My experience as an Oil Analyst at an oil and energy trading company

December 20, 2022 by Youssef LOURAOUI

In this article, Youssef LOURAOUI (Bayes Business School, MSc. Energy, Trade & Finance, 2021-2022) shares his experience as an oil market analyst during a summer internship at Petroineos Trading Ltd.

About the company

Petroineos is a joint venture between PetroChina International London (PCIL) and the INEOS Group for refining and energy trading. PetroChina is one of the world’s major oil and gas enterprises and the INEOS is a refining and petrochemical group. This joint venture is a young and ambitious firm that was founded in 2011 with a dynamic approach to business and a strong ambition for long-term success.

In Petroineos, there are three sections which handle trading in different products: crude, refined products, and power and emissions. Petroineos’s annual trading volume exceeds 70 million tonnes, with assets worth around $6 billion and trading revenue in excess of $30 billion (Figure 1).

Its strategically positioned refineries in Grangemouth (Scotland) and Lavera (France), is among the oldest crude oil refineries in Europe. It provides gasoline to both domestic (Scotland and
France) and international markets, while also sustaining local economies. Every year, the two refineries process about 360,000 barrels of crude oil per day and create over 16 million tonnes of oil products.

Figure 1. Petroineos Trading Ltd. key numbers.
Petroineos Trading Ltd. numbers
Source: Petroineos Trading Ltd. (2022)

My internship

I was affected along with seven other interns, each with their own specific project that had to be completed throughout the 12-week internship. The first days were intense because we had to deal with jargon, practitioners’ concepts, and the dynamic nature of the trading floor. Fortunately, I had the opportunity to be supervised by a seasoned analyst during the first month of the internship, which allowed me step up my skills and be up and running for the next two months. There was a healthy mix of autonomous work and fruitful discussions with the other colleagues. This forced me to be independent in my job while also working in groups when required, allowing for some flexibility in how I performed.

The highlight of this internship was the relationships created with my fellow interns. It really helped a lot to make this experience so enjoyable. The teamwork, the feedbacks, the help that was offered among interns was really amazing; it created a strong bond inside and outside the office. It was a blessing to meet and learn from them as they are all well accomplished individuals who have a bright future in front of them.

By the end of my internship, I felt that I improved significantly in terms of notions regarding crude oil market, time management, relationships with my colleagues and good memories overall.

Missions

During this internship, I was assigned to the analysis department, which was in charge of providing reports and market updates for the major commodity markets. I did my internship in extraordinary times for the oil and commodity markets as it was a few months after the beginning of the Ukrainian-Russian conflict. At that time, the world was experiencing a global energy crisis, a shock of extraordinary scope and complexity. This crisis reminded us of the events of the 1970s (Oil shocks 73-79). This crisis had many dimensions, including coal, oil, gas, food security, and climate change. Governments around the world are seeking for an equilibrium that would deliver a good energy mix while retaining affordable and secure resources for their people, not only reducing reliance on a single commodity.

Natural gas spot prices had reached unprecedented highs, the equivalent of USD 250 for a barrel of oil. Furthermore, the crisis had fueled inflationary pressures and raised the prospect of a recession, as well as a massive USD 2 trillion windfall for fossil fuel producers above their 2021 net revenue. In response to energy shortages and high costs, governments have invested well over USD 500 billion, primarily in industrialized nations, to protect consumers from the immediate consequences of inflation (especially in gas and oil).

Keeping this in mind, I was assigned coverage on the impact of Western sanctions on the Russian crude oil market. Every market participant seeked to predict the impact of the sanctions on the number of barrels shipped from Russia.

Is Russia going to reduce its crude oil export since Europe represented alone more than 80% of its exports volume before the war?
Who are the new market players that are profiting from this situation and capitalizing on a discounted Russian crude oil in the international crude oil market?
Are there any patterns that can help in better understanding the crude flows?

Those three questions captured the importance of the analysis that had to be conducted in order to give sound and well detailed answers in order to capitalize with trading strategies that could leverage this information.

The main task was the redaction of the report which was shared across the whole company. The basic idea of this report was to give a micro overview on a weekly basis on the main changes that can impact the Urals market. I was in charge of analysing the vessels movement from the main ports in Russia and capture their discharging patterns in order to extract valuable information into the main discharging regions that are profiting from this market.

I also collaborated on other analyses with another intern from the Data Science department, which involved analyzing alternative data to identify any interesting signals that may be used as a trading strategy. In addition, I shared two further quantitative analyses involving econometric relationships to analyze Russian and global oil demand in analysis to other factors of relevance. The projects were incredibly interesting, and the insights were also helpful in understanding the complexities of collecting insights in an environment where analysts are surrounded by noisy data that must be filtered in order to communicate valuable information.

The main conclusions of the Russian coverage:

India and China as the main actors profiting from the discounted price of Urals

Russia becomes the highest exporter of crude oil to India (Urals crude). The pattern change since the war unfolded. Historically, India imported oil from Irak and Saudi Arabia. This interest is based on the decision by the Asian countries to capitalise on a devaluated Russian oil price in the international crude oil market, which reached at some point of the war 30$/bbl difference with the main international oil benchmark (Brent). According to the Indian energy minister, they want to lock in the best price available in the international crude oil market (Figure 1).

According to US treasury Janet Yellen, this trend will continue, profiting from the western price cap on russian crude oil. G7 countries agreed in September to implement the price cap, which the US government hopes will be in place by December 5 when an EU embargo on the shipment of Russian crude comes into force. Under the mechanism, European companies will be permitted to transport and insure shipments of Russian oil to third countries as long as it is sold below a fixed price — an effort to limit the impact of the sanctions on global oil flows but ensure Russia’s earns less from the trade.

Dislocation of the market between Europe and Asia

Europe decreased importantly its dependence from Russian crude oil after the war in Ukraine. There is a shifting of actors in this market, with Asia skyrocketing demand compared to previous years because of the attractivity of the Urals in the international crude oil market. Also, if we shrink oil price volatility to its components, we see that:

External factors, other than supply and demand, play a more important role now specifically policy issues are more important than ever, accounting for more than 25% of oil volatility
20 years ago we could explain 90% of oil volatility by supply and demand, now this rate dropped to 65%.

Required skills

I would mention two main skills: market understanding and programming expertise. It is very beneficial to stay on top of market news, as it is good industry practice (especially for an analyst position) to understand the many market events that affect the specific commodity covered. As most businesses strive for automation, acquiring and mastering a programming language can only benefit future analysts. It has become a wider pre-requirement for most analyst positions.

Key learning

Key numbers

Some key numbers to have in mind to understand the crude oil market:

Estimation according to a reliable source: 1.43 trillion barrels left in the ground (2022)
Estimated part of oil consumption in most developed economies (around 30-45% of crude oil in the energy mix)
Estimated daily production per day in the world: around 100mb/d
Russia produce approximately 10% of the world daily crude oil demand. Urals production (the most traded grade of crude in the Russian oil market) was fluctuating on average around the 2mb/d threshold

Refinery margins

Refinery margin is derived from the difference between the refinery cost (buying crude) and the profit (selling refined product).

Refinery margin and crack spread

Crack spread represents the differential between the price of crude oil and the price of products refined. It is an industry specific metric to assess refining profitability. Crack refers also to the chemical process of decomposing the crude oil into different petroleum products. As different variables affect the price of crude oil and its refined products, this has an implication for refining margins.

Implementation of a crack strategy

Single product crack: Differential of one barrel of crude with one barrel of refined product
Multi product crack: Use of different refined products to secure a margin

Trading the crack spread

Either long or short crack: If long crack, confident view that refinery margins will strengthen (either crude oil price decreasing or products demand increasing)
If short crack, confident view that refinery margins are worsening, either because crude oil price increase or decrease of products demand

Reading crack spread as market signal

If crack widens, refined products more expensive than crude oil price, market expects that crude oil price will increase (to tighten back the spread to historical norm)
If crack tightens, refined products are sold cheaper than the price of crude oil, market expects that refined products (will reduce production) in order to get more expensive to widen the spread

Why should I be interested in this post?

This post will help any student looking to break into the work of oil or energy trading, but more generally for any analyst position, to have a grasp of the main concepts and skills that are required in the market and have a better understanding of the energy industry.

Useful resources

Business Analysis

Petroineos

Financial Times (2022) Russia becomes India’s top oil supplier as sanctions deflate price.

About the author

The article was written in December 2022 by Youssef LOURAOUI (Bayes Business School, MSc. Energy, Trade & Finance, 2021-2022).

Asset allocation techniques

December 19, 2022 by Youssef LOURAOUI

In this article, Youssef LOURAOUI (Bayes Business School, MSc. Energy, Trade & Finance, 2021-2022) presents the concept of asset allocation, a pillar concept in portfolio management.

This article is structured as follows: we introduce the notion of asset allocation, and we use a practical example to illustrate this notion.

Introduction

An investment portfolio is a collection of assets that are owned by an investor. Individual assets, such as bonds and stocks, as well as asset baskets, such as mutual funds or exchange-traded funds, can be employed. When constructing a portfolio, investors often consider both the projected return and risk. A well-balanced portfolio includes a wide range of investments to benefit from diversification.

The asset allocation is one of the processes in the portfolio construction process. At this point, the investor (or fund manager) must divide the available capital into a number of assets that meet the criteria in terms of risk and return trade-off, while adhering to the investment policy, which specifies the amount of exposure an investor can have and the amount of risk the fund manager can hold in his or her portfolio.

The next phase in the process is to evaluate the risk and return characteristics of the various assets. The analyst develops economic and market expectations that can be used to develop a recommended asset allocation for the customer. The distribution of equities, fixed-income securities, and cash; sub asset classes, such as corporate and government bonds; and regional weightings within asset classes are all decisions that must be taken in the portfolio’s asset allocation. Real estate, commodities, hedge funds, and private equity are examples of alternative assets. Economists and market strategists may set the top-down view on economic conditions and broad market movements. The returns on various asset classes are likely to be altered by economic conditions; for example, equities may do well when economic growth has been surprisingly robust whereas bonds may do poorly if inflation soars. These situations will be forecasted by economists and strategists.

The top-down approach

A top-down approach begins with assessment of macroeconomic factors. The investor examines markets and sectors based on the existing and projected economic climate in order to invest in those that are predicted to perform well. Finally, funding is evaluated for specific companies within these categories.

The bottom up approach

A bottom-up approach focuses on company-specific variables such as management quality and business potential rather than economic cycles or industry analysis. It is less concerned with broad economic trends than top-down analysis is, and instead focuses on company particular.

Types of asset allocations

Arnott and Fabozzi (1992) divide asset allocation into three types: 1) policy asset allocation; 2) dynamic asset allocation; and 3) tactical asset allocation.

Policy asset allocation

The policy asset allocation decision is a long-term asset allocation decision in which the investor aims to assess a suitable long-term “normal” asset mix that represents an optimal mixture of controlled risk and enhanced return. The strategies that offer the best prospects of achieving strong long-term returns are inherently risky. The strategies that offer the greatest safety tend to offer very moderate return opportunities. The balancing of these opposing goals is known as policy asset allocation. The asset mix (i.e., the allocation among asset classes) is mechanistically altered in response to changing market conditions in dynamic asset allocation. Once the policy asset allocation has been established, the investor can focus on the possibility of active deviations from the regular asset mix established by policy. Assume the long-run asset mix is established to be 60% equities and 40% bonds. A variation from this mix under certain situations may be tolerated. A decision to diverge from this mix is generally referred to as tactical asset allocation if it is based on rigorous objective measurements of value. Tactical asset allocation does not consist of a single, well-defined strategy.

Dynamic asset allocation

The term “dynamic asset allocation” can refer to both long-term policy decisions and intermediate-term efforts to strategically position the portfolio to benefit from big market swings, as well as aggressive tactical strategies. As an investor’s risk expectations and tolerance for risk fluctuate, the normal or policy asset allocation may change. It is vital to understand what aspect of the asset allocation decision is being discussed and in what context the words “asset allocation” are being used when delving into asset allocation difficulties.

Tactical asset allocation

Tactical asset allocation broadly refers to active strategies that seek to enhance performance by opportunistically adjusting the asset mix of a port- folio in response to the changing patterns of reward available in the capi- tal markets. Notably, tactical asset allocation tends to refer to disciplined techniques for evaluating anticipated rates of return on various asset classes and constructing an asset allocation response intended to capture larger rewards.

Asset allocation application: an example

For this example, lets suppose the fictitious following scenario with real data involved:

Mr. Dubois recently sold his local home construction company in the south of France to a multinational homebuilder with a nationwide reach. He accepted a job as regional manager for that national homebuilder after selling his company. He is now thinking about the financial future for himself and his family. He is looking forward to his new job, where he enjoys his new role and where he will earn enough money to meet his family’s short- and medium-term liquidity demands. He feels strongly that he should not invest the profits of the sale of his company in real estate because his income currently rely on the state of the real estate market. He speaks with a financial adviser at his bank about how to invest his money so that he can retire comfortably in 20 years.

The initial portfolio objective they created seek a nominal return goal of 7% with a Sharpe ratio of at least 1 (for this example, we consider the risk-free rate to be equal to zero). The bank’s asset management division gives Mr Dubois and his adviser with the following data (Figure 1) on market expectations.

Figure 1. Risk, return and correlation estimates on market expectation.
Time-series regression
Source: computation by the author (Data: Refinitiv Eikon).

In order to replicate a global asset allocation approach, we shortlisted a number of trackers that would represent our investment universe. To keep a well-balanced approach, we took trackers that would represent the main asset classes: global equities (VTI – Vanguard Total Stock Market ETF), bonds (IEF – iShares 7-10 Year Treasury Bond ETF and TLT – iShares 20+ Year Treasury Bond ETF) and commodities (DBC – Invesco DB Commodity Index Tracking Fund and GLD – SPDR Gold Shares). To create the optimal asset allocation, we extracted the equivalent of a ten-year timeframe from Refinitiv Eikon to capture the overall performance of the portfolio in the long run. As captured in Figure 1, the global equities was the best performing asset class during the period covered (13.02% annualised return), followed by long term bond (4.78% annualised return) and by gold (4.65% annualised return).

Figure 2. Asset class performance (rebased to 100).
Time-series regression
Source: computation by the author (Data: Refinitiv Eikon).

