the regular VIX calculation, which uses bid-ask midpoints from SPX options across two expirations, the SOQ uses opening trade prices from a single SPX option expiration selected to provide the required 30-day measure. If a selected option has no opening trade price, its bid-ask midpoint is used instead. The resulting SOQ determines the final settlement value of the expiring derivatives.

Price Formation in VIX Futures

A VIX futures price reflects the price at which market participants are willing to trade exposure to the VIX Index at a specified future expiration. It is not necessarily an unbiased forecast of the VIX level at that date. The VIX measures expected 30-day volatility derived from S&P 500 index options, whereas a VIX futures contract provides exposure to the value of this index at its own expiration. Futures prices therefore reflect expectations about future volatility conditions, risk premia and the balance of buying and selling pressure.

The relationship between futures prices across expiration dates is described by the VIX futures term structure. In contango, longer-dated contracts trade above nearer-dated contracts; in backwardation, nearer-dated contracts trade above longer-dated ones. Contango is frequently observed in relatively calm markets, while backwardation can emerge during periods of market stress, when demand for near-term volatility exposure increases sharply. However, an upward-sloping futures curve does not necessarily imply that the market expects the spot VIX to rise. The curve also reflects risk premia and supply and demand for volatility exposure across different maturities.

Trading activity can influence both the overall level of futures prices and the relative pricing of contracts with different expiration dates. Mixon and Onur (2019) examine how non-dealer trading activity relates to VIX futures pricing. Their findings highlight the role of aggregate net demand in the overall level of futures prices and the relationship between trading activity in individual contracts and the shape of the futures curve. This is relevant to the roll effect, which describes the contribution to returns from replacing a futures contract approaching expiration with a later-dated contract. In contango, maintaining a long position typically involves selling a nearer-dated contract and buying a more expensive deferred contract. This can create a headwind for returns, although the realised outcome depends on subsequent futures-price movements and the investor’s rolling strategy.

Price Formation in VIX Options

The price of a VIX option depends on the possible VIX settlement values at expiration relative to the option’s strike price. A call option has a positive payoff when the final settlement value exceeds the strike, while a put option has a positive payoff when the settlement value falls below the strike. The option premium reflects the potential payoffs, the time remaining until expiration, interest rates and the market’s assessment of the distribution of possible settlement values.

VIX futures play an important role in standard VIX option valuation. Because the VIX Index itself is not directly tradable, VIX futures provide market-based information about volatility priced for specific future dates. The relevant futures price is an important input in option valuation, but it does not determine the option premium on its own. The strike price, time to expiration, interest rates and the distribution of possible settlement values also influence the premium.

Option prices vary across strike prices and expiration dates. Implied volatility, calculated from observed option premiums using an option-pricing model, provides a common measure for comparing these prices. The implied volatility skew describes variation in implied volatility across strike prices, while the term structure describes variation across option maturities. These patterns show how the market prices options with different payoff profiles and maturities.

Tong, Huang and Wang (2022) compare VIX option pricing models that use the VIX Index and VIX futures as state variables, meaning market variables used as model inputs. Their findings indicate that models incorporating VIX futures prices perform better empirically than models relying on the VIX Index alone and several alternative market variables. This highlights the importance of futures-market information in modelling and valuing standard VIX options.

Trading Activity and Market Liquidity

In this section, we examine the trading activity and relative scale of the S&P 500 and VIX derivatives markets by comparing the average daily volumes of their futures and options contracts. The data are sourced from CME Group and Cboe.

Figure 6 below compares the average daily trading volume of E-mini S&P 500 futures and VIX futures in March 2026. E-mini S&P 500 futures (ES) are standardized futures contracts on the S&P 500 Index, with a contract multiplier of $50 per index point. ES recorded an average daily volume of 2.2 million contracts (CME Group), compared with 0.318 million contracts for VIX futures (Cboe).

Figure 6. Average daily number of contracts of S&P 500 and VIX futures (March 2026)

Source: computation by the author (CME Group and Cboe data).

Figure 7 below compares the average daily number of contracts (a proxy for trading activity) of S&P 500 Index (SPX) options and VIX options during Q1 2026. SPX options recorded an average daily volume of 4.855 million contracts (Cboe), while VIX options recorded 0.994 million contracts (Cboe). The figures represent average daily volume across January, February, and March 2026.

Figure 7. Average daily number of contracts of S&P 500 and VIX options (Q1 2026)

Source: computation by the author (Cboe data).

The figures provide an indication of the relative trading activity in S&P 500 and VIX derivatives, but they should be interpreted with caution. Average daily trading volume, measured in contracts, differs from the monetary or notional value of transactions. A higher contract volume does not necessarily imply a proportionally larger market in terms of economic exposure or liquidity. Furthermore, trading volume alone does not fully capture liquidity, which also depends on bid-ask spreads, market depth (number of contracts available in the market at a given price), and execution costs (bid-ask spreads, commissions, and the price impact of a trade). Therefore, the figures are best interpreted as a comparison of trading activity rather than a direct measure of market size or relative liquidity.

Why should I be interested in this post?

VIX futures and options are important instruments for market participants to take positions on future volatility, hedge portfolios against changes in volatility, and manage exposure to market risk. Understanding these instruments therefore provides a practical perspective on the management of volatility risk in financial markets.

Useful resources

Business resources

CME Group (2026) March 2026 Trading Volume.

Cboe Global Markets (2026) March 2026 Monthly Volume Statistics.

Cboe Global Markets (2026) VIX Futures.

Cboe Global Markets (2026) VIX Options.

Academic references

Black F. (1976) The pricing of commodity contracts, Journal of Financial Economics, 3, 167-179.

Carr P. and L. Wu (2006) A tale of two indices. The Journal of Derivatives, 13(3), 13-29.

Mixon S. and Onur E. (2019) Derivatives Pricing When Supply and Demand Matter: Evidence from the Term Structure of VIX Futures. The Journal of Futures Markets, 39(9), 1035-1055.

Tong C., Huang Z. and Wang T. (2022) Do VIX Futures Contribute to the Valuation of VIX Options? The Journal of Futures Markets, 42(9), 1644-1664.

Whaley R. E. (2009) Understanding the VIX. The Journal of Portfolio Management, 35(3), 98-105.

Zhang J. E. and Y. Zhu (2006) VIX futures. Journal of Futures Markets, 26(6), 521-531.

About the author

The article was written in October 2026 by Saral BINDAL (Indian Institute of Technology Kharagpur, Metallurgical and Materials Engineering, 2024-2028 & Research assistant at ESSEC Business School). His interests include tracking geopolitical developments and analyzing their direct impact on macroeconomic factors such as inflation, trade balances, and currency volatility, with a focus on using data to quantify these global economic ripple effects.

Discover all posts by Saral BINDAL

Financial Consolidation Step by Step: What I Learned as an Intern at Evernex

October 15, 2026October 15, 2026 by Mathis HOUROU

In this article, Mathis HOUROU (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2022-2026) shares his experience as a consolidation intern at Evernex. He explains how a group builds its consolidated accounts, step by step.

About the company

Evernex is a global provider of third-party maintenance (TPM) for data center equipment. It maintains and repairs servers, storage and network devices, but it is not their manufacturer. Clients keep their hardware running for longer and pay less than with the support of the manufacturer. The company is based in Paris.

Logo of Evernex.

Source: Evernex.

Evernex also manages spare parts, recycles equipment, erases data in a secure way and moves data centers. It offers rental and financing of IT hardware as well. In November 2024, 3i described a presence in more than 165 countries and more than 500,000 IT systems maintained.

The group has grown through acquisitions. The investment company 3i entered its capital in October 2019. Since then, Evernex has bought companies such as Storex in South Africa (2020), XSi in the United States (2022) and Ultra Support in the United Kingdom (2024). Ultra Support was the seventh acquisition since the investment of 3i. From an accounting point of view, each new company has its own ledger, its own currency and its own accounting habits. This is why consolidation matters a lot for this group.

Evernex IT maintenance.

Source: Evernex.

My internship

I work in the consolidation team in Paris. My internship lasts six months and started in September 2026. The team produces the accounts of the group every month. The work starts with the closing of each subsidiary and ends with the figures shown to top management.

I knew consolidation from my courses at ESSEC Business School. In practice, it is different. The numbers do not come from one ledger. They come from many entities. My role is to check that they fit together.

My missions

My main mission is to follow the monthly closing of the subsidiaries. They are in several countries and on several continents. Each month, I check that their accounts are complete and consistent before they enter the group figures. It is the first control of the team.

I also reconcile intercompany balances. This means checking that two entities of the group record the same transaction in the same way. For example, one entity books an invoice in December and the other one books it in January. This creates a difference of a few thousand euros. I look for the cause and ask the local accountant to correct it.

I follow indicators such as DSO (Days Sales Outstanding), DPO (Days Payable Outstanding) and bad debt. Bad debt is the part of the client invoices that may never be paid. I take part in the IFRS restatements used to calculate the EBITDA of the group, and I review the P&L of the entities. I also prepare slides for the CFO and the COO, with a focus on the expenses part of the EBITDA.

Required skills and knowledge

You need solid accounting bases, especially debit and credit, and a good knowledge of IFRS. I use Excel every day to check and compare large amounts of data. Rigor matters, because a small error in one entity can change the group figures.

Communication matters too. I often write to accountants in other countries and time zones. My requests must be clear and polite, so that they can explain or correct a number quickly.

What I learned

The quality of the group accounts starts with the quality of each local closing. I also learned to look at a number from the point of view of the group. A small difference in one entity can become a real issue when you compare it with the other side of the transaction.

Financial concepts related to my internship

The main concept of this post is financial consolidation. It is the core of my daily work. I define it first. Then I follow the process step by step and explain the purpose of each step.

What is financial consolidation?

Financial consolidation combines the financial statements of several legal entities into one set of accounts. The group is treated as if it were a single company. According to the IFRS Foundation, IFRS 10 defines consolidated financial statements as the statements of a parent and its subsidiaries, presented as those of a single economic entity.

Why do we need it? The performance of a group is not the sum of the accounts of its entities. Take an example. Entity A invoices EUR 200,000 of maintenance services to Entity B. Both belong to the same group. A records EUR 200,000 of revenue. B records EUR 200,000 of costs. For the group, nothing happened with the outside world. The group only paid itself. Without correction, the revenue and the costs of the group are both overstated by EUR 200,000.

Two other problems must be solved. The entities can work in different currencies. They can also use different accounting rules. The consolidation process solves these three problems one after the other. It starts with one question: which entities are in the group? This is the consolidation scope. When Evernex acquired Ultra Support in November 2024, a new entity entered the scope with all its accounts.

Example of a financial consolidation process flow.

Source: Mastek.

The steps below follow my own experience. They can differ a little from this example, because each group organizes its process in its own way.

Step 1: Local closing in each entity

Each subsidiary closes its own books first. The local team records its last entries, reconciles its bank and client accounts and produces a trial balance.

Purpose: the group accounts can only be as reliable as the local ones. An error that stays in this step will travel through the whole process.

Step 2: Reporting package and validation

Each entity sends its figures to the consolidation team in a standard reporting package. The local accounts are mapped to the chart of accounts of the group, so that a line such as revenue means the same thing everywhere. Checks flag missing data, accounts that do not balance and unusual changes from one month to the next.

Purpose: get comparable data and find the obvious errors early, while the local accountant can still fix them quickly.

Step 3: IFRS restatements

Local accounts often follow local rules. The group reports under IFRS. So the consolidation team adjusts the figures to the same standard. Leases are a good example. Under IFRS 16, a lessee records most leases on its balance sheet, as a right of use asset and a lease liability. Imagine that a subsidiary books a rent of EUR 120,000 per year as a simple expense in its local accounts. In the group accounts, this rent is replaced by a depreciation and an interest expense. The EBITDA of the group is then EUR 120,000 higher, because the rent is no longer in the operating expenses.

Purpose: measure all entities with the same rules, so that they can be added together and compared.

Step 4: Intercompany reconciliation

Before any elimination, the team checks that both sides of each internal transaction match. If entity A shows a receivable of EUR 200,000 on entity B, B must show a payable of EUR 200,000 to A. If B shows only EUR 195,000, the gap of EUR 5,000 has to be explained. The usual causes are an invoice booked in different months, a different exchange rate or a simple booking error.

Purpose: make the next steps possible, because an elimination works only if both sides agree. Handling intercompany activity is described as one of the most time consuming parts of the close (Workday). It is also where I spend a large part of my time as an intern.

Step 5: Currency translation

Subsidiaries report in their local currency. The group reports in one currency. Under IAS 21, assets and liabilities are translated at the closing rate. Income and expenses are translated at the rate of the transaction date, usually approximated by the average rate of the period. Equity items stay at historical rates.

Take an example. A UK subsidiary has assets of GBP 1,000, liabilities of GBP 400 and equity of GBP 600. This equity is made of a share capital of GBP 500 and a profit of GBP 100 for the year. The closing rate is 1.20, the average rate is 1.15 and the historical rate is 1.10 euros per pound. Assets become EUR 1,200 and liabilities EUR 480, so the net assets are EUR 720. On the equity side, the share capital becomes EUR 550 and the profit EUR 115, which makes EUR 665. The balance sheet does not balance by itself. The missing EUR 55 is recorded in equity, in a line called the cumulative translation adjustment (CTA).

Steps to calculate the cumulative translation adjustment (CTA).

Source: HighRadius.

Purpose: express everything in one currency, and keep the effect of exchange rates visible in equity instead of hiding it in the result of the year.

Step 6: Eliminations

Now the group removes everything that exists only inside the group. There are three main eliminations:

Internal revenue and costs: in our example, once the gap is corrected, the EUR 200,000 of revenue of A is cancelled against the EUR 200,000 of costs of B.
Internal receivables and payables: the receivable of A and the payable of B are removed from the balance sheet.
Investments and equity: the investment of the parent in a subsidiary is cancelled against the equity of this subsidiary. If the parent owns less than 100%, the share of the other shareholders is shown separately as non-controlling interests.

Purpose: avoid double counting, so that the group figures show only the transactions with the outside world.

Step 7: Review, analysis and reporting

The last step is the review of the consolidated balance sheet, income statement and cash flow statement. The team compares them with the budget and with the previous month. It explains the main changes and checks key indicators such as EBITDA or DSO. The figures are then presented to the management.

Purpose: make sure that every number can be explained. Consolidated accounts are a legal obligation. They are also the tool that managers use to take decisions.

Why should I be interested in this post?

If you want to work in asset management, equity research, audit or corporate finance, you will read consolidated accounts all the time. Knowing how they are built helps you understand what the numbers say. It explains, for example, why a strong dollar can change the reported results of a group, or why an acquisition makes the revenue jump.

This knowledge also helps if you look for an internship in consolidation, controlling or FP&A. These teams sit at the center of the information of the group and work with all the other departments. My advice to understand consolidation: follow one transaction from the local ledger to the group accounts.

Useful resources

Evernex

Evernex Evernex website

3i 3i-backed Evernex continues its international growth with acquisition of Ultra Support

Evernex Evernex completes its first acquisition since 3i’s investment and expands its presence in Eastern and Southern Africa

Evernex IT Maintenance: Complete guide for business IT teams

IFRS

IFRS Foundation IFRS 10 Consolidated Financial Statements

IFRS Foundation IAS 21 The Effects of Changes in Foreign Exchange Rates

IFRS Foundation IFRS 16 Leases

Business resources

Mastek Oracle Financial Consolidation & Close Service (FCCS) Guide

HighRadius A Guide to Cumulative Translation Adjustment (CTA) with Examples

Workday Financial consolidation and close process guide

About the author

The article was written in October 2026 by Mathis HOUROU (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2022-2026).

▶ Discover all posts by Mathis HOUROU.

My experience as an M&A Analyst intern at Dilcap Invest

October 5, 2026October 4, 2026 by William LONGIN

In this article, William LONGIN (MSc Financial Mathematics at Queen Mary University of London, 2026-2027) shares his experience as an M&A Analyst intern at Dilcap Invest, an independent M&A advisory boutique.

Dilcap Invest

Dilcap Invest is an independent M&A Advisory Boutique founded in 2025 by Christophe AUGER. The firm is headquartered in the historic Hôtel de la Marine, overlooking Place de la Concorde in Paris. The firm focuses on European mid-market companies, primarily on transactions with values below €100 million, with particular expertise in proptech, technology, financial services, and services for senior citizens.

The firm combines three complementary activities: strategic and governance advisory services; corporate finance advisory on acquisitions, disposals, fundraising, and restructuring transactions and direct equity investments in growing companies. Its boutique structure allows for direct senior involvement and close interaction with clients throughout the transaction process.

Logo of Dilcap Invest

Source: Dilcap Invest.

Christophe AUGER studied at Université Paris Dauphine and completed the financial analyst program of the Société Française des Analystes Financiers (SFAF). Building on experience in financial analysis, investment banking, corporate M&A, and entrepreneurship, he developed expertise in strategic and financial decision-making. Dilcap Invest’s boutique model provides clients with direct access to senior professionals and enables the firm to dedicate significant time and resources to a limited number of mandates.

My missions

During my internship, I worked on several dimensions of the M&A advisory process. My assignments combined financial analysis, valuation, market research, transaction screening, and business development. This gave me a practical view of how financial analysis is transformed into strategic recommendations and, eventually, into transaction opportunities.

Financial modeling and analysis

As an M&A analyst covering mid-cap companies, I built and maintained fully integrated three-statement financial models to analyze companies’ historical performance and forecast their future financial position. These models linked the income statement, balance sheet, and cash flow statement and incorporated operating assumptions, revenue projections, working capital, capital expenditure and depreciation, and debt schedules.

