Alpha

November 20, 2021 by Youssef LOURAOUI

In this article, Youssef LOURAOUI (Bayes Business School, MSc. Energy, Trade & Finance, 2021-2022) elaborates on the concept of alpha, one of the fundamental parameters for portfolio performance measure.

This article is structured as follows: we introduce the concept of alpha in asset management. Next, we present some interesting academic findings on the alpha. We finish by presenting the mathematical foundations of the concept.

Introduction

The alpha (also called Jensen’s alpha) is defined as the additional return delivered by the fund manager on the overall performance of the portfolio compared to the market performance (Jensen, 1968). A key issue in finance (and particularly in portfolio management) has been evaluating the performance of portfolio managers. The term ‘performance’ encompasses at least two independent dimensions (Sharpe, 1967): 1) The portfolio manager’s ability to boost portfolio returns by successful forecasting of future security prices; and 2) The portfolio manager’s ability to minimize (via “efficient” diversification) the amount of “insurable risk” borne by portfolio holders.

The primary hurdle to evaluating a portfolio’s performance in these two categories has been a lack of a solid grasp of the nature and assessment of “risk”. Risk aversion appears to predominate in the capital markets, and as long as investors accurately perceive the “riskiness” of various assets, this indicates that “risky” assets must on average give higher returns than less “risky” assets. Thus, when evaluating portfolios’ performance, the implications of varying degrees of risk on their returns must be considered (Sharpe, 1967).

One way of representing the performance is by linking the performance of a portfolio to the security market line (SML). Figure 1 depicts the relation between the portfolio performance in relation to the security market line. As illustrated in Figure 1 below, Fund A has a negative alpha as it is located under the SML, implying a negative performance of the fund manager compared to the market. Fund B has a positive alpha as it is located above the SML, implying a positive performance of the fund manager compared to the market.

Figure 1. Alpha and the Security Market Line

Source: Computation by the author.

You can download below an Excel file with data to compute Jensen’s alpha for fund performance analysis.

Academic Literature

Jensen develops a risk-adjusted measure of portfolio performance that quantifies the contribution of a manager’s forecasting ability to the fund’s returns. In the first empirical study to assess the outperformance of fund managers, Jensen aimed at quantifying the predictive ability of 115 mutual fund managers from 1945 to 1964. He looked at their ability to produce returns above the expected return given the risk level of each portfolio. Not only does the evidence on mutual fund performance indicate that these 115 funds on average were unable to forecast security prices accurately enough to outperform a buy-and-hold strategy, but there is also very little evidence that any individual fund performed significantly better than what we would expect from mutual random chance. Additionally, it is critical to highlight that these conclusions hold even when fund returns are measured net of management expenses (that is assume their bookkeeping, research, and other expenses except brokerage commissions were obtained free). Thus, on average, the funds did not appear to be profitable enough in their trading activity to cover even their brokerage expenses.

Mathematical derivation of Jensen’s alpha

The portfolio performance metric given below is derived directly from the theoretical results of Sharpe (1964), Lintner (1965a), and Treynor (1965) capital asset pricing models. All three models assume that (1) all investors are risk-averse and single-period expected utility maximizers, (2) all investors have identical decision horizons and homogeneous expectations about investment opportunities, (3) all investors can choose between portfolios solely based on expected returns and variance of returns, (4) all transaction costs and taxes are zero, and (5) all assets are infinitely fungible. With the extra assumption of an equilibrium capital market, each of the three models produces the following equation for the expected one-period return defined by (Jensen, 1968):

E(r): the expected return of the fund
r_f: the risk-free rate
E(r_m): the expected return of the market
β(E(r_m) – r_f): the systematic risk of the portfolio
α: the alpha of the portfolio (Jensen’s alpha)

Why should I be interested in this post?

If you are a business school or university student, this post will help you to understand the fundamentals of investment.

Useful resources

Academic research

Fama, Eugene F. 1965. The Behavior of Stock Market Prices.Journal of Business 37, 34-105.

Fama, Eugene F. 1967. Risk, Return, and General Equilibrium in a Stable Paretian Market. Chicago, IL: University of Chicago.Unpublished manuscript.

Fama, Eugene F. 1968. Risk, Return, and Equilibrium: Some Clarifying Comments. Journal of Finance, 23, 29-40.

Lintner, John. 1965a. Security Prices, Risk, and Maximal Gains from Diversification. Journal of Finance, 20, 587-616.

Lintner, John. 1965b. The Valuation of Risk Assets and the Selection of Risky Investments in Stock Portfolios and Capital Budgets.Review of Economics and Statistics 47, 13-37.

Markowitz, H., 1952. Portfolio Selection. The Journal of Finance, 7, 77-91.

Sharpe, William F. 1963. A Simplified Model for Portfolio Analysis. Management Science, 19, 425-442.

Sharpe, William F. 1964. Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk. Journal of Finance, 19, 425-442.

Sharpe, William F. 1966. Mutual Fund Performance. Journal of Business39, Part 2: 119-138.

Treynor, Jack L. 1965. How to Rate Management of Investment Funds.Harvard Business Review 18, 63-75.

Business analysis

JP Morgan Asset Management, 2021.Glossary of investment terms: Alpha

About the author

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

Security Market Line (SML)

November 20, 2021 by Youssef LOURAOUI

In this article, Youssef LOURAOUI (Bayes Business School, MSc. Energy, Trade & Finance, 2021-2022) presents the Security Market Line (SML), a key concept in asset pricing derived from the Capital Asset Pricing Model (CAPM).

This article is structured as follows: we first introduce the concept of Security Market Line (SML). We then present the mathematical foundations of the SML. We finish by presenting an investment strategy that can be implemented relying on the SML.

Security Market Line

The SML reflects the risk-return combinations accessible in the capital market at any given time for all risky assets. Investors would choose investments based on their risk appetites; some would only consider low-risk investments, while others would welcome high-risk investments. The SML is derived from the Capital Asset Pricing Model (CAPM), which describes the trade-off between risk and expected return for efficient portfolios.

The expected relationship between risk and return is depicted in Figure 1. It demonstrates that as perceived risk increases, investors’ required rates of return increase.

Figure 1. Security Market Line.

Source: Computation by the author.

Under the CAPM framework, all investors will choose a position on the capital market line by borrowing or lending at the risk-free rate, since this maximizes the return for a given level of risk. Whereas the CML indicates the rates of return of a specific portfolio, the SML represents the risk and return of the market at a given point in time and indicates the expected returns of individual assets. Also, while the measure of risk in the CML is the standard deviation of returns (total risk), the measure of risk in the SML is the systematic risk, or beta. Figure 2 depicts the SML line combined with four different assets. Asset A and B are above the SML line, which implies that they are overvalued. Asset C and D are below the SML which implies that they are undervalued. From Figure 2, we can implement an investment strategy by going long if the asset or portfolio lies under the SML and going short if the asset or portfolio is greater than the SML.

Figure 2. Security Market Line with a plot of different assets.

Source: Computation by the author.

Mathematical foundation

The SML plots an individual security’s expected rate of return against systematic, undiversifiable risk. The risk associated with an individual risky security is determined by the volatility of the security’s return, not by the market portfolio’s return. Individual risky securities bear a proportional share of the systematic risk. The only risk that an investor should be compensated for is systematic risk, which cannot be neutralized through diversification. This risk is quantified using the beta, which refers to a security’s sensitivity to market fluctuations. The slope of the SML is equal to the market risk premium and reflects the risk-reward trade-off at a particular point in time. We can define the line of the SML as:

Mathematically, we can deconstruct the SML as:

SML_formula

Where

E(R_i) represents the expected return of asset i
R_f is the risk-free interest rate
β_i measures the systematic risk of asset i
E(R_M) represents the expected return of the market
E[R_M – R_f] represents the market risk premium.

Beta and the market factor

William Sharpe (1964), John Lintner (1965), and Jan Mossin (1966) independently developed the Capital Asset Pricing Model (CAPM). The CAPM was a significant evolutionary step forward in capital market equilibrium theory because it allowed investors to value assets correctly in terms of risk. The CAPM makes a distinction between two forms of risk: systematic and specific risk. Systematic risk refers to the risk posed by the market’s basic structure, its participants, and all non-diversifiable elements such as monetary policy, political events, and natural disasters. By contrast, specific risk refers to the risk inherent in a particular asset and so is diversifiable. As a result, the CAPM solely captures systematic risk via the beta measure, with the market’s beta equal to one, lower-risk assets having a beta less than one, and higher-risk assets having a beta larger than one.

In the late 1970s, the portfolio management industry sought to replicate the market portfolio return, but as financial research advanced and significant contributions were made, it enabled the development of additional factor characteristics to capture additional performance. This resulted in the development of what is now known as factor investing.

Estimation of the Security Market Line

You can download an Excel file with data to estimate the Security Market Line.

Why should I be interested in this post?

The security market line is frequently used by portfolio managers and investors to determine the suitability of an investment product for inclusion in a portfolio. The SML is useful for determining whether a security’s expected return is favourable in comparison to its level of risk. The SML is frequently used to compare two similar securities that offer approximately the same rate of return to determine which one has the lowest inherent market risk in relation to the expected rate of return. Additionally, the SML can be used to compare securities of comparable risk to determine which one offers the highest expected return for that level of risk.

If you are a business school or university undergraduate or graduate student, this content will help you in broadening your knowledge of finance.

Useful resources

Academic research

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

Lintner, J. 1965a. The Valuation of Risk Assets and the Selection of Risky Investments in Stock Portfolios and Capital Budgets. The Review of Economics and Statistics 47(1): 13-37.

Lintner, J. 1965b. Security Prices, Risk and Maximal Gains from Diversification. The Journal of Finance, 20(4): 587-615.

Mossin, J. 1966. Equilibrium in a Capital Asset Market. Econometrica, 34(4): 768-783.

Reilly, R. K., Brown C. K., 2012. Investment Analysis & Portfolio Management, Tenth Edition.

Sharpe, W.F. 1963. A Simplified Model for Portfolio Analysis. Management Science, 9(2): 277-293.

Sharpe, W.F. 1964. Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk. The Journal of Finance, 19(3): 425-442.

About the author

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

Capital Market Line (CML)

January 30, 2024November 20, 2021 by Youssef LOURAOUI

In this article, Youssef LOURAOUI (Bayes Business School, MSc. Energy, Trade & Finance, 2021-2022) presents the Capital Market Line (CML), a key concept in asset pricing derived from the Capital Asset Pricing Model (CAPM).

This article is structured as follows: we first introduce the concept. We then illustrate how to estimate the capital market line (CML). We finish by presenting the mathematical foundations of the CML.

Capital Market Line

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

Let us consider two cases: 1) when investors have access to risky assets only; 2) when investors have access to risky assets and a risk-free asset (earning a constant interest rate, 2% for example below).

Risky assets

In the case of risky assets only, the efficient frontier (the set of optimal portfolios) is represented below in Figure 1.

Figure 1. Efficient frontier with risky assets only.
img_Simtrade_CML_graph_1
Source: Computation by the author.

Risky assets and a risk-free asset

In the case of risky assets and a risk-free asset, the efficient frontier (the set of optimal portfolios) is represented below in Figure 2. In this case, the efficient frontier is a straight line called the Capital Market Line (CML).

Figure 2. Efficient frontier with risky assets and a risk-free asset.

Source: Computation by the author.