After analyzing the historical return on the assets retained, as well as their volatility and covariance (and correlation), we can apply Mean-Variance portfolio optimization to determine the optimal portfolio. The optimal asset allocation would be the end outcome of the optimization procedure. The optimal portfolio, according to Markowitz’ seminal study on portfolio construction, will seek to create the best risk-return trade-off for an investor. After performing the calculations, we notice that investing 42.15% in the VTI fund, 30.69% in the IEF fund, 24.88% in the TLT fund, and 2.28% in the GLD fund yields the best asset allocation. As reflected in this asset allocation, the investor intends to invest his assets in a mix of equities (about 43%) and bonds (approximately 55%), with a marginal position (around 3%) in gold, which is widely employed in portfolio management as an asset diversifier due to its correlation with other asset classes. As captured by this asset allocation, we can clearly see the defensive nature of this portfolio, which relies significantly on the bond part of the allocation to operate as a hedge while relying on the equities part as the main driver of returns.

As shown in Figure 3, the optimal asset allocation has a better Sharpe ratio (1.27 vs 0.62) and is captured farther along the efficient frontier line than a naive equally-weighted allocation . The only portfolio with the needed characteristics is the optimal one, as the investor’s goal was to attain a 7% projected return with a minimum Sharpe ratio of 1.

Figure 3. Optimal asset allocation and the Efficient Frontier plot.
Time-series regression
Source: computation by the author (Data: Refinitiv Eikon).

Will this allocation, however, continue to perform well in the future? The market’s reliance on future expectations, return, volatility, and correlation predictions, as well as the market regime, will ultimately determine how much the performance predicted by this study will really change in the future.

Excel file for asset allocation

You can find below the Excel spreadsheet that complements the example above.

Why should I be interested in this post?

The purpose of portfolio management is to maximize (expected) returns on the entire portfolio, not just on one or two stocks for a given level of risk. By monitoring and maintaining your investment portfolio, you can build a substantial amount of wealth for a variety of financial goals, such as retirement planning. This post facilitates comprehension of the fundamentals underlying portfolio construction and investing.

Useful resources

Academic research

Arnott, R. D., and F. J. Fabozzi. 1992. The many dimensions of the asset allocation decision. In Active asset allocation, edited by R. Arnott and F. J. Fabozzi. Chicago: Probus Publishing.

Fabozzi, F.J., 2009. Institutional Investment Management: Equity and Bond Portfolio Strategies and Applications. I (4-6). John Wiley and Sons Edition.

Pamela, D. and Fabozzi, F., 2010. The Basics of Finance: An Introduction to Financial Markets, Business Finance, and Portfolio Management. John Wiley and Sons Edition.

About the author

The article was written in December 2022 by Youssef LOURAOUI (Bayes Business School, MSc. Energy, Trade & Finance, 2021-2022).

Quantitative equity investing

December 19, 2022 by Youssef LOURAOUI

In this article, Youssef LOURAOUI (Bayes Business School, MSc. Energy, Trade & Finance, 2021-2022) elaborates on the concept of quantitative equity investing, a type of investment approach in the equity trading space.

This article follows the following structure: we introduce the quantitative equity investing. We present a review of the major types of quantitative equity strategies and we finish with a conclusion.

Introduction

Quantitative equity investing refers to funds that uses model-driven decision making when trading in the equity space. Quantitative analysts program their trading rules into computer systems and use algorithmic trading, which is overseen by humans.

Quantitative investing has several advantages and disadvantages over discretionary trading. The disadvantages are that the trading rule cannot be as personalized to each unique case and cannot be dependent on “soft” information such human judgment. These disadvantages may be lessened as processing power and complexity improve. For example, quantitative models may use textual analysis to examine transcripts of a firm’s conference calls with equity analysts, determining whether certain phrases are commonly used or performing more advanced analysis.

The advantages of quantitative investing include the fact that it may be applied to a diverse group of stocks, resulting in great diversification. When a quantitative analyst builds an advanced investment model, it can be applied to thousands of stocks all around the world at the same time. Second, the quantitative modeling rigor may be able to overcome many of the behavioral biases that commonly impact human judgment, including those that produce trading opportunities in the first place. Third, using past data, the quant’s trading principles can be backtested (Pedersen, 2015).

Types of quantitative equity strategies

There are three types of quantitative equity strategies: fundamental quantitative investing, statistical arbitrage, and high-frequency trading (HFT). These three types of quantitative investing differ in various ways, including their conceptual base, turnover, capacity, how trades are determined, and their ability to be backtested.

Fundamental quantitative investing

Fundamental quantitative investing, like discretionary trading, tries to use fundamental analysis in a systematic manner. Fundamental quantitative investing is thus founded on economic and financial theory, as well as statistical data analysis. Given that prices and fundamentals only fluctuate gradually, fundamental quantitative investing typically has a turnover of days to months and a high capacity (meaning that a large amount of money can be invested in the strategy), owing to extensive diversification.

Statistical arbitrage

Statistical arbitrage aims to capitalize on price differences between closely linked stocks. As a result, it is founded on a grasp of arbitrage relations and statistics, and its turnover is often faster than that of fundamental quants. Statistical arbitrage has a lower capacity due to faster trading (and possibly fewer stocks having arbitrage spreads).

High Frequency Trading (HFT)

HFT is based on statistics, information processing, and engineering, as the success of an HFT is determined in part by the speed with which they can trade. HFTs focus on having superfast computers and computer programs, as well as co-locating their computers at exchanges, actually trying to get their computer as close to the exchange server as possible, using fast cables, and so on. HFTs have the fastest trading turnover and, as a result, the lowest capacity.

The three types of quants also differ in how they make trades: Fundamental quants typically make their deals ex ante, statistical arbitrage traders make their trades gradually, and high-frequency traders let the market make their transactions. A fundamental quantitative model, for example, identifies high-expected-return stocks and then buys them, almost always having their orders filled; a statistical arbitrage model seeks to buy a mispriced stock but may terminate the trading scheme before completion if prices have moved adversely; and, finally, an HFT model may submit limit orders to both buy and sell to several exchanges, allowing the market to determine which ones are hit. Because of this trading structure, fundamental quant investing can be simulated with some reliability via a backtest; statistical arbitrage backtests rely heavily on assumptions on execution times, transaction costs, and fill rates; and HFT strategies are frequently difficult to simulate reliably, so HFTs must rely on experiments.

Table 1. Quantitative equity investing main categories and characteristics.

Source: Source: Pedersen, 2015.

Conclusion

Quants run their models on hundreds, if not thousands, of stocks. Because diversification eliminates most idiosyncratic risk, firm-specific shocks tend to wash out at the portfolio level, and any single position is too tiny to make a major impact in performance.

An equity market neutral portfolio eliminates total stock market risk by being equally long and short. Some quants attempt to establish market neutrality by ensuring that the long side’s dollar exposure equals the dollar worth of all short bets. This technique, however, is only effective if the longs and shorts are both equally risky. As a result, quants attempt to balance market beta on both the long and short sides. Some quants attempt to be both dollar and beta neutral.

Why should I be interested in this post?

It may provide an opportunity for investors to diversify their global portfolios. Including hedge funds in a portfolio can help investors obtain absolute returns that are uncorrelated with typical bond/equity returns.

For practitioners, learning how to incorporate hedge funds into a standard portfolio and understanding the risks associated with hedge fund investing can be beneficial.

Understanding if hedge funds are truly providing “excess returns” and deconstructing the sources of return can be beneficial to academics. Another challenge is determining whether there is any “performance persistence” in hedge fund returns.

Getting a job at a hedge fund might be a profitable career path for students. Understanding the market, the players, the strategies, and the industry’s current trends can help you gain a job as a hedge fund analyst or simply enhance your knowledge of another asset class.

Useful resources

Academic research

Pedersen, L. H., 2015. Efficiently Inefficient: How Smart Money Invests and Market Prices Are Determined. Chapter 9 : 133 – 164. Princeton University Press.

About the author

The article was written in December 2022 by Youssef LOURAOUI (Bayes Business School, MSc. Energy, Trade & Finance, 2021-2022).

Optimal portfolio

December 19, 2022 by Youssef LOURAOUI

In this article, Youssef LOURAOUI (Bayes Business School, MSc. Energy, Trade & Finance, 2021-2022) presents the concept of optimal portfolio, which is central in portfolio management.

This article is structured as follows: we first define the notion of an optimal portfolio (in the mean-variance framework) and we then illustrate the concept of optimal portfolio with an example.

Introduction

An investor’s investment portfolio is a collection of assets that he or she possesses. Individual assets such as bonds and equities can be used, as can asset baskets such as mutual funds or exchange-traded funds (ETFs). When constructing a portfolio, investors typically evaluate the expected return as well as the risk. A well-balanced portfolio contains a diverse variety of investments.

An optimal portfolio is a collection of assets that maximizes the trade-off between expected return and risk: the portfolio with the highest expected return for a given level of risk, or the portfolio with the lowest risk for a given level of expected return.

To obtain the optimal portfolio, Markowitz sought to optimize the following dual program:

The first optimization seeks to maximize expected return with respect to a specific level of risk, subject to the short-selling constraint (weights of the portfolio should be equal to one).

img_SimTrade_implementing_Markowitz_2

The second optimization seeks to minimize the variance of the portfolio with respect to a specific level of expected return, subject to the short-selling constraint (weights of the portfolio should be equal to one).

img_SimTrade_implementing_Markowitz

Mathematical foundations

The investment universe is composed of N assets characterized by their expected returns μ and variance-covariance matrix V. For a given level of expected return μ_P, the Markowitz model gives the composition of the optimal portfolio. The vector of weights of the optimal portfolio is given by the following formula:

With the following notations:

w_P = vector of asset weights of the portfolio
μ_P = desired level of expected return
e = identity vector
μ = vector of expected returns
V = variance-covariance matrix of returns
V^-1 = inverse of the variance-covariance matrix
t = transpose operation for vectors and matrices

A, B and C are intermediate parameters computed below:

img_SimTrade_implementing_Markowitz_2

The variance of the optimal portfolio is computed as follows

img_SimTrade_implementing_Markowitz_3

To calculate the standard deviation of the optimal portfolio, we take the square root of the variance.

Optimal portfolio application: the case of two assets

To create the optimal portfolio, we first obtain monthly historical data for the last two years from Bloomberg for two stocks that will comprise our portfolio: Apple and CMS Energy Corporation. Apple is in the technology area, but CMS Energy Corporation is an American company that is entirely in the energy sector. Apple’s historical return for the two years covered was 41.86%, with a volatility of 35.11%. Meanwhile, CMS Energy Corporation’s historical return was 13.95% with a far lower volatility of 15.16%.

According to their risk and return profiles, Apple is an aggressive stock pick in our example, but CMS Energy is a much more defensive stock that would serve as a hedge in our example. The correlation between the two stocks is 0.19, indicating that they move in the same direction. In this example, we will consider the market portfolio, defined as a theoretical portfolio that reflects the return of the whole investment universe, which is captured by the wide US equities index (S&P500 index).

As captured in Figure 1, CMS Energy suffered less severe losses than Apple. When compared to the red bars, the blue bars are far more volatile and sharp in terms of the size of the move in both directions.

Figure 1. Apple and CMS Energy Corporation return breakdown.
Time-series regression
Source: computation by the author (Data: Bloomberg)

After analyzing the historical return on both stocks, as well as their volatility and covariance (and correlation), we can use Mean-Variance portfolio optimization to find the optimal portfolio. According to Markowitz’ foundational study on portfolio construction, the optimal portfolio will attempt to achieve the best risk-return trade-off for an investor. After doing the computations, we discover that the optimal portfolio is composed of 45% Apple stock and 55% CMS Energy corporation stock. This portfolio would return 26.51% with a volatility of 19.23%. As captured in Figure 2, the optimal portfolio is higher on the efficient frontier line and has a higher Sharpe ratio (1.27 vs 1.23 for the theoretical market portfolio).

Figure 2. Optimal portfolio.
Optimal portfolio plot 2 asset
Source: computation by the author (Data: Bloomberg)

You can find below the Excel spreadsheet that complements the example above.

Optimal portfolio application: the general case

We generated a large time series to obtain useful results by downloading the equivalent of 23 years of market data from a data provider (in this example, Bloomberg). We generate the variance-covariance matrix after obtaining all necessary statistical data, which includes the expected return and volatility indicated by the standard deviation of the returns for each stock during the provided period. Table 1 depicts the expected return and volatility for each stock retained in this analysis.

Table 1. Asset characteristics of Apple, Amazon, Microsoft, Goldman Sachs, and Pfizer.
img_SimTrade_implementing_Markowitz_spreadsheet_1
Source: computation by the author.

We can start the optimization task by setting a desirable expected return after computing the expected return, volatility, and the variance-covariance matrix of expected return. With the data that is fed into the appropriate cells, the model will complete the optimization task. For a 20% desired expected return, we get the following results (Table 2).

Table 2. Asset weights for an optimal portfolio.
Optimal portfolio case 1
Source: computation by the author.

To demonstrate the effect of diversification in the reduction of volatility, we can form a Markowitz efficient frontier by tilting the desired anticipated return with their relative volatility in a graph. The Markowitz efficient frontier is depicted in Figure 1 for various levels of expected return. We highlighted the portfolio with 20% expected return with its respective volatility in the plot (Figure 3).

Figure 3. Optimal portfolio plot for 5 asset case.
Optimal portfolio case 1
Source: computation by the author.

You can download the Excel file below to use the Markowitz portfolio allocation model.

Why should I be interested in this post?

The purpose of portfolio management is to maximize the (expected) returns on the entire portfolio, not just on one or two stocks. By monitoring and maintaining your investment portfolio, you can build a substantial amount of wealth for a variety of financial goals such as retirement planning. This post facilitates comprehension of the fundamentals underlying portfolio construction and investing.

Useful resources

Academic research

Pamela, D. and Fabozzi, F., 2010. The Basics of Finance: An Introduction to Financial Markets, Business Finance, and Portfolio Management. John Wiley and Sons Edition.

Markowitz, H., 1952. Portfolio Selection. The Journal of Finance, 7(1): 77-91.

About the author

The article was written in December 2022 by Youssef LOURAOUI (Bayes Business School, MSc. Energy, Trade & Finance, 2021-2022).

Long-short equity strategy

December 17, 2022 by Youssef LOURAOUI

In this article, Youssef LOURAOUI (Bayes Business School, MSc. Energy, Trade & Finance, 2021-2022) presents the long-short equity strategy, one of pioneer strategies in the hedge fund industry. The goal of the long-short equity investment strategy is to buy undervalued stocks and sell short overvalued ones.

This article is structured as follow: we introduce the long-short strategy principle. Then, we present a practical case study to grasp the overall methodology of this strategy. We conclude with a performance analysis of this strategy in comparison with a global benchmark (MSCI All World Index).