The models were typically structured into separate tabs for historical financial statements, assumptions, revenue build-up, operating projections, working capital, depreciation and capital expenditure, debt schedules, supporting calculations, valuation outputs, and sensitivity analyses. For example, during my internship I learned the importance of formatting with color coding (blue for inputs, black for formulas, green for links, red for checks) and learned many Excel short cuts allowing me to bypass the usage of a mouse.

Market research and transaction screening

During my internship I was brought to perform some market research on housing promoters based in various cities in France. As part of a mission that could potentially have a lot of upside and take advantage of a fragmented market. When it comes to complex capitalistic moves, Dilcap has a real expertise in understanding financial health of companies and competitive dynamics between actors in a market. Then the company contacted the presidents of each firm to discuss the projects. It showed the link between going from financials then narrowing it down to pre-deposition of CEO’s to pass the torch or be open to merging opportunities.

Comparable companies and transaction analysis

I also worked on valuation analysis using comparable companies and precedent transactions. I regularly used CFNews to monitor M&A activity and identify transactions relevant to the companies and sectors being analyzed. The platform enabled me to identify comparable transactions, analyze observed valuation multiples (EV/EBITDA and EV/Revenue), and benchmark them against prevailing market conditions to support assumptions used in valuation exercises. I also leveraged the database to analyze historical transactions, identify the most active acquirers, understand sector-specific dynamics, and track trends across the mid-cap market. In addition, I prepared weekly market briefings for the team, summarizing key announced transactions, market developments, and strategic insights that could impact ongoing mandates or business development opportunities.

The Répertoire.finance Project

I contributed to the development of repertoire.finance, an online platform connecting business owners and executives with independent M&A advisory firms. By working on repertoire.finance, I helped identify and onboard specialized advisory boutiques, structured their profiles by documenting sector expertise, transaction size, geographic coverage, and mandate focus, and contributed to improving the quality and consistency of the information available on the platform. The objective was to simplify the process of selecting an M&A advisor by providing business leaders with a comprehensive and transparent overview of the independent advisory landscape. This project strengthened my understanding of the French M&A advisory market, its key participants, and their respective positioning, while giving me valuable insight into the criteria companies use when selecting advisors for strategic transactions.

Concepts to master

EBITDA and Adjusted EBITDA

EBITDA (Earnings Before Interest, Taxes, Depreciation, and Amortization) is one of the most widely used financial metrics in mergers and acquisitions because it measures a company’s operating profitability independently of its financing structure, tax environment, and non-cash accounting charges. By excluding interest, taxes, depreciation, and amortization, EBITDA provides a standardized measure of operating performance that allows investors and advisors to compare companies across industries and capital structures. It also serves as the primary earnings metric used in valuation, with Enterprise Value typically expressed as a multiple of EBITDA (EV/EBITDA).

Adjusted EBITDA is a normalized version of EBITDA that excludes non-recurring, exceptional, or non-operating items that are not considered representative of the company’s ongoing performance. Typical adjustments include one-time legal expenses, restructuring costs, acquisition-related fees, litigation settlements, gains or losses from asset disposals, and, in founder-owned businesses, excess owner compensation or personal expenses. The objective is to present a sustainable earnings figure that more accurately reflects the company’s future operating capacity.

Adjusted EBITDA plays a critical role in M&A negotiations because it directly impacts valuation. Since enterprise value is commonly determined by applying an EV/EBITDA multiple, even small adjustments to EBITDA can significantly affect the purchase price. Consequently, sell-side advisors seek to identify and justify legitimate normalization adjustments that maximize value, while buy-side advisors carefully scrutinize each adjustment during financial due diligence to ensure it accurately reflects the company’s recurring operating performance.

Quality of Earnings (QoE)

Quality of Earnings (QoE) is a financial due diligence analysis that assesses whether a company’s reported earnings accurately reflect its sustainable and recurring operating performance. Rather than focusing solely on accounting profitability, a QoE review examines the underlying drivers of earnings to determine whether EBITDA is supported by normal business operations or distorted by one-time, non-recurring, or non-operating items. The objective is to provide potential buyers with a reliable measure of normalized earnings that can serve as the basis for valuation and purchase price negotiations.

A Quality of Earnings analysis typically includes a detailed review of revenue recognition policies, customer concentration, gross margins, operating expenses, working capital trends, seasonality, accounting policies, and cash conversion. It also identifies adjustments to EBITDA, such as restructuring costs, litigation expenses, acquisition-related fees, non-recurring gains or losses, and other exceptional items that should be excluded from normalized earnings. In addition, a QoE report evaluates whether historical financial performance is sustainable and highlights any financial risks or accounting issues that could impact future profitability.

In M&A transactions, the Quality of Earnings report is one of the most important components of financial due diligence. Buyers rely on it to validate the seller’s reported Adjusted EBITDA, assess the sustainability of future cash flows, and identify potential risks before completing an acquisition. Because enterprise value is typically based on Adjusted EBITDA, the conclusions of a QoE analysis can have a direct impact on valuation, purchase price negotiations, and transaction structure.

Information Memorandum

An Information Memorandum (IM), also referred to as a Confidential Information Memorandum (CIM), is a comprehensive marketing document prepared by the sell-side M&A advisor to present a company to potential buyers after they have signed a Non-Disclosure Agreement (NDA). Its purpose is to provide prospective acquirers with a detailed understanding of the business, its financial performance, competitive positioning, growth opportunities, and investment rationale, enabling them to evaluate the acquisition opportunity and prepare a non-binding offer.

An Information Memorandum typically includes an overview of the company and its history, descriptions of its products and services, market and industry analysis, customer and supplier relationships, management team, business strategy, operational model, historical financial statements, key performance indicators (KPIs), and management’s financial forecasts. It also highlights the company’s competitive advantages, growth drivers, and value creation opportunities while addressing key risks and market dynamics. Financial information is often presented with normalized or Adjusted EBITDA figures to facilitate valuation using comparable transaction and trading multiples.

The Information Memorandum is a central document in the M&A sale process and serves as the primary source of information for interested buyers during the initial stages of due diligence. It is designed to generate competitive interest, support management presentations, and provide sufficient information for bidders to submit indicative offers before entering a more detailed due diligence phase. A well-prepared IM balances transparency with commercial positioning, presenting the business accurately while emphasizing the key strengths and investment merits that underpin its valuation.

Why should you be interested in this post?

Whether you are preparing for investment banking interviews, starting an internship in M&A, or simply looking to strengthen your understanding of corporate finance, this guide brings together some of the most important concepts used in real transactions. It provides clear, practical explanations of key topics such as EBITDA, Adjusted EBITDA, Quality of Earnings (QoE), Information Memorandums, valuation multiples, and financial modeling, making complex M&A concepts accessible and directly applicable in a professional setting.

Word of conclusion

My internship as an M&A Analyst at Dilcap Invest provided me with invaluable exposure to the full lifecycle of middle-market M&A transactions. Through financial modeling, valuation analyses, market research, transaction screening, and the preparation of marketing materials, I developed both the technical expertise and commercial awareness required in investment banking. Working closely with experienced professionals allowed me to strengthen my analytical skills, attention to detail, and ability to communicate complex financial information in a clear and structured manner. Beyond the technical aspects, this experience deepened my understanding of the strategic considerations that drive acquisitions and disposals, while reinforcing my interest in corporate finance and mergers and acquisitions. It confirmed my ambition to pursue a career in investment banking, where I can continue to develop my financial and advisory skills while contributing to high-impact transactions.

Useful resources

Dilcap Invest

Répertoire.finance

Interview with Christophe Auger, founder of Répertoire.finance

About the author

The article was written in October 2026 by William LONGIN (MSc Financial Mathematics at Queen Mary University of London, 2026-2027).

▶ Discover all posts by William LONGIN.

How to download financial data with R

October 10, 2026September 27, 2026 by Hadrien PUCHE

Any financial analysis starts with data. Whether you want to analyze a stock, build a portfolio, measure risk, create a valuation model or develop trading strategies, the first step is always the same: obtaining financial data.

You could download data manually from websites such as Yahoo! Finance or Investing.com, but this quickly becomes tedious and time-consuming. It also limits the amount of data you can work with.

R allows us to automate this process and retrieve large amounts of financial information in just a few lines of code.

In this article, Hadrien PUCHE (ESSEC Business School, Grande École Program, Master in Management, 2023-2027) will help you to:

Download historical stock prices and market indices with R
Explore, clean, and visualize xts time-series data
Compute basic statistics and historical distributions
Compare multiple securities
Build the foundation needed for more advanced financial analysis

But first, what financial data can we actually download?

Financial professionals use many different categories of data across individual assets as well as portfolios and funds.

Market data (Easily downloadable for free via Yahoo! Finance)

Individual asset prices (e.g., individual stocks, corporate bonds)
Portfolios and funds (e.g., ETFs, mutual funds)
Currency exchange rates (e.g., EUR/USD)
Market indices (e.g., S&P 500)

Macroeconomic data (Available via the St. Louis Fed – FRED)

Inflation and Consumer Price Index (CPI)
Interest rates and bond yields
GDP growth and unemployment

Not all data sources are freely available. Many professional investors rely on paid platforms such as Bloomberg or FactSet to access fundamental accounting data (revenue, cash flows) and alternative data (satellite imagery, sentiment). Fortunately, market prices and macroeconomic indicators can easily be accessed for free using R for research and learning purposes.

While you can download macroeconomic data using specialized packages like fredr, we will keep things simple in this article and focus purely on extracting and modeling market prices using the open-source quantmod package.

A step-by-step guide

Follow the next steps to download your first financial dataset with R.

Step 1: Instal the required packages

If you have not yet installed R, refer to the setup guide published earlier in this series to configure your execution environment (RStudio).

Once your environment is ready, install the required packages by running this in your console (you only need to do this once):

install.packages(c("quantmod", "PerformanceAnalytics"))

Here is what these packages do:

quantmod: Short for Quantitative Financial Modelling Framework, it is a widely used R package for downloading and analyzing financial market data, including data from Yahoo! Finance.
xts: Short for eXtensible Time Series, this package is automatically installed with quantmod. It provides data structures specifically designed for time-indexed data.
PerformanceAnalytics: A package of econometric functions used to calculate returns and risk metrics.

Step 2: Import your R packages

Most financial analysis scripts begin by loading the packages required for the analysis using the library() function.

Create a new R script file. You can also save it wherever you want. Paste the following script and run it (as a reminder, you need to select the code you want with your mouse before running it):

library(quantmod)
library(PerformanceAnalytics)

Step 3: Download your first data

To download financial data, we use ticker symbols, which identify securities or market instruments within a given exchange or data provider. For example, AAPL represents Apple.

We will use the getSymbols() function. By passing arguments to the function, we can customize the output. Setting auto.assign = FALSE assigns the dataset directly to a variable that we can name aapl_data.

Let us download daily data for Apple’s stock price over the last five years (from 01/01/2019 to 31/12/2024 at that time)t.

# Download historical Apple stock data
aapl_data <- getSymbols("AAPL", src = "yahoo", from = "2019-01-01", to = "2024-01-01", auto.assign = FALSE)
 
# Display the first 5 rows
head(aapl_data, 5)

You will obtain this xts table with the following columns:

Open: opening price at the beginning of the trading day
High: highest price during the trading day
Low: lowest price during the trading day
Close: closing price at the end of the trading day
Volume: transaction volume during the trading day
Adjusted: closing price adjusted for stock splits and dividends

A screenshot from RStudio showing the output table of the getSymbols query

To keep things simple for this guide, we will focus strictly on the raw Close price. quantmod provides a convenient helper function called Cl() that instantly extracts just the closing price column from the dataset.

# Extract only the closing price
aapl_close <- Cl(aapl_data)
head(aapl_close, 3)

Add this code to your script, then highlight it with your mouse, and press run. Your RStudio should now display this:

A screenshot from RStudio showing the new output

Step 4: Use more precise queries for historical context

As financial analysts, we routinely extract specific timeframes to understand how assets behave under macroeconomic stress. Because our data is stored as an xts object, R makes it incredibly easy to slice time-series data using date ranges.

For example, examining the COVID-19 market shock in early 2020 provides a useful illustration of extreme market volatility. Let’s isolate Apple’s stock specifically during the COVID-19 market shock and initial recovery (January to June 2020):

# Isolate the COVID-19 crash using xts date subsetting (YYYY-MM-DD/YYYY-MM-DD)
covid_crash <- aapl_close["2020-01-01/2020-06-30"]
 
# Plot the isolated data
plot(covid_crash, main = "AAPL Stock Price - COVID-19 Crash & Recovery", col = "red", lwd = 2)

The output of the previous code cell showing the COVID crash

Step 5: Download the data for multiple stocks at the same time

Downloading multiple stocks is necessary for financial analysis that often requires comparing securities, analyzing sectors, or building a portfolio. Instead of issuing separate requests and risking misaligned dates, we can fetch all tickers at once.

Let’s download the data for six of the largest US banks: JPMorgan Chase, Bank of America, Wells Fargo, Citigroup, Goldman Sachs, and Morgan Stanley. Analyzing this sector is a classic way to measure the impact of interest rates on the broader economy.

# Define the major US bank tickers
bank_tickers <- c("JPM", "BAC", "WFC", "C", "GS", "MS")
 
# Download data into the global environment
getSymbols(bank_tickers, src = "yahoo", from = "2019-01-01", to = "2024-01-01")
 
# Extract only the closing prices and merge them into a single matrix
bank_prices <- merge(Cl(JPM), Cl(BAC), Cl(WFC), Cl(C), Cl(GS), Cl(MS))
 
head(bank_prices, 3)

The result is an xts object in which each column represents a stock and each row corresponds to a trading date.

Screenshot of the output of the previous cell showing the US Banks matrix

This table format is ideal for portfolio analysis and benchmarking. To save it for external use, you can export it as a CSV file:

# Save the data frame as a CSV file
write.csv(as.data.frame(bank_prices), file = "us_banks_data.csv")

Now that you have successfully downloaded your financial data, let’s see how you can clean it and then use it.

Inspecting and cleaning the dataset

Financial datasets may contain missing values (NA) for various reasons, including trading suspensions, differences in trading calendars, listing dates, or data-provider issues. Missing observations should be identified before computing returns or risk measures, as they may affect subsequent calculations. For this introductory example, we simply remove rows containing missing values using na.omit(). In applied financial analysis, however, the appropriate treatment depends on the source of the missing data and the objective of the analysis.

In R, we can easily remove any rows containing missing data using the na.omit() function.

# Check for missing values (returns the total count)
sum(is.na(aapl_close))

# Clean missing values by dropping rows with NAs
aapl_close <- na.omit(aapl_close)

# View the last few rows of the cleaned data
tail(aapl_close, 5)

A screenshot from RStudio showing the output of the tail function

Vizualizing your data

Let’s create our first chart to visualize the evolution of Apple’s stock price using the chartSeries() function, which is built specifically for financial time series.

chartSeries(aapl_close, 
            name = "Apple Stock Price", 
            theme = chartTheme("white"), 
            TA = NULL) # TA = NULL removes technical indicators for a clean chart

A screenshot of RStudio with the stock price visualization chart output

We have now:

Downloaded market data from Yahoo! Finance
Extracted the closing price and cleaned the data
Created a time-series plot to visualize stock prices

These core steps form the basis of empirical financial research and quantitative models.

Computing basic statistics and historical distributions

To evaluate stock performance and risk, we compute basic descriptive statistics. First, we calculate daily returns using the Return.calculate() function from the PerformanceAnalytics package.

# Calculate daily percentage returns (and remove the first NA row)
aapl_returns <- Return.calculate(aapl_close)
aapl_returns <- na.omit(aapl_returns)
 
# Compute summary statistics
mean_return <- mean(aapl_returns)
volatility <- sd(aapl_returns)
skew <- skewness(aapl_returns)
kurt <- kurtosis(aapl_returns)

print(paste("Mean Daily Return:", round(mean_return, 5)))
print(paste("Daily Volatility (Std Dev):", round(volatility, 4)))

Plotting historical distributions

Histograms display the frequency distribution of daily returns, helping us inspect distribution symmetry and tail risks.

# Return distribution histogram
hist(aapl_returns, breaks = 50, col = "salmon", main = "Historical Daily Return Distribution", xlab = "Daily Return")

The output of the previous cell – distribution histogram

Normalizing stock prices and computing returns

All stocks have different nominal prices. If Tesla trades at $350 and Nvidia at $220, it does not mean that Tesla performed better. To establish an accurate comparison, we execute two fundamental computations:

Price normalization: We normalize all historical time series to a base index of 100, ensuring a standardized starting point.
Return calculation: We compute periodic returns to measure performance independently of the nominal price level.

# Clean any missing data
bank_prices <- na.omit(bank_prices)

# Harmonize prices to Base 100 (Divide every row by the first row, multiply by 100)
normalized <- sweep(bank_prices, MARGIN = 2, STATS = as.numeric(bank_prices[1,]), FUN = "/") * 100
 
# Plot the performance comparison
plot(normalized, legend.loc = "topleft", main = "Performance Comparison (Base = 100)", ylab = "Growth of $100")

The output of the previous cell showing normalized prices

We can also compute daily returns across all stocks in one line:

bank_returns <- na.omit(Return.calculate(bank_prices))
head(bank_returns, 3)

This is a standard technique used by portfolio managers and equity analysts to compare growth trajectories.