The CML joins the risk-free asset and the tangency portfolio, which is the intersection with the efficient frontier with risky assets only. We can reasonably conclude from Figure 2 that, to increase expected return, an investor has to increase the amount of risk he or she takes to attain returns higher than the risk-free interest rate. As a result, the Sharpe ratio of the market portfolio equals the slope of the CML. If the Sharpe ratio is more than the CML, an investment strategy can be implemented, such as buying assets if the Sharpe ratio is greater than the CML and selling assets if the Sharpe ratio is less than the CML (Drake and Fabozzi, 2011).

Investors who allocate their money between a riskless asset and the risky market portfolio M can expect a return equal to the risk-free rate plus compensation for the number of risk units σ_P) they accept. This result is in line with the underlying notion of all investment theory: investors perform two services in the capital markets for which they might expect to be compensated. First, they enable someone else to utilize their money in exchange for a risk-free interest rate. Second, they face the risk of not receiving the promised returns in exchange for their invested capital. The term E(r_M)- R_f) / σ_M refers to the investor’s expected risk premium per unit of risk, which is also known as the expected compensation per unit of risk taken.

Figure 3 represents the Capital Market Line which connect the risk-free asset to the efficient frontier line. The straight line in Figure 3 represents a combination of a risky portfolio and a riskless asset. Any combination of the risk-free asset and Portfolio A is similarly outperformed by some combination of the risk-free asset and Portfolio B. Continue drawing a line from R_f to the efficient frontier with increasing slopes until you reach Portfolio M’s point of tangency. All other possible portfolio combinations that investors could build are outperformed by the collection of portfolio possibilities along Line R_f-M, which is the CML. The CML, in this sense, represents a new efficient frontier that combines the Markowitz efficient frontier of risky assets with the ability to invest in risk-free securities. The CML’s slope is (E(r_M)- R_f) / σ(_M), which is the highest risk premium compensation that investors can expect for each unit of risk they take on (Reilly and Brown, 2012) (Figure 3).

If we fully invest our cash on the risk-free rate, we would be exactly on the y axis with an expected return of 2%. Each time we move along the curve that connects the risk-free rate to the optimum market portfolio, we allocate less weight to the risk-free rate, and we overweight more on riskier assets (Point A). Points M represents the optimal risky portfolio in the efficient frontier line, which minimizes the overall portfolio variance. It would have a weighting of 45% in stock A and a 55% in stock B, which would offer a 26.23% annualized return for a 17.27% annualized volatility. Point B represents a portfolio composition that is based on a leveraged position of 140% on the optimal risky portfolio and a short position on the risk-free asset of -40% (Figure 3).

Figure 3. Efficient frontier with different points.

Source: Computation by the author.

Mathematical representation

We can define the CML as the line that is tangent to the efficient frontier which connects the risk-free asset with the market portfolio:

img_SimTrade_CML_equations_0

Where:

σ_P: the volatility of portfolio P
R_f: the risk-free interest rate
E(R_M): the expected return of the market M
σ_M: the volatility of the market M
E[R_M– R_f]: the market risk premium.

The expected return of the portfolio can be computed as:

img_SimTrade_CML_equations_1

The Sharpe Ratio is shown in parenthesis, and it compares the performance of an investment, such as a security or portfolio, to the performance of a risk-free asset after adjusting for risk. It is calculated by dividing the difference between the investment returns and the risk-free return by the standard deviation of the investment returns. It denotes the additional amount of return that an investor receives for each unit of risk increase (Sharpe, 1963). We can define it mathematically as:

img_SimTrade_CML_equations_2

We can identify the following relationship between the slope of the CML and the Sharpe ratio of the market portfolio, defined mathematically as follows:

img_SimTrade_CML_equations_3

A simple strategy for stock selection is to buy assets with Sharpe ratios that are higher than the CML and sell those with Sharpe ratios that are lower. Indeed, the efficient market hypothesis implies that beating the market is impossible. As a result, all portfolios should have a Sharpe ratio that is lower than or equal to the market. As a result, if a portfolio (or asset) has a higher Sharpe ratio than the market, this portfolio (or asset) has a higher return per unit of risk (i.e. volatility), which contradicts the efficient market hypothesis. The alpha is the abnormal excess return over the market return at a given level of risk.

Why should I be interested in this post?

Sharpe ratio is a popular tool for assessing portfolio risk/return in finance. The Sharpe ratio informs the investor precisely which portfolio has the best performance among the available options. This simplifies the investor’s decision-making process. The higher the ratio, the greater the return for each unit of risk.

If you are a business school or university undergraduate or graduate student, this content will help you in broadening your knowledge of finance.

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.

Lintner, J. 1965a. The Valuation of Risk Assets and the Selection of Risky Investments in Stock Portfolios and Capital Budgets. The Review of Economics and Statistics 47(1): 13-37.

Lintner, J. 1965b. Security Prices, Risk and Maximal Gains from Diversification. The Journal of Finance, 20(4): 587-615.

Mossin, J. 1966. Equilibrium in a Capital Asset Market. Econometrica, 34(4): 768-783.

Reilly, R. K., Brown C. K., 2012. Investment Analysis & Portfolio Management, Tenth Edition.

Sharpe, W.F. 1963. A Simplified Model for Portfolio Analysis. Management Science, 9(2): 277-293.

Sharpe, W.F. 1964. Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk. The Journal of Finance, 19(3): 425-442.

About the author

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

Active Investing

November 20, 2021 by Youssef LOURAOUI

In this article, Youssef LOURAOUI (Bayes Business School, MSc. Energy, Trade & Finance, 2021-2022) elaborates on the concept of active investing, which is a core investment strategy that relies heavily on market timing and stock picking as the two main drivers of financial performance.

This article is structured as follows: we introduce the concept of active investing in asset management. Next, we present an overview of the academic literature regarding active investing. We finish by presenting some basic principles on active investing.

Introduction

Active investing is an approach for going beyond matching a benchmark’s performance and instead aiming to outperform it. Alpha may be calculated using the CAPM framework, by comparing the fund manager’s expected return with the expected market return (Jensen, 1968). The search for alpha is done through two very different types of investment approaches: stock picking and market timing.

Stock picking

Stock picking is a method used by active managers to select assets based on a variety of variables such as their intrinsic value, the growth rate of dividends, and so on. Active managers use the fundamental analysis approach, which is based on the dissection of economic and financial data that may impact the asset price in the market.

Market timing

Market timing is a trading approach that involves entering and exiting the market at the right time. In other words, when rising outlooks are expected, investors will enter the market, and when downward outlooks are expected, investors will exit. For instance, technical analysis, which examines price and volume of transactions over time to forecast short-term future evolution, and fundamental analysis, which examines the macroeconomic and microeconomic data to forecast future asset prices, are the two techniques on which active managers base their decisions.

Review of academic literature on active investing

As fund managers tried strategies to beat the market, financial literature delved deeper into the mechanism to achieve this purpose. Jensen’s groundbreaking work in the early ’70s gave rise to the concept of alpha in the tracking of a fund’s performance to distinguish between the fund’s manager’s ability to generate abnormal returns and the part of the returns due to luck (Jensen, 1968).

Jensen develops a risk-adjusted measure of portfolio performance that quantifies the contribution of a manager’s forecasting ability to the fund’s returns. He used the measure to quantify the predictive ability of 115 mutual fund managers from 1945 to 1964—that is, their ability to produce returns above those expected given the risk level of each portfolio.

Not only does the evidence on mutual fund performance indicate that these 115 funds on average were unable to forecast security prices accurately enough to outperform a buy-and-hold strategy, but there is also very little evidence that any individual fund performed significantly better than what we would expect from mutual random chance. Additionally, it is critical to highlight that these conclusions hold even when fund returns are measured net of management expenses (that is assume their bookkeeping, research, and other expenses except brokerage commissions were obtained free). Thus, on average, the funds did not appear to be profitable enough in their trading activity to cover even their brokerage expenses.

Core principles of active investing

First principle: market efficiency varies between asset classes.

Investment information is not always readily available in all markets. For less efficient asset classes, an “active” management strategy offers a larger possibility to outperform the market, whereas a “passive” investment strategy may be more appropriate for highly efficient asset classes. In other words, there are compelling advantages for incorporating both active and passive techniques into an overall portfolio.

For example, Wall Street analysts cover a huge portion of US large size shares, making it harder to locate cheap companies. For this highly efficient asset class, a passive investment strategy may be more cost-effective in some cases. On the other side, emerging market equities are sometimes under-researched and difficult to appraise, providing an active manager with additional opportunities to identify mispriced companies. The critical point here is to notice the distinctions and then make the appropriate decisions.

Second principle: market efficiency varies across asset classes.

Within practically every asset class, active and passive management strategies can alternate as winners periodically. Even the most efficient asset classes can occasionally benefit from active management over passive. The reason is substantially distinct from the one stated in Principle One. Principle Two is related to the “Grossman-Stiglitz Paradox”: If markets are fully efficient, there is no reason to investigate them; yet markets can only be perfectly efficient for as long as they are regularly investigated. When investors run out of patience researching stocks in a highly efficient market, passive investment becomes appealing, reopening the door to opportunities for active research. This can result in an annual cycle of active/passive trends.

In some investing environments, active strategies have tended to benefit investors more, while passive strategies have tended to outperform in others. For instance, active managers may outperform more frequently than passive managers when the market is turbulent, or the economy is deteriorating. On the other way, when certain securities within the market move in lockstep or when stock valuations are more consistent, passive strategies may be preferable. Investors may gain from combining passive and active strategies in a way that exploits these insights, depending on the opportunity in various areas of the capital markets. Market conditions, on the other hand, vary constantly, and it frequently takes an intelligent eye to determine when and how much to skew toward passive rather than active investments (Morgan Stanley, 2021).

It’s worth noting that attaining consistently successful active management has historically been more challenging in some asset classes and segments of the market, such as large US company stocks. As a result, it may make sense to be more passive in certain areas and more active in asset classes and segments of the market where active investing has historically been more rewarding, such as overseas stocks in emerging markets and smaller U.S. corporations (Morgan Stanley, 2021).

Why should I be interested in this post?

If you are a business school or university undergraduate or graduate student, this content will help you in broadening your knowledge of finance.

Useful resources

Academic research

Grossman, S., Stiglitz, J., 1980. On the impossibility of Informationally efficient markets. The American Economic Review, 70(3), 393-408.

Lintner, J. 1965a. The Valuation of Risk Assets and the Selection of Risky Investments in Stock Portfolios and Capital Budgets. The Review of Economics and Statistics 47(1): 13-37.

Lintner, J. 1965b. Security Prices, Risk and Maximal Gains from Diversification. The Journal of Finance, 20(4): 587-615.

Mangram, M.E., 2013. A simplified perspective of the Markowitz Portfolio Theory. Global Journal of Business Research, 7(1): 59-70.

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

Mossin, J. 1966. Equilibrium in a Capital Asset Market. Econometrica, 34(4): 768-783.

Sharpe, W.F. 1963. A Simplified Model for Portfolio Analysis. Management Science, 9(2): 277-293.

Sharpe, W.F. 1964. Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk. The Journal of Finance, 19(3): 425-442.

Business analysis

Forbes, 2021. Active or Passive investing? Two principles provide the answer

JP Morgan Asset Management, 2021. Investing

Morgan Stanley, 2021. Active vs Passive management

About the author

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

Social trading

November 19, 2021 by Daksh GARG

Social trading

In this article, Daksh GARG (ESSEC Business School, Master in Strategy & Management of International Business (SMIB), 2020-2021) explains how social networks and online communities have led to emergence of new way of trading called social trading. This article answers the following questions: What is social trading? What are the platforms on which social trading is being done? And what are some of the potential disadvantages of putting your money and following the trend of social trading?

What is social trading?

Social trading in the stock market uses the wisdom of the online community to develop strategies and portfolios that best fit the investment needs of the community members.