Introduction

According to Credit Suisse, a long-short strategy can be defined as follows: “Long-short equity funds invest on both long and short sides of equity markets, generally focusing on diversifying or hedging across particular sectors, regions, or market capitalizations. Managers have the flexibility to shift from value to growth; small to medium to large capitalization stocks; and net long to net short. Managers can also trade equity futures and options as well as equity related securities and debt or build portfolios that are more concentrated than traditional long-only equity funds.”

This strategy has the particularity of potentially generate returns in both rising and falling markets. However, stock selection is key concern, and the stock picking ability of the fund manager is what makes this strategy profitable (or not!). The trade-off of this approach is to reduce market risk but exchange it for specific risk. Another key characteristic of this type of strategy is that overall, funds relying on long-short are net long in their trading exposure (long bias).

Equity strategies

In the equity universe, we can separate long-short equity strategies into discretionary long-short equity, dedicated short bias, and quantitative.

Discretionary long-short

Discretionary long-short equity managers typically decide whether to buy or sell stocks based on a basic review of the value of each firm, which includes evaluating its growth prospects and comparing its profitability to its valuation. By visiting managers and firms, these fund managers also evaluate the management of the company. Additionally, they investigate the accounting figures to judge their accuracy and predict future cash flows. Equity long-short managers typically predict on particular companies, but they can also express opinions on entire industries.

Value investors, a subset of equity managers, concentrate on acquiring undervalued companies and holding these stocks for the long run. A good illustration of a value investor is Warren Buffett. Since companies only become inexpensive when other investors stop investing in them, putting this trading approach into practice frequently entails being a contrarian (buy assets after a price decrease). Because of this, cheap stocks are frequently out of favour or purchased while others are in a panic. Traders claim that deviating from the standard is more difficult than it seems.

Dedicated short bias

Like equity long-short managers, dedicated short bias is a trading technique that focuses on identifying companies to sell short. Making a prediction that the share price will decline is known as short selling. Similar to how purchasing stock entails profiting if the price increases, holding a short position entail profiting if the price decreases. Dedicated short-bias managers search for companies that are declining. Since dedicated short-bias managers are working against the prevailing uptrend in markets since stocks rise more frequently than they fall (this is known as the equity risk premium), they make up a very small proportion of hedge funds.

Most hedge funds in general, as well as almost all equity long-short hedge funds and dedicated short-bias hedge funds, engage in discretionary trading, which refers to the trader’s ability to decide whether to buy or sell based on his or her judgement and an evaluation of the market based on past performance, various types of information, intuition, and other factors.

Quantitative

The quantitative investment might be seen as an alternative to this traditional style of trading. Quants create systems that methodically carry out the stated definitions of their trading rules. They use complex processing of ideas that are difficult to analyse using non-quantitative methods to gain a slight advantage on each of the numerous tiny, diversified trades. To accomplish this, they combine a wealth of data with tools and insights from a variety of fields, including economics, finance, statistics, mathematics, computer science, and engineering, to identify relationships that market participants may not have immediately fully incorporated in the price. Quantitative traders use computer systems that use these relationships to generate trading signals, optimise portfolios considering trading expenses, and execute trades using automated systems that send hundreds of orders every few seconds. In other words, data is fed into computers that execute various programmes under the supervision of humans to conduct trading (Pedersen, 2015).

Example of a long-short equity strategy

The purpose of employing a long-short strategy is to profit in both bullish and bearish markets. To measure the profitability of this strategy, we implemented a long-short strategy from the beginning of January 2022 to June 2022. In this time range, we are long Exxon Mobile stock and short Tesla. The data are extracted from the Bloomberg terminal. The strategy of going long Exxon Mobile and short Tesla is purely educational. This strategy’s basic idea is to profit from rising oil prices (leading to a price increase for Exxon Mobile) and rising interest rates (leading to a price decrease for Tesla). Over the same period, the S&P 500 index has dropped 23%, while the Nasdaq Composite has lost more than 30%. The Nasdaq Composite is dominated by rapidly developing technology companies that are especially vulnerable to rising interest rates.

Overall, the market’s net exposure is zero because we are 100% long Exxon Mobile and 100% short Tesla stock. This strategy succeeded to earn significant returns in both the long and short legs of the trade over a six-month timeframe. It yielded a 99.5 percent return, with a 36.8 percent gain in the value of the Exxon Mobile shares and a 62.8 percent return on the short Tesla position. Figure 1 shows the overall performance of each equity across time.

Figure 1. Long-short equity strategy performance over time
Time-series regression
Source: computation by the author (Data: Bloomberg)

You can find below the Excel spreadsheet that complements the example above.

Performance of the long-short equity strategy

To capture the performance of the long-short equity strategy, we use the Credit Suisse hedge fund strategy index. To establish a comparison between the performance of the global equity market and the long-short hedge fund strategy, we examine the rebased performance of the Credit Suisse index with respect to the MSCI All-World Index. Over a period from 2002 to 2022, the long-short equity strategy index managed to generate an annualised return of 5.96% with an annualised volatility of 7.33%, leading to a Sharpe ratio of 0.18. Over the same period, the MSCI All World Index managed to generate an annualised return of 6.00% with an annualised volatility of 15.71%, leading to a Sharpe ratio of 0.11. The low correlation of the long-short equity strategy with the MSCI All World Index is equal to 0.09, which is closed to zero. Overall, the Credit Suisse hedge fund strategy index performed somewhat slightly worse than the MSCI All World Index, but presented a much lower volatility leading to a higher Sharpe ratio (0.18 vs 0.11).

Figure 2. Performance of the long-short equity strategy compared to the MSCI All-World Index across time.
Time-series regression
Source: computation by the author (Data: Bloomberg)

You can find below the Excel spreadsheet that complements the explanations about the Credit Suisse hedge fund strategy index.

Why should I be interested in this post?

Long-short funds seek to reduce negative risk while increasing market upside. They might, for example, invest in inexpensive stocks that the fund managers believe will rise in price while simultaneously shorting overvalued stocks to cut losses. Other strategies used by long-short funds to lessen market volatility include leverage and derivatives. Understanding the profits and risks of such a strategy might assist investors in incorporating this hedge fund strategy into their portfolio allocation.

Useful resources

Academic research

Pedersen, L. H., 2015. Efficiently Inefficient: How Smart Money Invests and Market Prices Are Determined. Princeton University Press.

Business Analysis

BlackRock Long-short strategy

BlackRock Investment Outlook

Credit Suisse Hedge fund strategy

Credit Suisse Hedge fund performance

Credit Suisse Long-short strategy

Credit Suisse Long-short performance benchmark

About the author

The article was written in December 2022 by Youssef LOURAOUI (Bayes Business School, MSc. Energy, Trade & Finance, 2021-2022).

Fama-MacBeth two-step regression method: the case of K risk factors

December 12, 2022 by Youssef LOURAOUI

Fama-MacBeth two-step regression method: the case of K risk factors

This article is structured as follows: we introduce the Fama-MacBeth two-step regression method. Then, we present the mathematical foundation that underpins their approach for K risk factors. We provide an illustration for the 3-factor mode developed by Fama and French (1993).

Introduction

Risk factors are frequently employed to explain asset returns in asset pricing theories. These risk factors may be macroeconomic (such as consumer inflation or unemployment) or microeconomic (such as firm size or various accounting and financial metrics of the firms). The Fama-MacBeth two-step regression approach found a practical way for measuring how correctly these risk factors explain asset or portfolio returns. The aim of the model is to determine the risk premium associated with the exposure to these risk factors.

The first step is to regress the return of every asset against one or more risk factors using a time-series approach. We obtain the return exposure to each factor called the “betas” or the “factor exposures” or the “factor loadings”.

The second step is to regress the returns of all assets against the asset betas obtained in Step 1 using a cross-section approach. We obtain the risk premium for each factor. Then, Fama and MacBeth assess the expected premium over time for a unit exposure to each risk factor by averaging these coefficients once for each element.

Mathematical foundations

We describe below the mathematical foundations for the Fama-MacBeth regression method for a K-factor application. In the analysis, we investigated the Fame-French three factor model in order to understand their significance as a fundamental driver of returns for investors under the Fama-MacBeth framework.

The model considers the following inputs:

The return of N assets denoted by R_i for asset i observed every day over the time period [0, T].
The risk factors denoted by F_k for k equal from 1 to K.

Step 1: time-series analysis of returns

For each asset i from 1 to N, we estimate the following linear regression model:

Fama-French time-series regression

From this model, we obtain the β_{i, Fk} which is the beta associated with the k^th risk factor.

Step 2: cross-sectional analysis of returns

For each period t from 1 to T, we estimate the following linear regression model:

Fama-French cross-sectional regression

Application: the Fama-French 3-Factor model

The Fama-French 3-factor model is an illustration of Fama-MacBeth two-step regression method in the case of K risk factors (K=3). The three factors are the market (MKT) factor, the small minus big (SMB) factor, and the high minus low (HML) factor. The SMB factor measures the difference in expected returns between small and big firms (in terms of market capitalization). The HML factor measures the difference in expected returns between value stocks and growth stock.

The model considers the following inputs:

The return of N assets denoted by R_i for asset i observed every day over the time period [0, T].
The risk factors denoted by F_MKT associated to the MKT risk factor, F_SMB associated to the MKT risk factor which measures the difference in expected returns between small and big firms (in terms of market capitalization) and F_HML associated to 𝐻𝑀𝐿 (“High Minus Low”) which measures the difference in expected returns between value stocks and growth stock

Step 1: time-series regression

img_SimTrade_Fama_French_time_series_regression

Step 2: cross-sectional regression

img_SimTrade_Fama_French_cross_sectional_regression

Figure 1 represents for a given period the cross-sectional regression of the return of all individual assets with respect to their estimated individual beta for the MKT factor.

Figure 1. Cross-sectional regression for the market factor.
Cross-section regression for the MKT factor Source: computation by the author.

Figure 2 represents for a given period the cross-sectional regression of the return of all individual assets with respect to their estimated individual beta for the SMB factor.

Figure 2. Cross-sectional regression for the SMB factor.
Cross-section regression for the SMB factor Source: computation by the author.

Figure 3 represents for a given period the cross-sectional regression of the return of all individual assets with respect to their estimated individual beta for the SMB factor.

Figure 3. Cross-sectional regression for the HML factor.
Cross-section regression for the HML factor Source: computation by the author.

Empirical study of the Fama-MacBeth regression

Fama-MacBeth seminal paper (1973) was based on an analysis of the market factor by assessing constructed portfolios of similar betas ranked by increasing values. This approach helped to overcome the shortcoming regarding the stability of the beta and correct for conditional heteroscedasticity derived from the computation of the betas for individual stocks. They performed a second time the cross-sectional regression of monthly portfolio returns based on equity betas to account for the dynamic nature of stock returns, which help to compute a robust standard error and assess if there is any heteroscedasticity in the regression. The conclusion of the seminal paper suggests that the beta is “dead”, in the sense that it cannot explain returns on its own (Fama and MacBeth, 1973).

Empirical study: Stock approach for a K-factor model

We collected a sample of 440 significant firms’ end-of-day stock prices in the US economy from January 3, 2012 to December 31, 2021. We calculated daily returns for each stock as well as the factor used in this analysis. We chose the S&P500 index to represent the market since it is an important worldwide stock benchmark that captures the US equities market.

Time-series regression

To assess the multi-factor regression, we used the Fama-MacBeth 3-factor model as the main factors assessed in this analysis. We regress the average returns for each stock against their factor betas. The first regression is statistically tested. This time-series regression is run on a subperiod of the whole period from January 03, 2012, to December 31, 2018. We use a t-statistic to explain the regression’s behavior. Because the p-value is in the rejection zone (less than the significance level of 5%), we can conclude that the factors can first explain an investor’s returns. However, as we will see later in the article, when we account for a second regression as proposed by Fama and MacBeth, the factors retained in this analysis are not capable of explaining the return on asset returns on its own. The stock approach produces statistically significant results in time-series regression at 10%, 5%, and even 1% significance levels. As shown in Table 1, the p-value is in the rejection range, indicating that the factors are statistically significant.

Table 1. Time-series regression t-statistic result.
Cross-section regression Source: computation by the author.

Cross-sectional regression

Over a second period from January 04, 2019, to December 31, 2021, we compute the dynamic regression of returns at each data point in time with respect to the betas computed in Step 1.

That being said, when the results are examined using cross-section regression, they are not statistically significant, as indicated by the p-value in Table 2. We are unable to reject the null hypothesis. The premium investors are evaluating cannot be explained solely by the factors assessed. This suggests that factors retained in the analysis fail to adequately explain the behavior of asset returns. These results are consistent with the Fama-MacBeth article (1973).

Table 2. Cross-section regression t-statistic result.
Source: computation by the author.

Excel file

You can find below the Excel spreadsheet that complements the explanations covered in this article.

Why should I be interested in this post?

Fama-MacBeth made a significant contribution to the field of econometrics. Their findings cleared the way for asset pricing theory to gain traction in academic literature. The Capital Asset Pricing Model (CAPM) is far too simplistic for a real-world scenario since the market factor is not the only source that drives returns; asset return is generated from a range of factors, each of which influences the overall return. This framework helps in capturing other sources of return.

Useful resources

Academic research

Brooks, C., 2019. Introductory Econometrics for Finance (4th ed.). Cambridge: Cambridge University Press. doi:10.1017/9781108524872

Fama, E. F., MacBeth, J. D., 1973. Risk, Return, and Equilibrium: Empirical Tests. Journal of Political Economy, 81(3), 607–636.

Roll R., 1977. A critique of the Asset Pricing Theory’s test, Part I: On Past and Potential Testability of the Theory. Journal of Financial Economics, 1, 129-176.

American Finance Association & Journal of Finance (2008) Masters of Finance: Eugene Fama (YouTube video)

About the author

The article was written in December 2022 by Youssef LOURAOUI (Bayes Business School, MSc. Energy, Trade & Finance, 2021-2022).

Fama-MacBeth regression method: the stock approach vs the portfolio approach

December 12, 2022 by Youssef LOURAOUI

Fama-MacBeth regression method: the stock approach vs the portfolio approach

In this article, Youssef LOURAOUI (Bayes Business School, MSc. Energy, Trade & Finance, 2021-2022) presents the Fama-MacBeth regression method used to test asset pricing models and addresses the difference when applying the regression method on individual stocks or portfolios composed of stocks with similar betas.