Common pitfalls

When working with market data in R, beginners often run into the same issues:

Using the wrong ticker symbol
Comparing stocks without normalizing prices (Base 100)
Forgetting that markets are closed on weekends and holidays
Failing to clean and handle missing values (NA) using na.omit()
Using raw closing prices when adjusted prices are required: for long-term performance analysis, adjusted prices are generally preferable because they account for stock splits and dividends.

Overall, don’t forget to always inspect and clean your data before starting your analysis.

Exercises

Exercise 1: Basic data retrieval and price visualization (RACE)

Ferrari N.V. (RACE) presents an interesting case study in market dynamics: it is a car manufacturer that acts as a high-end luxury franchise. Its deliberate production scarcity, multi-year order backlogs, and immense pricing power decouple it from typical automotive boom-and-bust cycles.

Using the ticker symbol RACE, download the last five years of daily market data.

Your tasks:

Use the appropriate R functions to display the first 5 rows and the last 5 rows of the dataset to verify data integrity.
Extract the closing price and generate a line chart plotting the price over the entire 5-year period. Observe how its price trajectory reflects Ferrari’s distinctive positioning at the intersection of the automotive and luxury industries.

Exercise 2: time-series extraction and volume analysis on Tesla (TSLA)

Using the ticker symbol TSLA, extract the market data for the precise calendar period from January 1, 2022, to December 31, 2024 (using the from and to parameters).

Your tasks:

Identify the peak (highest closing price) and the trough (lowest closing price) over this period using the max() and min() functions.
Extract the Volume column (using Vo()) and calculate the average daily trading volume, a fundamental metric used by analysts to assess market liquidity.

Exercise 3: Comparative performance and risk profiling on Chinese tech companies

Chinese technology stocks often experience unique market cycles driven by distinct domestic regulatory environments and macroeconomic factors. Using the last five years of daily market data, compare the performance and risk characteristics of the following three US-listed ADRs:

Alibaba (BABA)
Baidu (BIDU)
PDD Holdings (PDD)

Questions:

Which stock achieved the highest total cumulative return?
Which stock was most volatile (highest standard deviation of daily returns)?
Which one offered the best risk-adjusted profile over the period? Use the harpe ratio or any risk-adjusted measure

Download the solutions

To help you check your work and experiment further, you can download the complete R Script containing the full code, charts, and commentary for all exercises.

Download Solutions (.r Script)

What’s next?

Now that you know how to download financial data, compute returns, and analyze basic performance and risk measures in R, you are ready to delve into more advanced quantitative and corporate finance topics.

If you want to learn more about other programming languages, check out these two articles to learn how to install Python on your computer and use it to download financial data:

▶ Hadrien PUCHE How to Install and Run Python on Your Computer (A Step-by-Step Guide)

▶ Hadrien PUCHE How to download and model financial data with Python

About the Author

This article was written in September 2026 by Hadrien PUCHE (ESSEC Business School, Grande École Program, Master in Management, 2023-2027).

▶ Discover all posts by Hadrien PUCHE

How to install and run R on your computer (a step-by-step guide)

October 12, 2026September 27, 2026 by Hadrien PUCHE

Understanding and writing R code can be a valuable skill for your career. While general-purpose programming languages such as Python are more widely used, R is particularly well suited to statistical analysis, data visualization, and quantitative research.

In this article, Hadrien PUCHE (ESSEC Business School, Grande École Program, Master in Management, 2023-2027) will help you to:

Understand the core components of the R statistical ecosystem for finance
Compare development setups (RStudio Desktop vs. Visual Studio Code)
Install R alongside essential build tools (RTools / Xcode)
Set up RStudio Desktop as a purpose-built workspace
Install econometric packages via CRAN (like quantmod)
Run a test script

But first, what is R exactly?

Historically, R was created in 1993 as an open-source implementation of the S language, developed at Bell Labs for statistical computing.

Setting up your R workspace can be straightforward. We will rely on CRAN (the Comprehensive R Archive Network), R’s main public repository for packages, to install the packages required for our analysis and their dependencies. Let’s walk through deploying a professional quantitative workspace for R.

Quick vocabulary for beginners

Before we dive in, let’s define a few technical terms you will encounter frequently:

Package: A collection of reusable R functions, data, and documentation designed for a specific purpose. For example, quantmod provides tools for quantitative financial analysis and financial data retrieval.
Library: A directory on your computer, where your installed packages are stored. You will use the library() command in your code to load them. While developers often use the terms package and library interchangeably, technically you install a package into your library.
Dependency: A package that another package requires in order to work properly. R manages these dependencies automatically, so you do not have to take care of them, but do not be surprised if R installs many more packages than you initially requested.
Build Tools (Rtools / Xcode): Background software required by your computer to translate (or “compile”) raw source code into executable instructions. R frequently compiles financial packages directly on your machine, making these essential to prevent errors.

Choosing your development environment: RStudio or Visual Studio Code?

You generally have two choices when it comes to writing R code: RStudio Desktop and Visual Studio Code (VS Code).

RStudio Desktop: An Integrated Development Environment (IDE) built specifically for R. It features a 4-pane layout that lets you simultaneously view your scripts, console, environment variables (data frames loaded in memory), and charts.
Visual Studio Code: VS Code is a highly versatile code editor. You can run R in VS Code by installing the R extension and configuring the required R packages. This is a good choice if you plan to mix multiple programming languages in the same project, though configuring VS Code for R requires a bit more effort than RStudio.

RStudio Desktop 4-pane layout
RStudio Desktop Interface

Visual Studio Code running R
Visual Studio Code configured for R

For this guide, we will focus on setting up R and integrating it with RStudio, as it offers a purpose-built user experience for R.

Understanding the R architecture

The R architecture operates as follows:

Base R: The underlying computational engine that calculates the math and runs the logic.
Build tools (RTools / Xcode):oftware required to compile R packages from source when precompiled binary versions are not available. Most beginners will install packages from binaries, but having these tools available can prevent installation problems with packages that require compilation.
CRAN: The Comprehensive R Archive Network. This is the centralized, strictly regulated global repository for R packages.

Step-by-step installation guide

Step 1: Install R and build tools

First, we must install R. RStudio will not function without it.

Go to the official CRAN Download Page.
For Windows:
- Click Download R for Windows > base > Download the latest R executable and install it using default settings.
- Go back to the Windows page, click Rtools, and install the version matching your R installation. This is critical for compiling quantitative packages later.
For macOS:
- Click Download R for macOS and select the .pkg matching your chip (Apple Silicon or Intel).
- To ensure packages compile correctly, open your Mac Terminal and run xcode-select --install to get the necessary developer tools.

Step 2: Install RStudio

Now, we install the integrated development environment (IDE) that we will use to write and execute R code.

Head to the Posit RStudio Desktop website.
Download the free version corresponding to your operating system (Windows or macOS).
Run the installer. RStudio will normally detect the R installation completed in Step 1 automatically.

Step 3: Install packages from CRAN

Because R uses centralized package repositories such as CRAN, we can install the packages required for our financial analysis directly from the R console in RStudio.

Launch RStudio.
In the Console pane (bottom-left), type the following command and press Enter. This will reach out to CRAN and download the essential tools for market data and time-series analysis:


# Install quantmod for data retrieval, xts for time-series, and PerformanceAnalytics for risk metrics
install.packages(c("quantmod", "xts", "PerformanceAnalytics", "ggplot2"))

💡 Quick fix tip: R may occasionally ask whether you want to install a package from source when a binary version is also available. For beginners, the binary version is usually the simplest option. Installing from source may require Rtools on Windows or the Xcode Command Line Tools on macOS.

a screenshot of the output of the script when downloading the packages

Checking that everything is working as intended

Let’s verify your infrastructure by writing a short script that pulls actual market data.

In RStudio, go to File > New File > R Script.
Paste the following quantitative code into the top-left editor pane.
Highlight all the text and press Ctrl+Enter (Windows) or Cmd+Enter (macOS) to run it.

# Load the quantitative financial modeling library
library(quantmod)
 
# Download historical financial data for Apple via Yahoo Finance API
getSymbols("AAPL", src = "yahoo", from = "2023-01-01", to = "2024-01-01", auto.assign = TRUE)
 
# Display the first 5 rows of the time-series array in the console
print(head(AAPL))
 
# Generate a financial chart with volume and Bollinger Bands for volatility analysis
chartSeries(AAPL, 
            name = "Apple Inc. (AAPL) Historical Prices", 
            theme = chartTheme("white"), 
            TA = c(addVo(), addBBands()))

A screenshot of RStudio after running the test script
After running the script, your RStudio should look like this

If the AAPL dataset appears in your top-right Environment pane, the data prints in your Console, and a professional candlestick chart renders in your bottom-right Plots pane, your R setup is fully operational.

Next steps & use cases

With R correctly configured, you are now equipped to tackle complex econometric and financial challenges. A good next step would be to learn how to download financial data with R.

You can learn how to do so in this article: How to download financial data with R

Useful resources

▶ CRAN (The Comprehensive R Archive Network): The main public repository for R packages, R distributions, and documentation. CRAN provides a centralized infrastructure for distributing and maintaining thousands of R packages used in statistical computing, econometrics, and quantitative research.

About the author

This article was written in September 2026 by Hadrien PUCHE (ESSEC Business School, Grande École Program, Master in Management, 2023-2027).

▶ Discover all posts by Hadrien PUCHE

How to download and model financial data with Python

October 20, 2026September 27, 2026 by Hadrien PUCHE

You could download data manually from websites such as Yahoo! Finance or Investing.com, but this quickly becomes tedious and time-consuming. It also limits the amount of data you can work with.

Python allows us to automate this process and retrieve large amounts of financial information in just a few lines of code.

In this article, Hadrien Puche (ESSEC Business School, Grande École Program, Master in Management, 2023-2027) will help you understand how to:

Download historical stock prices with Python
Explore and visualize market data
Compute basic statistics and historical distributions
Compare multiple securities
Learn more about the CAPM
Build the foundation needed for more advanced financial analysis

What financial data can we download?

Financial professionals use many different categories of data across individual assets as well as portfolios and funds.

Market data

Individual asset prices (e.g., individual stocks, corporate bonds)
Portfolios and funds (e.g., ETFs, mutual funds)
Currency exchange rates
Commodity prices
Bond yields

Company fundamentals

Revenue
Earnings
Margins
Cash flows

Macroeconomic data

Inflation
Interest rates
GDP growth
Unemployment

Alternative data

News
Social media sentiment
Satellite imagery
Credit card spending

Not all data sources are freely available. Many professional investors rely on paid platforms such as Bloomberg, FactSet, Capital IQ or Morningstar to access standardized, high-frequency, and point-in-time data.

Fortunately, stock market data specifically can easily be accessed for free using Python for research and learning purposes.

In this article, we will use the open-source yfinance library to download historical market data that you will then be able to model and use for any financial analysis project you may have.

A step-by-step guide

Follow the next steps to download your first financial data with Python 🙂

Step 1: Installing the required libraries

If you have not yet installed Python, refer to the setup guide to configure your execution environment (such as Jupyter Notebook or Anaconda).

Once your environment is ready, install the required packages:

pip install yfinance pandas numpy matplotlib

or inside Jupyter Notebook:

!pip install yfinance pandas numpy matplotlib

We will use:

yfinance to retrieve market data
pandas to manipulate data structures
numpy for financial and mathematical operations
matplotlib to create charts

Step 2: Import our Python packages

Most data analysis scripts begin by importing the packages required for the analysis. In Python, packages provide reusable code and functionality that extend Python’s core capabilities.

import yfinance as yf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

The aliases (yf, pd, np, plt) make the code shorter and easier to read.

Step 3: Download our first data set

To download financial data, we use ticker symbols, which identify securities or market instruments within a given exchange or data provider. For example, AAPL represents Apple.

When calling a Python function, we can customize its behavior by passing arguments such as period (e.g., "5y" for 5 years) or specific start and end dates.

As an example, let us download daily data for Apple stock price over the last five years (Yahoo! Finance ticker: AAPL). The data will be stored in a data frame (df) that we can name df_apple.

df_aapl = yf.download("AAPL", period="5y")

print(df_aapl.head())

You will obtain this table with the following columns:

Close: closing price at the end of the trading day
High: highest price during the trading day
Low: lowest price during the trading day
Open: opening price at the beginning of the trading day
Volume: transaction volume during the trading day

An screenshot from VSC showing the output table of this Yfinance query

The data is stored in a Pandas DataFrame. This is a popular two-dimensional, tabular data structure with labeled axes (rows and columns).

To inspect its structure, type the following code:

df_aapl.info()

A screenshot from VSC showing the output of df_aapl.info()

Step 4: Using more precise queries for historical context

Instead of downloading a rolling period (like “5y”), we can isolate specific market events by passing exact start and end dates to the download function. As financial analysts, we routinely extract specific timeframes to understand how assets behave under macroeconomic stress.

For example, analyzing the COVID-19 market crash in early 2020 offers invaluable insights into extreme volatility, liquidity crunches, and rapid V-shaped recoveries. Let’s download and plot Apple’s stock specifically during the height of the pandemic shock (January to June 2020):

# Isolate the COVID-19 crash and initial recovery phase
covid_crash = yf.download("AAPL", start="2020-01-01", end="2020-06-30")

# Plot the isolated data
plt.figure(figsize=(10, 5))
plt.plot(covid_crash.index, covid_crash["Close"], color="#d9534f", linewidth=2)
plt.title("AAPL Stock Price - COVID-19 Crash & Recovery (Early 2020)")
plt.xlabel("Date")
plt.ylabel("Price ($)")
plt.grid(True, linestyle="--", alpha=0.6)
plt.show()

The output of the previous code cell

We could use this same technique to analyze other pivotal periods, such as:

A central bank interest rate tightening cycle (e.g., the Fed’s 2022-2023 rate hikes)
The 2008 Global Financial Crisis (if analyzing older datasets)
Specific earnings announcement windows

Step 5: Downloading multiple stocks at the same time

Downloading multiple stocks is necessary for financial analysis that often requires comparing securities, building a portfolio, or testing trading strategies like pairs trading.

Instead of issuing separate requests for each stock (which risks hitting API rate limits or misaligning dates), it is far more efficient to fetch all tickers at once in a single batch query.

To make this practical, let’s download the data for the “Magnificent Seven”. These seven mega-cap tech companies (Apple, Microsoft, Alphabet, Amazon, Meta, Nvidia, and Tesla) have heavily dominated market capitalization and driven a massive portion of the S&P 500’s returns in recent years.

# Define the Magnificent 7 tickers
mag7_tickers = ["AAPL", "MSFT", "GOOGL", "AMZN", "META", "NVDA", "TSLA"]

# Download the closing prices for all 7 stocks simultaneously
prices = yf.download(mag7_tickers, period="5y")["Close"]
 
print(prices.head())

The result is now again a matrix where each column represents a stock, and each row represents a trading day.

Screenshot of the output of the previous cell

This table format is ideal for portfolio analysis, benchmarking, and performance comparisons, and can be used to draw any kind of graphs.

Note that the table’s columns are displayed in two groups. Depending on the display width, Jupyter Notebook or VS Code may wrap or truncate wide DataFrames. You can export the DataFrame to a CSV file if you prefer to inspect the complete dataset in a spreadsheet application.

# save the dataframe as a csv
prices.to_csv('mag_7_data.csv')

Screenshot of the output of the previous cell

Now that you successfully downloaded your financial data, let’s see how you can clean it and then use it.

Inspecting and cleaning the dataset

Financial datasets may contain missing values (NaN) for various reasons, including differences in trading calendars, trading suspensions, listing dates, or data-provider issues. Missing observations should be identified before computing returns or risk measures. For this introductory example, we simply remove rows containing missing values. In applied financial analysis, however, the appropriate treatment depends on the source of the missing data and the objective of the analysis.

If left unaddressed, these missing data points will break your mathematical functions and severely distort your return and volatility calculations. The code below checks how many missing values exist in each column, and then removes (drops) any rows containing them. In some situations you might want to forward-fill these gaps to preserve the timeline, but dropping them is the safest thing to do for now.

# Check for missing values
print(df_aapl.isnull().sum())
# Clean missing values by dropping rows with NaNs
df_aapl = df_aapl.dropna()
# Print the first rows of the dataframe
df_aapl.head()

df_aapl.head()

A screenshot from VSC showing the output of the cleaning code cell

To view the last rows of the dataframe, replace head() by tail():

Output of the VSC cell when we switch to tail()

Visualizing the stock price with graphs or charts

Let’s create our first graph to visualize the evolution of Apple’s stock price.

plt.figure(figsize=(10, 5))
plt.plot(df_aapl.index, df_aapl["Close"], label="AAPL Close Price")
plt.title("Apple Stock Price")
plt.xlabel("Date")
plt.ylabel("Price ($)")
plt.legend()
plt.show()

A screenshot of VSC with the stock price visualization cell output

We have now:

Downloaded market data from Yahoo! Finance
Stored and cleaned the data in a dataframe
Created a time-series plot to visualize stock prices

These core steps form the basis of empirical financial research and quantitative models.

Computing basic statistics & historical distributions

To evaluate stock performance and risk, we compute basic descriptive statistics for both prices and financial returns: minimum, maximum, mean, variance, standard deviation, skewness, and kurtosis. Although descriptive statistics can also be computed for price levels, risk analysis generally focuses on returns, whose distributions are more economically meaningful.