Social trading is security trading on a digital platform where investors derive their investment decisions from information or signals provided by other traders in the community. Social trading enables experienced retail investors to share their investments strategies or portfolios with the online community. It provides the opportunity to observe the ideas and to discuss, comment or even copy them. The concept of social trading combines the investment ideas of traders with a leader follower principle. The star traders are called signal providers. They share their investment ideas with the online community. Members of the community execute their strategies and portfolios in a real money account or in a virtual account where no real funds are at risk. This is illustrated in Figure 1.

Figure 1. Social trading: interaction between the experienced trader and social traders.

Source: IG

What do you need to know before you start social trading?

Social trading has removed the barriers to financial inclusion, but it is also said to downplay a lot of the knowledge required to negotiate in financial markets. One of the largest faults a social trader can make is thinking it is risk free. All trading involves risk, and traders are likely to make a loss at one point or another. The idea of trusting a judgement of third person – while retaining all the risk of loss – is seen as a large drawback of social trading. Financial markets require knowledge and patience, and social trading can potentially help you skip a few steps, but it does so at the expense of experience. You might take on some other persons, but you implement plan should be unique to you and your aims. Although the strategies of others can be used to create some guidance for your trades, their plans will be suited to their own goal. Each individual perceives risk differently so, so trading the way someone else would isn’t always necessarily a good idea.

How to get started with social trading?

Select a social trading platform/broker

There are a lot of different platforms available in the market. But make sure the platform provides all of the functions you want to enable you to utilize it successfully and is trustworthy.

Select and research the traders you want to learn and copy from

The platforms will provide you with recommendations of traders that you can copy and learn from, but it is essential to do your own research. Research the markets they trade, the strategies they use and the risk parameters. Do your due diligence.

Allocate a set amount to each trader

Allocate a portion of your capital to each trader you follow. Think of this as your risk management. You will have researched the trader you are tracking, and now it’s about diversifying your investment into each trader. For traders with a high-risk strategy, you may want to allocate less money to traders with a lower risk strategy.

Figure 2 gives some of the popular social trading platforms emerged in past few years.

Figure 2. Popular social trading platforms.

Source: www.diaman.eu/blog

Why should I be interested in this post?

Investing in financial markets such as stocks, foreign exchange (forex) and cryptocurrencies requires extensive knowledge and experience. You need to be up to date with news events affecting the business world, and also have the capability of reading charts, analytics reports and understand trends. If you lack this capacity, chances are you may lose your money in the market. Social trading platforms might come in handy in this phenomenon, but you should always be careful and aware of the risks. This article provides a good introduction who is starting their journey in social trading.

Useful resources

IG Social trading explained Accessed November 2, 2021.

CMC markets Social trading Accessed November 2, 2021.

About the author

The article was written in November 2021 by Daksh GARG (ESSEC Business School, Master in Strategy & Management of International Business (SMIB), 2020-2021).

Use of AI in investment banking

November 19, 2021 by Daksh GARG

Use of AI in investment banking

In this article, Daksh GARG (ESSEC Business School, Master in Strategy & Management of International Business (SMIB), 2020-2021) explains how the use of AI is transforming the investment banking industry.

What is artificial intelligence?

Artificial intelligence (AI) generally refers to the ability of machines to exhibit human-like intelligence and a degree of autonomous learning. An example could be training machines to recognize patterns and solve complex problems by analyzing those patterns.

Recently there has been a push to work with AI in the investment industry. The conditions to do so are optimal now, or at least more so than in the past. There have great leaps in computer processing abilities, allowing the cost of quality processing tech to decrease. There has also been a sharp increase in the availability of accessible data, which AI can use as needed. This has led to AI being cost effective enough to be used in carrying out investment guidance and analysis.

When can AI add value?

There have been several implementations of AI across the broad spectrum of investment banking , and the rapid pace of adoption paves the way for greater efficiencies in almost all departments and functions. AI has the potential to completely transform the competitive dynamics given its indispensability in investment banking. Much like a chain reaction, investment banks will adopt AI if even one of their peers adopts it, and they already have. This will keep going until all of them rely on the accuracy and effectiveness of AI in their daily trade and investment making decisions. By its very nature, investment banking is a highly competitive industry, and intelligent tools can augment every facet from trade processing to defining structured products and projecting returns on capital, simulating market conditions to support accurate and fast decision making, to even improving the customer experience. AI will also enhance back-office functions, ensuring quicker and easier settlements with minimal human error.

Figure 1. Use of Artificial intelligence / Machine learning AI/ML deployment across various components of financial services industry.
Use of AI/ML in financial services industry.

Use of AI/ML in financial services industry.

Source: Refinitiv AI/ML survey August 2020

Some current use cases

There are several use cases already being adopted for use of AI and ML in the financial services industry. Some of the prominent examples of companies using AI are:

ING

ING leverages the power of AI to empower bond traders make faster and more accurate pricing decisions, with Katana, an AI tool that uses predictive analytics to ensure traders are quoting the right price when buying and selling bonds for their clients, based entirely on historic and real-time streaming data.

Barclays

The UK investment bank takes its AI initiatives very seriously, with the whole subsidiary dedicated to making the investment bank’s vision of becoming “the most AI savvy workforce in the UK” come true. Payments and trade decisions for their short term structured products are handled by their in-house AI.

UBS

UBS uses machine learning and neural networks for facilitating accurate decision making of its traders on the floor, including the allocation of funds and analyzing real time data to enhance the performance of its high performing traders.

JP Morgan Chase

The American investment banking giant is using AI to interpret loan agreements. As a part of its AI agenda, its now famous Contract Intelligence AI system saved 360,000 hours of mundane work, interpreting and recording contract clauses, freeing up support staff to focus on high value delivery.

Figure 2. Potential use cases of AI in various components of investment banking.

Source: Refinitiv AI/ML survey August 2020

The challenges towards wide-spread adoption of AI in investment baking

We cannot assume that AI will replace analysts anytime soon. There are a lot of nuanced human interactions and decisions for the machines to acquire at their current development phase. At this stage in the AI managing processes, leading analysis, and making judgment calls can be very dangerous. Like humans, the machines have to follow training for some time before performing as they are designed. Human supervision on these systems will be essential. After the collapse of many banks in 2008 there has been a wide spread adoption of AI. We are talking about more effective gathering data, from hours to mere seconds. Artificial Intelligence enables investment bankers to focus more on the transactions rather than the immense piles of hard work. There are certain costs towards adoption and a potential job loss also. In 2000, Goldman Sachs’ cash equities trading desk was holding 600 traders. Today, only two traders are left on the desk with machines doing the heavy lifting of the work.

Conclusion

Banks are automating their processes day by day. Despite the increasing and better application of AI in banking and financial industry contributing towards innovation, its adoption in the industry is still at the stage of infancy. Low level of maturity, infrastructure, reluctant industry adoption, increased technical complexity and high costs are the major reasons preventing financial services firms for large scale adoption. But this would change in future.

Why should I be interested in this post?

Artificial Intelligence is emerging in the digital era that we know now, expected to be the next big thing. This article shows the benefits and challenges of AI in the investment banking industry.

Useful resources

McKinsey (19/09/2020) AI-bank of the future: Can banks meet the AI challenge? Accessed November 2, 2021.

Forbes (31/10/2020) The State Of AI Adoption In Financial Services Accessed November 2, 2021.

About the author

The article was written in November 2021 by Daksh GARG (ESSEC Business School, Master in Strategy & Management of International Business (SMIB), 2020-2021).

The IRR, XIRR and MIRR functions in Excel

June 4, 2024November 19, 2021 by Léopoldine FOUQUES

The IRR, XIRR and MIRR functions in Excel

In this article, Léopoldine FOUQUES (ESSEC Business School, Global Bachelor of Business Administration, 2017-2021) presents the IRR function in Excel to compute the internal rate of return of a series of cash flows.

About Excel

Excel is by far the most used financial modeling tool across the world to build models and perform analysis. Knowing which Excel function to use can help employees in the financial sector (financial analysts, fund managers, risk managers, traders, etc.) to work faster and build a more powerful model.

The internal rate of return (IRR)

Definition

The computation of the internal rate of return (IRR) is based on the net present value (NPV) of an investment. In financial modelling, an investment is represented by a series of cash flows: CF₀, CF₁, CF₂, …, CF_T. For a classic investment, the first cash flow, CF₀, is negative (outflow) and the future cash flows, CF₁, CF₂, …, CF_T are positive (inflows).

The net present value (NPV) of an investment is computed according to the following formula:

where r is the discount rate that takes into account the risk of the project.

The IRR corresponds to the value of the discount rate for which the NPV is equal to 0:

The IRR is the solution of a non-linear equation:

Use in finance

One of the most important functions is the Internal Rate of Return (IRR) function, as it’s an easy function to compare an investment’s return, based on a series of cash flows.

The function is very useful in financial modeling. Indeed, it’s frequently used to compare scenarios before deciding about a project. An example is when a company is presented with two opportunities: one is investing in a new factory and the second is expanding its existing factory.

By using IRR, we can estimate the IRR for each scenario and verify which one is higher than the average cost of capital of the business (the Weighted Average Cost of Capital or WACC) is a calculation of a firm’s cost of capital in which each category is proportionally weighted).

The Excel functions to compute the IRR

Building a math-based calculation is time-consuming and complicated, so Excel offers three functions for the calculation of the internal rate of return: IRR, MIRR, and XIRR.

The IRR function

The IRR function uses one required argument and one optional:

The values: they represent the series of cash flows, including net income value and investments.
The guessed number for the expected internal rate of return. If omitted, the function will default to 0.1 (= 10%).

You can download the Excel file below in which I illustrate the use of the IRR function in Excel based on a simple example.

Note that the IRR corresponds to a period rate. Monthly cash flows lead to a monthly IRR, quarterly cash flows lead to a quarterly IRR; and annual cash flows lead to an annual IRR. As, in practice, the standard is to work annual rates, monthly and quarterly IRR have to annualized.

Note that the use of the IRR function assumes that the period between each cash flow is the same (equal-size payment periods), for example one year.

From the IRR function to the XIRR function

If the period between each cash flow is not the same, the IRR function should not be used. It is the case with monthly cash flows as the months of the year may contain 28, 29, 30 or 31 days.

In this case, the XIRR function comes into play to calculate a correct internal rate of return, taking into consideration the periods of different sizes.

The XIRR function has three arguments:

The values
The dates for cash outflows and inflows.
The guessed number for the expected internal rate of return (optional argument).

You can download the Excel file below in which I illustrate the use of the IRR and XIRR functions in Excel based on a simple example.

From the IRR function to the MIRR function (Modified Internal Rate of Return)

The MIRR function is quite the same as the IRR function, except that it takes into consideration both the cost of borrowing the initial investment funds (discount rate) and reinvestment rates for future cash flows.

In contrast to IRR, MIRR assumes that cash flows from a project are reinvested at the firm’s cost of capital (rate of return on a portfolio company’s existing securities).

To compute the MIRR, the Excel function uses the following parameters:

The values
The guessed number for the expected internal rate of return (optional argument).
The financial rate: the finance rate of interest paid
The reinvest rate: the interest rate earned from the reinvested profit

Where FV represents the Future Value of positive cash flows at the cost of capital for the company, PV represents the Present Value of negative cash flows at the financing cost of the company, and T represents the number of periods.

You can download the Excel file below in which I illustrate the use of the IRR and MIRR functions in Excel based on a simple example.

Some of the accountants say that the MIRR function is less valid than the other because not all the flows are reinvested fully. Although we can use a less important interest rate to compensate the partial investment; but we think the best approach will be the inclusion of the three calculations (IRR, XIRR, and MIRR).