This article is structured as follow: we introduce the Fama-MacBeth testing method. Then, we present the mathematical foundation that underpins their approach. We conduct an empirical analysis on both the stock and the portfolio approach. We conclude with a discussion on econometric issues.

Introduction

As a reminder, the Fama-MacBeth regression method is composed on two steps: step 1 with a time-series regression and step 2 with a cross-section regression.

The first step is to regress the return of every stock against one or more risk factors using a time-series regression. We obtain the return exposure to each factor called the “betas” or the “factor exposures” or the “factor loadings”.

The second step is to regress the returns of all stocks against the asset betas obtained in the first step using a cross-section regression for different periods. We obtain the risk premium for each factor used to test the asset pricing model.

The implementation of this method can be done with individual stocks or with portfolios of stocks as proposed by Fama and MacBeth (1973). Their argument is the better stability of the beta when considering portfolios. In this article we illustrate the difference with the two implementations.

Fama and MacBeth (1973) implemented with individual stocks

We downloaded a sample of daily prices of stocks composing the S&P500 index over the period from January 03, 2012, to December 31, 2021 (we selected the stocks present from the beginning to the end of the period reducing our sample from 500 to 440 stocks). We computed daily returns for each stock and for the market factor retained in this study. To represent the market, we chose the S&P500 index, an important global stock benchmark capturing the US equity market.

The procedure to derive the Fama-MacBeth regression using the stock approach can be achieved as follow:

Step 1: time-series regression

We compute the beta of the stocks with respect to the market factor for the period covered (time-series regression). We estimate the beta of each stock related to the S&P500 index. The beta is computed as the slope of the linear regression of the stock return on the market return (S&P500 index return). This time-series regression is run on a subperiod of the whole period from January 03, 2012, to December 31, 2018.

Step 2: cross-sectional regression

Over a second period from January 04, 2019, to December 31, 2021, we compute the dynamic regression of returns at each data point in time with respect to the betas computed in Step 1.

With this procedure, we obtain a risk premium that would represent the relationship between the stock returns at each data point in time with their respective beta for the sample analyzed.

Test the statistical significance of the results obtained from the regression

Results in the time-series regression using the stock approach are statistically significant. As shown in Table 1, the p-value is in the rejection area, which implies that the factor that the market factor can be considered as a driver of return.

Table 1. Time-series regression t-statistic result.
img_SimTrade_Fama_MacBeth_cross_sectional_regression_stat_result Source: computation by the author.

However, when analyzed in the cross-section regression, the results are not statistically significant anymore. As shown in Table 2, the p-value is not in the rejection area. We cannot reject the null hypothesis (H0: non significance of the market factor). Market factor alone cannot explain the premium investors are considering.
This means that the market factor fails to explain properly the behavior of asset returns, which undermines the validity of the CAPM framework. These results are in line with the Fama-MacBeth paper (1973).

Table 2. Cross-section regression t-statistic result.
img_SimTrade_Fama_MacBeth_cross_sectional_regression_stat_result Source: computation by the author.

You can find below the Excel spreadsheet that complements the explanations covered in this part of the article (implementation of the Fama and MacBeth (1973) method with individual stocks).

Fama and MacBeth (1973) implemented with portfolios of stocks

The procedure to derive the Fama-MacBeth regression using the portfolio approach can be achieved as follow:

Step 1: time-series regression

We first compute the beta of the stocks with respect to the market factor for the period covered (time-series regression). We estimate the beta of each stock related to the S&P500 index. The beta is computed as the slope of the linear regression of the stock return on the market return (S&P500 index return). This time-series regression is run on a subperiod of the whole period from January 03, 2012, to December 31, 2015. We build twenty portfolios based on stock betas ranked in ascending order. The betas of the portfolios are then estimated again on a subperiod from January 04, 2016, to December 31, 2018.

It is challenging to maintain beta stability over time. Fama-MacBeth aimed to remedy this shortcoming through its novel technique. However, some issues must be addressed. When betas are calculated using a monthly time series, the statistical noise in the time series is significantly reduced in comparison to shorter time frames (i.e., daily observation). When portfolio betas are constructed, the coefficient becomes considerably more stable than when individual betas are evaluated. This is due to the diversification impact that a portfolio can produce, which considerably reduces the amount of specific risk.

Step 2: cross-sectional regression

Over a second period from January 03, 2019, to December 31, 2021, we compute the dynamic regression of portfolio returns at each data point in time with respect to the betas computed in Step 1.

With this procedure, we obtain a risk premium that would represent the relationship between the portfolio returns at each data point in time with their respective beta for the sample analyzed.

Test the statistical significance of the results obtained from the regression

Results in the cross-section regression using the portfolio approach are not statistically significant. As captured in Table 3, the p-value is not in the rejection area, which implies that the factor is statistically insignificant and that the market factor cannot be considered as a driver of return.

Table 3. Cross-section regression with portfolio approach t-statistic result.
img_SimTrade_Fama_MacBeth_Portfolio_cross_sectional_regression_stat_result Source: computation by the author.

You can find below the Excel spreadsheet that complements the explanations covered in this part of the article (implementation of the Fama and MacBeth (1973) method with portfolios of stocks).

Econometric issues

Errors in data measurement

Because regression uses a sample instead of the entire population, a certain margin of error must be accounted for since the authors derive estimated betas for the sample.

Asset return heteroscedasticity

In econometrics, heteroscedasticity is an important concern since it results in unequal residual variance. This indicates that a time series exhibiting some heteroscedasticity has a non-constant variance, which renders forecasting ineffective because the time series will not revert to its long-run mean.

Asset return autocorrelation

Standard errors in Fama-MacBeth regressions are solely corrected for cross-sectional correlation. This method does not fix the standard errors for time-series autocorrelation. This is typically not a concern for stock trading, as daily and weekly holding periods have modest time-series autocorrelation, whereas autocorrelation is larger over long horizons. This suggests that Fama-MacBeth regression may not be applicable in many corporate finance contexts where project holding durations are typically lengthy.

Why should I be interested in this post?

Useful resources

Academic research

Brooks, C., 2019. Introductory Econometrics for Finance (4th ed.). Cambridge: Cambridge University Press. doi:10.1017/9781108524872

Fama, E. F., MacBeth, J. D., 1973. Risk, Return, and Equilibrium: Empirical Tests. Journal of Political Economy, 81(3), 607–636.

Roll R., 1977. A critique of the Asset Pricing Theory’s test, Part I: On Past and Potential Testability of the Theory. Journal of Financial Economics, 1, 129-176.

American Finance Association & Journal of Finance (2008) Masters of Finance: Eugene Fama (YouTube video)

About the author

The article was written in December 2022 by Youssef LOURAOUI (Bayes Business School, MSc. Energy, Trade & Finance, 2021-2022).

Fama-MacBeth regression method: Analysis of the market factor

December 12, 2022 by Youssef LOURAOUI

In this article, Youssef LOURAOUI (Bayes Business School, MSc. Energy, Trade & Finance, 2021-2022) presents the Fama-MacBeth two-step regression method used to test asset pricing models. The seminal paper by Fama and MacBeth (1973) was based on an investigation of the market factor by evaluating portfolios of stocks with similar betas. In this article I will elaborate on the methodology and assess the statistical significance of the market factor as a fundamental driver of return.

This article is structured as follow: we introduce the Fama-MacBeth testing method used in asset pricing. Then, we present the mathematical foundation that underpins their approach. I then apply the Fama-MacBeth to recent US stock market data. Finally, I expose the limitations of their approach and conclude to discuss the generalization of the original study to other risk factors.

Introduction

The two-step regression method proposed by Fama-MacBeth was originally used in asset pricing to test the Capital Asset Pricing Model (CAPM). In this model, there is only one risk factor determining the variability of returns: the market factor.

The first step is to regress the return of every asset against the risk factor using a time-series approach. We obtain the return exposure to the factor called the “beta” or the “factor exposure” or the “factor loading”.

The second step is to regress the returns of all assets against the asset betas obtained in Step 1 during a given time period using a cross-section approach. We obtain the risk premium associated with the market factor. Then, Fama and MacBeth (1973) assess the expected premium over time for a unit exposure to the risk factor by averaging these coefficients once for each element.

Mathematical foundations

We describe below the mathematical foundations for the Fama-MacBeth two-step regression method.

Step 1: time-series analysis of returns

The model considers the following inputs:

The return of N assets denoted by R_i for asset i observed over the time period [0, T].
The risk factor denoted by F for the market factor impacting the asset returns.

For each asset i (for i varying from 1 to N) we estimate the following time-series linear regression model:

Fama MacBeth time-series regression

From this model, we obtain the following coefficients: α_i and β_i which are specific to asset i.

Figure 1 represents for a given asset (Apple stocks) the regression of its return with respect to the S&P500 index return (representing the market factor in the CAPM). The slope of the regression line corresponds to the beta of the regression equation.

Figure 1. Step 1: time-series regression for a given asset (Apple stock and the S&P500 index).
Source: computation by the author.

Step 2: cross-sectional analysis of returns

For each period t (from t equal 1 to T), we estimate the following cross-section linear regression model:

Fama MacBeth cross-section regression

Figure 2 plots for a given period the cross-sectional returns and betas for a given point in time.

Figure 2 represents for a given period the regression of the return of all individual assets with respect to their estimated individual market beta.

Figure 2. Step 2: cross-section regression for a given time-period.

Source: computation by the author.

We average the gamma obtained for each data point. This is the way the Fama-MacBeth method is used to test asset pricing models.

Empirical study of the Fama-MacBeth regression

The seminal paper by Fama and MacBeth (1973) was based on an analysis of the market factor by assessing constructed portfolios of similar betas ranked by increasing values. This approach helped to overcome the shortcoming regarding the stability of the beta and correct for conditional heteroscedasticity derived from the computation of the betas for individual stocks. They performed a second time the cross-sectional regression of monthly portfolio returns based on equity betas to account for the dynamic nature of stock returns, which help to compute a robust standard error and assess if there is any heteroscedasticity in the regression. The conclusion of the seminal paper suggests that the beta is “dead”, in the sense that it cannot explain returns on its own (Fama and MacBeth, 1973).

Empirical study: Stock approach

We downloaded a sample of end-of-month stock prices of large firms in the US economy over the period from March 31, 2016, to March 31, 2022. We computed monthly returns. To represent the market, we chose the S&P500 index.

We then applied the Fama-MacBeth two-step regression method to test the market factor (CAPM).

Figure 3 depicts the computation of average returns and the betas and stock in the analysis.

Figure 3. Computation of average returns and betas of the stocks.
Source: computation by the author.

Figure 4 represents the first step of the Fama-MacBeth regression. We regress the average returns for each stock with their respective betas.

Figure 4. Step 1 of the regression: Time-series analysis of returns
Source: computation by the author.

The initial regression is statistically evaluated. To describe the behavior of the regression, we employ a t-statistic. Since the p-value is in the rejection area (less than the significance limit of 5 percent), we can deduce that the market factor can at first explain the returns of an investor. However, as we are going deal in the later in the article, when we account for a second regression as formulated by Fama and MacBeth (1973), the market factor is not capable of explaining on its own the return of asset returns.

Figure 5 represents Step 2 of the Fama-MacBeth regression, where we perform for a given data point a regression of all individual stock returns with their respective estimated market beta.

Figure 5. Step 2: cross-sectional analysis of return.
Source: computation by the author.

Figure 6 represents the hypothesis testing for the cross-sectional regression. From the results obtained, we can clearly see that the p-value is not in the rejection area (at a 5% significance level), hence we cannot reject the null hypothesis. This means that the market factor fails to explain properly the behavior of asset returns, which undermines the validity of the CAPM framework. These results are in line with Fama-MacBeth (1973).

Figure 6. Hypothesis testing of the cross-sectional regression.
Source: computation by the author.

Excel file for the Fama-MacBeth two-step regression method

You can find below the Excel spreadsheet that complements the explanations covered in this article to implement the Fama-MacBeth two-step regression method.

Limitations of the Fama-McBeth approach

Selection of the market index

For the CAPM to be valid, we need to determine if the market portfolio is in the Markowitz efficient curve. According to Roll (1977), the market portfolio is not observable because it cannot capture all the asset classes (human capital, art, and real estate among others). He then believes that the returns cannot be captured effectively and hence makes the market portfolio, not a reliable factor in determining its efficiency.

Furthermore, the coefficients estimated in the time-series regressions are sensitive to the market index chosen for the study. These shortcomings must be taken into account when assessing CAPM studies.

Stability of the coefficients

The beta of individual assets are not stable over time. Fama and MacBeth attempted to address this shortcoming by implementing an innovative approach.

When betas are computed using a monthly time-series, the statistical noise of the time series is considerably reduced as opposed to shorter time frames (i.e., daily observation).

Using portfolio betas makes the coefficient much more stable than using individual betas. This is due to the diversification effect that a portfolio can achieve, reducing considerably the amount of specific risk.

Conclusion

Why should I be interested in this post?

Useful resources

Academic research

Brooks, C., 2019. Introductory Econometrics for Finance (4th ed.). Cambridge: Cambridge University Press. doi:10.1017/9781108524872

Fama, E. F., MacBeth, J. D., 1973. Risk, Return, and Equilibrium: Empirical Tests. Journal of Political Economy, 81(3), 607–636.

Roll R., 1977. A critique of the Asset Pricing Theory’s test, Part I: On Past and Potential Testability of the Theory. Journal of Financial Economics, 1, 129-176.

American Finance Association & Journal of Finance (2008) Masters of Finance: Eugene Fama (YouTube video)

Business Analysis

NEDL. 2022. Fama-MacBeth regression explained: calculating risk premia (Excel). [online] Available at: [Accessed 29 May 2022].

About the author

The article was written in December 2022 by Youssef LOURAOUI (Bayes Business School, MSc. Energy, Trade & Finance, 2021-2022).

Women in Finance (Northeastern University)

December 8, 2022 by Aastha DAS

Women in Finance (Northeastern University)

In this article, Aastha DAS (ESSEC Business School, Bachelor’s in Business Administration, Exchange Student from Northeastern University) presents the “Women in Finance” association at Northeastern University.

Background on women in finance

In almost all industries around the world, women are underrepresented at the top, excluding few sectors like nursing and education. One industry where women are highly underrepresented is the financial services. The Deloitte Center for Financial Services revealed the statistic that of the largest financial institutions in the US, only six out of 107 are run by female CEOs in 2019. This can be attributed to how women and men start on par during the start of their careers in finance, but men are more motivated to grow in the ranks to the C-level executive (C like Chief Executive Officer, Chief Financial Officer, Chief Investment Officer, Chief Economist, etc.). There are significantly fewer precedents for women in high levels of finance making it difficult for aspiring women in finance to find role models and mentors to rely on as a guide, especially in venture and private equity.