# Calculate daily percentage returns
df_aapl['Return'] = df_aapl['Close'].pct_change()

# Compute summary statistics for Price and Returns
stats_df = pd.DataFrame({
    'Metric': ['Min', 'Max', 'Mean', 'Variance', 'Std Dev', 'Skewness', 'Kurtosis'],
    'Price ($)': [
        df_aapl['Close'].min().item(),
        df_aapl['Close'].max().item(),
        df_aapl['Close'].mean().item(),
        df_aapl['Close'].var().item(),
        df_aapl['Close'].std().item(),
        df_aapl['Close'].skew().item(),
        df_aapl['Close'].kurtosis().item()
    ],
    'Daily Return': [
        df_aapl['Return'].min().item(),
        df_aapl['Return'].max().item(),
        df_aapl['Return'].mean().item(),
        df_aapl['Return'].var().item(),
        df_aapl['Return'].std().item(),
        df_aapl['Return'].skew().item(),
        df_aapl['Return'].kurtosis().item()
    ]
})

print(stats_df)

Plotting historical distributions

Histograms display the frequency distribution of prices and daily returns, helping us inspect price trends, distribution symmetry, and tail risks.

fig, axes = plt.subplots(1, 2, figsize=(14, 5))

# Price distribution
axes[0].hist(df_aapl['Close'].dropna(), bins=30, color='skyblue', edgecolor='black')
axes[0].set_title('Historical Price Distribution')
axes[0].set_xlabel('Price ($)')
axes[0].set_ylabel('Frequency')

# Return distribution
axes[1].hist(df_aapl['Return'].dropna(), bins=50, color='salmon', edgecolor='black')
axes[1].set_title('Historical Daily Return Distribution')
axes[1].set_xlabel('Daily Return')
axes[1].set_ylabel('Frequency')

plt.tight_layout()
plt.show()

Normalizing stock prices and computing returns

All stocks have different nominal prices. If Tesla trades at $350 and Nvidia at $220, it does not mean that Tesla is worth more than Nvidia or performed better.

To establish an accurate comparison across these assets, we must execute two fundamental computations:

Price harmonization: we normalize all historical time series to a base index of 100, to ensure a standardized starting point.
Return calculation: we compute the periodic returns to get the actual performance in % rather than the absolute variation.

normalized = prices / prices.iloc[0] * 100

plt.figure(figsize=(10, 5))
plt.plot(normalized.index, normalized)
plt.title("Performance Comparison (Base = 100)")
plt.xlabel("Date")
plt.ylabel("Growth of $100")
plt.legend(prices.columns)
plt.show()

the output of the previous cell

We can also compute daily returns across all stocks:

returns = prices.pct_change().dropna()
print(returns.head())

the output of the previous cell

The chart now shows how much each investment would have grown from the same starting value.

This is a standard technique used by portfolio managers and equity analysts.

Case study: the Capital Asset Pricing Model (CAPM)

In empirical finance, evaluating an individual asset requires isolating the return generated by the broader market from the return specific to the company itself. The Capital Asset Pricing Model (CAPM) provides the foundational framework to decompose this risk.

The model decomposes the return of an individual asset over a given time period into three components: the risk-free rate, a market systematic factor and a firm-specific factor. The model is expressed through the following equation:

r_t = r_f + β(r_m – r_f) + ε_t

Where:

r_t is the return of the stock (e.g., Apple).
r_f is the risk-free interest rate (e.g., the 13-week Treasury Bill, ^IRX).
β (Beta) represents the stock’s sensitivity to market movements (systematic risk).
r_m – r_f is the excess return of the market index (e.g., the S&P 500, ^GSPC).
ε_t (Epsilon) represents the idiosyncratic return associated with firm-specific risk not explained by the market.

By downloading these three time series simultaneously, we can calculate the stock’s Beta and isolate its firm-specific residual risk.

# Download asset (AAPL), market benchmark (S&P 500), and risk-free rate (13-week T-Bill)
market_data = yf.download(["AAPL", "^GSPC", "^IRX"], start="2022-01-01", end="2024-12-31")["Close"].dropna()

# Compute daily percentage returns for the stock and the market
returns_df = market_data[["AAPL", "^GSPC"]].pct_change().dropna()
 
# Convert the annualized risk-free yield (^IRX) to a daily rate
daily_rf = (market_data["^IRX"] / 100) / 252
returns_df["Rf"] = daily_rf
 
# Calculate the excess returns: (r_t - r_f) and (r_m - r_f)
excess_aapl = returns_df["AAPL"] - returns_df["Rf"]
excess_market = returns_df["^GSPC"] - returns_df["Rf"]
 
# Compute Market Beta: Covariance(stock, market) / Variance(market)
cov_matrix = np.cov(excess_aapl, excess_market)
beta = cov_matrix[0, 1] / cov_matrix[1, 1]
 
# Isolate Epsilon (the firm-specific residual risk)
# Rearranging the CAPM equation: epsilon = (r_t - r_f) - beta * (r_m - r_f)
epsilon = excess_aapl - (beta * excess_market)
 
print(f"Calculated Beta: {beta:.4f}")
print(f"Mean Firm-Specific Return (Epsilon): {epsilon.mean():.6f}")
print(f"Idiosyncratic Risk (Epsilon Std Dev): {epsilon.std():.4f}")

Common pitfalls

When working with market data, beginners often run into the same issues:

Using the wrong ticker symbol
Comparing stocks without normalizing prices
Forgetting that markets are closed on weekends and holidays
Failing to clean and handle missing values (NaN) in the dataset
Failing to check whether price series are raw or adjusted for stock splits and dividends

Overall, always inspect and clean your data before starting your analysis.

Exercises

Exercise 1: Basic data retrieval and price visualization (MSFT)

Microsoft is a mature mega-cap technology company, and a cornerstone of most global equity portfolios. Retrieving and inspecting its historical data is a perfect starting point to practice basic YFinance commands.

Using the ticker symbol MSFT, download the last five years of daily market data.

Your tasks:

Use the appropriate pandas functions to display the first 5 rows and the last 5 rows of the dataset to verify data integrity (checking for correct start/end dates).
Generate a line chart plotting the closing price over the entire 5-year period to visualize its long-term market trend.

Exercise 2: time-series extraction and volume analysis on Tesla (TSLA)

Tesla is renowned for its high historical volatility and massive retail trading interest. The 2022-2024 window was particularly eventful for growth and electric vehicle stocks, marked by shifting supply chains and a rapid rise in interest rates. Isolating this exact timeframe allows us to analyze the stock’s behavior under changing macroeconomic conditions.

Using the ticker symbol TSLA, extract the market data for the precise calendar period from January 1, 2022, to December 31, 2024 (using the start and end parameters).

Your tasks:

Identify the peak (highest closing price) and the trough (lowest closing price) over this period to grasp the magnitude of the stock’s price swings.
Calculate the average daily trading volume, a fundamental metric used by analysts to assess market liquidity and ongoing investor interest.

Exercise 3: Comparative performance and risk profiling on Chinese tech companies

Chinese technology stocks often experience unique market cycles driven by distinct domestic regulatory environments and macroeconomic factors. Using their US-listed ADRs (American Depositary Receipts), compare the performance and risk characteristics of three major players over the last five years:

Alibaba (BABA)
Baidu (BIDU)
PDD Holdings (PDD)

Questions:

Which stock achieved the highest total cumulative return?
Which stock was most volatile (highest standard deviation of daily returns)?
Which one offered the best risk-adjusted profile (e.g., highest Sharpe ratio) over the period?

Download the solutions

To help you check your work and experiment further, you can download the complete Jupyter Notebook containing the full code, charts, and commentary for all exercises.

Download Solutions (.ipynb)

What’s next?

Now that you know how to download financial data, perform basic computations, and control for market risk, you are ready to delve into advanced quantitative and corporate finance topics.

About the author

This article was written in September 2026 by Hadrien PUCHE (ESSEC Business School, Grande École Program, Master in Management, 2023-2027).

▶ Discover all posts by Hadrien PUCHE

How to install and run Python on your computer (a step-by-step guide)

October 7, 2026September 27, 2026 by Hadrien PUCHE

In finance, the ability to rapidly acquire, clean, and manipulate data is a key skill that can help you gain an edge over other students and job applicants. While Excel (with VBA) remains widely used and is sufficient for most basic financial modeling, such as a DCF valuation, Python offers far more scalability, automation, and mathematical power than Excel.

In this article, Hadrien PUCHE (ESSEC Business School, Grande École Program, Master in Management, 2023-2027) will help you to:

Understand the core components of a Python environment for finance
Choose the most secure and efficient development setup for financial data
Install Miniconda and manage isolated virtual environments
Set up Visual Studio Code (VS Code) as your primary coding workspace
Run a test script to download and visualize real stock market data

No computer science background is required to start using Python.

Quick vocabulary for beginners

Before we dive in, let’s demystify a few technical terms you will encounter frequently:

Python: A popular, high-level programming language created in 1991 by Guido van Rossum (and named after the BBC comedy series Monty Python’s Flying Circus). Today, Python is widely used in quantitative finance and data science because of its simple syntax and vast ecosystem of financial tools.
Library / Package: A collection of pre-written code created by other developers so you don’t have to reinvent the wheel (e.g., pandas for data tables, yfinance for downloading stock market prices).
Dependency: A package that another package needs in order to work properly.
Environment: An isolated “sandbox” on your computer containing a specific version of Python and specific libraries, preventing projects from interfering with one another.
IDE (Integrated Development Environment): The visual software app where you write, edit, and test your code (e.g., Visual Studio Code).
Extension: An add-on (like an app from an App Store) that adds extra features to your IDE.

In this first article, we will focus on helping you set up Python on your computer so that you can start learning how to use it. We will guide you step by step through setting up a professional local workspace and testing that everything is working properly. Once that is done, you will find a list of follow-up articles at the end to explore real-world financial use cases.

Choosing your development environment

A development environment is simply the ecosystem of software tools you use to write, manage, and execute your code. When selecting a workspace for Python, you have three main choices:

Local workspaces (like Visual Studio Code): The standard choice for finance. Running your code locally (on your own computer) gives you full control over your local file systems, execution speed, and (most importantly) data privacy. In finance, working with proprietary trading algorithms or confidential client data means you cannot upload sensitive information to unvetted third-party servers.
Cloud notebooks (like Google Colab): Cloud platforms are convenient for quick experiments because they require zero installation. However, they are generally unsuitable for professional financial workflows. You do not have full control over code execution or environment stability, and uploading confidential financial datasets or proprietary logic to public cloud infrastructure poses significant security and compliance risks.
AI-native code editors (like Cursor or Windsurf): These editors heavily integrate AI to generate code automatically. While powerful for experienced developers, relying on AI tools too early prevents beginners from learning core programming logic, syntax, and debugging skills. It is far better to understand the core mechanics manually first.

In this article, we will focus exclusively on establishing a local workspace using Visual Studio Code (VS Code),which is a widely used tool to get comfortable with professional Python coding.

We will also use Jupyter Notebooks (files ending in .ipynb). Unlike traditional Python scripts (files ending in .py) that execute the entire code at once, Jupyter Notebooks allow you to write and run code in individual “cells.” This block-by-block structure is especially powerful in finance for several reasons:

Isolating code: You can work on and execute specific parts of your code independently (e.g., downloading data once, then tweaking the math in a separate cell without re-downloading).
Immediate feedback: Data tables, charts, and outputs are displayed directly below the specific cell you just ran, and you do not have to execute the entire code each time.
Easier debugging: By testing your logic piece-by-piece, identifying and fixing errors becomes significantly faster.
Better examples, tutorials, or exercises: You can mix executable code with explanatory text and financial formulas, making it the perfect format for case studies and tutorials.

As your code grows, using a Jupyter Notebook will be more and more useful.

What you need to install (and why)

Before installing anything, let’s understand how the different components of your workspace fit together:

Miniconda (which includes Python & Conda): Python comes with a comprehensive standard library, but financial and data analysis typically require additional packages such as pandas, NumPy, matplotlib, and yfinance. To perform financial analysis, you need external packages/libraries like pandas or yfinance. Conda is a tool that manages these packages and isolates them into dedicated virtual environments.
Note on Anaconda vs. Miniconda: Anaconda is a big download that comes bundled with hundreds of packages you may never use. I suggest using Miniconda because it is a lightweight version, containing only Conda and Python, allowing us to keep your setup clean and fast.
Virtual Environments: Why do we need them? If you install every package into one single base Python installation, different projects will eventually require conflicting versions of the same library (a “dependency collision”), causing your scripts to crash. Virtual environments keep each project’s tools safely separated.
Visual Studio Code (VS Code): A clean user interface where you write, edit, and debug your code. VS Code connects seamlessly to your Conda virtual environment to execute your scripts.

How the architecture works

The diagram below illustrates how your development setup functions:

A graph showing the links between the user, VS Code, Miniconda, and Python.
Figure 1: How the User, VS Code, Miniconda Environment, and Python Engine interact.

You (the User) interact directly with VS Code to write commands and inspect results.
VS Code sends your code to your isolated Miniconda Virtual Environment (e.g., my_environment that you can create to store the packages that you will use in your own code).
Inside this environment, the Python Engine processes the math and logic, drawing upon the installed financial libraries (like yfinance and pandas).
The execution results (tables, charts, output logs) are sent back to VS Code for you to view.

As a fun side note: you can technically write code in almost any text editor! For a fun take on how far you could take this, check out this video.

Step-by-step installation guide

Step 1: Install Miniconda (Python + Conda)

Conveniently, downloading and installing Miniconda automatically installs Python, so this will be our first step.

Head to the official Miniconda Download Page, select the installer for your operating system (Windows or macOS), and complete the installation using the recommended default settings.

Step 2: Install Visual Studio Code and Extensions

Visual Studio Code (VS Code) will serve as your Integrated Development Environment (IDE). As a quick reminder, an IDE is the main visual software application, where you will actually write, edit, test, and debug your code. You can think of it as the central command dashboard for all your financial programming projects.

Download & Install: Go to the official VS Code website, download the installer for Windows or macOS, and follow the standard installation instructions.
Install Essential Extensions: Launch VS Code. Click on the Extensions icon on the left-hand Activity Bar (or press Ctrl+Shift+X on Windows / Cmd+Shift+X on Mac). Think of extensions as add-ons from an app store that give VS Code superpowers. Search for and install:
- Python (by Microsoft) – Provides syntax highlighting, code completion, and interpreter selection.
- Jupyter (by Microsoft) – Enables interactive execution of code cells inside .ipynb notebook files.

VS Code Extensions Marketplace showing Python extension by Microsoft
Make sure to install the official Python and Jupyter extensions in VS Code.

Step 3: Create your virtual environment via the Terminal

Now, we will create a clean, isolated Conda environment named my_environment where our financial packages will live.

Open your command line interface:
- Windows 11 / 10: Open the Start menu, search for Anaconda Prompt, and click to open it. (Alternatively, you can open Windows Terminal / PowerShell, but Anaconda Prompt automatically initializes Conda for you).
- macOS: Open the Terminal app (press Cmd + Space, type “Terminal”, and press Enter).
Run the following Conda & pip commands one by one:

# 1. Create an isolated environment named ‘my_environment’ with Python 3.11
conda create –name my_environment python=3.11 -y

# 2. Activate your new environment
conda activate my_environment

# 3. Upgrade pip and install core financial analysis libraries
pip install –upgrade yfinance pandas numpy matplotlib notebook –no-cache-dir

Pro-tip: Whenever you need to install additional packages in the future, open your terminal, activate your environment (conda activate my_environment), and run pip install [package_name].

Step 4: Connect VS Code to your environment

Now that your environment and libraries are ready, you need to tell VS Code to use my_environment to run your code.

Open a workspace folder: In VS Code, go to File > Open Folder… and select or create a dedicated folder on your computer (e.g., finance_python). It does not matter where it is, you simply need somewhere to store your code files.
Create your files: Click the New File icon in the Explorer sidebar to create two files:
- test.py (.py file is to store Python code)
- notebook.ipynb (.ipynb is the file extension name used for Jupyter notebooks)
Select the Python interpreter: Open test.py. Press Ctrl+Shift+P (Windows) or Cmd+Shift+P (macOS) to open the Command Palette, type Python: Select Interpreter, and press Enter.
- VS Code should automatically list my_environment. Click on it.
- If it doesn’t appear automatically: Click Enter interpreter path… > Find… and navigate directly to the executable file:
  - Windows: C:\Users\YourUsername\miniconda3\envs\my_environment\python.exe
  - macOS: /Users/YourUsername/miniconda3/envs/my_environment/bin/python3
Select Jupyter Kernel: Open notebook.ipynb. Click Select Kernel in the top-right corner of the window, choose Python Environments…, and select your my_environment path.

An image of the VS Code menu with notebook.ipynb and test.py created
Once this is done, your IDE should look just like this.

Quick Troubleshooting Tips:

“No matching commands” error: If typing Python: Select Interpreter gives no results, click inside the test.py editor window first to wake up the Python extension, or click Select Python Interpreter in the bottom-right status bar.
Environment missing from the list: Make sure you activated the environment in terminal at least once, or use the direct path navigation detailed above.