Limits of the IRR

The non-linear equation for obtaining the IRR may have one solution, several solutions or no solution according to the sequence of cash flows. These represent limits of the IRR as an investor would like one value when estimating its investment.

Another limit of the IRR as a decision criterion for investing is that the result is not in agreement with the decision criterion based on NPV, which represents the value created by the investment.

You can download the Excel file below in which I provide an example to illustrate the limit of the IRR when selecting investment when two projects are available.

Useful resources

Mazars Excel IRR Function And Other Ways To Calculate IRR In Excel

About the author

The article was written in November 2021 by Léopoldine FOUQUES (ESSEC Business School, Global Bachelor of Business Administration, 2017-2021).

My experience as Consultant in Energy & Utilities at Atos Worldgrid

November 19, 2021 by Léopoldine FOUQUES

My experience as Consultant in Energy & Utilities at Atos Worldgrid

In this article, Léopoldine FOUQUES (ESSEC Business School, Global Bachelor of Business Administration, 2017-2021) shares her experience as Consultant in Energy & Utilities at Atos Worldgrid.

About Atos Worldgrid

Founded in 1997, Atos is a French multinational firm specialized in information technology (IT), headquartered in Bezons, France. Member of the CAC 40 index, Atos has been positioned as a leader in cloud infrastructure, cybersecurity, and super calculator and operates in 71 countries.

As a leader in digital transformation, Atos supports its customers in large-scale industrial projects and covers a wide range of sectors: financial services and insurance, healthcare, manufacturing, public sector and defense, resources and services, and telecommunications and media.

Since 2010, Atos has specialized in a secure and decarbonized digital through the brand “Atos Worldgrid”. The purpose of Worldgrid is to offer an extensive ecosystem of technology partners and implement critical Information technology / Operational technology (IT/OT) convergence projects. It provides services along the entire energy value chain to over 250 clients worldwide, operating all the energy sources, as oil & gas, biofuels, wind, solar, sea, hydro, coal, and nuclear, and covers all the distributed grid, from the generation to the retail.

My experience at Atos

My interest in the technology field was further intensified during my studies at ESSEC by following the course on blockchain and big data. That is why I came to work for Atos, a leading IT group in the digital transition, for a two-year apprenticeship.

Working as Consultant in Energy & Utilities in the Atos Worldgrid entity, I became aware of the interest of science applied to ecology to address current challenges, particularly those related to the digital and energy transition. During this apprenticeship, I became familiar with a complex and fast-moving environment. I put into practice what I had learned since the beginning of my studies and contributed to many projects with a proven innovative dimension. Thus, I had the opportunity to work on a wide variety of missions. In particular, I conducted several studies on the energy sector, contributed to the design of commercial offers for startups, and developed a platform, in partnership with Méteo-France. These missions allowed me to put into practice my managerial qualities and my customer orientation. On the other hand, my curiosity, and my appetite for the subjects I studied led me to work on information systems and asset management for companies in the energy and utility sector.

Why is necessary to put digital into the energy sector?

Atos is one of the leaders in services for Smart Cities (intelligent and connected cities). The enterprise has a wide range of solutions for cities and local authorities, covering consulting, systems implementation, user-centered design and managed services.

During my internship I had the opportunity to work on many subjects that are part of Smart Cities projects. Furthermore, I became aware that digital was at the heart of all transformation projects.

For many actors, the Smart City will be the “natural” environment of the improved human being. Therefore, we intend to provide the city with digital knowledge and technologies. Faced with the need for a change in ecological strategy, the value of new technologies is becoming increasingly important in discussions. The conjunction of ecology and IT was also widely discussed at the Davos Economic Forum in January 2020. It is in this context that Big Data initiatives have multiplied.

In the emerging context of Smart Cities or connected cities in which uses are rethought and resources are optimized, Big Data is becoming increasingly important. Indeed, faced with the exponential volume of data produced by various actors, controlling it becomes an essential tool that cities must appropriate. The data would enable the development of new services for the city, optimize existing ones and reconcile citizens’ expectations with current policies. Finally, is the central element of the most innovative projects, and constitute most city modernization projects. These technologies open the door to new conjunctures and represent the foundation stone of the cities of tomorrow.

The valorization of this data is one of the main challenges for territories and leads to the rethinking of new tools. Thanks to the mass of data that can be collected, both public and private players can anticipate future innovations and developments and thus lead the city towards a sustainable and responsible approach. The use cases are many and are very often based on the triptych: mobility, energy management and street services, i.e. services for both local or national authorities and consumers.

At the heart of the connected city, the smart energy grids must learn to be resilient.

In order to succeed in the energy transition, the electricity system is undergoing considerable change: development of renewable energies, deployment of electric vehicles, the emergence of new use cases, etc. To facilitate this transition, the energy grid must become more efficient. But the smart grid is not born smart, it becomes smart. Atos Worldgrid aims to make its cities a reality. The solutions support governments in their mission to provide intelligent integrated services to their citizens and economic partners.

In 2010, The French Parliament adopted the Law on National Commitment for the Environment : The Grenelle II Law. The Grenelle II law includes energy and climate concerns in the French objectives, with the aim of reducing consumption and greenhouse gas emissions. In addition, the law accelerates the deployment of renewable energy. Among other things, the state is committed to building at least 500 wind turbines per year.

Faced with this development of natural energies, the question of grid stability becomes essential. Indeed, these weather-sensitive energies are not predictable, and a bad estimate of the production can lead to very strong price fluctuations and a shortage of energy. The renewable energy produced must be estimated accurately to avoid a large gap between the energy produced and the energy sold, as the energy produced is not linear. A financial imbalance has repercussions on the whole value chain, from producer to final consumer. To prevent producers from producing too much or too little or aggregators from disturbing the balance of the grid by buying too much or too little, tools must be designed. This is the main challenge for companies like Atos and to achieve a transition to renewable energy.

Source: Wikimedia

At the heart of these smart grids and this revolution, the deployment of smart meters, such as Linky, developed by Atos, is today an essential building block for the success of this transition. Installed directly in French homes, it measures the daily electricity consumption of the home and transmits this data to the network operator. Its advantages are numerous. Firstly, its scope is ecological since it facilitates the insertion of renewable energy sources into the energy network. By collecting information on household consumption, renewable energies are better distributed and optimized, which contributes to the flexibility and therefore the stability of the network. Also, the consumer is also more widely integrated into the process. With smart grids, it is consumption that adapts to production and thus gives the consumer a new role. The Linky smart meter is an essential building block in this logic.

To support the energy transition and encourage the massive integration of renewable energy sources and new uses of mobility, network infrastructures have had to adapt quickly. In addition to the new dimensioning of the network, which has become a smart grid, the digitization functions resulting from Big Data have provided optimization and control tools for better operational performance. These advanced models have made it possible to improve the observability and controllability of the network, for better control of renewable energy connections. Atos is active in this area. In collaboration with Méteo France, It has developed service platform that uses Machine Learning algorithms to estimate the production of renewable energy in detail.

What I learned during my internship

During my work placement I acquired certain consulting skills, and I also learned to work in a team and to manage teams, to lead working groups. I have acquired both managerial and technical skills.

Above all, I discovered a sector that was totally unknown to me. As I progressed in the missions, I became familiar with a specialized vocabulary and gradually understood the technical workings. This experience in a digital services company was very enriching, both personally and professionally, and gave me the desire to continue in this direction.

Useful resources

smartgrids Introduction aux smart grids

European Commission Smart grids and meters

European Commission Energy modelling

About the author

The article was written in November 2021 by Léopoldine FOUQUES (ESSEC Business School, Global Bachelor of Business Administration, 2017-2021).

Bollinger Bands

November 18, 2021 by Jayati WALIA

Bollinger Bands

In this article, Jayati WALIA (ESSEC Business School, Grande Ecole Program – Master in Management, 2019-2022) presents the popular Bollinger bands used in technical analysis.

This post is organized as follows: we introduce the concept of Bollinger bands and provide an illustration with Apple stock prices. We delve into the interpretation of Bollinger bands as port and resistance price levels used to define buy and sell trading signals. We then present the techniques to compute the Bollinger bands and finally discuss their limitations.

Introduction

In the 1980s, John Bollinger, a long-time market technical analyst, developed a technical analysis tool for trading in securities. At that time, it was presumed that volatility was a static quantity, a property of a security, and if it changed at all, it would happen in a long-term period. After some experimentation, Bollinger figured that volatility was indeed a very dynamic quantity and a moving average computed on a time period (typically 20 days) with bands drawn above and below at intervals could be determined by a multiple of standard deviation.

Unlike a percentage calculation from a simple moving average, Bollinger bands simply add and subtract a standard deviation (or a multiple of the standard deviation, usually two). The tool thus represents the volatility in the prices of the security which is measured by the standard deviation of the prices of the security. The bands are used to understand the overbought or oversold levels for a security and to follow the price trends. The indicator/tool comprises of three main bands, an upper band, a lower band, and a middle band.

The middle band is a simple moving average (SMA), which is usually computed over a rolling period of 20 trading days (about a calendar month). The upper and the lower bands are positioned two standard deviations away from the SMA. The change in the distance of the upper and lower bands from the SMA determine the price strength (which is the strength of price trend of stock relative to overall market trend) and the lower and the upper levels for the stock prices. Bollinger bands can be applied to all financial securities traded in the market including equities, forex, commodities, futures, etc. They are used in multiple time frames (daily, weekly and monthly) and can be even applied to very short-term periods such as hours.

Figure 1 represents the evolution of the price of Apple stocks with the Bollinger bands for the period January 2020 – September 2021.

Figure 1. Bollinger bands on Apple stock.
Bollinger bands Apple stock
Source: computation by the author (data source: Bloomberg).

Figure 2 illustrates for the price of Apple stocks the link between the Bollinger bands and volatility measured by the standard deviation of prices. The lower the volatility, the narrower the bands.

Figure 2. Bollinger’s bands and volatility
Bollinger bands and volatility Apple stock
Source: computation by the author (data source: Bloomberg).

How to interpret Bollinger bands

Traders use the Bollinger bands to determine the strength of the price trend of a stock. The upper and lower bands measure the degree of volatility in prices over time. The width between the bands widens as the volatility in the stock prices increases and indicates a strong trend in the price movement. Conversely, the width between the bands narrows as the volatility decreases, indicating that the price of the security is range-bound. When this width is extremely narrow and contracting, it indicates that there can be a potential breakout in the price movement soon and is referred to as “Bollinger squeeze”. If the price crosses the upper band, it may indicate that the movement will be in an uptrend, and If the price crosses the lower band, it may indicate that the movement will be in a downtrend.

If the price hits the upper band, it indicates an overbought level in the security, and when the price hits the lower band, it indicates an oversold level. When the price crosses the upper band, traders consider it to a positive signal to buy the stock as the price trend is in an upward direction and shows great strength. Similarly, when the price crosses the lower band, traders consider it to a positive signal to sell the stock as the price trend is in a downward direction and shows great strength.

In other words, Bollinger bands act as dynamic resistance and support levels for the price of the security. Thus, once prices touch either of the upper or lower band levels, they tend to return back to the middle of the band. This phenomenon is referred to as the “Bollinger bounce” and many traders rely extensively on this strategy when the market is ranging and there is no clear trend that they can identify.

Calculation

The three bands of the Bollinger bands are calculated using the following formula:

Middle Band

The middle band is the simple moving average (SMA) over a 20-day rolling period. To calculate the SMA, we compute the average of the closing prices of the stock over the past 20 days.