Figure 1. Women in Finance 2022 semester recap.

Source: Northeastern University.

There is a growing rate of initiatives trying to bolster women into the industry with the gender gap reducing exponentially in MBA programs, yet women still only account for a small ratio of finance staff at top global business schools. Nonprofit organizations like “Girls Who Invest” help diversity initiatives support women to bring young women into the world of finance through internships and mentoring programs, providing them with the best possible foundation.

Women need these programs and mentors to show them that they are capable of getting through any hardship in the path of achieving C-level executive positions and success, starting from as young as high school. These programs help to spark interest in the field and show them that there is so much more to the industry than seen regularly by someone not as exposed to the different sectors.

One industry where women are highly underrepresented is the upper levels of financial services management and investment services with the Deloitte Center for Financial Services revealing the statistic that of the largest financial institutions in the US, only six out of 107 are run by female CEOs in the USA, based on a 2019 rate.

The unfortunate statistic of the “broken rung” describes how women in lower entry level positions are promoted into managerial and C-level positions at a significantly lower rate than men. The broken rung can be defined as women having misfortune in promotion and this creates a ripple effect with progressive organizational grades creating disproportionate levels of women in the organizational hierarchy, especially involving diversity in senior leadership.

Misconceptions also root from stereotypes discouraging women from entering the finance space under the belief that they do not necessarily have the work-life balance as men as they should have to take care of families while working fulltime, but this belief is changing as women in finance are supported and men are also bolstered to take more responsibility within the home.

Women in Finance at Northeastern University

At Northeastern University, I have been involved in Women in Finance (WiF) since my first year starting with being involved in the Peer Mentorship program and then also acting as a Peer Mentor in my sophomore year and being a mentee in the Alumni Mentorship program. I also enjoyed being a part of the E-Board at Women in Finance as a Research Associate in Spring 2022 as part of the Street Talk. I thoroughly enjoyed being on the Street Talk as I felt as I was able to give back to the Women in Finance community through the works of the team, creating new ideas for the initiative, while also having the pleasure of being on the E-Board and getting to know all the other members and creating connections with one another. I also enjoyed being a part of the Professional program as I got to gain much insight from my alumni mentor, where I learned more firsthand information about the field, I am interested in. This upcoming Spring 2023, I will be taking on the role of President for the organization to best represent the Women in Finance initiative at Northeastern.

Missions of the organization

There are several organizations at many universities encouraging women in the business world and even entrepreneurship, but few have separate organizations for finance which is what draws me to this. The Women in Finance Initiative at Northeastern University strives to continue to uphold WiF’s goal to “educate, empower, support, and mentor” students by offering interesting and engaging programming and events that help students navigate finance. As members of the board, representing the organization with many endorsers and supports, WiF hopes to help provide the resources and instill confidence in female students, so they are empowered to pursue strong co-ops, internships, and leadership opportunities. Specifically, helping equip students with the skills to be successful in finance roles is valuable. The Wall Street Prep Series is an excellent program for students the organization has created to allow enrollment into a financial basics course with access to the certificate to learn incredibly valuable skills that can set them apart in interviews and on the job. Likewise, the mentorship programs provide invaluable guidance for students, and even use it in interviews and resumes. Continuing these programs and working to develop new ones allows WiF to fulfill its goal to educate students, and in doing so, helps with students’ confidence. Providing support like this allows women to realize their potential, which is another overarching goal.

The organization also has its the Executive Speaker Series (ESS) is an opportunity for undergraduate women, open to an interdisciplinary audience, to learn the stories and wisdom from female executives. In the past this has included positions across the C-level: CEO, CFO, CIO, and Chief Economist. These executives have traditionally worked in either the finance industry or come from some studying or other background in finance to further inspire women pursuing a business degree, particularly in finance and/or accounting.

Figure 2. Women in Finance Executive Speaker Series (ESS).
Women in Finance speaker series
Source: Northeastern University.

Acquiring financial skills

As many women explore the world of finance, it is important to build technical skills, and this is where the workshops from WiF come to use. The goal of workshops and skill series this semester is to help build members’ technical skills for the purpose of interviewing for co-ops or full-time work. We want our members to be competitive as technical interviewing becomes more popular with higher level positions. Technical skills include but are not limited to financial modeling, investing, personal finance, excel, PowerPoint, and learning how to use Bloomberg. This Fall we are already committed to hosting a Bloomberg Education Series through Northeastern’s virtual terminals.

The opportunities like the NYC and Fidelity Treks are something that not many other organizations offer. Each spring Women in Finance offers educational and networking Treks to companies in both Boston and New York City. The Freshman NYC Trek is where students go on a 3-day trip to New York and can visit various firms to learn about various career paths with a focus on Investment Banking and Consulting. Virtual Treks and networking opportunities in the form of a career education series are also regularly offered. Career Education panels are programmed to include but are not limited to, a Private Equity Panel, a Corporate Finance Panel, a Navigating Investment Banking Panel, and a Restructuring Panel.

Looking forward

Continuing to develop more unique programming like those that draw more students to the organization helps keep our organization up to date with the everchanging markets and world of finance. In the long term, because of the value of those opportunities, WiF could even become a draw to the university of prospective students. Continuing to have corporate sponsors can help fund more events like the treks while also helping to potentially build out more co-op/internship experiences for students with our sponsors. Given how male-dominated finance is, Women in Finance at Northeastern is a safe space for women to explore the field and know their worth and to encourage them to pursue roles they otherwise many not and provide a supportive community for females in finance.

Moving forward, ensuring that Women in Finance can help many more generations of young woman aspiring to be in this sector and the business world, I hope to create more long-lasting connections for the organization to allow more diverse parts of the finance field. I also hope to ensure that each semester remains consistent with the past while maintaining a level of improvement if necessary.

Why should I be interested in this article?

It is worth reading this article because of the underrepresentation of women in the finance industry and how that is pivoting to change over time and the impact that women can make at a high level. It can be daunting to enter a field like this, especially with so many controversial opinions and stereotypes. I would not be where I am in my career with an Investment Banking internship, financial services co-op, and upcoming M&A solutions consulting internship in NYC without this organization and I owe it to Northeastern University’s Women in Finance Initiative for providing me with the resources, support, mentorship, and confidence to put myself where I may not have felt I belonged before. I am not a finance concentration or major at my university, yet I learned that anyone can go into a role of finance, and that one is not just constricted as it is an open playing field, as long as you apply yourself and find the path of finance you want to go through, by exposing yourself to the different opportunities in finance like investment banking, equity research, sales & trading, asset management, wealth management, venture capital, angel investing, private equity, hedge funds, global capital markets, and so many more untapped industries for women to climb the ranks in.

Useful resources

Women in Finance (Northeastern University)

Women in Finance – Program

Women in Finance (LinkedIn)

Women in Finance (Instagram)

Gender and Finance

About the author

The article was written in December 2022 by Aastha DAS (ESSEC Business School, Bachelor’s in Business Administration, Exchange Student from Northeastern University).

My experience as an investment banking analyst intern at G2 Capital Advisors

December 22, 2024December 8, 2022 by Aastha DAS

My experience as an investment banking analyst intern at G2 Capital Advisors

In this article, Aastha DAS (ESSEC Business School, Bachelor’s in Business Administration, Exchange Student from Northeastern University) shares her experience during a summer internship as an investment banking analyst at G2 Capital Advisors.

About the company

G2 Capital Advisors is a boutique, low-middle market investment bank which is sector-focused with an integrated and multi-product approach, creating an uncommon full-service product line. G2 provides unique solutions in the investment banking realm including specializing in buy-side and sell-side advisory, capital markets, and restructuring, with different teams allocated to each part of those practices. Most analysts specialize in one of the products while associates and vice presidents tend to cover more product lines while specializing a certain industry in the business practice, further supporting the C-Level executive heading their respective industry specialty group.

G2 Capital Advisors logo
Women in Finance
Source: G2 Capital Advisors.

G2 follows a unique business model in which their managing directors and leads of industry practices are from a background of sector success, and not necessarily banking careers, like Industrials & Manufacturing, Technology & Business Services, Consumer & Retail, and Transportation & Logistics. The culture and core values at G2 Capital Advisors revolve around dedication to their clients, to be able to provide the best possible, creative yet lucrative solutions for their issues. Their main business practice remains Restructuring and Investment Banking.

Through remaining tenacious in all their business processes, they are able to create a full-servicing bank which can provide accountability in an honest and respectful manner, further differentiating themselves.

My Internship

My Missions

I worked as an intern in the summer of 2022 for the Investment Banking practice at G2 Capital Advisors. I was mainly responsible for supporting the analysts and sometimes supported the associates in the buy-side and sell-side business practices. As the size of this boutique lower-middle market investment bank is a lot smaller than a lot of other banks, my experience was more unique than most investment banking summer analyst positions.

I got holistic views on the whole firm as I got experience in intensive levels in three of four of their business practices: Technology & Business Services, Consumer & Retail, and Transportation & Logistics, on both the buy-side and sell-side investment banking advisory. On the buy-side, I created extensive market maps for clients to source their clients and potential new acquisitions for them. Here I was also able to perform also as a research analyst for M&A and equity research on active advisory and restructuring deals throughout the summer by using Excel to curate and develop market maps, historical financial analysis, and prepare for engagement with clients. I was the forefront of the intern class through ensuring that all the submissions from the group were of top quality for all curated presentation materials including tailored pitch books, Confidential Information Memorandums, deal sheets, and teasers. This helped prepare me, the other interns, as well as the analysts and associates for client presentations, oftentimes doing more research than necessary, to stay ahead of competitors. I also aided in the company’s outreach initiatives through drafting many press releases and research presentations for transaction announcements and quarterly industry reports specifically for the Consumer & Retail and Transportation & Logistics business practices.

In my final weeks as an intern, I was able to generate my own comparables and financial models to aid associates for many ongoing deals. All the interns were also responsible for a research presentation of any of the business practices and I delivered a presentation on Consumer & Retail. In this presentation, I sourced new portfolio companies, hedge funds, and private equity firms for G2 to create connections with and evaluated the intrinsic value of creating relations with each of these different sub-sectors and companies and how it better aligns with G2’s goals to provide industry specialized support for clients. I have since gotten feedback from several of the managing directors and leads in the Consumer & Retail team that many of the suggested partnerships have rendered successful and are in process of deal-making with due diligence underway.

Required Skills and Knowledge

The Investment Banking sector at G2 Capital Advisors is arguably the most profitable business practice and there was a high learning curve to going into this internship. I had to quickly learn the sell-side and buy-side business practices to best support the analysts and associates so that we were able to deliver the best market maps and materials to the clients and our managing directors/deal managers. Along with the steep learning curve of investment banking concepts, I also had to adapt to the G2 form of financial modeling as I had learned it already from a club at my university called Bull & Bear Equity Research club. It is also necessary to develop and come prepared with many soft skills like humility, generosity to always give a helping hand, self-discipline, time management, conflict resolution, and high analytical/critical thinking. As an aspiring intern in the investment banking and advisory space, it is wise to stay up to date with financial news, so it is recommended to read/listen to news through podcasts like the NYTimes Daily, New York Times, Wall Street Journal, The Economist. Most higher-level executives are well versed with financial news and do not need to think twice about it and this is a good tactic to incorporate in beginner’s careers to ensure the interns are knowledgeable on all that is going on in the market, in light of any swift changes.

What I Learned

My internship at G2 Capital Advisors gave me a good understanding of the composition of the entire financial institution and the operation of the financial market as well as investment banking through allowing me to master my Excel capabilities, relationship building skills with clients and other employees, while learning technical skills as well like financial terms and everything that is necessary in the different advisory processes. The knowledge I had previously from taking microeconomics, macroeconomics, macroeconomic theory, financial management, Wall Street Prep, and financial accounting aided me in the internship to create a solidified foundation to grow in the industry. I also learned how lucrative a career in investment banking is because of how heavily technical it can get with developing models, but also how personable it can make you through creating special relationships with many different market leaders, clients, and investors, creating a well-rounded employee in the financial services space.

Key Financial Concepts

Here are three useful financial concepts I learned in the Investment Banking department at G2 Capital Advisors.

Buy-side vs sell-side

Buy-Side – is the side of the financial market that buys and invests large portions of securities for the purpose of money or fund management.

Buy-Side – is the other side of the financial market, which deals with the creation, promotion, and selling of traded securities to the public.

M&A Sell-Side Advisory Process Explained

One of the most in-depth processes I learned was the Sell-side process for investment banking: Detailing my insight below

Process and Timeline

1. Winning the Mandate

a. Pitch
b. Engagement Review Memo
c. Engagement Letter Signing

2. Preparing for sale

a. Definite strategy (who, how much, what process)
b. Draft Teaser, Executive Summary, CIM
c. Kick-off Meeting
d. Organize Financials
e. Create Projections
f. Prepare non-disclosure agreement

3. Marketing

a. Launch process

i. Contact Buyers
ii. Receive preliminary bids
iii. Manage deal processes
iv. Send teaser and NDA
v. Investor meetings/emails
vi. Draft Letter of Interest bid
vii. Draft Management Presentation
viii. Set up data room and due diligence

b. Letter of Interest Bid Deadline

i. Receive final LOIs
ii. Board meetings
iii. Management presentation
iv. Negotiate with lead bidders
v. Execute LOI

4. Confirmatory Due Diligence

a. Enter into exclusivity agreement with one bidder post negotiation
b. Kick-off diligence meeting
c. Facilitate due diligence

5. Closing

a. Proceeds waterfall deliverables
b. Present finalized deal terms and fairness opinion
c. Get board approval
d. Signing and closing
e. Invoice deliverable sent
f. Transaction review memo

What is Restructuring?

Restructuring is a unique concept in investment banking which entails growth and special situations in and out of the court with both a debtor and creditor side, based on what is best fit for the firm. It helps provide clients with clear solutions to ensure sustainable long-term stability. This is usually a practice which is enacted, and advisors are called in when a company wants to change its structure completely or significantly in both financial and operational aspects, during times of financial pressures where clear restructuring of the business is necessary. It often involves revising debt options, operations, and forms of limiting financial detriment while still improving the firm.

A company will often use restructuring advisory when there is debt difficulty, especially regarding consolidating to pay their bondholders. It is also possible to incorporate operation restructure to help cut costs in payroll and/or in the size of assets through significant sales.