Testing your installation

Now that VS Code is connected to my_environment, let’s run a simple test script to confirm that our setup can successfully fetch market data and display a stock chart. Open test.py or notebook.ipynb, paste the code below, and execute it:

# Import yfinance to download stock market data directly from Yahoo Finance
import yfinance as yf

# Import matplotlib.pyplot (aliased as 'plt') to create financial charts and plots
import matplotlib.pyplot as plt

# Download historical stock data for Apple Inc. (AAPL)
print("Fetching financial data from Yahoo Finance using yfinance...")
df = yf.download('AAPL', start='2023-01-01', end='2024-01-01')

# Display the first 5 rows of the downloaded data in the terminal / output window
print("\nFirst 5 rows of AAPL market data:")
print(df.head())

# Plot historical closing prices
plt.figure(figsize=(10, 5))
plt.plot(df['Close'], label='AAPL Close Price', color='#1d4ed8', linewidth=1.5)
plt.title('Apple Inc. (AAPL) Historical Close Prices - 2023')
plt.xlabel('Date')
plt.ylabel('Price ($)')
plt.grid(True, linestyle='--', alpha=0.5)
plt.legend()
plt.show()

If the market dataset downloads and a clean line chart of Apple’s stock price appears, congratulations! You have successfully configured a professional, local Python environment for financial engineering.

This is how the output should look like if everything is working correctly:

An image of the VS Code menu with notebook.ipynb and test.py created

Congratulations! You have successfully configured a professional, local Python environment, ready for financial engineering

A quick tip for installing additional packages

As you have seen, packages such as yfinance, matplotlib, and pandas extend Python with useful functionality for financial analysis. If you need to install an additional package while working in a Jupyter Notebook, you can use the %pip command directly in a notebook cell, provided that the appropriate Python environment is selected as the active kernel. For example, the following command installs seaborn, a high-level statistical data visualization library built on top of Matplotlib:

%pip install seaborn

An image of the VS Code menu with notebook.ipynb and test.py created

Next steps & financial use cases

Now that your environment is fully operational, you are ready to start applying Python to quantitative finance. Explore this article to learn how to use Python to download and use financial market data.

▶ Hadrien Puche How to download and model financial data with Python

If you are interested in programming languages and would like to learn another useful skill, explore these two articles about how you could use the programming language R to help you in your financial analysis:

▶ Hadrien Puche How to install and run R on your computer (A step-by-step guide)

▶ Hadrien Puche How to download financial data with R

About the Author

This article was written in September 2026 by Hadrien PUCHE (ESSEC Business School, Grande École Program, Master in Management, 2023-2027).

▶ Discover all posts by Hadrien PUCHE

AMM: Market Making in Decentralized Finance

September 10, 2026September 10, 2026 by Prof. François LONGIN

Calculateur AMM

Constant-Product Automated Market Maker (AMM): Price Calculator

This application calculates the average transaction price and the final price (marginal price after the transaction) for a constant-product AMM defined by x × y = k. The following convention is used: buy means that the user buys asset x and pays with asset y, while sell means that the user sells asset x and receives asset y.

Pool Parameters

Initial reserve of asset x (ETH)

Initial reserve of asset y (USDC)

Transaction

Direction Buy Sell

Quantity traded (Δx)

Results

Average transaction price

Final price after the transaction

Total amount traded

Chart

VIX Data and Statistical Properties

September 3, 2026 by Saral BINDAL

In this article, Saral BINDAL (Indian Institute of Technology Kharagpur, Metallurgical and Materials Engineering, 2024-2028 & Research assistant at ESSEC Business School) examines the statistical properties of historical VIX data and applies statistical methods to model its dynamics.

Introduction

The Chicago Board Options Exchange (CBOE) Volatility Index, or VIX, is a real-time market index designed to measure the market’s expectation of 30-day forward-looking annualized volatility in the US equity market. It is option-based, calculated using the market prices of S&P 500 index options to gauge expected volatility. The VIX construction methodology can be found in the article CBOE Volatility Index

In this article, we analyze the VIX index using historical market data to study its statistical properties, and discuss the methods used to model its dynamics in quantitative finance.

Historical Data

The VIX Index Historical data is publicly available from the Chicago Board Options Exchange (CBOE) and is updated on a daily basis. The analysis in this article is based on daily closing values of the VIX obtained from this data.

Figure 1 below illustrates the time series plot of VIX daily closing values from January 1990 to August 2026. From the figure, we can observe that during periods of high uncertainty, the VIX tends to rise significantly, as seen during the Global Financial Crisis (2007–2009) and the COVID-19 pandemic (2019–2020). We can also see the VIX rising as of now amid the ongoing Middle East crisis, although not to the same extent as observed during previous periods of heightened uncertainty.

Figure 1. VIX Index Historical Data (1990-2026)

Source: computation by the author.

Distributional Characteristics

Using historical VIX data from 1990 to 2026, Figure 2 illustrates the empirical distribution of daily VIX closing values. The histogram, overlaid with a kernel density estimate (KDE), provides a visual representation of the distribution’s underlying probability density. The corresponding first four moments of the distribution are reported below:

Table 1. VIX Distribution Statistics
Table of VIX Distribution Statistics
Source: computation by the author.

From the above table, we can observe that the data is right-skewed, which indicates that volatility tends to remain relatively low under normal market conditions but can rise sharply during periods of financial stress. The long right tail therefore reflects the occurrence of volatility shocks and tail-risk events, such as the Global Financial Crisis (2007–2009), the COVID-19 market shock (2020), and other episodes of significant financial or geopolitical uncertainty. Moreover, a high excess kurtosis indicates fatter tails compared to a normal distribution, implying a higher probability of observing extreme VIX levels. From the histogram, we observe that the VIX is concentrated around a level of 15 for a large proportion of the sample. This suggests that, under normal market conditions, the VIX tends to fluctuate around this level, which can be interpreted as its long-term level of the market uncertainty

Figure 2. Historical Distribution and Kernel Density Estimation of the Daily VIX levels
Historical Distribution of the Daily VIX levels
Source: computation by the author.

Time-Series Properties

The VIX exhibits several well-documented time-series properties that distinguish it from traditional financial asset prices. Unlike equity prices, whose levels are generally characterized by non-stationary dynamics, the VIX exhibits pronounced mean reversion and persistence, together with sharp spikes during episodes of market stress. The mean-reverting behaviour of the VIX is particularly noteworthy because it resembles a fundamental feature of interest-rate dynamics, which has long been incorporated into models such as the Vasicek (1977) and Cox, Ingersoll, and Ross (1985). The mean-reverting specifications have similarly been used to capture the tendency of volatility to return toward a long-run level.

However, the two processes differ substantially in their temporal dynamics. Interest-rate persistence typically reflects gradual adjustments in response to macroeconomic conditions and monetary policy, with mean reversion occurring over relatively longer horizons. In contrast, the VIX responds rapidly to changes in market expectations: episodes of financial stress can trigger abrupt upward spikes, followed by relatively rapid mean reversion toward lower levels. Thus, while both exhibit persistent and mean-reverting dynamics, the VIX is distinguished by its faster adjustment, pronounced asymmetry, and sharp responses to market stress.

Methods to Model VIX Dynamics

VIX dynamics exhibit several distinctive characteristics, particularly mean reversion and persistence, which require different modelling approaches. This section examines how various econometric models capture these features, ranging from mean-reverting and Log-VIX models to HAR, ARCH/GARCH, and stochastic volatility models.

Mean-Reverting Models

Early approaches to modelling volatility indices treated volatility as a mean-reverting stochastic process. A commonly used specification is the Cox-Ingersoll-Ross (CIR) process, originally developed for interest-rate modelling by Cox, Ingersoll and Ross (1985) and subsequently applied to volatility derivatives by Grünbichler and Longstaff (1996). Grünbichler and Longstaff modelled the volatility index using a mean-reverting square-root process and derived pricing formulas for volatility futures and options.

The dynamics are given by

CIR Formula

V_t represents the volatility index at time t
κ is the speed of mean reversion
θ is the long-run level towards which the process tends to revert
σ is the diffusion parameter controlling the magnitude of random fluctuations
W_t is a standard Brownian motion

The term κ(θ − V_t)dt represents the mean-reverting component, which pulls the process towards its long-run level θ, while σ√V_tdW_t represents the diffusion component, capturing random fluctuations in the volatility index.

The CIR framework was subsequently examined empirically in the context of VIX futures by Zhang and Zhu (2006). They estimated a stochastic variance model using historical VIX data and used it to derive and evaluate VIX futures prices.

An alternative mean-reverting modelling approach is the Ornstein-Uhlenbeck (OU) process (Uhlenbeck and Ornstein, 1930), which assumes a constant diffusion coefficient:

OU Formula

Unlike the CIR process, the OU process is Gaussian and can theoretically take negative values. This makes the OU process unsuitable for modelling the VIX level directly, since the VIX is strictly positive. A common way to retain the mean-reverting OU structure while ensuring a positive VIX is therefore to model log(VIX) rather than the VIX level itself.

Log-VIX Models

A natural way to address the possibility of negative values is to model the logarithm of the VIX. Let

VIX Log-Change Formula

A mean-reverting logarithmic process can then be written as

Mean Reverting Log Formula

or equivalently,

Mean Reverting Log Formula

Since

VIX Formula

the resulting modelled VIX is strictly positive. The logarithmic transformation therefore preserves the mean-reverting structure while avoiding the negative-value problem associated with modelling the VIX level using a Gaussian process.

Mean-reverting in the log models were studied by Detemple and Osakwe (2000) in the context of volatility option valuation. Their model provides an early theoretical foundation for modelling volatility through a log-normal mean-reverting process. The approach was subsequently applied directly to the VIX by Bao (2013), who developed a mean-reverting logarithmic model for the spot VIX and extended it to incorporate jumps and stochastic volatility.

Heterogeneous Autoregressive Model

Although continuous-time mean-reverting models capture the tendency of volatility to move towards a long-run level, empirical evidence indicates that the VIX also exhibits substantial persistence across different time horizons. Fernandes, Medeiros and Scharth (2014) conducted a detailed analysis of the time-series properties of the VIX and found evidence of long-range dependence. They therefore employed Heterogeneous Autoregressive (HAR) models to model and forecast the VIX.

The HAR framework captures persistence by allowing past VIX observations over different horizons to affect the current value. A simplified HAR model can be expressed as

HAR Formula

where

HAR Weekly Component Formula

and

HAR Monthly Component Formula

The three components represent information from daily, weekly, and monthly horizons, respectively. This allows the HAR-VIX model to capture the persistence of the VIX across different time horizons while remaining relatively simple and parsimonious. The HAR framework was originally introduced by Corsi (2009) to model the heterogeneous dynamics of realized volatility. Fernandes, Medeiros and Scharth (2014) adapted this framework to the VIX and showed that its multi-horizon structure provides a useful representation of the strong persistence and long-range dependence in the VIX.

Stochastic Variance of the VIX

The volatility of the VIX itself can vary over time. Rather than assuming that the diffusion coefficient is constant, stochastic-volatility models allow the volatility governing VIX fluctuations to evolve as a separate stochastic process.

Kaeck and Alexander (2013) investigate continuous-time models of VIX dynamics that explicitly incorporate stochastic volatility of volatility. Their analysis considers several one- and two-factor continuous-time models, including affine and non-affine specifications and models with jumps, using VIX data over an extended period.

Conceptually, the model can be represented as

Stochastic Volatility VIX Formula

where

VIX_t represents the VIX index
V_t represents the instantaneous variance governing movements in log-VIX and is itself stochastic
κ controls the mean-reversion speed of log-VIX
θ represents the long-run level of log-VIX
W_t is a Brownian motion driving log-VIX
Z_t represents the size of a jump in log-VIX
J_t is a jump-counting process, with dJ_t representing the occurrence of jumps

Stochastic Variance Formula

κ_v controls the mean-reversion speed of the variance process
θ_v represents the long-run level of the variance process
σ_v controls the volatility of the variance process, representing the volatility-of-volatility
W_t^v is a Brownian motion driving the variance process

The key distinction is therefore that the variance of VIX fluctuations is no longer constant. It becomes a state variable that evolves over time.

This additional source of randomness allows the model to capture changes in the intensity of VIX fluctuations and provides a more flexible representation of the sharp and persistent movements observed during periods of financial stress. Kaeck and Alexander’s analysis specifically examines whether stochastic volatility of volatility improves the ability of continuous-time models to describe VIX dynamics.

ARCH and GARCH Models

The VIX also exhibits volatility clustering, where periods of high volatility tend to be followed by high volatility, while periods of low volatility tend to be followed by low volatility. This suggests that the variance of log change in VIX is not constant over time. ARCH and GARCH models capture this feature by allowing the conditional variance of log change in VIX to evolve over time.

Let the log change in the VIX be defined as

VIX Log Change Formula

An ARCH or GARCH model can then be used to model the conditional variance of these changes. The standard GARCH(1,1) specification is

GARCH Formula

where

r_t represents the log change in the VIX at time t
μ represents the conditional mean of the VIX log change
ε_t represents the innovation or shock at time t
σ_t² represents the conditional variance of the VIX log change
ω represents the long-run variance component
α measures the immediate effect of new shocks on conditional variance
β measures the persistence of conditional variance over time

The αε_t−1² term captures the immediate impact of a new shock, while the βσ_t−1² term captures the persistence of previously elevated variance. A high value of β therefore indicates that periods of high variation in the VIX tend to persist.

The ARCH model was introduced by Engle (1982), while Bollerslev (1986) extended it to the more general GARCH framework. For the VIX, these models are useful for studying time-varying conditional variance and volatility clustering in VIX changes, rather than the mean-reverting behaviour of the VIX level itself.

Empirical Analysis of VIX

We use the complete historical daily VIX closing values (1990 – 2026) to estimate the parameters of the CIR model described above. Since the CIR model is specified in continuous time while the data are observed at daily intervals, the process is first discretized using the Euler-Maruyama approximation:

Discretized CIR Formula

where Δt; = 1/252 for daily observations and ε_t+1 ∼ N(0,1). This discretization implies that, conditional on the previous day’s VIX value, the expected value and variance of the next observation are:

Conditional Expected Value and Variance Formula

Assuming the discretized process is conditionally normally distributed, these expressions allow us to construct a likelihood for the observed VIX data. The log-likelihood is:

Log Likelihood Formula

The CIR parameters are then estimated by choosing the values that maximize this log-likelihood:

Maximum Likelihood Estimator Formula

This gives an estimated mean-reversion speed of κ = 4.8618, a long-run VIX level of θ = 19.4237, and a diffusion parameter of σ = 5.1952.

These estimated parameters are then used to simulate a possible future path of the VIX. The simulation starts from the observed VIX value of 14.63 on 13^th August 2026 and projects the VIX forward over 252 trading days using the CIR dynamics. The mean-reverting component pulls the process towards the estimated long-run level, while the diffusion component introduces random fluctuations around this tendency.

Figure 3 compares the historical VIX with one simulated path generated by the estimated CIR model. The historical series shows the large fluctuations and sharp spikes observed in the VIX over the sample period, while the simulated path represents one possible future realization starting from the current VIX level of 14.63. The dotted line at 19.42 represents the estimated long-run level of the VIX. The simulated path fluctuates around this level and exhibits a tendency to move towards it, illustrating the mean-reverting behavior implied by the CIR model.

You can download the Excel file with complete historical data used for the above calculations below.

Figure 3. Historical Path and Multiple CIR Simulated Paths for the VIX

Source: computation by the author.

You can download the Python code below to reproduce the CIR parameter estimation and simulated VIX paths presented above.

Alternatively, you can download the R code below with the same functionality as in the Python file.

Why should I be interested in this post?

For anyone interested in finance or a career in trading, understanding the statistical properties of VIX and how it is modelled is very important. As one of the most widely used measures of market uncertainty and expected volatility, it serves as an important tool for market analysis, risk assessment and numerous volatility-based trading strategies.

Useful resources

Academic research on option pricing

Black F. and M. Scholes (1973) The pricing of options and corporate liabilities. Journal of Political Economy, 81(3), 637-654.

Black, F. (1976), “Studies of Stock Price Volatility Changes”, Proceedings of the Business and Economics Section of the American Statistical Association, 177-181.

Cox J. C., J. E. Ingersoll and S. A. Ross (1985) A theory of the term structure of interest rates. Econometrica, 53(2), 385-407.

Hull J.C. (2022) Options, Futures, and Other Derivatives, 11th Global Edition, Chapter 15 – The Black-Scholes-Merton model, 338-365.

Merton R.C. (1973) Theory of rational option pricing. The Bell Journal of Economics and Management Science, 4(1), 141-183.

Uhlenbeck G. E. and L. S. Ornstein (1930) On the theory of the Brownian motion. Physical Review, 36(5), 823-841.

Academic research on VIX

Bao Q. (2013) Mean-Reverting Logarithmic Modelling of VIX. MPRA Paper, No. 46413.

Bollerslev T. (1986) Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327.

Corsi F. (2009) A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics, 7(2), 174-196.

Cox J. C., J. E. Ingersoll and S. A. Ross (1985) A theory of the term structure of interest rates. Econometrica, 53(2), 385-407.

Detemple J. and C. Osakwe (2000) The valuation of volatility options. European Finance Review, 4(1), 21-50.

Engle R. F. (1982) Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987-1007.

Fernandes M., M. C. Medeiros and M. Scharth (2014) Modelling and predicting the CBOE market volatility index. Journal of Banking & Finance, 40, 1-10.

Grünbichler A. and F. A. Longstaff (1996) Valuing futures and options on volatility. Journal of Banking & Finance, 20(6), 985-1001.

Jiang G. J. and Y. S. Tian (2005) The model-free implied volatility and its information content. The Review of Financial Studies, 18(4), 1305-1342.

Kaeck A. and C. Alexander (2013) Continuous-time VIX dynamics: On the role of stochastic volatility of volatility. International Review of Financial Analysis, 28, 46-56.

Uhlenbeck G. E. and L. S. Ornstein (1930) On the theory of the Brownian motion. Physical Review, 36(5), 823-841.