SMA 20 days

To compute the upper and lower bands, we need first to compute the standard deviation of prices.

img_std_dev_bollinger_bands

Upper band

The upper band is calculated by adding the SMA and the standard deviation times two:

Bollinger upper band

Lower band

The lower band is calculated by subtracting the standard deviation times two from the SMA:

Bollinger lower band

Limitations of Bollinger bands

Bollinger bands are considered to be lagging indicators since they represent the simple moving average which is based on the historical stock prices. This means that the indicator is not very useful in predicting the future price patterns as the indicator signals a price trend when it has already started to happen.

To benefit from the Bollinger bands, traders often combine this indicator with other technical tools like the Relative Strength Index (RSI), Stochastic indicators and Moving Averages Convergence-Divergence (MACD).

▶ Jayati WALIA Trend Analysis and Trading Signals

▶ Jayati WALIA Moving averages

▶ Jayati WALIA Standard deviation

Useful resources

Bollinger bands

Fidelity: Technical Indicators: Bollinger Bands

About the author

The article was written in November 2021 by Jayati WALIA (ESSEC Business School, Grande Ecole Program – Master in Management, 2019-2022).

Trend Analysis and Trading Signals

November 18, 2021 by Jayati WALIA

Trend Analysis and Trading Signals

In this article, Jayati WALIA (ESSEC Business School, Grande Ecole Program – Master in Management, 2019-2022) presents an overview of trend analysis and trading signals in stock price movements.

This post is organized as follows: we introduce the concept of trends used in technical analysis and its link with support and resistance price levels used to define buy and sell trading signals. Then, we present the different types of trends and discuss the time frame for their analysis. Trends based on straight lines, moving averages and the Fibonacci method are presented in detail with examples using Moderna, Intel, Adobe and Apple stock prices.

Introduction

Trend Analysis is one of the most important areas of technical analysis and is key to determining the overall direction of movement of any financial security. The analysis of trends in asset prices is used to find support and resistance price levels and in fine generate buy and sell trading signals when these support and resistance price levels are broken.

Support and resistance

The support and resistance are specific points on the price chart of any security which can be used to identify trade entry and exit points. The support refers to the price level at which price generally bounces upwards and buying trend is strongest. Likewise, the resistance price is a price level at which selling power is strongest and the price of the security struggles to break above the resistance. The support and resistance levels can act as potential entry and exit points for any trade since it is at these levels that the price can either “break-out” of the current trend or continue moving in the same direction. The support and resistance can be determined by using prices or Japanese candlesticks.

Ways to define trends

The two main ways to define trends in financial markets are straight lines and moving averages. Straight lines simply give static support and resistance levels that do not change over time. Moving averages give dynamic support and resistance levels that are continuously adjusted over time. Another popular method to define trends is the Fibonacci method.

Trends based on straight lines

Overview

Trend lines are indicators to identify the trends in the price chart of a security within a time frame (say one week or one month). Trend analysis using trend lines takes specific price levels or zones that correspond to support and resistance. An uptrend is based on the principle of higher highs and higher lows; similarly, a downtrend is based of lower highs and lower lows.

These price levels are the major zones where the market seems to respond by making a strong advance or decline. If the stock prices are in an uptrend, it shows an increasing demand for the stock and if the stock prices are in downtrend, it shows an increasing supply for the stock.

Trend lines can be built by connecting two or more prices (peaks or troughs) in either direction of a stock price movement on a time frame determined by the trader (1 hour, 1 day, 1 week, etc.) over a period (3 months, 6 months, 12 months, etc.). For a trend line to be valid, a minimum of two highs or lows should be used. The more times price movement touches a trend line, the more accurate is the trend indicated by the line.

Different types of trends using straight lines

The use of market trends in technical analysis in financial markets is based on the concept that past movements in the prices of the stock provides an overview of the future movement. Note that such an approach is in contradiction with the Market Efficiency Hypothesis (EMH) developed by Fama (1970), which states that the best prediction of the price of tomorrow is the price of today (past prices being useless).

The prices of any financial asset in the market follows three major trends: up, down and sideways trends.

Up trend

When the stock prices follow an uptrend, it means the prices are reaching higher highs and higher lows on a pre-determined time frame (decided by the trader). The higher high of a stock price is the highest it reaches in each time frame and the lower lows is the lowest it reaches in that time frame. The constant rise and fall in the stock prices show that the market sentiments are bullish about the stock and the trader tries to buy the stock when it is at its lowest in the uptrend.

The following figure shows an upward channel trend in Moderna stock prices using Japanese
candlesticks. As observed in the graph, both the upper and lower trend lines connect minimum two peaks and troughs respectively. As the price crosses the upper trend line (resistance level), it enters an uptrend (or a bullish trend) indicating a buy signal.

Figure 1. Uptrend in Moderna stock.

Uptrend in Moderna stock

Source: computation by the author (data source: Bloomberg).

Down trend

A downtrend comprises of lower highs and lower lows in the prices of the stock. The stock prices follow a downward sloping trend, which shows a bearish sentiment in the stock. The traders resist to enter in a long position when the stock prices are in down trend.

The following figure shows Intel stock prices in a downtrend (or bearish trend) represented by upper and lower straight trends lines. When the price crosses the lower trend line (support level), it will enter into a downtrend indicating a sell signal.

Figure 2. Downtrend in Intel stock.

Downtrend in Intel stock

Source: computation by the author (data source: Bloomberg).

Sideways trend

In such a trend, the stock prices move in a sideways direction and the highs and lows of the stock price are constant for a period of time. Such price movements make it difficult for the trader to predict the future price movements of the stock. The trader trading in this stock tries to anticipate potential breakouts above the resistance level or below the support level. He or she enters in a long position when the price of the stock breaks the upper resistance level. Also, he or she can benefit from the sideways movement by entering in a long position when the stock prices retrace from the support level, to enjoy the stream of profits till the price reaches the resistance level.

Figure 3. Sideways trend in Adobe stock.

Sideways trend in Adobe stock

Source: computation by the author (data source: Bloomberg).

Trends based on moving averages

Overview

A moving average is an indicator to interpret the current trend of a stock price. A moving average basically shows the price fluctuations in a stock as a single curve and is calculated using previous price, hence it is a lagging indicator.

To measure the direction and strength of a trend, moving averages strategy involves price averaging to establish a baseline. For instance, if price moves above the average, the indicated trend is bullish and if it moves below the average, the trend is bearish. Moving averages are also used in development of other indicators such as Bollinger’s bands and Moving Average Convergence Divergence (also known as MACD).

The moving average indicator can be of many types, but the simple moving average (SMA) and exponential-weighted moving average (EWMA) are most commonly used. An n-period SMA can be calculated simple by taking the sum of the closing prices of a stock for the past ‘n’ time-periods divided by ‘n’.

Crossovers of moving averages is a common strategy used by traders wherein two or more moving averages can help determine a more long-term trend. Basically, if a short-term MA crosses above a long-term MA, the crossover indicates a downtrend and vice-versa indicates an uptrend. Traders can utilize it establish their position in the stock.

Example: Apple stock

Consider below the APPLE stock price chart using Japanese candlesticks. The lines in blue and yellow indicate 20-day (or 20-period) SMA and 50-day SMA respectively. We can observe that while the 2 lines are indicative of the movement of stock price fluctuations, the 20-day SMA is closer to the actual price movement and responds more quickly to price change.

We can also observe a crossover in the moving averages wherein the 20-day MA is crossing below the 50-day MA indicating a down trend in price movement.

Figure 4. Moving averages on Apple stock.

Moving averages in Apple stock

Source: computation by the author (data source: Bloomberg).

Fibonacci Levels

Fibonacci levels are a commonly used trading indicator in technical analysis that provides support and resistance levels for price trends. These levels can be used to determine more accurate entry and exit points by measuring or predicting the retracements before the continuation of a trend.

Fibonacci retracement levels are counted on numbers of the Fibonacci sequence (0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55 and so on). Each number (say 13) amounts to approximately 61.8% of the following number (13/21=0.618), 38.2% of the number after (13/34=0.382), and 23.6% of the number after (13/55=0.236).

Fibonacci analysis can be applied when there is an evident trend in prices. Whenever a security moves either upwards or downwards sharply, it tends to retrace back a little before its next move. For example, consider a stock that moved from $50 to $70, it is likely to retrace back to, say, $60 before moving to $90. Fibonacci levels can be used to identify these retracement levels and provide opportunities for the traders to enter new positions in the trend direction.

Example: Moderna stock

Consider below the Moderna stock price chart using Japanese candlesticks. We can see an evident uptrend (indicated by the straight trendlines in blue). The Fibonacci retracement levels have been plotted and we can notice that the ‘61.8% Fibonacci level’ intersects the rising trend line. Thus, it can serve as a potential support level. Further, it can also be observed that the price bounces from the 61.8% level before rising up again and it would have been a good entry point for a trader to take up a long position in the stock.

Figure 5. Fibonacci levels on Moderna stock.

Fibonacci levels in Moderna stock

Source: computation by the author (data source: Bloomberg).

Time frame

Trends also can vary among different time frames. For example, an overall uptrend on the weekly time frame can include a downtrend on the daily time frame, while the hourly is going up. Multiple time frame analysis can thus help traders understand the bigger picture. Some trends are seasonal while others are part of bigger cycles.

The trend analysis can be done on different time horizons (including short term, intermediate term, and long term) to identify the price trends for different trading styles.

Useful resources

Academic articles

Fama E.F. (1970) Efficient Capital Markets: A Review of Theory and Empirical Work, The Journal of Finance 25(2): 383-417.

About the author

The article was written in November 2021 by Jayati WALIA (ESSEC Business School, Grande Ecole Program – Master in Management, 2019-2022).

Credit risk

November 18, 2021 by Jayati WALIA

Credit risk

In this article, Jayati WALIA ESSEC Business School, Grande Ecole Program – Master in Management, 2019-2022) presents credit risk.

Introduction

Credit risk is the risk of not receiving promised repayments due to the counterparty (a corporate or individual borrower) failing to meet its obligations and is typically used in context of bonds and traditional loans. The counterparty risk, on the other hand, refers to the probability of potential default on a due obligation in derivatives transactions and also affects the credit rating of the issuer or the client. The default risk can arise from non-payments on any loans offered to the institution’s clients or partners.

With bank failures in Germany and the United States in 1974 led to the setup of the Basel Committee by central bank governors of the G10 countries with the aim of improving the quality of banking supervision globally and thus devising a credible framework for measuring and mitigating credit risks. Banks and financial institutions especially need to manage the credit risk that is inherent in their portfolios as well as the risk in individual transactions. Banks also need to consider the relationships between credit risk and other risks. The effective management of credit risk is a critical component of a comprehensive approach to risk management and essential to the long-term success of any banking organisation.

Credit risk for banks

For most banks, debts (on the assets side of their balance sheet – banking book) are the largest and most obvious source of credit risk. However, sources of credit risk (counterparty risk) also exist through other the activities of a trading (on the assets side of their balance sheet – trading book), and both on and off the balance sheet. Banks increasingly face credit risk (counterparty risk) in various financial instruments other than loans, including interbank transactions, trade financing, bonds, foreign exchange transactions, forward and futures contracts, swaps, options, and in the extension of commitments and guarantees, and the settlement of transactions.

Risk management

Exposure to credit risk makes it essential for banks to have a keen awareness of the need to identify, measure, monitor and control credit risk as well as determine that they hold adequate capital against these risks and are adequately compensated in case of a credit event.