Internal restructuring often entails operations, processes, departments, supply chains, executive board, and even ownership change, further enabling the firm to grow profitable while growing. This is when investment banks come in to help with the negotiation of restructuring plans to input financial and legal advisors and potentially even gain aid from investors and appointing new CEOs to implement the new changes and propel the firm forward.

Merger & Acquisition Activism

Activism a particularly new space in the investment banking world but one worth keeping tabs on because of how much it changes and how volatile it can make certain deals. In activist investing, there is usually a sign of change occurring through a catalyst which prompts activist investors to reveal themselves. This is an investment strategy where an investor comes into and/or attempts to pursue poorly-run companies with share prices that have gone down recently, usually an investor which much potential. The activist investor usually takes in a large stake in the company which reveals their interest and pushes for changes because of their vast equity, in efforts to turnaround the company for the better. This hopefully results in price increases for the security.

It is necessary to stay weary of activist investors because they may not always have the company’s best interests at hand. For this reason, shareholder trust is a large factor for activist investors. Most of the most successful activist investors are public figures and not necessarily hedge funds. They often use aggressive and confrontational tactics to pressure the management teams of public companies. It is necessary to grow public and shareholder trust along with public attention to grow their platform to endorse the suggested recommendations.

Why should I be interested in this article?

It is worth reading this article because of the topic it discusses in the popular investment banking space. It is necessary to note how well-rounded investment banking can make an individual but also the uniqueness of this post entails how the experience was at a boutique low-middle market investment bank with full servicing to reveal how one smaller firm can do so much in efforts to create the most impactful and creative solutions to business issues and M&A deals.

Useful resources

G2 Capital Advisors

Financier Worldwide Magazine (June 2019) The rising influence of shareholder activism in M&A transactions: recent trends in the UK

About the author

The article was written in December 2022 by Aastha DAS (ESSEC Business School, Exchange Student from Northeastern University – Bachelor’s in Business Administration).

Why is Apple’s new iPhone 14 release line failing in the first few months?

December 8, 2022 by Aastha DAS

Why is Apple’s new iPhone 14 release line failing in the first few months?

In this article, Aastha DAS (ESSEC Business School, Bachelor’s in Business Administration, Exchange Student from Northeastern University) discusses events about Apple’s products, their impact on Apple’s share price and the link with market efficiency.

Brief reminder of the facts

On Tuesday October 18th, 2022, Apple stocks saw a downturn after the announcement of the limit of one of its iPhone suppliers for the newly released iPhone 14 Plus due to demand issues.

Figure 1. Event about Apple.

Source: Bloomberg.

With the new release, Apple took a large risk with eliminating certain failing lines like the “mini” model. Within the iPhone 14 range, the largest changes and upgrades were to the “Pro” models in hopes of diversifying its product line while pricing remained consistent in appropriate increases, as done in the past. Unfortunately, this has been highly unsuccessful with many reports revealing how the sales of the “iPhone 14” line have been subpar of expectations.

Impact on company

This is concerning for the company since Apple had increased its sales projections in the few weeks prior to the iPhone 14 family release in September as it does annually and many of its suppliers had already started making preparations for a 7% boost in orders after the release. This incident had direct financial consequences on the company as the stock immediately dropped by $4 from $145. There are mixed reports on consumers’ preferences to buy either the iPhone 14 Plus or iPhone 14 as preferences between the features and affordability of the two vary greatly. It is difficult for the company to gauge the fluctuations in demand, especially as the new iPhone 14 has not been doing as well as anticipated. This can also be attributed to the decrease in global demand because of surging inflation and the impending recession and war in Ukraine. The smartphone market is projected to decrease by 6.5% this year, 2022. Following an official announcement in a press release from Apple, the stock price immediately dropped in regard to the production halt for the iPhone 14 Plus at one of the plants in China. Apple shares fell 3.9% on the New York stock Exchange (NYSE) on Wednesday morning to $145.90. The shares are additionally also down about 18% this year in 2022, compared to a 23% drop in the S&P 500 Index. Still, many professionals state they are not surprised about this due to more preference toward the more premium models of the iPhone 14 family. The share price quickly leveled out but it revealed how volatile the stock is to the market and each decision they make.

Figure 2. Event about Apple.
Apple share price
Source: Google Finance.

Relations to market efficiency

Market efficiency involves a market where the current price of a stock/security quickly reflects information of that security and/or its respective company in a wholly rational manner.

There are many ways to evaluate a market’s efficiency, even as novice market watchers, starting with reevaluating the lag in the time that information is released regarding a security to when it is reflected in the security’s price. The changes in price are usually a product of announcements that are novel and unexpected which can be compared to the press release by Apple to limit the supply chain of its iPhone 14 production as this is uncommon for the company to do, so soon after the company’s fall release, as it does annually. It also relates to a company’s share price in relation to its earnings per share outcomes and the share price growing following the EPS, in an efficient market, as EPS growth reveals positive growth for long-term investors, and it is still optimistic to observe that Apple has managed to grow EPS by 28%/year over the past three years. This restores faith in the stock as though it has proven to be volatile, it regulates itself and has clearly been on the rise in a long-term perspective, revealing sustainable growth. A real positive is seen with Apple’s similar EBIT margins to 2021 as revenue grew by 12% to $388B USD.

At this point in time, the Apple security can be seen as semi-strong efficiency. This can be attributed to how public Apple is with there being much historical market data and public information like company accounts, hundreds of reports on the renowned company which regularly are reflected in the company’s stock price.

Figure 3. Apple financial statistics.

Source: Forbes Digital Covers.

Why did I choose this event?

I chose this event as a financial event of Apple’s stock taking a downturn dip because it reveals much about the smartphone and personal electronics market despite being a quite small event in the trajectory of its iPhone releases. This shows how smartphones will also suffer from raising inflation and the Ukrainian-Russia war despite popular demand and so-called need for smartphones like the iPhone. I am also an avid consumer of Apple products and find it interesting how emotional many stakeholders are based on how they react to even the smallest aspects of its product line. It reveals how despite the rationality of the market being beneficial, human beings chose to act on fear and precautionary measures to ensure that they will be safer rather than opting in favor of risk, within reason.

Why should you be interested in this topic?

There are many reasons why it is important to stay on top of the regular markets and this article discusses a company which is regularly changing in the markets. As a SimTrade student, or anyone interested in financial markets, market efficiency is a key aspect to refer to when making financial decisions and trading. It is worthwhile to consider companies with strong efficiency and those which do not, allowing a broader outlook into how they might function. It is necessary to see if there is a possibility of beating the market because any information available to a trader is already involved in the market price so it is difficult to beat it for the higher returns.

Useful resources

SimTrade course Market information

Yahoo! Finance (October 25, 2022) Here’s Why We Think Apple (NASDAQ:AAPL) Is Well Worth Watching

Apple Newsroom (November 6, 2022) Update on supply of iPhone 14 Pro and iPhone 14 Pro Max

Bloomberg (September 28, 2022) Apple Ditches iPhone Production Increase After Demand Falters

About the author

The article was written in December 2022 by Aastha DAS (ESSEC Business School, Bachelor’s in Business Administration, Exchange Student from Northeastern University).

A quick review of the M&A – Real Estate job…

December 5, 2022 by Louis DETALLE

A quick review of the M&A – Real Estate job…

In this article, Louis DETALLE (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023) explains how is working as an M&A Analyst in Real Estate any different from being a general M&A.

Let’s recall what M&A is and then, we will assess how working in the Real Estate sector makes it different

M&A (Mergers & Acquisitions) is a profession that advises companies wishing to develop their external growth, i.e. growth through the acquisition of another company or through a merger with it. M&A mandates are therefore carried out on the side of the company that wishes to acquire another company, “buy-side”, or on the side of a company that wishes to be acquired, “sell-side”.

Therefore, an analyst in M&A with a Real Estate focus will only work on Real Estate-related topics. For example, clients may be property managers, real estate companies, property developers or hotel groups. By the same logic, the subjects encountered and studied in the M&A pitches will be centered around the real estate sector. These include shopping centers, entire housing estates, office towers, hotel chains and their hotel stock.

What does an analyst in M&A – Real Estate work on?

The tasks of an M&A analyst are diverse and include, for example, drawing up a business plan, modelling different scenarios and strategies in Excel, and drafting information memorandums (IMs) on the various deals in progress. With a real estate asset that you are trying to value for instance, the case scenarios that you will anticipate, will describe the possibility that you do not receive 100% of the rent, but perhaps only 30% for the first 6 months of the project.

All these skills on the real estate sector are cumulated to the ones you acquire as an M&A analyst and are then widely used for the mergers and acquisitions of companies, in the development of their external strategy, in their financial evaluation or in the analysis of databases.

Again, the financial analysis tools for the real estate sector are not the same and are specific to Real Estate. For example, analysts will focus on the possibility that rents for a property will not come in and will try to estimate whether the estimated average occupancy rate is realistic. When valuing by stock market or transactional comparables, the comparables used will be functions of the capitalization rate and not EBITDA or EBIT multiples.

What are the main exits for an M&A Analyst of the Real Estate sector?

There are a lot of opportunities, but of course largely limited to the real estate sector. This is because there are many different jobs and companies in the real estate world: asset managers, real estate companies, real estate management of a large company, etc. It is also possible to join the real estate teams of a private equity fund in order to move to the investor side.

Resources

Youtube Interview with a Deloitte Manager in M&A Real Estate

BNP Paribas Real Estate – Presentation of all Real Estate jobs

About the author

The article was written in December 2022 by Louis DETALLE (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023).

A quick review of Wealth Management’s job…

December 5, 2022 by Louis DETALLE

A quick review of Wealth Management’s job…

In this article, Louis DETALLE (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023) explains what an Wealth Manager works on, on a daily basis.

What does Wealth Management consist in?

Private banking is a specialized advisory service offered by financial institutions to individual or institutional clients with substantial financial resources and assets. The clients targeted by this type of activity may be company directors such as Bernard Arnault or François Pinault. Clients may also be wealthy families who maintain and grow their assets through the solutions offered by private bankers.

To do this, institutions that practice private banking employ various experts who cover all the issues related to the optimization of an estate: private bankers, tax lawyers, asset managers and notaries.

What does a Wealth Manager work on?

As stated above, the Wealth Manager should be able to propose a property investment (such as the Pinel Law in France for example), an investment in traditional life insurance or even an investment in a private equity management company. The diversity of investment products can be very great, and it is therefore important for the Wealth Manager to understand his client’s needs in order to offer him the solution that is best suited to his needs.

This is particularly true in an independent wealth management firm but much less so in a division of a large bank with wealth management activities. Indeed, in an independent asset management firm, fund managers will have to invent new investment solutions. The private bank employees of a large bank, on the other hand, will only be able to offer products created by the bank to which they are attached.

For this reason, wealth management work varies greatly depending on the type of structure in which the wealth manager works. Indeed, the proportion of “commercial” work will be much greater for a “non-independent” private banker, since he or she will not be as involved in the construction and development of the solutions proposed. For independent private banks, the work of creating investment solutions and finding investment opportunities will logically be more significant.

What are the different levels of clients that wealth managers can deal with?

In this respect, four main levels of segmentation of high-net-worth clients can be distinguished, hierarchized according to the thresholds of assets managed by the firm or the private bank.

Upper Affluent : The Upper Affluent have assets between €100,000 and €1,000,000. In order to develop their wealth, these clients mainly turn to the so-called premium investment offers of retail banks or small independent wealth management firms.

High Net Worth Individuals (HNWI): High Net Worth Individuals (HNWI) have assets of up to €5,000,000. HNWIs generally make up a large proportion of the client base of both independent and non-independent private banks.

Very High Net Worth Individuals (Very HNWI): These are individuals with an estimated wealth of between €5,000,000 and €30,000,000. This very wealthy clientele is generally advised by the wealth management departments of independent private banks and private banks attached to the network banks. It should be noted that this type of individual constitutes the so-called “premium” clients of these departments.

The “above €30 million”: The most affluent individuals (with assets of over €30,000,000) are the main clients of family offices. They are external organizations of individuals related to a single family and advise them on all aspects of their wealth management. Family offices employ people who are often experienced and have a wide range of skills.

Resources

Article about the different jobs that exist in the management of financial resources: asset management, wealth management, family office…

Youtube Top 20 Wealth Manager Interview Q&A

Youtube Natixis wealth management department’s website

About the author

The article was written in December 2022 by Louis DETALLE (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023).

What are green bonds?

December 5, 2022 by Louis DETALLE

What are green bonds?

In this article, Louis DETALLE (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023) explains what are the different green bonds and what are they used for.

Green bond

A green bond is a debt security, often issued by a company or public entity on the market to enable it to finance projects related to the ecological transition. These securities function in the same way as traditional bonds, characterized by an interest rate, a repayment schedule, etc. However, green bonds are specific in that the projects financed by this type of bond must be oriented towards preserving the environment. From 2013 onwards, the green bond market has experienced very strong growth worldwide: almost $275 billion of broadly defined green bonds were issued between 2013 and 2017, including over $100 billion in the last year. In 2021, global green bond sales reached a record $513 billion, according to Bloomberg. Despite this explosion of green bonds, the craze for this type of bonds is to be qualified as they remain very marginal compared to conventional bonds, especially in the current context of the war in Ukraine.

What are the main green bond issuers?

Green bonds issuers can consist mainly of states and governments. For example, Europe has an important place in this market: almost 45% of green bond issues in 2019 were denominated in euros, compared to 26% in dollars. Indeed, France was the first country to issue a significant size of green bond, followed closely by Germany, Belgium, Ireland and the Netherlands. 225 billion in green bonds as part of the European recovery plan.

Outside Europe, the US and China are the largest issuers of green bonds. They account for almost 32% of such issues.

On the other hand, green bonds issuers can also consist of large companies. In France, Suez (the water and waste group) issued a first green bond for €2.6 billion in May 2022. This transaction met with strong demand as it was oversubscribed by about 2.9 times by 200 European institutional investors, the group said in a statement. In the meantime, large companies, particularly in the energy sector, have also launched green bond issues with, in France, Engie, EDF or in real estate with the Icade group. The SNCF also issued a green bond in 2016, becoming the first railway infrastructure manager in the world to adopt such an approach. 900 million euros were issued in the first year, then 1 billion in 2017, the largest green issue for a French company, and 500 million in 2019.