Whaley R. E. (2009) Understanding the VIX. Journal of Portfolio Management, 35(3), 98-105.

Zhang J. E. and Y. Zhu Y. (2006) VIX futures. Journal of Futures Markets, 26(6), 521-531.

VIX Data

CBOE Global Markets (2026) VIX Historical Data.

About the author

The article was written in September 2026 by Saral BINDAL (Indian Institute of Technology Kharagpur, Metallurgical and Materials Engineering, 2024-2028 & Research assistant at ESSEC Business School). His interests include tracking geopolitical developments and analyzing their direct impact on macroeconomic factors such as inflation, trade balances, and currency volatility, with a focus on using data to quantify these global economic ripple effects.

Discover all posts written by Saral BINDAL.

Volkswagen in China: How Localization Strategy Built Success and Created New Challenges

September 2, 2026 by Bochen LIU

In this article, Bochen LIU (Queen’s Smith School of Business, BCom 2023–2027; ESSEC BBA Exchange Program, Fall 2025) explains how Volkswagen’s localization strategy has become a key marketing approach for maintaining competitiveness in the Chinese automobile market. By adapting products, branding, digital communication, and consumer engagement to local preferences, Volkswagen demonstrates how international companies can strengthen their market position through localization rather than standardization.

Volkswagen in China

Volkswagen has been one of the most successful foreign automobile brands in China since entering the market in the 1980s. Through joint ventures with SAIC and FAW, Volkswagen developed a strong business presence by combining its global automotive expertise with local market knowledge. Rather than simply exporting global models, Volkswagen adopted a localization strategy by adapting its products, production processes, and marketing activities to better meet the needs and preferences of Chinese consumers. This approach allowed Volkswagen to establish a strong market position and become one of the leading international automobile brands in China.

The success of Volkswagen in China was highly dependent on its ability to make products that could suit the taste of the Chinese market. For instance, one of the first successful products made by Volkswagen in China was the Santana, which had high reliability and suitability for use by the Chinese consumers. Later, another model known as Lavida was designed specifically for the Chinese market. This model was made taking into consideration the needs of the Chinese consumers for spaciousness and comfort. This shows that the company managed to localize its global brand in the local market by making products that could suit the needs of the Chinese customers.

The Chinese automobile market has been transformed in recent years. In the past, VW could take advantage of its global reputation, engineering skills, and reliability to gain success in the Chinese market. However, today, the customers give more attention to electric cars, intelligent systems, and digital products. The development of Chinese companies producing electric cars, such as BYD and NIO, is posing new challenges for the company. In conclusion, while Volkswagen’s localization strategy has been successful in helping the company enter the Chinese market, it needs to adapt constantly.

From Localization to Competitive Advantage in China

Volkswagen’s localization strategy is important because Chinese consumers increasingly evaluate vehicles based on factors beyond traditional brand reputation. While Volkswagen previously benefited from its global reputation for reliability and engineering quality, the Chinese automotive market has shifted toward digital technology, intelligent driving features, and user experience. Therefore, adapting products and marketing communication to local preferences has become essential for maintaining customer relevance.

For example, Volkswagen has invested in localized technology and product development to better respond to Chinese consumers’ expectations for intelligent vehicles. Features such as intelligent cockpit systems, localized infotainment functions, and digital services allow Volkswagen to compete with domestic brands that have strong technological advantages. Without these adaptations, Volkswagen risks losing younger Chinese consumers who increasingly prioritize innovation and connectivity over traditional brand value.

Consulting: structuring and reducing uncertainty

Market localization requires market analysis procedures. As part of its business strategy, the company constantly gathers consumer data, analyzes competitors, and tracks shifts in consumer demands. Tools like the STP model (segmentation, targeting, and positioning), SWOT analysis, and consumer journey mapping make this easier.

As a result, Volkswagen uses market analysis tools to identify potential customer groups and create more relevant marketing campaigns.

Financial analysis: measuring and pricing risk

The effectiveness of localization can be evaluated through measurable business indicators, including market share, customer acquisition, sales growth, and brand preference. These metrics allow managers to determine whether localized marketing investments generate sustainable returns.

Improved localization often increases customer satisfaction and strengthens long-term brand loyalty, ultimately reducing marketing costs while improving overall business performance.

Challenges in China’s Changing Automotive Market

The Chinese automotive market has experienced significant uncertainty due to the rapid development of electric vehicles, government support for new energy vehicles, and changing consumer expectations. Unlike traditional market risks that can be estimated through historical data, these changes have created new competitive conditions that Volkswagen could not fully predict based on its previous success.

These changes require Volkswagen to continuously adapt its marketing strategy rather than relying only on its previous competitive advantages. For decades, Volkswagen benefited from its strong reputation for quality and reliability in China. However, the rise of domestic EV manufacturers has shifted competition toward technology, connectivity, and user experience. Therefore, Volkswagen’s future competitiveness depends on its ability to localize not only its products but also its digital and innovation strategies.

From managing risk to building resilience

Volkswagen builds long-term resilience by making localization an ongoing strategic capability rather than a temporary marketing campaign. Continuous investment in local research and development, partnerships with Chinese technology companies, and localized communication channels enable the company to respond more quickly to changing market conditions.

This adaptive approach allows Volkswagen to strengthen customer relationships while maintaining competitiveness in an increasingly dynamic automotive industry.

Why should I be interested in this post?

Volkswagen’s experience demonstrates that successful international marketing requires more than global brand recognition. Companies must understand local consumers, adapt their value proposition, and continuously refine their marketing strategy to remain competitive.

For business and marketing students, this case illustrates how localization, consumer insight, and strategic positioning can transform global brands into locally relevant market leaders, providing valuable lessons for international marketing and brand management.

Useful resources

Volkswagen Newsroom

Volkswagen Group China

American Marketing Association (AMA)

About the author

The article was written in August 2026 by Bochen LIU (Queen’s Smith School of Business, BCom 2023–2027; ESSEC BBA Exchange Program, Fall 2025).

▶ Discover all posts by Bochen LIU

Haste Does Not Bring Success”: What SimTrade Taught Me About Patience, Discipline, and Market Judgment

August 31, 2026 by François Longin

In this article, Feitong GUO (The Chinese University of Hong Kong, Shenzhen, Accounting and Data Analytics, 2023–2027; ESSEC Business School, BBA Exchange Program, Spring 2026) explains how SimTrade changed her understanding of patience, risk and decision-making under uncertainty.

“欲速则不达”: haste does not bring success

Confucius warned that “欲速则不达，见小利则大事不成”: if we pursue speed or become distracted by small gains, we may fail to achieve the larger objective. Before taking SimTrade, I was often impatient in simulations. A short-term profit could make me close a position too early, while an unexpected loss could make me abandon my reasoning and rush into the next round. The course taught me that good trading is not constant action. It is the discipline to observe, form a view, define acceptable risk and wait until the evidence justifies a decision.

What the simulations revealed

Across market-making exercises in an order-driven market with a limit book, I experienced both premature exits and positions that moved sharply against me. I also learned that quoting a wider spread does not automatically create profit: other participants may offer better prices, execution is uncertain and inventory can become risky when the market changes direction. I therefore stopped treating every price movement as a command to act. Instead, I began to ask what information had changed, whether the movement was ordinary noise or a regime shift, and whether my original assumptions still held.

This distinction also clarified the difference between patience and procrastination. Procrastination means neither acting nor thinking. Patience is active: observing the market, updating expectations, defining price and risk limits, and being ready to act. It is not stubbornly holding a losing position. When evidence invalidates the original thesis, discipline means accepting the loss rather than defending a sunk cost.

Volatility, uncertainty and the need to wait

The following FRED charts connect my simulation experience with real-market evidence. They do not provide a trading rule or predict the next crash. Instead, they show why patience must be combined with preparation for rare but consequential changes.

Figure 1. CBOE Volatility Index (VIX) and the S&P 500.
CBOE VIX and S&P 500
Source: Federal Reserve Bank of St. Louis FRED, using CBOE and S&P Dow Jones Indices data.

Figure 1 shows that sharp increases in expected volatility can coincide with steep equity-market declines, most visibly during the 2020 shock. The relationship is not a mechanical buy-or-sell signal, but it illustrates how quickly the market environment can change. My lesson is that patience cannot mean ignoring downside risk: a trader should establish risk limits before uncertainty rises, because calm observation becomes harder once prices move abruptly.

Figure 2. Long-run history of the CBOE Volatility Index, with a high-volatility threshold.
Long-run CBOE VIX with high-volatility threshold
Source: Federal Reserve Bank of St. Louis FRED, using CBOE data.

Figure 2 places volatility spikes in a longer historical context. High-volatility episodes are intermittent, while calmer conditions occupy much of the sample. This helps explain why overreacting to every small movement can be costly, but also why a routine trading rhythm must include contingency plans. We cannot know the exact timing of the next extreme episode; we can only avoid confusing the absence of a shock with the absence of risk.

Figure 3. Absolute daily changes in the S&P 500 and the VIX.
Absolute daily S&P 500 changes and CBOE VIX
Source: Federal Reserve Bank of St. Louis FRED, using CBOE and S&P Dow Jones Indices data.

Figure 3 compares realized daily market movements with the VIX, a forward-looking measure based on options prices. The two tend to rise together in stressed periods, but they do not match exactly because they describe different horizons and information sets. This distinction mirrors SimTrade: observing what has already happened is not the same as forecasting what may happen next. Better judgment requires both evidence from realized outcomes and an explicit view of future uncertainty.

Behavioral finance behind my mistakes

The disposition effect

Selling a winning position too quickly while hesitating over a losing one resembles the disposition effect documented by Shefrin and Statman. Short-term gains feel concrete and easy to secure, whereas realizing a loss feels like admitting that the original judgment was wrong. Recognizing this bias helped me separate emotional comfort from decision quality.

Sunk costs and rational perseverance

Time, money and effort inevitably influence emotions, even though sunk costs should not determine the next decision. Perseverance is rational only when the expected future benefit still justifies the remaining risk. This principle is relevant beyond trading: in analytics projects, I should not defend a model merely because I spent time building it. I should keep, revise or abandon it according to evidence.

From trading discipline to business and data analytics

SimTrade strengthened three qualities that I want to bring to business and data analytics: patient reasoning, reflection and a long-term orientation. Data rarely explains itself. Analysts must distinguish signal from noise, test assumptions, investigate unexpected results and wait for sufficient evidence without becoming passive. The course showed me that a good decision is not defined only by whether one trade makes money. It is defined by whether the process was consistent, explainable and responsive to new information.

Why should I be interested in this post?

For students interested in finance, consulting or analytics, simulations offer a safe environment in which to discover how emotion enters supposedly rational decisions. The most transferable lesson is not a particular order or strategy. It is the habit of slowing down before acting: specify the objective, interpret the evidence, control the downside and review the result. “Haste does not bring success” is therefore not an argument for inactivity; it is a framework for disciplined action.

Useful resources

Academic research

Arkes, H. R., & Blumer, C. (1985) The Psychology of Sunk Cost, Organizational Behavior and Human Decision Processes, 35(1), 124–140.

Kahneman, D., & Tversky, A. (1979) Prospect Theory: An Analysis of Decision under Risk, Econometrica, 47(2), 263–291.

Shefrin, H., & Statman, M. (1985) The Disposition to Sell Winners Too Early and Ride Losers Too Long: Theory and Evidence, The Journal of Finance, 40(3), 777–790.

Confucius, The Analects, Book XIII, “Zi Lu”.

Market data

Federal Reserve Bank of St. Louis FRED: Measuring uncertainty and volatility with FRED data

SimTrade

SimTrade course catalogue

SimTrade simulation catalogue

About the author

The article was written in August 2026 by Feitong GUO (The Chinese University of Hong Kong, Shenzhen, Accounting and Data Analytics, 2023–2027; ESSEC Business School, BBA Exchange Program, Spring 2026).

▶ Discover all articles by Feitong GUO .

From Verification to Evaluation: How Audit Shaped My Approach to Nonprofit Investment

August 31, 2026 by Feitong GUO

In this article, Feitong GUO (The Chinese University of Hong Kong, Shenzhen, Accounting and Data Analytics, 2023-2027; ESSEC Business School, BBA Exchange Program, Spring 2026) reflects on how her experience as an Audit Assistant at Ernst & Young (EY) and her work as a Project Consultant at A Better Community (ABC) taught her two complementary ways of supporting reliable decisions: verifying evidence against established standards and designing an evaluation framework when the standards themselves must first be defined.

About the organizations

EY and ABC operate in very different environments, but both transform complex information into structured evidence for decision-makers. EY does so through established assurance and review procedures, while ABC provides professional services that help social organizations and funders make more informed decisions.

Ernst & Young (EY) is a global professional-services organization providing assurance, consulting, strategy and transactions, and tax services. I worked as an Audit Assistant in July and August 2025. To respect confidentiality, I do not identify the financial institutions or funds involved.

Logo of EY.

Source: the company.

A Better Community (ABC), founded in 2008, mobilizes professional volunteers to provide consulting, research, digital, impact-investment and philanthropic-advisory services to social organizations. Since August 2025, I have served as a Volunteer Management Team Member and Project Consultant. I have participated in three volunteer-recruitment seasons and two project seasons, supporting a nonprofit educational research organization and a foundation investment project.

Logo of A Better Community.

Source: the organization.

From applying standards to designing them

Verification in audit

At EY, my tasks included assisting with audit working papers, reviewing credit files and checking financial information. In credit-file reviews, I examined borrower information such as loan amounts, interest rates, collateral or guarantees, repayment capacity and the bank’s internal loan-risk classification. Completeness meant more than confirming that every required field had been filled in. I also checked whether the information was supported by relevant documents and remained consistent across client reports, audit materials and credible public sources. When the sources did not agree, I recorded the discrepancy and sought clarification rather than selecting the figure that appeared most convenient.

I also supported valuation-related checks for selected investment projects held by funds. The purpose was to verify parts of the existing estimates, not to perform the valuation independently. I organized project information and valuation schedules, traced operational inputs such as annual production volumes, prices and forecasts to annual reports and other supporting materials, and checked formulas and cross-references between different tables. When figures did not match, I followed their sources and calculation paths to locate the problem. If the discrepancy could not be resolved, I documented it and raised a specific question with the client team or my mentor.

These tasks could be repetitive, but they were not trivial. A small inconsistency in a date, amount, formula or supporting document could affect the reliability of the output. Audit taught me to preserve an evidence trail, distinguish a fact from an inference and know when escalation was necessary. I contributed to the process rather than owning the final audit conclusion, yet that role showed me how disciplined verification supports trust.

Evaluation in nonprofit consulting

In ABC’s foundation investment project, the question moved upstream. The client wanted a reusable methodology to screen potential partner organizations and inform future funding decisions. Our team was not merely checking whether information met an existing standard; we first had to decide what a reasonable and usable standard should contain. I was responsible for researching nonprofit-screening methodologies and contributing to the indicator framework and its validation.

Our emerging framework has three sequential layers: compliance, communication and value. Compliance is an entry gate. It may include checks of registration status, annual reports, financial disclosures, penalties and abnormal operating information through credible public sources. If an organization does not pass the required compliance checks, it does not proceed to the communication assessment. This prevents strong publicity from compensating for a fundamental compliance concern.

For organizations that pass the first gate, the communication layer distinguishes current capacity from future potential. Current capacity may be reflected in the volume, frequency, clarity and audience response of existing content. Potential depends less on polished videos or follower counts and more on whether the organization operates a credible project, can provide verifiable stories and evidence, is willing to cooperate in content development, and has a mission that can be meaningfully communicated to the foundation’s target audience. The value layer then asks whether the project addresses a genuine need, has a reasonable intervention logic and is aligned with the funder’s objectives.

The same form, but a different decision problem

At first sight, my work at EY and ABC followed a similar process. In both cases, information had to be collected, entered into a structured table, assessed and transformed into a decision-oriented output. In EY credit-file reviews, the output contributed to an internal loan-risk classification. At ABC, the framework is intended to support the screening of nonprofit organizations and future funding decisions.

The fundamental difference lies in who defines the methodology. At EY, the required fields, supporting documents, assessment standards and classification rules had already been established. My responsibility was to research and verify the information, complete the required fields, check consistency and identify issues that needed clarification. At ABC, our team is helping to design the table itself: its questions, evidence requirements, gates, scoring logic and final decision rules.

The two settings also use different forms of internal consistency. In valuation checks at EY, I examined numerical relationships: for example, how operational assumptions entered one schedule and affected another calculation. In the ABC framework, the relationship is procedural. An organization must pass the compliance gate before its communication potential and project value are assessed. In both cases, a result is credible only when the path from evidence to conclusion can be traced.

Why the evaluation-criteria model appears here

Figure 1. Evaluation criteria developed by the Organisation for Economic Co-operation and Development’s Development Assistance Committee (OECD-DAC).

Source: KfW Development Bank, based on the OECD-DAC evaluation criteria.

The OECD-DAC framework evaluates development interventions through six complementary criteria: relevance, coherence, effectiveness, efficiency, impact and sustainability. It appears here because our team first examined established evaluation methodologies before designing a framework adapted to nonprofit screening and funding decisions. The framework shows that evaluation requires several distinct questions and that each criterion should clarify what evidence an evaluator needs.

However, we did not copy the OECD-DAC model directly. It is primarily designed to evaluate development interventions, whereas our client needs to screen organizations before selecting partners and allocating future funding. We therefore used it as a methodological reference and adapted its multi-dimensional logic to our sequential compliance, communication and value framework. It informed our thinking, but it is not our final framework.