Financial regulation

The Basel Committee on Banking Supervision has developed influential policy recommendations concerning international banking and financial regulations in order to exercise judicious corporate governance and risk management (especially credit and operational risks), known as the Basel Accords. The key function of Basel accords is to set banks’ capital requirements and ensure they hold enough cash reserves to meet their respective financial obligations and henceforth survive in any financial and/or economic distress. Common risk parameters such as exposure at default, probability of default, etc. are calculated in accordance with specifications listed under the Basel accords and quantify the exposure of banks to credit risk enabling efficient risk management.

Credit risk modelling: overview

Credit risk modelling is done by banks and financial institutions in order to calculate the chances of default and the net financial losses that may be incurred in case of occurrence of default event. The three main components used in credit risk modelling as per advanced IRB (Interest ratings based) approach under Basel norms aimed at describing the exposure of the bank to its credit risk are described below. These risk measures are converted into risk weights and regulatory capital requirements by means of risk weight formulas specified by the Basel Committee.

Probability of default (PD)

The probability of default (PD) is the probability that a borrower may default on its debt over a period of one year. There are two main approaches to estimate PD. The first is the ‘Judgemental Method’ that takes into account the 5Cs of credit (character, capacity, capital, collateral and conditions). The other is the ‘Statistical Method’ that is based on statistical models which are automated and usually a more accurate and unbiased method of determining the PD.

Exposure at Default (EAD)

The exposure at default (EAD) is the predicted expected amount outstanding in case the borrower defaults and essentially is dependent upon the amount to which the bank was exposed to the borrower at the time of default. It changes periodically as the borrower repays his payments to the lender.

Loss given default (LGD)

The loss given default LGD refers to the amount expected to lose by the lender as a proportion of the EAD. Thus, LGD is generally expressed as a percentage.

LGD = (EAD – PV(recovery) – PV(cost))/EAD

With:
PV(recovery) = Present value of recovery discounted till time of default
PV(cost) = Present value of cost of lender discounted till time of default

For instance, a borrower takes a $50,000 auto loan from a bank for purchasing a vehicle. At the time of default, loan has an outstanding balance of $40,000. EAD would thus be $40,000.

Now, the bank takes over the vehicle and sells it for $35,000 for recovery of loan. LGD will be calculated as ($40,000 – $35,000)/$40,000 which is equal to 12.5%. Note that we have assumed the present value of cost here as 0.

Expected Loss

The expected loss is case of default is thus calculated to be PD*EAD*LGD and banks use this methodology in order to better estimate their credit risk and be prepared for any losses to be incurred thus implementing risk management.

Credit Rating

Credit rating describe the creditworthiness of a borrower entity such as a company or a government, which has issued financial debt instruments like loans and bonds. It also applies to individuals who borrow money from their banks to finance the purchase of a scar or residence. It is a means to quantify the credit risk associated with the entity and essentially signifies the likelihood of default.

Credit risk assessment for companies and governments is generally performed by a credit rating agencies which analyses the internal and external, qualitative and quantitative attributes that drive the economic future of the entity. Some examples of such attributes include audited financial statements, annual reports, analyst reports, published news articles, overall industry analysis and future trends, etc.

A credit agency is deemed to provide an independent and impartial opinion of the credit risk and consequent ratings they issue for any entity. Rating agencies S&P Global, Moody’s and Fitch Ratings currently dominate 85% of the global ratings market (as of 2021).

About the author

The article was written in November 2021 by Jayati WALIA (ESSEC Business School, Grande Ecole Program – Master in Management, 2019-2022).

Working for a Private Bank

November 17, 2021 by William ANTHONY

Working for a Private Bank

In this article, William ANTHONY (ESSEC Business School, Global Bachelor of Business Administration, Exchange Student from the University of Bath, 2021-2022) shares his experience as a Sales & Marketing intern in a private bank.

The company

Union Bancaire Privée (UBP SA) was founded in 1969 by Edgar de Picciotto in Switzerland. With CHF 161.1 billion in assets under management, UBP is one of the largest private banks in Switzerland, as well as one of the best capitalised banks in the country. UBP specializes in the fields of wealth management on behalf of private and institutional clients. The bank’s head office is in Geneva, Switzerland, and it employs 1,827 people as of November 2021 according to LinkedIn.

Source: Union Bancaire Privée.

My internship at UBP

In the autumn of 2018, I worked in the Sales & Marketing department of Union Bancaire Privée (UBP), London. At the private bank, I helped support a multi-national team using my command of French on a daily basis, particularly as regards frequent communication with the UBP head office in Geneva. During this time, I also developed familiarity with the commercial applicability of SalesForce and LinkedIn, which allowed me to identify and send newsletters to potential clients.

While with UBP, I also helped organise a roadshow for departmental seniors and this allowed me to meet other company managers which developed my understanding of both finance and marketing within a client facing environment.

Key concepts related to my work

The Law of Averages

The principle holds that future events are likely to occur as to balance out past deviations from an expected average. For me this was important as it related to the likelihood that my leads (potential clients) would reply to newsletters and convert into clients. It also allowed me to manage my expectations and remain positive in an environment that required a strong willingness to succeed.

Work hard, play hard

One of the benefits of working in a client-facing role was that I often met prospective clients in social settings like restaurants, bars, or conferences. This was a fantastic biproduct of the team I worked with, which allowed me to learn more about the industry in a more informal manner. It also allowed me to witness first-hand the connections that I created between the bank and prospective clients. On a sidenote, all bills were also expensed to UBP which made the whole ordeal rather merry.

Money

“Happiness is not in the mere possession of money; it lies in the joy of achievement, in the thrill of creative effort.” The quote by Franklin D. Roosevelt really echoes with me as through multiple internships and work placements, I’ve learnt that the people you work with and the genuine appreciation for the job you’re doing outweighs the benefits of a higher salary in worse conditions. From cleaning out storage rooms to drinking with clients at the Ritz, the enjoyment and passion for one’s work and comradery between colleagues affects my work ethic and drive to succeed. Money can rarely be the ultimate objective, it is a biproduct of a passion and acts as compensation for your time and expertise.

Useful resources

Union Bancaire Privée (UBP)

About the author

The article was written in November 2021 by William ANTHONY (ESSEC Business School, Global Bachelor of Business Administration, Exchange Student from the University of Bath, 2021-2022).

Peloton’s uphill battle with the world’s return to order

November 17, 2021 by William ANTHONY

Peloton’s uphill battle with the world’s return to order

In this article, William ANTHONY (ESSEC Business School, Global Bachelor of Business Administration, Exchange Student from the University of Bath, 2021-2022) discusses the market reaction to Peloton’s quarterly results.

Description of the firm

Peloton is an American company which produces exercise equipment and media streaming. Its best-seller ‘Gen 3 Peloton bike’ is available in the US, UK, Germany, Australia, and parts of Canada. With significant increases in sales during the COVID pandemic, Peloton became a public company via IPO on September 26th, 2019. At the time of writing, the company is currently valued at $16.85Bn (Market Capitalization as of 07/11/2021).

Source: Peloton.

Description of the event

Peloton reported weakening sales on the 4^th of November 2021 and a larger loss than expected for its first fiscal quarter. As consumers begin to return to gyms, a step closer to a pre-pandemic environment, John Foley, Peloton CEO, admits to ongoing “supply chain constraints” and “demand uncertainty amidst re-opening economies”. The fall in demand growth for Peloton’s exercise equipment has subsequently lowered the demand for its subscription-based products which has also contributed to lower profit margins.

My explanation of the market reaction to the event

The weaker than expected sales growth report led to a decrease in Peloton’s share price by nearly 40% from $86 to $60 as illustrated in Figure 1 below. In their fiscal first quarter, Peloton’s loss per share was expected at $1.07 but was instead $1.25; revenue was expected at $810.7 million but was instead $805.2 million. Despite a surge of 148% in sales and marketing expenses to $284.3 million, which represents around 35% of Peloton’s revenue, the firm was still unable to show strong growth figures coming out of the Covid-19 pandemic. This is extremely worrying and underlines Peloton’s “challenged visibility” (John Foley, 2021).

CNBC’s Jim Cramer advised his audience to sell Peloton on the next bounce after what he called a “disastrous” quarterly result. When looking at gyms like Planet Fitness Inc which have increased by nearly 20% in the last 5 days in share price, I believe there is reason for worry for Peloton shareholders. The lack of direction from Peloton management regarding a post-pandemic market and ongoing supply chain challenges leaves Peloton with a ‘sell’ market consensus and for me justifies its cliff-diving share price.

Figure 1. Peloton stock chart.

Source: Google Finance.

Justification of my choice of the event and the firm

I chose this event because it is very recent, it is also a great example of how news regarding stagnant growth can negatively affect a company’s share price. In this example, one of the top fitness companies in the world that gained massive momentum over the global COVID pandemic, saw all its shareholders lose up to 40% of their investment in one single after-hours trading session. Here we have witnessed how ruthless the market can be in just a couple of hours.

My thoughts on the market efficiency

Eugene Fama distinguishes three levels of informational market efficiency:

Weak efficiency: all information contained in past stock market data (prices and transaction volumes) is already reflected in today’s price.
Semi-strong efficiency: in addition to the information contained in historical stock market data, all public information (company accounts, analyst reports, etc.) is already reflected in today’s price.
Strong efficiency: all information, public as well as private, is already reflected in today’s price.

I believe in Peloton’s case that the market has shown itself to have semi-strong efficiency. Within hours of private information being made public, the company share price reflected an adjusted rate relating to the newly released quarterly report information. As a result of the market incorporating the news into the price with such rapidity, the market was hard to ‘beat’ due to shareholders willingness to sell their shares.

Useful resources

Academic references

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

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

Business

Peloton’s website

Peloton’s financial results

About the author

The article was written in November 2021 by William ANTHONY (ESSEC Business School, Global Bachelor of Business Administration, Exchange Student from the University of Bath, 2021-2022).

How to compute the IRR in Excel

November 13, 2021 by Jérémy PAULEN

How to compute the IRR in Excel

Jeremy PAULEN

In this article, Jérémy PAULEN (ESSEC Business School, Global Bachelor of Business Administration, 2019-2023) explains everything about the IRR function in Excel, which is used to compute the internal rate of return of a series of cash flow to evaluate the financial performance of an investment in relative terms.

What is the IRR?

The IRR represents the internal rate of return of an investment. It is closely related to the net present value (NPV) of the investment as the IRR is the discount rate that makes the NPV equal to zero.

Consider an investment represented by a series of cash flows CF₀, CF₁, CF₂, …, CF_T, which take into account the revenues and expenses of the project computed or forecasted at time 0 leading to capital inflows and outflows for the firm. The NPV of this investment is given by:

where r is the discount rate that takes into account the risk of the project.

The IRR corresponds to the value of the discount rate for which the NPV is equal to 0:

The IRR is the solution of a non-linear equation:

Note that this equation may have one solution, several solutions or no solution according to the sequence of cash flows.

The internal rate of return (IRR) is an important indicator in the decision-making process as it measures the financial performance of a project. The IRR is a relative measure as its unit is a percentage. The NPV is an absolute measure as its unit is the euro, the dollar, etc.

It makes it possible to measure the future financial performance of a project or a company. The higher the IRR is, the more interesting it is to launch the project.

The IRR can therefore be used in the case of a choice to be made between different investment perspectives, but also to evaluate the company’s share buyback programs.

A limit of using the IRR method is that it does not consider the size of a project. Cash flows are simply compared to the amount of capital outlay generating those cash flows. In other words, considering two projects A and B, the IRR of A may be lower than the IRR of B, while the NPV of A may be higher than the NPV of B.

The IRR function in Excel

How to use the IRR function in Excel?

In Excel, you can get the IRR function in the “Formulas” tab.
You can also type “= IRR (value, [guess])” in the cell where you want to compute the IRR.