US companies have been slower to embrace green bonds, but with a total of $30 billion in green bonds issued in the first 10 months of 2019, US corporate green bond issuance has jumped by 60%. PepsiCo has obtained 1 billion dollars from investors for its inaugural operation in 2019. These 30-year green bonds will be used to finance projects that reduce the use of non-recycled plastic in the manufacture of bottles, limit the consumption of water in its production processes and, more generally, reduce its carbon footprint. The UDR real estate group is one of the recent issuers. In February 2019, it was telecoms giant Verizon that raised $1 billion, attracting eight times more demand than supply.

How are the green bonds regulated?

In the European Union, the regulation of European green bonds is still at the draft stage. The EU is taking further steps to implement its strategy on financing sustainable growth and energy transition.

The EU Permanent Representatives have given the green light to the Council’s position on a proposal to create European green bonds. The regulation concerned sets out uniform requirements for bond issuers who wish to use the name “European Green Bond” or “EuGB. For the latter, the main interest is that this regulation would provide a registration system and a monitoring framework for European green bond issuers.

Environmentally sustainable bonds are one of the main instruments for financing investments in green technologies, energy and resource efficiency, and sustainable transport and research infrastructure. The Council announced that it is ready to enter into negotiations with the European Parliament in order to reach agreement on a final version of the text that will have to be accepted by all Member States.

Outside the EU, in the US, China and elsewhere, green bond regulation is still in its infancy. This raises a major concern: actors can issue green bonds without using the funds for environmental purposes. For example, according to the Climate Bonds Initiative, only half of China’s green bonds comply with international standards. It is precisely for this reason that regulations are more necessary than ever to avoid a green bond fashion

Resources

French State’s Website about green bonds

An article by BNP Paribas about the EU regulation on ESG criterias

An article by Les Echos on how the US are defining new regulations in order to fight the plague of greenwashing

About the author

The article was written in December 2022 by Louis DETALLE (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023).

A quick review of the corporate lawyer’s job…

December 5, 2022 by Louis DETALLE

A quick review of the corporate lawyer’s job…

In this article, Louis DETALLE (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023) explains what a corporate lawyer works on, on a daily basis.

How a corporate lawyer is different from any other lawyer?

A corporate lawyer (also called a business lawyer) is first and foremost a lawyer, so he has studied law. However, he specializes in commercial and company law. He can also add banking law, tax law, industrial property law, mergers and acquisitions or stock exchange law to his skills.

Unlike the classical lawyer in the common sense of the term, the corporate lawyer works in a law firm or directly with a large company, advising it on all legal aspects of its activities. They only plead in cases of litigation, whereas a lawyer specialized in criminal law will plead much more often and in various criminal cases.

What does a corporate lawyer work on?

The tasks of a corporate lawyer are varied and depend on the specialty he or she practices within a business law firm. Indeed, the latter may have a dominant advisory role, i.e., he will accompany corporate clients on all their issues such as company takeovers, share transfers, debt issues… On the other hand, the corporate lawyer may also work with a litigation focus, i.e., he will specialize in defending the interests of his clients when they are the target of a lawsuit, or when they sue a third party. In any case, the corporate lawyer is a genuine advisor. The corporate lawyer must lead his clients to make the best strategic choice for them and must defend their interests against the opposing party.

The daily advisory missions of the corporate lawyer

In relation to corporate clients, the business lawyer will have to participate in his clients’ projects by ensuring that they respect a well-adapted legal framework. This will be an important part of the lawyer’s work to ensure that the client is not in conflict with the law and the regulations specific to its sector. The corporate lawyer will also need to assist clients in M&A transactions in the same way that an investment banker will. In this respect, the corporate lawyer will participate in the negotiations on the amount of the transaction and will pay particular attention to the various clauses and legal documents relating to the transaction. Whether it is the drafting of a Non-Disclosure Agreement, a Letter of Intent, a Non-Binding Offer or the signing of the Share Purchase Agreement, the business lawyer will have to supervise all these legal documents in order to protect his client as best as possible. The business lawyer will also have to assist his clients in the drafting and supervision of the various contracts relating to the company’s partners.

The punctual litigation missions of the corporate lawyer

On the other hand, the corporate lawyer will be responsible for advising and representing his clients in possible litigation. This will consist of determining the rights and duties of his clients in case of litigation and pleading in court if necessary. This aspect of a business lawyer’s work may seem less recurrent, but it is nonetheless crucial because it is precisely when a client is being sued that he or she needs the business lawyer most.

How to become a corporate lawyer?

In France, after a baccalaureate, the future corporate lawyer must enroll in a law faculty to obtain at least a Master 1 or a Master of Law. Afterwards, they can specialize in business law and obtain a Master 2 in business law, financial law, or tax law. They can also choose to continue their studies at a university abroad or take a master’s degree at a business school, which they will enter by admission based on their qualifications.

Once they have their master’s degree, the future business lawyer will have to join a regional center for professional training of lawyers or CRFPA to obtain the certificate of aptitude for the profession of lawyer or CAPA, commonly known as the “bar exam” and become a business lawyer.

Resources

An comprehensive interview of a corporate lawyer

Youtube Conference Business Lawyer: between myths and realities

About the author

The article was written in December 2022 by Louis DETALLE (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023).

Forex exchange markets

December 2, 2022 by Nakul PANJABI

Forex exchange markets

In this article, Nakul PANJABI (ESSEC Business School, Grande Ecole Program – Master in Management, 2021-2024) explains how the foreign exchange markets work.

Forex Market

Forex trading can be simply defined as exchange of a unit of one currency for a certain unit of another currency. It is the act of buying one currency while simultaneously selling another.

Foreign exchange markets (or Forex) are markets where currencies of different countries are traded. Forex market is a decentralised market in which all trades take place online in an over the counter (OTC) format. By trading volume, the forex market is the largest financial market in the world with a daily turnover of 6.6 trillion dollars in 2019. At present, it is worth 2,409 quadrillion dollars. Major currencies traded are USD, EUR, GBP, JPY, and CHF.

Players

The main players in the market are Central Banks, Commercial banks, Brokers, Traders, Exporters and Importers, Immigrants, Investors and Tourists.

Central banks

Central banks are the most important players in the Forex Markets. They have the monopoly in the supply of currencies and therefore, tremendous influence on the prices. Central Banks’ policies tend to protect aggressive fluctuations in the Forex Markets against the domestic currency.

Commercial banks

The second most important players of the Forex market are the Commercial Banks. By quoting, on a daily basis, the foreign exchange rates for buying and selling they “Make the Market”. They also function as Clearing Houses for the Market.

Brokers

Another important group is that of Brokers. Brokers do not participate in the market but acts as a link between Sellers and Buyers for a commission.

Types of Transactions in Forex Markets

Some of the transactions possible in the Forex Markets are as follows:

Spot transaction

As spot transaction uses the spot rate and the goods (currencies) are exchanges over a two-day period.

Forward transaction

A forward transaction is a future transaction where the currencies are exchanged after 90 days of the deal a fixed exchange rate on a defined date. The exchange rate used is called the Forward rate.

Future transaction

Futures are standardized Forward contracts. They are traded on Exchanges and are settled daily. The parties enter a contract with the exchange rather than with each other.

Swap transaction

The Swap transactions involve a simultaneous Borrowing and Lending of two different currencies between two investors. One investor borrows the currency and lends another currency to the second investor. The obligation to repay the currencies is used as collateral, and the amount is repaid at forward rate.

Option transaction

The Forex Option gives an investor the right, but not the obligation to exchange currencies at an agreed rate and on a pre-defined date.

Peculiarities of Forex Markets

Trading of Forex is not much different from trading of any other asset such as stocks or bonds. However, it might not be as intuitive as trading of stocks or bonds because of its peculiarities. Some peculiarities of the Forex market are as follows:

Going long and short simultaneously

Since the goods traded in the market are currencies themselves, a trade in the Forex market can be considered both long and short position. Buying dollars for euros can be profitable in cases of both dollar appreciation and euro depreciation.

High liquidity and 24-hour market

As mentioned above, the Forex market has the largest daily trading volume. This large volume of trading implies the highly liquid feature of Forex Assets. Moreover, Forex market is open 24 hours 5 days a week for retail traders. This is due to the fact that Forex is exchanged electronically over the world and anyone with an internet connection can exchange currencies in any Forex market of the world. In fact for Central banks and related organisations can trade over the weekends as well. This can cause a change in the price of currencies when the market opens to retail traders again after a gap of 2 days. This risk is known as Gapping risk.

High leverage and high volatility

Extremely high leverage is a common feature of Forex trades. Using high leverage can result in multiple fold returns in favourable conditions. However, because of high trading volume, Forex is very volatile and can go in either upward or downward spiral in a very short time. Since every position in the Forex market is a short and long position, the exposure from one currency to another is very high.

Hedging

Hedging is one of the main reasons for a lot of companies and corporates to enter into a Forex Market. Forex hedging is a strategy to reduce or eliminate risk arising from negative movement in the Exchange rate of a particular currency. If a French wine seller is about to receive 1 million USD for his wine sales then he can enter into a Forex futures contract to receive 900,000 EUR for that 1 million USD. If, at the date of payment, the rate of 1 million USD is 800,000 EUR the French wine seller will still get 900,000 EUR because he hedged his forex risk. However, in doing so, he also gave up any gain on any positive movement in the EUR-USD exchange rate.

Useful resources

Academic resources

Solnik B. (1996) International Investments Addison-Wesley.

Business resources

DailyFX / IG The History of Forex

DailyFX / IG Benefits of forex trading

DailyFX / IG Foreign Exchange Market: Nature, Structure, Types of Transactions

About the author

The article was written in December 2022 by Nakul PANJABI (ESSEC Business School, Grande Ecole Program – Master in Management, 2021-2024).

A quick review of the Equity Research analyst's job…

March 31, 2024November 28, 2022 by Louis DETALLE

A quick review of the Equity Research analyst’s job…

In this article, Louis DETALLE (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023) explains what an Equity Researcher works on, on a daily basis.

What does Equity Research consist in?

The objective of equity research is to make buy or sell recommendations on stocks to advise investors on their asset allocation. In doing so, the Equity Research team will closely monitor certain stocks to see if the stock is outperforming or underperforming. In doing so, they will closely monitor the share price and sell their monitoring as a service to determine whether to buy or sell a share.

This equity research service is therefore sold to investors in the financial markets to provide them with a comprehensive financial analysis, as well as advice on whether to buy or sell particular securities. The analysis report presented by an equity analyst is used by investment banks and private equity firms to evaluate the company for an initial public offering (IPO), a leveraged buy-out (LBO), alliances and others. Therefore, all these investors constitute clients of Equity Research teams.

Most banks have Equity Research teams such as Societe Generale Bank, UBS, BNP Paribas, Barclays, Goldman Sachs and Citi for instance. In short, equity research analysts are mainly employed by investment banks (BNP, Citi, Barclays, etc.), investment funds (KKR, Blackstone, Bpifrance) or asset managers (BlackRock, Vanguard, Amundi).

An equity research analyst is specialised in a specific sector such as automotive, aerospace, healthcare, telecoms and biotech. The advantage of doing that is that the banks will have extremely complementary profiles that will be able to deal with the analysis of many companies of the same sector. They will have the benefit of hindsight trough building their knowledge of comparable companies. The analyst will often even develop a special expertise on a particular company, which he or she will follow closely.

What does an analyst in Equity Research work on?

As explained above, an equity research analyst will follow the release of sector or company specific information to write a note for subsequent use by the clients as part of their investment strategy. Therefore, the work of the equity research analyst will be primarily information research, reading quarterly financial reports, and press releases which may provide information on the company’s performance to date compared to expectations. The analyst will also look for information on upcoming mergers and acquisitions (M&A) or divestment transactions, the announcement of new partnerships or possible disposal plans.

As for the sectoral notes, the analyst will delve into the reading of documents from the major international institutions for all the data relating to global and entire sectors.
Once this research work is completed, equity research analysts proceed to forecast results through financial modelling: they use historical data to understand how the results were obtained and they confront these historical performances with the constraints of the future environment in order to anticipate how the company will perform. This modelling will enable them to forecast short-, medium- and long-term stock performance and the behavior to adopt in order to make the most of it.

This work will therefore be carried out in the form of a synthesis and by drafting studies for investors and reacting to specific news items.

Finally, a last type of task will consist of answering clients’ questions by telephone during morning meetings in order to give them recommendations for the day.

Why do Equity Research jobs appeal so much to business school students?

First of all, it should be noted that this profession combines corporate finance skills with financial market experience, which is rare! Indeed, the Equity Research analyst will carry out various financial analyses which will be used to issue trading recommendations on the financial markets. A financial profession in such a situation is rare, which is a first strong argument.

In addition, it is the dynamic working environment that investment banking constitutes that attracts young graduates. Equity Research is marked by a culture of high standards and maximum commitment, with highly responsive teams and extremely competent colleagues. Working in a high-powered team though quite small teams enables an analyst to quickly gain knowledge on a sector or a client.

The position of Equity Research in front of clients also makes the job really interesting because the Equity Research Analyst is at the core of the clients’ investment strategies. Because as we have seen together, such a job requires the ability to manage theoretical models and market trends in order to give clients a good insight of what is to expect for the day. For that matter, an Equity Research career can be very challenging and gives plenty of responsibilities, and this is what young graduates seek for.

Resources

Equity Research Interview Questions with answers

Youtube An analyst in Equity Research’s Youtube Interview

Youtube How to do the Equity Research of a company?

About the author

The article was written in November 2022 by Louis DETALLE (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023).

A quick interview with an Asset Manager at Vontobel…

November 28, 2022 by Louis DETALLE

A quick interview with an Asset Manager at Vontobel…

In this article, Louis DETALLE (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023) interviews an asset manager at Vontobel to better understand their daily work.

Hello, what is your background?

I went to business school and chose to do an internship in finance at Blackrock. When I left ESCP Business School, Blackrock offered me a job and that’s how I launched my career in Finance. I got into Vontobel later, as I had experienced many aspects of Asset Management.

Could you explain what Vontobel Asset Management does?

Vontobel is an asset management company, which means that it invests the funds of clients such as banks and insurance companies that do not necessarily have the required expertise.

Our investment strategy must therefore ensure the growth of our clients’ funds while combining several factors such as risk, investment horizon and the profitability objective sought by the client.

What is your role as Managing Director here at Vontobel?

I am responsible for the global development of bond sales, so I have to make sure that our representatives around the world present these bond products well by making sure they have access to all the necessary marketing materials such as tenders. I also have to understand the market behaviour and the expectations of our clients in order to define the best possible strategy.