A practical illustration of the framework

Consider a nonprofit organization with a legally registered status, an active social-media account and an education project that appears attractive to potential donors. These characteristics would not immediately produce a positive recommendation. The compliance layer would first verify its registration, annual reports, financial disclosures and relevant risk information. If the organization passed this gate, the communication layer would distinguish current performance, such as publication frequency and audience engagement, from future potential, including the availability of credible project stories, supporting evidence and willingness to cooperate. The value layer would then examine whether the project addresses a genuine need and is aligned with the foundation’s objectives.

This staged process is intended to avoid two common errors: treating polished communication as proof of project value, and treating a lack of current communication resources as proof that an organization has no communication potential. The detailed indicators are still being developed and will need practical testing before the methodology is delivered.

Validation before delivery

A framework may look logical in a presentation and still fail in practice. Another project group will apply the draft indicators to real cases. Their work may reveal unavailable data, overlapping criteria, vague scoring anchors or outputs that do not match the client’s decision needs. We will then revise the definitions, evidence requirements, gates and scoring rules. This process resembles audit in an important way: a conclusion must be traceable to evidence. It also resembles analytics because the model must be tested against observed results rather than defended simply because effort has already been invested in it.

Required skills and knowledge

Both experiences required attention to detail, information organization and professional judgment. Audit placed greater emphasis on accuracy, documentation, reconciliation and escalation. Nonprofit consulting required methodology research, framework design, stakeholder alignment and the ability to translate broad ideas into observable indicators. Across both settings, the most important skill was asking what decision the information was meant to support before deciding how to collect or process it.

What I learned

EY taught me how to use a methodology responsibly, while ABC taught me how difficult it is to design one responsibly. Applying an existing framework requires accuracy, traceability and consistency. Designing a new framework requires the same discipline, but also demands decisions about definitions, evidence, thresholds and trade-offs. Together, the two experiences changed my understanding of evaluation: a reliable result depends not only on the quality of the information entered into a table, but also on the quality of the methodology behind the table.

Economic, financial and business concepts related to my experience

Due diligence and information reliability

Due diligence reduces information asymmetry by checking the reliability, completeness and relevance of evidence before a decision. Credit-file review, valuation checks and nonprofit compliance screening all apply this logic, even though their procedures and stakeholders differ. In each setting, important claims should be traceable to identifiable sources.

Multi-criteria decision analysis

Funding decisions rarely depend on a single metric. Multi-criteria decision analysis makes trade-offs explicit by defining dimensions, evidence and scoring rules. Some conditions may function as gates rather than compensable scores. In our emerging framework, compliance serves this role: communication strength cannot compensate for failure to meet a mandatory compliance requirement.

Model validation and iteration

An evaluation framework is a model of reality. Validation asks whether different reviewers can apply it consistently, whether the necessary evidence is available and whether its outputs are useful for the intended decision. Testing and iteration are therefore part of responsible design, not signs that the first draft failed.

Why should I be interested in this post?

For students interested in audit, consulting, impact investing or business analytics, these experiences show a progression from checking information to structuring judgment. Entry-level work may begin with documents, confirmations and meeting records, but those tasks build the discipline needed for higher-level analysis. The challenge is to connect every task to its decision purpose: what is being verified, what uncertainty remains and what evidence would justify the next step?

Useful resources

Academic research and reports

Gamvros, I., Nidel, M., & Raghavan, N. R. S. (2006) Investment Analysis and Budget Allocation at Catholic Relief Services, Interfaces, 36(5), 400-406.

Salimi, N., & ten Have, W. (2024) Aiding Philanthropic Venture Capitalists in Selecting Nonprofit Organisations, Journal of Philanthropy and Marketing, 29(1), e1834.

Bayarri, M. J., Berger, J. O., Higdon, D., Kennedy, M. C., Kottas, A., Paulo, R., Sacks, J., Cafeo, J. A., Cavendish, J., Lin, C. H., & Tu, J. (2002) A Framework for Validation of Computer Models, NISS Technical Report 128.

OECD: Evaluation criteria

Business and organization resources

EY: About us

A Better Community: About

A Better Community: Consulting services

KfW Development Bank: Evaluation criteria and rating scale

About the author

The article was written in August 2026 by Feitong GUO (The Chinese University of Hong Kong, Shenzhen, Accounting and Data Analytics, 2023-2027; ESSEC Business School, BBA Exchange Program, Spring 2026).

▶ Discover all articles by Feitong GUO .

Know yourself and know your opponent, and you will never be defeated – Sun Tzu

August 6, 2026 by Bochen LIU

In this article, Bochen LIU (Queen’s Smith School of Business, BCom 2023–2027; ESSEC BBA Exchange Program, Fall 2025) analyzes how the famous Chinese strategic quote “知己知彼，百战不殆” (know yourself and know your opponent, and you will never be defeated), derived from Sun Tzu’s The Art of War, functions as a foundational principle in modern Business strategy, particularly in positioning, segmentation, and competitive analysis.

Know Yourself, Know Your Opponent: Applying Sun Tzu’s Strategy to Modern Business Competition

The Chinese strategic philosophy “知己知彼，百战不殆” (know yourself and know your opponent, and you will never be defeated), originating from Sun Tzu’s The Art of War, continues to provide valuable insights for modern business strategy and competitive analysis. Although this principle was originally developed in the context of military strategy, its underlying logic remains highly relevant in today’s corporate environment. Companies must understand their own capabilities while continuously analyzing competitors, customers, and external market conditions. Knowledge of both internal resources and external competition enables organizations to develop stronger positioning strategies and maintain competitive advantages.

In the current business environment, companies face uncertainty caused by changing consumer behaviour, technological development, and increasing competition. Having a high-quality product or service alone does not guarantee success. Organizations must understand their own strengths, recognize their weaknesses, and evaluate how competitors influence customer expectations and market conditions. Therefore, the principle of “knowing yourself and knowing your opponent” can be transformed into a practical business framework through Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis, which allows companies to evaluate internal capabilities and external challenges.

Understanding “Know Yourself”

“Know yourself” refers to the process through which companies understand their internal resources, capabilities, and limitations. From a business perspective, this involves evaluating internal factors such as brand reputation, product quality, pricing strategies, operational efficiency, financial resources, and customer relationships.

Understanding internal strengths allows companies to identify areas where they possess competitive advantages. For example, companies with strong brand recognition may not compete with competitors through lower prices. Instead, they can create value through customer loyalty, premium positioning, and differentiated customer experiences. Similarly, organizations with unique technologies or specialized expertise can use these capabilities to distinguish themselves from competitors.

However, knowing oneself also requires recognizing internal weaknesses. Companies that fail to understand their limitations may develop unrealistic strategies that cannot be effectively implemented. For example, a business may provide high-quality products but lack sufficient distribution channels, marketing resources, or operational capacity to support expansion.

Therefore, “know yourself” does not simply mean identifying positive characteristics. It requires an objective evaluation of both strengths and weaknesses so that companies can develop realistic and effective strategies.

Understanding “Know Your Opponent”

“Know your opponent” refers to understanding competitors, customers, and external market conditions. Businesses operate in environments where competitors continuously adjust their strategies, introduce new products, and influence consumer expectations. Without sufficient knowledge of the external environment, companies may lose their competitive position.

Competitor analysis allows businesses to understand how other organizations attract customers, differentiate their products, and respond to market changes. For example, companies operating in the education industry need to analyze competitors’ pricing strategies, teaching methods, customer acquisition approaches, and brand positioning before developing their own marketing strategies.

Knowing your opponent does not mean copying competitors’ strategies. Instead, it enables businesses to identify opportunities for differentiation and develop unique value propositions. By understanding competitors’ strengths and weaknesses, companies can discover underserved customer segments and create stronger market positions.

Therefore, “know your opponent” represents a continuous process of monitoring external changes and adapting business strategies according to evolving market conditions.

Applying “Know Yourself and Know Your Opponent” Through SWOT Analysis

SWOT analysis is one of the most effective frameworks for applying Sun Tzu’s principle to modern business strategy. The framework evaluates four key areas: Strengths, Weaknesses, Opportunities, and Threats. Strengths and weaknesses represent internal factors related to “knowing yourself,” while opportunities and threats represent external factors related to “knowing your opponent.”

SWOT matrix
Source: the author.

Strengths and Weaknesses: Internal Analysis

Strengths refer to internal advantages that allow companies to compete effectively. These advantages may include strong brand recognition, innovative products, loyal customers, advanced technology, or efficient operations. By identifying strengths, companies can develop strategies that maximize their existing competitive advantages.

Weaknesses refer to internal limitations that may reduce competitiveness. These may include high operating costs, limited resources, weak brand awareness, or inefficient processes. Recognizing weaknesses allows organizations to improve internal capabilities and prevent potential problems from affecting future growth.

Opportunities and Threats: External Analysis

Opportunities refer to external conditions that create potential growth possibilities. These may include emerging customer demands, technological development, new market segments, or changing consumer preferences. Companies that successfully identify opportunities can adapt their strategies and expand their market presence.

Threats refer to external factors that may negatively influence business performance. These include new competitors, changing consumer behaviour, economic uncertainty, and increasing price competition. Understanding threats allows companies to prepare appropriate responses before competitors gain an advantage.

Through SWOT analysis, businesses can transform the abstract principle of “know yourself and know your opponent” into a structured decision-making process. This framework allows organizations to connect internal capabilities with external market conditions and develop more effective strategies.

Real Business Example: Starbucks’ Competitive Strategy

Starbucks Corporation provides a clear example of how “know yourself and know your opponent” can be applied through SWOT analysis. The company demonstrates how understanding internal capabilities and external competition can support long-term competitive success.

From an internal perspective, Starbucks’ major strengths are its global brand recognition and unique customer experience. Starbucks is not simply a company that sells coffee; it creates value through store atmosphere, personalized beverages, customer service, and strong brand identity. These advantages allow Starbucks to build customer loyalty and maintain premium pricing compared with many traditional coffee providers.

However, Starbucks also faces internal weaknesses. Operating thousands of locations worldwide requires significant investment in employees, store management, supply chains, and quality control. These high operating costs create challenges when Starbucks competes with lower-cost coffee providers. Recognizing these weaknesses allows the company to improve efficiency and adapt its operations.

From an external perspective, Starbucks identifies opportunities by analyzing changes in consumer behaviour. The increasing popularity of digital ordering, mobile payment systems, and personalized products provides opportunities for Starbucks to strengthen customer relationships. By understanding customer expectations, Starbucks can develop services that better match market demands.

At the same time, Starbucks must understand competitive threats. In markets such as China, Starbucks faces increasing competition from local coffee brands that compete through lower prices, rapid expansion, and digital platforms. Instead of competing only through price, Starbucks relies on its strengths, including global branding, customer experience, and product innovation, to maintain differentiation.

This example demonstrates that successful businesses need both internal and external analysis. Starbucks maintains competitiveness because it understands its own capabilities while continuously monitoring competitor strategies and market changes.

Conclusion

The principle of “know yourself and know your opponent” remains an important concept in contemporary business strategy. Organizations cannot achieve sustainable success by focusing only on their internal capabilities. They must also understand competitors, customers, and external environmental factors that influence market performance.

SWOT analysis provides a practical method for implementing this principle because it combines internal evaluation with external market analysis. Through SWOT analysis, businesses can create more effective strategies, improve market positioning, and develop sustainable competitive advantages.

Sun Tzu’s strategic thinking therefore continues to provide valuable lessons for modern organizations. Companies that accurately understand themselves and their opponents are better prepared to compete in dynamic markets.

Useful resources

The Art of War (Chinese Text Project)

Encyclopaedia Britannica – The Art of War

American Marketing Association (AMA)

About the author

The article was written in August 2026 by Bochen LIU (Queen’s Smith School of Business, BCom 2023–2027; ESSEC BBA Exchange Program, Fall 2025).

▶ Discover all posts by Bochen LIU

My experience as a machine learning research intern: from social media data to an IEEE publication

July 31, 2026 by Anirudh KUMAR

In this article, Anirudh KUMAR (B.S. Economics, IIT Kanpur, with a minor in Artificial Intelligence and Machine Learning) shares his experience as a machine learning research intern. From February to August 2024, he worked under the supervision of Professor Swagato Chatterjee on a study of how people respond to Facebook communication about hydrogen fuel cell vehicles. The project later became a paper in IEEE Transactions on Engineering Management.

About the research project

The project examined a practical communication problem. When an organization posts about a sustainable technology, which parts of the message are associated with likes, comments, shares, and other interactions? We chose hydrogen fuel cell vehicles because the technology is promising but still unfamiliar to many people. Organizations therefore have to explain both its immediate uses and its longer-term potential.

A single subject can be framed in very different ways. A post about a hydrogen-powered bus might discuss lower tailpipe emissions, give technical details about fuel-cell efficiency, announce a government pilot project, or warn about the cost of delaying cleaner transport. The technology is the same, but the topic, readability, and emotional tone are different. Our job was to turn those differences into variables that we could test.

My internship

My missions

My mission was to help build a reproducible pipeline from raw Facebook posts to research findings. I worked on text cleaning, topic modelling, sentiment and emotion measures, model comparison, charts, and short research briefs. Each stage had to answer a specific question: what was measured, why was it measured that way, and could another researcher reproduce the result?

Figure 1. From Facebook posts to research findings
Research workflow from Facebook data to text features, models, and findings
Source: Author’s own work.

The research workflow in practice

Step 1: collecting and structuring the data

The study used Facebook posts collected through CrowdTangle from February 2022 to February 2024. The search combined “hydrogen fuel” with terms such as vehicle, car, transport, train, airplane, traffic, and truck. We kept English-language posts and used the country of the page administrators to divide the sample.

The final dataset contained 9,672 posts: 4,106 observations from developed countries and 5,566 from developing countries. The developed-country sample was led by the United States, the United Kingdom, and Australia. The developing-country sample included India, China, the Philippines, South Africa, Pakistan, Myanmar, and observations from 72 other countries.

Our outcome variable was the total number of interactions with a post. We also recorded factors that could affect engagement even before considering the words, including the page’s followers and likes at the time of posting and whether the post was a photo, video, status update, or another format.

Step 2: cleaning the text with NLTK and spaCy

NLTK and spaCy are Python libraries for natural language processing. I used them to split text into tokens, remove material that did not help the analysis, and reduce related word forms to a common lemma. For example, lemmatization can map vehicles to vehicle and emissions to emission. This prevents the model from treating simple grammatical variations as unrelated ideas.

A simplified preprocessing example

Illustrative post before cleaning: “Hydrogen-powered buses are not yet cost competitive, but they can reduce urban emissions.”

Illustrative tokens after cleaning: “hydrogen”, “power”, “bus”, “not”, “yet”, “cost”, “competitive”, “reduce”, “urban”, “emission”.

The word not is important. If a standard stop-word list removed it, the sentence could appear more positive than the writer intended. I therefore did not treat preprocessing as a one-click operation. I inspected examples after URL and punctuation removal, after tokenization, after stop-word filtering, after lemmatization, and after the final features had been created. This is what I meant by checking the data after every major transformation.

Consistency mattered too. The forms EV, electric vehicle, and electric vehicles can refer to the same idea. If they are left as separate terms without review, topic frequencies and model inputs can become harder to interpret.

Step 3: finding topics with Latent Dirichlet Allocation

Latent Dirichlet Allocation, or LDA, is a topic model. It searches for groups of words that often occur together and represents each post as a mixture of those groups. I compared specifications with different numbers of topics and used coherence scores as one diagnostic. The final choice also had to produce topics that a reader could distinguish and name.

For developed countries, the model identified 12 topics. Examples included “Battery Technology and Toxicity” with words such as battery, lithium, toxic, electricity, EV, and metal; “Electric Aircraft and Aviation” with aircraft, plane, aviation, and flight; and “Electric Cars and Sustainable Transportation” with vehicle, electric, car, and powered. These three short-term topics had the strongest positive relationships with engagement relative to the reference topic in the final Poisson model.

For developing countries, the model identified 11 topics. Examples included “Government Green Energy Projects”, “Electric Vehicles and Battery Technology”, and “Future Energy Technologies”. The mix was different from the developed-country sample: immediate transport projects mattered, but longer-term energy planning also attracted attention.

This step taught me the difference between an output and a finding. LDA will always return word groups. The researcher still has to check whether those groups are stable, distinct, and useful for answering the research question.

Step 4: measuring sentiment and emotion

I used TextBlob and NLTK WordNet for sentiment measures and NRCLex for emotion categories. Sentiment summarizes whether the language is more positive or negative. NRCLex adds categories such as anticipation, trust, joy, surprise, fear, anger, disgust, and sadness.

The distinction matters because two negative messages can invite different reactions. A post expressing sadness about slow adoption may encourage passive reading, while a post warning about an urgent climate or energy risk may evoke fear and prompt comments or shares. In the final models, fear had the strongest positive relationship among the negative emotions in both country groups. Among positive emotions, anticipation was important in developed countries and trust was important in developing countries.

Dictionary-based scores are constructed variables, not direct readings of a person’s feelings. Negation, technical language, and context can change a sentence’s meaning. I therefore checked sample classifications instead of assuming that every automated label was correct.

Step 5: measuring readability

The study measured text complexity with the Gunning Fog Index. The index combines average sentence length with the share of complex words, usually words with three or more syllables:

Fog Index = 0.4 x [(words / sentences) + 100 x (complex words / words)]

Consider three ways to introduce the same subject. “Hydrogen cars are good” is easy to read but says little. “Hydrogen fuel cells produce electricity without tailpipe carbon emissions, although storage and refuelling remain costly” gives the reader both an accessible explanation and useful detail. A paragraph filled with unexplained terms such as proton-exchange membranes, electrochemical conversion, and volumetric energy density may be accurate but difficult for a general audience.