The IRR function uses the following arguments:

Values: The cash flow series. Cash flows include investment values and net income.
Guess: a number guessed by the user that is close to the expected internal rate of return

Example

Example: consider a new factory modeled by the following series of cash flows:

CF₀ = -$50,000 (initial cost)
CF₁ = +$5,000 (net cash flow in year 1)
CF₂ = +$8,000 (net cash flow in year 2)
CF₃ = +$13,500 (net cash flow in year 3)
CF₄ = +$18,800 (net cash flow in year 4)
CF₅ = +$20,500 (net cash flow in year 5)

Excel file to compute the IRR of a series of cash flows

You can download below a short video which illustrates how to compute the IRR of a series of cash flows with Excel.

Useful resources

Microsoft IRR function

About the author

The article was written in November 2021 by Jérémy PAULEN (ESSEC Business School, Global Bachelor of Business Administration, 2019-2023)

My Marketing Developer Experience

November 13, 2021 by Jérémy PAULEN

My Marketing Developer Experience

In this article, Jérémy PAULEN (ESSEC Business School, Global Bachelor of Business Administration, 2019-2023) shares his experience as a marketing developer at the Tennis Club of Rosheim.

Tennis Club of Rosheim

Tennis Club of Rosheim was founded in 1970 and is owned by David Livernais. The premises are located in the sports center of Rosheim, in Alsace. The Tennis Club had 191 members in 2021 and several partners. The Club therefore organizes many events every year.

The club wishes to create a relationship of trust with its members as well as a friendly atmosphere.

My internship at the Tennis Club of Rosheim

During my studies at ESSEC Business School, I had the opportunity to undertake an internship as a marketing developer at the Tennis Club of Rosheim.

Source: Tennis Club Rosheim

I was involved with the marketing department, which is in charge of promoting the club to potential new members. The promotion of a business takes place in several stages. First of all, through the development of a visual identity. The visual identity or graphic identity is a set of coherent visual elements which make it possible to identify the same entity through the different communication media that it uses. Example: Coca-Cola and the color red are almost synonymous with each other. The company uses its signature color on all of its products and all of its marketing materials.

Given my experience in the visual field and my different skills, I was given the responsibility of developing a new visual identity. The only restriction I had was to use the orange color.

I first had to create a logo. The logo is the pillar of a company’s image. A professional logo allows a company to stand out from its competitors, to convey its values and to tell a story. It therefore needs to be studied. The logo I created is made up of three elements:

A tennis ball in the center of the image to evoke the main activity of the club
The date the club was founded: 1970. Integrating a date reassures potential customers Because it implies professional knowledge and experience acquired over the years.
And last but not the least: the name of the club.

Source: Tennis Club Rosheim

I wanted a simple and uncluttered logo to reach as many people as possible.

I also had to create multiple posters to announce the different events. Each of my creations had a similar color code: orange, green and yellow. Orange is the color of cheerfulness, generosity, ambition, and abundance, but more importantly the color of the company. Green symbolizes hope and nature. Yellow is associated with joy, good humor, and radiance.

Promotion also passes through word, including emails or flyers. On several occasions I had to communicate by email with members and partners. Communicating through this media involves many codes. We must respect the codes of politeness and avoid any awkwardness. The emails are written and thus visible at all times.

Every day, the club president organized a general meeting where he talked about global debriefing. The various meetings make it possible to clarify the week’s objectives and to distribute the tasks to the workforce. They also present an opportunity to give feedback and to integrate each employee in the decision-making process. For my part, I took this opportunity to propose a new method of communication: Tiktok.

During my time at the TC Rosheim, I also had the opportunity to organize a large-scale event. Many competitors meet each year for a big competition. It was therefore necessary to find sponsors to finance this event, in our case the main partner was KARANTA. I also had to find volunteers to supervise the event and ensure it was smoothly run.

My key learning outcomes

During my internship at the Tennis Club of Rosheim, I had the opportunity to develop the following (soft) skills:

To manage a team
To meet deadlines
To improve communication skills
To understand the needs and to listen to the expectations of (internal) clients
The ability to learn and to improve my different skills

Three key terms

I develop below three key terms to understand my work.

Visual identity

The visual identity or graphic identity is a set of coherent visual elements which make it possible to identify the same entity through the different communication media that it uses.

A participatory leader

The participatory leader likes to study all the proposals before making a decision. He or she encourages his or her employees and values their work and achievements. A key element is feedback.

Feedback

Feedback is a step back, a testimony on an event that occurred. It allows time to check if the meaning of a message is understood, determine if help is required and verify if everybody is involved.

Useful resources

Tennis Club Rosheim

About the author

The article was written in November 2021 by Jérémy PAULEN (ESSEC Business School, Global Bachelor of Business Administration, 2019-2023).

My experience as a junior audit consultant at KPMG

November 10, 2021 by Pierre-Alain THIAM

My experience as a junior audit consultant at KPMG

In this article, Pierre-Alain THIAM (ESSEC Business School, Global Bachelor of Business Administration, 2019-2023) shares his experience as a junior audit consultant in the Audit & Consulting department at KPMG.

About KPMG

When we mention professional services networks, everyone is instantly thinking about the “Big Four”. During the last summer from May to July 2021, I had the opportunity to work in one of these companies, KPMG, as a Junior intern in Audit & Consulting.

KPMG’s letters are the sheer remnants of the company’s history. In March 1917, Piet Klijnveld and Jaap Kraayenhof created an accounting firm in Amsterdam called Klynveld Kraayenhof & Co. Then multiples mergers occurred and in 1979, Klynveld Kraayenhof & Co., McLintock Main LaFrentz from the United States and Deutsche Treuhandgesellschaft from Germany, formed Klynveld Main Goerdeler known as KMG. In 1987, KMG and Peat Marwick merged and had one of the biggest mergers at the time. Two of the biggest accounting firms of the 20th century chose to unify and ultimately formed KPMG as we know it today.

Nowadays, KPMG provides multiple finance services including mainly Audit, Management Consulting and Tax Consulting.

Source: KPMG

What I did during my internship

During my internship, I evolved in a global system. I tried to understand the rules and even complex expectations of a work organization. My goals were to adopt an action or work plan coherent with the challenges and success of the requested mission and work effectively in a multicultural and interdisciplinary team while communicating in a dynamic national and international environment. I had to focus on producing clear and well-organized working documents on various subjects (test on revenues, process of a system, bank reconciliation of two pension fund regimes for examples), that the highest graded employees like managers or even partners would review for the mission.

I was mainly involved in audit assignments. The activity entrusted to it was essentially the understanding of target companies: organization, processes, and risks. Then, the understanding of audit objectives and performing substantive controls to identify accounting anomalies, including fraud of all types. And last but not least, I had to understand and perform internal control assessment work. I did a total of four missions over three months, with a range from a national agricultural organization to an insurance and a pension fund.

What I learned during my internship

Throughout my time at KPMG, I learned that technical skills are not the most important skills needed to work in the audit sector. Of course, you need to master hard skills like knowing the basics of financial accounting, being able to find the equilibrium of a balance sheet or having solid foundations on Excel. Nonetheless, there is a big “relational” aspect of the job. Since you are almost always with new people and colleagues, soft skills are definitely required. If you find a way to show your ability to communicate with other people, it always results with a better quality of work for you and the people involved in your mission.

When working in the audit sector, I think there are three principal aspects you need to focus on: logic, flexibility, and tenacity.

Logic

That may sound surprising, but logic is the most important quality you will need. When we talk about audit, people instantly think about big formulas, accounting and working on numbers all the time. This vision is quite wrong, I know for a fact that audit is mostly a process. A process is the description and assessment of the procedures for granting aid to customers: monitoring of their activities, the respect of accounting procedures and the identification of the various risks and controls implemented. You need to ask yourself a lot of questions related to the activities of the company you are working with. The aim is to identify the risks, assess them, define control measures, and simulate different scenarios of incidents to be analyzed: basically, it is called risk management. For example, if you work with Orange on their mobile services, you will first ask them how they make money from this activity. If they tell you each call is counted as a transaction, we will try to know how they make sure a call is counted. Then if Orange answers us that they have antenna A that conveys information to antenna B, KPMG will then investigate the risks of this method and how it can be the cause of a loss of money for the client company. This example is very specific to a certain type of firm, and it directly leads us to the second quality you need to succeed: flexibility.

Flexibility

At KPMG you can work in every field you can imagine and in any country in the world. My first mission was in a pension fund for employees of a bank group, my fourth week I worked in an insurance company and the week just after I worked in a logistics company. Lastly, my final mission was in a company for the support of the rural development of a country.

As you can see, we jump from one subject to another pretty quickly. Versatility is key because you never know what you are going to work on and where you are going to work at. If you don’t like to disrupt your habits, the rhythm of a consulting firm will promptly become fiendish for you. The bright side of this atmosphere is that it constantly stimulates you intellectually and you are always learning. Working in this type of company is extremely formative, but it comes with its advantages and disadvantages, metaphorically it is a double-edged sword, and you will either love it or hurt yourself if you find the correct way to handle it or not. And this is precisely why tenacity is essential to stay on the course.

Tenacity

Sometimes the work can be hard, and you have to try a lot of ideas until you find a solution. This can be even more difficult when there is tension with the management of your client company. Some people are really tough to work with and they just don’t want you to succeed because it will be at their detriment. Therefore, you need to have good communication skills while standing strong in your positions and not letting people walk over you.

The threat cannot only come from the clients but from your own colleagues. Most of the people are benevolent, but it is a dog-eat-dog world and not everyone can get to the top.

Useful resources

KPMG

About the author

The article was written in November 2021 by Pierre-Alain THIAM (ESSEC Business School, Global Bachelor of Business Administration, 2019-2023).

What I learned from my time on the stock market

November 9, 2021 by Joshua WAN

What I learned from my time on the stock market

In this article, Joshua WAN (ESSEC Business School, Global Bachelor of Business Administration, Exchange Student from Stockholm University, Fall 2021) shares what he has learnt in the stock market.

I’m sure many who have chosen the Sim Trade course are active on the stock market and probably have been for some time. But maybe some of you have not yet ventured out on the world’s equity markets and maybe some of you are taking the SimTrade course in order to prepare yourself for trading with your own hard-earned money.

My story

I was 16 years old when I first decided that I wanted to start investing my spare money and saving it for the future. I had started my first year of Business Administration in upper secondary school and a friend told me that you could open an account on Avanza (Sweden’s largest brokerage firm) with your parents’ permission. After handing in all the required paperwork I transferred about €500 that I had left over from my summer job and was ready to do my first trade.

Back then €500 was a lot of money for me but in the context of the stock market it is just peanuts. What I realize now in hindsight is that this relatively small amount of money allowed me to learn valuable lessons about investing and financial markets without having any larger future consequences. I lost a large percentage of this money, but I did learn a lot from my mistakes. I wanted to share what I learned and maybe someone that wants to start investing can use these lessons to avoid making the rookie mistakes that I made.

Do your own research

One of the first things I learned was that you should always try to understand why you are investing in a specific stock and not just go off other people’s advice. I remember one of my friends telling me to invest in a certain company and he showed me the return that he made on the stock. I didn’t know much about the company, its market, and the future challenges. I did not know why I was investing in this company, so it is no surprise that this investment did not turn out very well. Now I’m not saying that you need to do a full fundamental or technical analysis, but you should understand the company you are investing in and why you are investing in it. You can read different recommendations and try to form your own opinion but don’t invest in something just because someone else tells you to.