How were you recruited for this position and what qualities do you think are required?

I was recruited in particular for my experience and knowledge of the various financial markets. Vontobel was looking for someone who had the ability to understand the client segments and the ability to manage teams, for example, I manage 50 people on a daily basis. Generally speaking, the higher you go, the less technical skills are required. A managing director (MD) will of course have to be able to master the financial issues of the day, but he or she will make the difference by his or her ability to lead a team to ever-improving results.

What do you like about this job?

What I like is the diversity of the subjects I deal with in my job. This job requires me to use my technical knowledge of investment products, stock markets and macroeconomic principles in the context of a client relationship.

I have to analyse both the financial markets and my clients’ needs. Understanding their psychology and the structure in which they evolve allows me to define offers in line with their needs. For instance, it is required of me to understand what the best investment opportunities are given the macroeconomic circumstances and the interest rates environment.

Do you have any advice for students who want to go into investment banking or asset management?

Before choosing which area of finance you want to work in, I think it’s important to identify the characteristics of each of these sectors. Investment banking is similar to corporate finance, so it is a very demanding job (including weekends) because you work on M&A and company IPOs. So an analyst in M&A will be required to work from 9:30 am until midnignt and later sometimes…Asset management is a market finance job, with the definition of investment strategies linked to the opening of the market. This is why this sector requires more reasonable hourly volumes, we are talking about 8 am to 8:30 pm. The level of remuneration will be less than the ever-increasing wages of M&A, an Asset Manager can start around 50 K€ per year but it will increase every year.

Resources

Vontobel

Youtube How to approach a job interview for Asset Management

About the author

The article was written in November 2022 by Louis DETALLE (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023).

The Money Changer and his Wife

February 5, 2024November 27, 2022 by Nakul PANJABI

The Money Changer and his Wife

In this article, Nakul PANJABI (ESSEC Business School, Grande Ecole Program – Master in Management, 2021-2024) talks about the The Money Changer and his Wife painting by Quentin Matsys and the related subject of foreign exchange markets.

About the The Money Changer and his Wife painting

This painting called ‘The Money Changer and his Wife’ was painted in 1514 by Quentin Matsys, a 16th century Flemish Artist. It originated at a time when Belgium saw an increase in the number immigrants from Spain and neighboring countries because of the Spanish Inquisition. The immigrants needed local money to buy goods and services in Belgium. Therefore, the business of money changers thrived at that time.

The Money Changer and his Wife painting

Source: Quentin Matsys (Louvre Museum).

In fact, the city of Antwerp was growing in importance at that time. Earlier, Bruges was the trading capital of Belgium. In 13th century, Bruges was the leading trade centre of the North-western Europe and the world’s ever first Stock Exchange was founded in Bruges. However, due to silting up of Zwin due to tidal inlet, Bruges lost access to the North Sea and suffered from economic decline.

At the end of the 15th Century, most foreign trading houses shifted from Bruges to Antwerp and Antwerp became the Sugar capital of Europe. The city attracted Sugar traders from all around the Europe and became a major trading hub. Moneylenders and Financiers capitalized this opportunity and Antwerp became developed an efficient Bourse (Stock Exchange) that even lent money to the English Government.

Money Changer

A money changer was a person who exchanged coins or currency of one country for that of another. Many European cities and towns produced their own coinage during the Middle Ages, and these coins frequently featured the faces of their rulers, such as the local bishop or baron.

It became necessary to exchange foreign coins for local ones at neighborhood money changers when visitors from the outside world—especially traveling merchants—came to town for a market fair.

A foreign coin would be evaluated by money changers for its type, condition, and legitimacy before being accepted as a deposit and having its value converted into local money. The money could then be withdrawn by the merchant in local currency to complete a transaction. This was the humble yet prosperous beginning of the modern day banking and foreign exchange.

As mentioned on the Louvre Museum website, through its deployment of Christian symbolism (representation of the Bible), this depiction of a money changer’s store acts as a moral lesson on the spiritual need to resist worldly temptations (the woman is not looking at the Bible but the money).

The two subjects are pictured sitting behind a table, half-length. They become the center of attention because of how tightly the scenario is framed. They are symmetrical to the letter. On the table in front of him, the man is busy weighing the pearls, gems, and gold pieces. This is keeping his wife from finishing the devotional book she is now reading, as seen by the image of the Virgin and Child.

This painting can currently be seen in the Louvre Museum in Abu Dhabi.

The History of Forex

Trading between tribes began as early as 6000 BC. People started trading by exchanging one good for another. This was followed by systems in which goods like salt and spices became common media of exchange.

Then around 6th century BC societies started using coins as a medium of exchange. The main reasons were portability, durability, divisibility, uniformity, limited supply and acceptability. Most coins were valued by the purity and type of metal used, its weight and its stamp. Gold coins became the most common form of currency around the world.

However, in the 1800’s countries started adopting Gold Standard. England, USA, and other major countries adopted a system in which a piece of paper (fiat currency) is equal to a certain value of gold that can be redeemed by the holder of this paper from the government. Since the notes were a promise to pay a certain level of gold from the government, the value of that paper became equal to the value of gold it was backed by.

During the First and Second World Wars, the limits of this system were tested when the European countries needed to print more money to pay for the war. This led to the creation of Bretton Woods System. In this system, all the major currencies would be pegged (fixed rate) to the US Dollar and the US Dollar would be pegged to gold. It made sense as USD was the benchmark currency at that time and US had the majority of gold reserves in the world. The shortage of gold relative to the US dollars in circulation brought an end to the Bretton Woods agreement in 1971.

After a series of failed agreements, countries made the switch to the free-floating system that we know today. In this system the value of one currency in other currency’s terms is decided by the relative supply and demand of the currencies. Before the Internet, the major players in this market were large financial institutions and central banks. With the internet, anybody with a computer and access to internet can access the forex market and exchange currencies. This changed everything not just for Forex market but also for other financial markets. Now investing in or trading with another country became extremely easy. The costs (spreads) were reduced, and the movement of money (capital) was faster than ever.

The History of Forex

Source: Daily FX.

Although the Forex Markets, as we know it, are relatively new, people have always been exchanging currencies for trade. This lead to a creation and prosperity of a very specific profession- Money Changing.

Useful resources

About the painting

Wikipedia The Money Changer and His Wife

Louvre Abu Dhabi The Money Changer and His Wife

Joy Of Museums virtual Tours “The Money Changer and His Wife” by Quentin Matsys

Brugge Brugge: History in a nutshell

About the foreign exchange

IG Benefits of forex trading

Daily FX The History of Forex

About the author

The article was written in November 2022 by Nakul PANJABI (ESSEC Business School, Grande Ecole Program – Master in Management, 2021-2024).

Market efficiency: the case study of Yes bank in India

November 26, 2022 by Aamey MEHTA

Market efficiency: the case study of Yes bank in India

In this article, Aamey MEHTA (ESSEC Business School, Master in Finance, Singapore campus, 2022-2023) explains the key financial concept of market efficiency.

What is Market Efficiency?

An informationally efficient market is a market in which the current price of a security fully, rationally, and quickly reflects all information of that security

We can measure the efficiency of a market by observing the lag between the time that information is received to the time that the security’s price reflects this information. If there is a large lag, then traders can make use of this information to generate positive returns. For efficient markets the price of a security should not be affected by information that is already expected. The changes in price should be due to new information, i.e., information that was unexpected. For example: if a company’s earning is expected to be $10M (market consensus) and their earnings are $10M, this should not cause a change in the company’s price. However, if the earnings were $20M or $5M, then the shock news will cause the stock price to move upwards or downwards.

Market efficiency and investment styles

In a perfectly efficient market investors should use a passive investment strategy. This is because in such a market it is not possible to beat the market. In efficient markets investors can expect the market value of an assets to be equal to its intrinsic value. Using an active strategy will result in underperformance compared to the market due to transaction costs. However, if the market is inefficient, then active investment strategies can result in a profit for the investor.

What factors affect market efficiency?

Generally, markets are neither perfectly efficient or inefficient. The degree of efficiency depends on the following factors: the number of market participants, the availability of Information, and impediments to trading.

Number of market participants

The higher the number of market participants the more efficient the market is. Market participants include investors, traders, analysts, and people who follow the market. The number of participants can vary over time and across countries. Some countries prevent foreigners from trading on their markets which reduces market efficiency.

Availability of Information

The more information that is available to the investors, more efficient the market is. The easier and cheaper it is to access the information the more efficient the market will be. The access to information should not favor one group over another and should be equally available to all participants. If participants have access to material nonpublic information about the firm they should not trade on this information as this would constitute insider trading which is illegal. In developed markets there is abundance of information, and the markets are efficient. Example: New York Stock Exchange. In less developed markets the availability of information is lower and hence markets are less efficient. Example: the forwards market.

Impediments to trading

Arbitrage refers to buying an asset in one market and simultaneously selling it in another market at a higher price. This buying and selling will continue till price in both the markets are the same and arbitrage is no longer possible. Impediments to trading such as high transaction costs will restrict arbitrage opportunities and allow for some mispricing of assets.

Short selling prevents assets from being overvalued and hence short selling improves market efficiency. Restrictions on short selling, such as inability to borrow stock cheaply will reduce efficiency.

Transaction and information costs

If the cost of gathering information, analysis and trading is more than the cost of trading misvalued assets markets will be inefficient. If after deducting costs, there is no risk adjusted returns to be made from trading based on publicly available information then the markets are said to be efficient.

Types of market efficiency

Weak form of market efficiency

This form of market efficiency states that current security prices fully reflect all currently available security market data. Thus, past price and volume information will have no predictive power over the future direction of security prices because price changes will be independent from one period to the next.

Semi-strong form of market efficiency

This form holds that security prices rapidly adjust without bias to the arrival of new public information. Current security prices fully reflect all publicly available information. This form says that security prices include all past security market and non-market information available to the public. Examples: Information on the financials reports published by the company, news about the company.

Strong form of market efficiency

This form states that security prices fully reflect all information from both public and private sources. The strong form includes all types of information: past security market information, public and private (insider) information. This means that no group of investors has monopolistic access to information relevant to the formation of prices and no one should be able to generate positive risk adjusted returns.

What do we know about the efficiency of the market?

Fama

Fama, in his paper Efficient Market Hypothesis defined a market to be “informationally efficient” if prices at each moment incorporate all available information about future values.

The efficient market hypothesis states:

Current prices incorporate all available information and expectations.
Current prices are the best approximation of intrinsic value.
Price changes are due to unforeseen events.
“Mispricings” do occur but not in predictable patterns that can lead to consistent outperformance.

The efficient market hypothesis does not state:

All investors are rational.
Prices are always right.
Prices should be stable.
Professional money managers can’t earn higher than market returns.

The Grossman-Stiglitz paradox

This paradox was proposed by Stanford Grossman and Joseph Stiglitz. They argue that perfectly informationally efficient markets are an impossibility since, if prices perfectly reflected available information, there is no profit to gathering information, in which case there would be little reason to trade and markets would eventually collapse.

Investors that purchase index funds or ETFs benefit at the expense of investors who pay for financial services either indirectly or directly via investing in actively managed funds.

Case study: yes bank

Yes Bank is an Indian Bank founded in 2004 by Rana Kapoor and Ashok Kapur, headquartered in Mumbai, India.

Yes bank is a private sector bank. In March 2020, Yes Bank faced a historical crisis. There are various reasons that led Yes bank to this crisis, they are, there were a large number of bad loans given by banks and depositors have withdrawn large numbers of amounts from the bank. There was no balance between the loan sheet and the depositors’ sheet. RBI put a 30 days moratorium on Yes Bank to save it.
A major effect of the yes bank crisis was that there was a big chance that other financial institutions could collapse. But the Reserve Bank of India took initiative and saved Yes Bank from major collapse.

In May 2020 shares of Yes Bank Ltd. fell as much as 84.65 percent intraday to Rs 5.65 apiece—the lowest on record—but pared some of the losses to traded 51.63 percent lower at Rs 17.80. The S&P BSE Sensex fell 1,450 points and NSE Nifty 50 slipped below 10,900. This, after the Reserve Bank of India on Thursday evening superseded the board of the lender and imposed curbs on its operations for a month.

stock chart of yes bank
Logo of Wells Fargo
Source: internet.

Useful resources

Academic resources

Fama E. (1970) Efficient Capital Markets: A Review of Theory and Empirical Work, Journal of Finance, 25,383-417.

Fama E. (1991) Efficient Capital Markets: II Journal of Finance, 46, 1575-617.

Grossman S.J. and J.E. Stiglitz (1980) On the Impossibility of Informationally Efficient Markets The American Economic Review, 70, 393-408.

Business resources

Yes bank

About the author

The article was written in November 2022 by Aamey MEHTA (ESSEC Business School, Master in Finance, Singapore campus, 2022-2023)

About the company

My internship

Missions

India and China as the main actors profiting from the discounted price of Urals

Dislocation of the market between Europe and Asia

Required skills

Key learning

Key numbers

Refinery margins

Refinery margin and crack spread

Implementation of a crack strategy

Trading the crack spread

Reading crack spread as market signal

Why should I be interested in this post?

Related posts on the SimTrade blog

Useful resources

Business Analysis

About the author

Introduction

The top-down approach

The bottom up approach

Types of asset allocations

Policy asset allocation

Dynamic asset allocation

Tactical asset allocation

Asset allocation application: an example

Excel file for asset allocation

Why should I be interested in this post?

Related posts on the SimTrade blog

Useful resources

Academic research

About the author

Introduction

Types of quantitative equity strategies

Fundamental quantitative investing

Statistical arbitrage

High Frequency Trading (HFT)

Conclusion

Why should I be interested in this post?

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Useful resources

Academic research

About the author

Introduction

Mathematical foundations

Optimal portfolio application: the case of two assets

Optimal portfolio application: the general case

Why should I be interested in this post?

Related posts on the SimTrade blog

Useful resources

Academic research

About the author

Introduction

Equity strategies

Discretionary long-short

Dedicated short bias

Quantitative

Example of a long-short equity strategy

Performance of the long-short equity strategy

Why should I be interested in this post?

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Academic research

Business Analysis

About the author

Fama-MacBeth two-step regression method: the case of K risk factors

Introduction

Mathematical foundations

Step 1: time-series analysis of returns

Step 2: cross-sectional analysis of returns

Application: the Fama-French 3-Factor model

Empirical study of the Fama-MacBeth regression

Empirical study: Stock approach for a K-factor model

Time-series regression

Cross-sectional regression

Excel file

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