The results showed an inverted U-shaped relationship between the Fog Index and interactions. Engagement rose as messages became more informative, then fell when the text became too complex. The precise turning point differed by context. In the developing-country analysis, the partial-dependence curve reached its maximum near a Fog score of 20.

Step 6: comparing explanatory and predictive models

We used Poisson regression to explain how readability, topics, and emotions were associated with the count of interactions. We then compared Random Forest, Support Vector Regression, XGBoost, and Poisson-based predictive models. The predictive exercise used both in-sample and out-of-sample root mean squared error, or RMSE. A lower out-of-sample RMSE means the model predicted unseen posts more accurately.

The comparison also showed why the training score cannot be the only criterion. In the developed-country sample, XGBoost achieved a low in-sample error but showed signs of overfitting. Random Forest provided a better balance. Its out-of-sample RMSE fell from 4,898 in the baseline model to 3,056 after topic information was added. Adding emotion variables changed the error to 3,171, so the larger feature set was not automatically better.

What the study found

Message complexity has a middle ground

Posts that were very simple could lack useful information, while highly technical posts could demand too much effort from a general reader. The highest engagement appeared between those extremes. For an organization, the practical lesson is to explain the technology clearly without removing the detail that makes the message informative.

Relevant topics differ across markets

In developed countries, short-term topics such as electric vehicles, battery technology, and aviation were more engaging than distant or futuristic themes. In developing countries, the pattern was mixed. Government green-energy projects and battery technology mattered, but future energy technologies also resonated. A single global content plan would miss these differences.

Trust and fear both matter

Trust can make a new technology feel credible, while fear can draw attention to the consequences of inaction. Neither result means that organizations should exaggerate. It means the emotional tone of a message is measurable and should be considered alongside the topic and readability.

Communicating the results

I converted the analysis into topic tables, model-comparison charts, feature-importance plots, and three short research briefs. One chart compared the out-of-sample RMSE of the four predictive approaches. Another summarized the 12 developed-country topics and the 11 developing-country topics. The briefs translated those outputs into communication questions: how technical should a post be, which topics fit each market, and which emotional signals require careful interpretation?

This work exposed weak explanations quickly. If I could not explain why Random Forest was preferred to XGBoost, or what a topic coefficient meant relative to the reference topic, the figure was not ready for another reader.

Required skills and knowledge

The internship required Python, natural language processing, regression, machine learning, and model evaluation. My economics courses helped me define variables and separate association from causation. My AI and machine-learning coursework helped me build the pipeline and compare models.

The work also required careful records. I kept track of cleaning rules, exclusions, topic specifications, model versions, and evaluation results. I treated the code as shared work. A co-author should be able to see what changed and why.

From internship to publication

The project developed into the article Drivers of Social Media Engagement on Organizational Communication on Sustainable Technological Innovation: Insights from Developed and Developing Countries, published in 2026 in IEEE Transactions on Engineering Management.

The manuscript went through several rounds of revision. I learned to treat a changed result as information rather than as a setback. If a coefficient, topic, or model ranking changed after a reasonable specification check, we needed to understand the reason before keeping the claim.

What I learned

The question comes before the model.

I now write the research question in plain language before opening a notebook. In this project, the question was not simply which algorithm predicts engagement best. We also needed to understand which message characteristics were associated with engagement and whether the relationships differed across country groups.

Small decisions need a record.

A stop-word choice can change the meaning of a sentence. A different number of LDA topics can change the labels used in the regression. Weeks later, those decisions are difficult to reconstruct from memory, so I keep a short log of exclusions, transformations, and model changes.

Prediction and explanation are different tasks.

Poisson regression helped us interpret relationships between the features and interaction counts. Random Forest was more useful for prediction. Neither result replaced the other. A strong research design needed both an interpretable explanation and an honest test on unseen data.

Financial concepts related to my internship

Investor attention

Attention is scarce in financial markets as well as on social media. Investors cannot read every earnings call, filing, news article, and management post. Topic and engagement analysis can help researchers study which firms or themes receive attention and whether that attention is associated with trading activity.

Sentiment and textual analysis

Earnings calls, annual reports, and management commentary contain language that can be measured systematically. The internship taught me to inspect how a sentiment variable was built before treating it as a signal. A financial sentence with negation or technical terminology can confuse the same dictionary methods used for social-media text.

Model risk

The XGBoost result offered a direct example of model risk. A model can fit the training data closely and perform poorly on new observations. Out-of-sample tests, sensitivity checks, and readable documentation are essential when a model may influence an investment or business decision.

How I use these lessons now

In my current work as an AI Growth Intern at Pocket FM, I apply the same method to a different problem: a GenAI workflow that converts Hindi audio scripts into Marathi. I work with editors to define quality criteria for emotional beats, narrative hooks, and regional expressions. A fluent translation is not enough if it changes the scene’s intent.

The research internship taught me to make those criteria explicit, inspect intermediate outputs, and document recurring errors. The tools have changed, but the habit of testing each transformation remains useful.

Why should I be interested in this post?

A published paper can make research look linear. My experience was less tidy. A cleaning rule affected the topics, the topics affected the models, and the models sometimes sent us back to an earlier decision. The practical work was in those links.

For students interested in finance, data science, or AI, the project shows how researchers can turn raw language into evidence. Technical skill is necessary, but the result is easier to trust when the examples, assumptions, and out-of-sample performance are visible.

Useful resources

Chatterjee, S., Ghatak, A., Meena, A. K. and Meena, P. (2026). Drivers of Social Media Engagement on Organizational Communication on Sustainable Technological Innovation: Insights from Developed and Developing Countries. IEEE Transactions on Engineering Management, 73. DOI: 10.1109/TEM.2026.3665711.

Blei, D. M., Ng, A. Y. and Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993-1022.

NLTK Project. Natural Language Toolkit documentation.

Explosion. spaCy usage documentation.

About the author

The article was written in July 2026 by Anirudh KUMAR, who completed a B.S. in Economics at IIT Kanpur with a minor in Artificial Intelligence and Machine Learning. He is an AI Growth Intern at Pocket FM and previously worked in product management at AryaVastra and machine-learning research under Professor Swagato Chatterjee. His interests include behavioral finance, empirical research, and AI applications in business.

▶ Discover all articles by Anirudh KUMAR.

Trading as Principal in Illiquid Markets: What No Finance Course Can Fully Prepare You For

July 24, 2026 by Isaac FAINSTEIN

In this article, Isaac FAINSTEIN, Director at Petrini Valores and Visiting Lecturer at IESEG School of Management (Lille), shares his professional experience as a trader in illiquid fixed income and emerging markets — and what practitioners know that most finance courses never cover.

About Petrini Valores

Petrini Valores is an Argentine broker-dealer specializing in fixed income, equities, derivatives, and financing. The firm operates as a market maker in illiquid corporate and provincial bonds, across multiple asset classes: peso-denominated, USD-denominated, inflation-linked, and dollar-linked instruments. It also participates as a member of underwriting syndicates in primary bond issuances.

As Director of the trading desk, I am responsible for pricing, execution, and risk management across these asset classes on a daily basis.

Logo of Petrini Valores.

Source: Petrini Valores.

Trading in practice: what the desk actually looks like

I have been trading fixed income and foreign exchange in Argentine markets for over fifteen years. Over that same period, I have taught applied finance courses at IESEG School of Management in Lille — courses built around the situations I encounter at the desk every week. What follows is an attempt to bridge those two worlds.

Agency, intermediation, and principal trading: three different jobs

Most finance programs teach students how to price securities. Fewer teach them what it actually feels like to put the firm’s capital at risk to make a market. The distinction between agency trading, intermediation, and principal trading is more consequential than most courses suggest.

In agency trading, you act on behalf of a client — executing their order in the market, taking no position yourself, earning a fee for the service. The client bears the market risk. You are their agent.

Intermediation — what practitioners often call riskless principal — is already a form of proprietary trading, technically speaking. You act as principal on both legs: you buy from one counterparty and simultaneously sell to another, earning the bid-ask spread. Because both legs close at the same time, your market exposure is minimal. You are not an agent of either side. You are a counterparty to both, just briefly, and without meaningful inventory risk.

Principal trading with inventory risk is something else entirely. The firm puts its own capital on the line with no guaranteed exit. You buy a bond from a client with no buyer lined up on the other side. You sell from your own inventory because a client needs to buy. You absorb the spread — and the full market risk that comes with holding the position until you can unwind it. The longer you hold, the more exposure you carry. This is the mode that no simulation fully replicates, and the one this article is about.

Pricing illiquid bonds: when there is no obvious answer

A large portion of my daily activity involves corporate and provincial bonds that do not trade on a liquid exchange. There is no visible order book. There is no Bloomberg mid-price that everyone agrees on. There is a fragmented OTC market where each dealer forms their own view of value.

When a client calls and asks for a bid or offer on one of these bonds, I have to produce a price — quickly, without full information. I know what I think the bond is worth. What I do not know is whether the client is a buyer or a seller.

This asymmetry is at the heart of market-making in illiquid securities. If I quote too tight a spread, I may find myself on the wrong side of a pre-arranged trade. A client may call five dealers simultaneously, collect our offers, and hit the best one — while already having a buyer on the other side paying more than my offer. In that case, I have sold bonds below what the market was willing to pay, and the client has effectively traded through me.

I use this scenario in class regularly. Students are always surprised. They assume that being a good trader means knowing what something is worth. It does — but it also means understanding the information game you are playing with the person on the other side of the phone.

Then there is the moment that every trader knows: you have priced the trade, the client has everything they need to decide, and then — nothing. They go to lunch. They are in a meeting. They are closing another trade. You are sitting there holding a price in a moving market, watching the bid shift while you wait for a response that may or may not come. No simulation I have seen fully replicates the specific discomfort of that moment.

Primary market underwriting: when commitment meets reality

Beyond secondary market activity, I participate as an underwriter in primary bond issuances for Argentine corporates, as part of the underwriting syndicate organized around each deal. This is a different kind of principal risk — one that is taken on before the bond even exists.

When a company decides to issue a bond, I commit to underwriting a portion of the deal. This is a real financial commitment: if investor demand is insufficient to cover the full issuance, I absorb the remainder onto my own book. In Argentina, primary markets typically use a Dutch auction format — investors submit bids specifying the coupon rate they are willing to accept and the quantity they want. The issuer then sets a clearing rate that satisfies the target issuance amount.

On auction day, I am simultaneously placing bonds with my own client base, managing my underwriting exposure, and monitoring where the clearing rate is likely to land. If I have covered my commitment with investor demand, I am in good shape. If not, the unsold portion of my underwriting commitment ends up on my balance sheet at the clearing rate — and I work that position off over the following days or weeks, offering it into a market that may or may not be ready to absorb it.

This is textbook principal risk. It is also something that very few students have any mental model for before entering the industry.

FX mismatches and capital controls: the Argentine laboratory

Argentina has operated with capital controls for years. At their peak, the gap between the official exchange rate and the blue-chip swap rate — a market-implied rate derived from the implicit FX embedded in cross-market bond transactions — reached several hundred percent. Today the gap has narrowed significantly, but the structure remains.

This creates situations that no standard finance course addresses. A bond denominated in dollars can be bought and sold in different currencies. If I buy a USD bond paying dollars and sell it against pesos, I receive pesos for an asset I paid for in dollars. I now have a currency mismatch on my book: I am effectively long pesos, short dollars. I can hedge that exposure immediately by buying back the dollars in the FX market, or — if I have a view that the implied exchange rate will move in my favor — I can hold the position and let it run.

The decision is not mechanical. It depends on my reading of the regulatory environment, the direction of the blue-chip swap rate, and how much currency risk I am willing to carry on the book at that moment. This is daily life on the desk. And it is very difficult to teach without the context that produces it.

When models break: the lesson of negative oil prices

In April 2020, front-month WTI crude oil futures briefly traded at negative prices. Physical storage constraints had overwhelmed the market’s mechanics, and sellers were willing to pay counterparties to take delivery of crude oil they had nowhere to store.

I watched it happen from the desk in real time. What struck me was not the price itself — it was the reaction across the industry. Many traders assumed it was a glitch. Some platforms were simply not built to display or process negative prices, and brokers whose systems could not show the quotes found themselves liable to clients who could not see — let alone act on — what was happening in the market. Several firms had to absorb losses because their technology had never contemplated the possibility.

I use this episode as an opening in class — not to explain futures mechanics, which students can read in any textbook — but to ask a different question: what do you do when the model produces an answer that the real world seems to reject? What is your decision framework when your screen shows something that looks impossible? The answer is that you need to understand the why behind the price before you can act on it. That understanding is not something you can look up in real time. Either you have built it, or you have not.

The most important rule on a trading desk

Every trader makes mistakes. A wrong-way position, a misread signal, a fat-finger entry. What separates good trading culture from bad is not the absence of errors — it is what happens in the first thirty seconds after one occurs.

The worst thing a trader can do is wait. Hiding a mistake, even briefly, turns a manageable problem into a serious one. A position that could have been closed at a small loss will compound. The bid-ask spread you avoided paying once will have widened by the time you are forced to act.

The most important rule on any trading desk is this: when you make a mistake, communicate it immediately. No fear of consequences should outweigh the cost of silence. A well-run desk creates an environment where immediate transparency is rewarded — because the alternative is invariably more expensive. This is not a financial concept. It is a cultural one. And it may be the most practically useful thing I can tell any student before they sit down at a real trading desk for the first time.

Argentina: the best trading school you never attended

With a World Cup recently concluded — and Argentina’s performance still fresh in everyone’s memory — there is a useful analogy worth making. Argentina’s best players did not all come through polished academies with perfect pitches and controlled conditions. Many learned on uneven surfaces, in chaotic environments, where improvisation and resilience were not optional. Those conditions, more often than not, produced technically complete and mentally durable players.

The same logic applies to trading in an environment like Argentina. Multiple asset classes, multiple yield curves, structural illiquidity, capital controls, and macroeconomic volatility — all simultaneously, all the time. Traders who come through this market and move to larger ones — Brazil, Mexico, or developed markets — typically find the transition smoother than expected. They have already navigated conditions that most traders in more liquid markets never face. When you learn to trade in the mud, the rest feels like solid ground.

Financial concepts related to this article

I present below four financial concepts central to my daily work as a trader in illiquid and emerging markets.

Principal trading and inventory risk

In principal trading, the broker-dealer buys or sells securities using its own capital, taking market risk onto its own balance sheet. This contrasts with agency trading, where the firm executes on behalf of a client and earns a fee, or with intermediation (riskless principal), where the firm matches both sides simultaneously and earns the bid-ask spread without holding inventory risk. The critical difference is time: in principal trading, the firm holds a position that may not be unwound immediately, and the longer it is held, the greater the market exposure.

Underwriting syndicate and book runner

In a primary bond issuance, several broker-dealers form an underwriting syndicate, each committing to place a portion of the deal with investors. The book runner is the lead of this syndicate — it manages the investor order book, coordinates pricing with the issuer, and oversees the allocation process. Other syndicate members, such as Petrini Valores in many Argentine corporate issuances, commit to their own underwriting tranche and are responsible for placing it with their client base. If a syndicate member cannot fully place its portion, the unsold bonds remain on its balance sheet at the clearing rate.

Dutch auction in primary bond markets

A Dutch auction is a price-discovery mechanism in which investors submit bids specifying both quantity and the coupon rate they are willing to accept. The issuer sets a single clearing rate that satisfies the target issuance amount. All successful bidders receive bonds at the clearing rate, regardless of their individual bids. This format is widely used in Argentine primary markets for corporate bond issuances.

Blue-chip swap rate and capital controls

In markets with capital controls, such as Argentina, the blue-chip swap rate (also known as the contado con liquidación or CCL rate) is an implied exchange rate embedded in cross-market bond transactions. It reflects the market’s assessment of currency value in the absence of free convertibility and can diverge significantly from the official rate. Managing positions across currencies in this environment requires an understanding of the regulatory framework and a constant read on the gap between official and market-implied rates.

Why should I be interested in this post?

If you are a finance student planning to work in sales and trading, fixed income, or any market-facing role, the situations described here are among the ones you will encounter earliest — and none of them are fully captured in a simulation or a pricing model.

The gap between finance education and market reality is not about knowledge. Most graduates know their bond math. The gap is about judgment: knowing how to act when information is incomplete, the counterparty is not responding, and the market is moving. Understanding how principal risk, illiquidity, and currency mismatches interact in real time is the difference between arriving prepared and arriving surprised.

Useful resources

Academic research

Gkillas K. and Longin, F. (2018) Financial market activity under capital controls: lessons from extreme events, Economics Letters, 171, 10-13.

Martellini, L., Priaulet, P., Priaulet, S. (2003) Fixed-Income Securities: Valuation, Risk Management and Portfolio Strategies, John Wiley & Sons.

Hull, J. C. (2021) Options, Futures, and Other Derivatives, 11th edition, Pearson.

Business resources

Petrini Valores — Argentine broker-dealer specializing in fixed income, equities, derivatives, and financing.

FINRA Tools and Calculators — public source for US bond transaction data and pricing context.

About the author

This article was written in July 2026 by Isaac FAINSTEIN, Director at Petrini Valores and Visiting Lecturer at IESEG School of Management (Lille), where he has taught applied finance and trading courses for over 10 years.

▶ Discover all articles by Isaac FAINSTEIN.

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