Don’t be too impulsive and emotional

The aforementioned investment did not go very well, and I quickly found myself in the red. After about 5 months my position had gone down by circa 40%. At this point I decided to sell the stock in what was more of an impulsive emotional decision than a rational one. I did not sell because the company had no upside, I sold because I did not want to see -40% every time I opened the app.

Warren Buffet once said, “The most important quality for an investor is temperament, not intellect” (Sarwa, 2021). He meant that you need to have discipline, patience and that you need to understand that the market will fluctuate. He also goes on to say that humans are irrational and are tempted to make trades when they feel like the market is working against them. Of course, you should not stay onboard a sinking ship but if you have invested in a good company then patience will often pay off.

If I would have kept my position in this firm until today, I would have had a return of over 500% and if I would have sold it at the all-time high, I could have had a return of 1,500%. This does however bring me to my next point.

The figure below shows the price evolution of PowerCell Sweden stocks over the period 2015-2021 (the stock investment that I am talking above).

Figure. Stock chart of PowerCell Sweden

Source: First North Stockholm

Learn from your mistakes but don’t beat yourself up

Even if you do your research and have patience, you will still have some investments that don’t turn out well and some big opportunities that you will miss. It’s very easy to get hung up on these mistakes but instead of just seeing the negatives you can use this opportunity to try and understand why you made the mistake. By understanding your mistakes, you can lay a foundation for better future decisions. From the investment I previously mentioned I learned the importance of patience and knowledge. I realize now that it would have been a perfect opportunity to buy more shares instead of selling. But even if it doesn’t feel very nice that I missed out on a huge return you can’t beat yourself up over it because there will always be something you could have done better.

Have a strategy

Before you start trading/investing you need to have a strategy. You should know if you are investing long term or short term, low-risk low-return or high-risk high-return or if you are targeting value or growth firms. Of course, you can mix all these strategies and use different ones for different investments, but you need to be aware of what you are doing and why you are doing it. It can also be useful to know how much money you are willing to lose and how much profit you are willing to walk away with so you can manage your position using stop-loss orders and take-profit orders.

Diversification

For many of you this is nothing new, but diversification is one of the most important things to think about when building an investment portfolio. By investing in different industries and different geographical markets, you can minimize the risk that is specific to each firm. Of course, you cannot diversify all risk, after a certain point the diversification effect is marginally decreasing and there is a systematic risk that you cannot diversify away. When I first started, I only owned 2 stocks and I was very heavily weighted in one of them so when this stock went down my whole portfolio also went down. Of course, it was hard to achieve significant diversification with such a small amount of capital, but I have since then learned the importance of diversification. Even on the worst days there is usually one or two stocks in my portfolio that are not in the red.

Some final thoughts

I’m sure most of this is old news to you but I wanted to share what I’ve learned from my early mistakes in case anyone here wants to but has not yet taken the step on to the financial markets. Even if you don’t want to be an active trader and just want to invest in mutual funds and ETFs, these lessons and principles can still be useful.

Useful resources

Sarwa (7th October 2021) Top 20 Warren Buffett Quotes To Inspire Your Investment Goals.

About the author

The article was written in November 2021 by Joshua WAN (ESSEC Business School, Global Bachelor of Business Administration, Exchange student from Stockholm University, Fall 2021).

Marketable Securitites

October 25, 2021 by Shruti CHAND

Marketable Securities

In this article, Shruti Chand (ESSEC Business School, Master in Management, 2020-2022) elaborates on the concept of marketable securities.

This read will help you get started with understanding marketable securities on the balance sheet of a business.

Introduction

Marketable securities are financial instruments that can be converted into cash easily by the firm. Marketable securities are recorded in the balance sheet along with other current assets because of their nature. You can find marketable securities on a firm’s balance sheet under the Cash and cash equivalents section.

Types of marketable securities:

There are various types of marketable securities available to the firm and that can appear in its balance sheet. The most common ones are:

Bankers acceptances

Bankers acceptance bills can be imagined as an instrument that functions like a post-dated cheque. The business which purchases a banker’s acceptance note can use this instrument to convert it into cash, with the bank guaranteeing the payment. The bank issues these cheques in exchange of a fee, i.e. issues it at a discount to a face value to the company.

Commercial paper

Commercial Paper (CP) bought by a company refers to a short-term and unsecured debt instrument issued by other companies. The maturity of commercial papers can be as short as a few days to 270 days typically. Again, just like banker’s acceptance, commercial papers are issued at a discount on the face value.

Certificate of Deposits

Certificate of Deposits (CD) are issued by banks that provide interest on their value. Most financial institutions offer these certificates with a blocked period in which the certificate holder keeps the certificate untouched. The institution also levies a penalty in case of early withdrawal.

Treasury Bills

Treasury Bills (T bills) are similar to certificates of deposits, but are issued by governments with maturity of one year or less. They are usually issued in fixed denomination at a discount on the face value. They are considered as one of the safest forms of investment and the discount rate is referred to risk free rate of the country.

Final Words:

Marketable securities are important as they can be sold on short notice to meet the financial obligation of the firm (to pay salaries to employees, to pay bills to providers, etc.).

Relevance to the SimTrade certificate

This post deals with Marketable Securities on the balance sheet, an important tool for investors to take investment decisions.

About theory

By taking the SimTrade course, you will know more about how investors can use various strategies to invest in order to trade in the market.

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About practice

By launching the series of Market maker simulations, you can extend your learning about financial markets and trading approaches.

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About the author

Article written by Shruti Chand (ESSEC Business School, Master in Management, 2020-2022).

Liabilities

October 25, 2021 by Shruti CHAND

Liabilities

In this article, Shruti CHAND (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2022) elaborates on the concept of liabilities.

This read will help you get started with understanding the liability side of the balance sheet.

Introduction

A liability is an obligation that a company has in return of economic benefits that the company has received in the past. Any kind of obligation or risk that are due to a third party can be termed as liability.

Liabilities are recorded on the balance sheet can be short-term or long-term in nature.

Liability vs Expense

It is important to know that liability is not an expense for the business. An expense is the cost of operation for the business and is recorded on the income statement of a business. Liabilities on the other hand is what the business owes to another party already as the economic benefit has been transferred in the past. It is recorded in the balance sheet of the company.

Liabilities are very important for a business as they finance the daily operations of the business. For expansion activities, for instance if a business wants to expand overseas, liability in form of bank loans will help the business acquire assets to make the move to another location. This loan facilitated by a bank for example will be recorded in the liabilities section in the balance sheet.

Structure of the Liabilities part of the balance sheet

The Liabilities part of the balance sheet can be structured as follows.

Current Liabilities

These are the company’s short-term obligation (Usually financial in nature) that are to be paid within a period of one year. Most noteworthy examples of current liabilities include:

Wages Payable: The total amount of salaries that the company owes to its employees.

Interest Payable: The credit that the business takes to finance short term needs of business operations accrues an interest. This interest in payable by the business in the short term and is recorded in the interest payable section of the balance sheet.

Dividends Payable: The total amount of dividends that the company owes to the investors against the stocks issued to them.

These items help the readers understand the level of obligations on the businesses due in a short period of time.

Non-current liabilities

These are obligations that are owed in a period longer than a year. Long term bonds, loans, etc. are a part of long-term/non-current liabilities. Companies usually issue bonds fulfil their long-term capital needs which are very common type of non-current liability. Other common examples of long-term liabilities include:

Debentures: Type of bond or debt instrument issued by the company unsecured against a collateral.

Bonds Payable: Long term debt instrument issued by companies and government which is a promise to pay at a future date and is issued at a discount in the current period.

Deferred tax liabilities: All that the company owed the government in the form of tax obligation that hasn’t been met yet by the company.

Final Word

Liability section of the balance sheet helps investors to assess the risk profile of a business. It is an important tool to measure the leverage taken by a firm to assess the risk level of the company within the industry and compare it with competitors in the same industry.

Relevance to the SimTrade certificate

This post deals with Liability side of the balance sheet, an important tool for investors to take investment decisions.

About theory

By taking the SimTrade course, you will know more about how investors can use various strategies to invest in order to trade in the market.

Take SimTrade courses

About practice

By launching the series of Market maker simulations, you can extend your learning about financial markets and trading approaches.

Take SimTrade courses

About the author

Article written by Shruti CHAND (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2022).

Assets

October 25, 2021 by Shruti CHAND

Assets

In this article, Shruti CHAND (ESSEC Business School, Master in Management, 2020-2022) elaborates on the concept of assets.

This read will help you get started with understanding Assets side on the balance sheet of a company.

Introduction

An asset can be defined as anything that has an economic value or future benefit. Assets are an important part of the balance sheet of a firm as it reports all that a business owns at a given point of time. Assets are economic resources that will generate cash flows in the future. Examples of assets include machinery, building, accounts receivable, etc.

All the value of assets that one sees on the balance sheet are typically recorded on historical cost, adjusted from time to time based on depreciation. The Assets side of the balance sheet states all the assets in the order of their liquidity, i.e., ease with which they can be converted into cash. Hence, current assets such as cash or cash equivalents, short-term investments, etc. are listed first followed by fixed non-liquid assets at the end (with the US and international framework).

Structure of the Assets part of the Balance Sheet

Types of Assets on the Balance Sheet:

Current Assets:

Any asset that can be converted into cash with ease (Typically within a timeframe of 1 year) is known as Current Asset.

Most common current assets on a balance sheet include cash and cash equivalents, inventory, accounts receivable, and other prepaid expenses.

Fixed Assets:

Fixed assets are resources that the business owns which cannot be converted into cash immediately. Most noteworthy fixed assets on a balance sheet include plants, buildings, machinery and equipment. There is a constant adjustment that is made to the value of these assets from time to time to reflect their current value.

Financial Assets:

This asset class represents the securities, corporate bonds, preferred equity and all other hybrid equity that is financial in nature that the business owns.

Intangible Assets:

These assets have no physical existence whatsoever, but since they still have value attached to it and generate benefit for the business, they are categorized as Intangible Assets. Examples of intangible assets include patents, copyrights, trademarks, goodwill and other intellectual property owned by the business.

Note that with the French presentation of the balance sheet, the least liquid assets appear in the top while the more liquid assets appear in the bottom of the balance sheet), and similarly the shareholders’ equity and the long-term liabilities appear in the top while the short-term liabilities appear in the bottom of the balance sheet.

Final Word:

Investors use the Assets side of the balance sheet to check the financial health of the business (profitability of the investments). One common way to assess the performance is to find out Asset turnover ratio which measures the revenues generated form the use of assets against sales.

The amount of which type of asset a business owns is dependent upon its area of operations. Some businesses are more capital intensive than others which might require them to have more fixed assets than others. Equipment manufacturers are going to have large number of fixed assets such as machinery while a tech business have almost no fixed assets (except intellectual property assessed over time accounted in intangibles).

Relevance to the SimTrade certificate

This post deals with Asset side of the balance sheet, an important tool for investors to take investment decisions.

About theory

By taking the SimTrade course, you will know more about how investors can use various strategies to invest in order to trade in the market.

Take SimTrade courses

About practice

By launching the series of Market maker simulations, you can extend your learning about financial markets and trading approaches.

Take SimTrade courses

About the author

Article written by Shruti Chand (ESSEC Business School, Master in Management, 2020-2022).

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Academic Literature

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Select and research the traders you want to learn and copy from

Allocate a set amount to each trader

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The IRR, XIRR and MIRR functions in Excel

About Excel

The internal rate of return (IRR)

Definition

Use in finance

The Excel functions to compute the IRR

The IRR function

From the IRR function to the XIRR function

From the IRR function to the MIRR function (Modified Internal Rate of Return)

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My experience as Consultant in Energy & Utilities at Atos Worldgrid

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