Contributors

Métier de Directeur financier

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Description du métier de Directeur financier

Chloé POUZOL

In this article, Chloé POUZOL (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2024) présente le métier de Directeur financier.

Que fait un directeur financier ?

L’objectif principal d’un directeur financier est de développer stratégiquement et financièrement l’entreprise pour laquelle il travaille. Ses missions sont variées et nombreuses. Il est tout d’abord responsable de garantir l’équilibre financier de l’entreprise et d’optimiser ses performances. Pour cela, il encadre les équipes financières et comptables, établit les budgets, assure le suivi de la trésorerie et des écarts avec le budget, gère le besoin en fonds de roulement (BFR) et s’occupe de la gestion des dettes en anticipant les besoins de financement de l’entreprise.

Ensuite, le directeur financier est chargé de conseiller la Direction Générale sur les investissements à réaliser. Il décide des placements à effectuer, des plans de financement et suit leur mise en œuvre.

Le directeur financier doit aussi représenter l’entreprise lors des rencontres, des négociations avec les partenaires financiers et des réunions de la Direction générale au sein même de l’entreprise. Il est responsable de l’organisation des réunions et assemblées générales (notamment de clôture des comptes et pour les reportings) .

Enfin, le directeur financier doit mettre en place des procédures de gestion et d’optimisation et faire des veilles réglementaires relatives au secteur d’activité de l’entreprise. Il supervise le recouvrement et le juridique.

Avec qui travaille un directeur financier ?

Le directeur financier doit avoir une appétence pour le travail en équipe. En effet, il est en relation avec tous les services de l’entreprise pour établir les budgets de trésorerie et surtout avec les équipes comptables, administratives et financières. Il dirige lui-même une équipe composée d’analystes financiers, de responsables de la trésorerie, de spécialistes du financement et d’experts des crédits internationaux. Le directeur financier est également en charge des relations extérieures avec les bailleurs de fonds (les actionnaires et les créanciers comme les banques) et les organismes privés ou publics (Etat, régulateurs, associations professionnelles, etc.) concernés par l’activité de l’entreprise.

Combien gagne un directeur financier ?

Le salaire d’un directeur financier dépend de son expérience professionnelle, de sa formation initiale, du secteur d’activité et de la taille de l’entreprise dans laquelle il travaille. Cependant, le salaire mensuel moyen s’élève généralement entre 5 000 € et 6 600 € brut ; mais il peut aller jusqu’à 25 000 € brut pour un directeur financier très expérimenté. En tant que directeur, il est également possible de toucher des bonus.

Quel positionnement dans la carrière ?

Travailler en tant que directeur financier permet une mobilité professionnelle importante. En effet, il vous est possible d’une part de continuer votre carrière dans la même entreprise en travaillant à la Direction Générale en tant que DG ou PDG par exemple, ou d’autre part, en changeant d’entreprise pour occuper à nouveau un poste de Directeur Financier ou au sein de la Direction Générale.

Quelle formation ?

Être muni d’un Bac+5 et sorti d’une école de commerce comme l’ESSEC correspondent à la formation académique nécessaire pour occuper ce poste.

Cependant, le poste de Directeur financier n’est pas accessible dès la diplomation. Il est, en effet, nécessaire d’acquérir plusieurs années d’expériences dans le domaine de la comtpabilité, de la finance, du contrôle de gestion ou de l’audit.

Compétences requises ?

Il est évident que chaque directeur financier est différent et qu’en fonction de l’entreprise dans laquelle il travaille et des équipes qu’il gère certaines compétences seront plus nécessaires que d’autres. Il est tout de même possible d’affirmer que parmi les hard skills nécessaire, une bonne connaissance des aspects fiscaux, comptables, juridiques et financiers est essentielle à acquérir. Les compétences techniques s’acquièrent principalement avec l’expérience. De même, le directeur financier doit faire preuve de leadership et avoir le sens de l’écoute afin de bien manager ses équipes. Enfin, une forte résistance au stress ainsi que la capacité de convaincre un public sont des compétences humaines et comportementales (soft skills) très valorisées pour ce poste.

Artilcles à lire sur le blog SimTrade

   ▶ All posts about Professional experiences

   ▶ Anne BARBERO Career in finance

   ▶ Alexandre VERLET Classic brain teasers from real-life interviews

Ressources utiles

Cegos Fiche métier directeur financier

A propos de l’auteure

Cet article a été écrit en mai 2022 par Chloé POUZOL (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2024).

Mon expérience de contrôleuse de gestion chez Edgar Suites

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Mon expérience de contrôleuse de gestion chez Edgar Suites

Chloé POUZOL

In this article, Chloé POUZOL (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2024) partage son experience de contrôleuse de gestion chez Edgar Suites.

L’entreprise : Edgar Suites

Edgar Suites est une start-up fondée en 2016 par Xavier O’QUIN, Maxime BENOIT et Grégoire BENOIT. Elle propose à ses clients de vivre une expérience au cœur de la ville en logeant dans une suite urbaine : un mix idéal entre appartement et hôtel. Pour cela, l’entreprise loue des locaux initialement occupés par des bureaux, qu’elle transforme en T1 (studio), T2 (2 pièces) et T3 (3 pièces).

Exemple de suite
Exemple de suite
Source: Edgar Suites

En mai 2021, Edgar Suites a levé 104 millions d’euros auprès du fonds d’investissement BC Partners. Depuis cette levée de fonds, l’entreprise a triplé son activité avec presque 150 suites urbaines à Paris, Levallois Perret, Bordeaux, Lille et Cannes.

Logo de l’entreprise Edgar Suites
Logo Edgar Suites
Source: Edgar Suites

Mes missions

En tant que stagiaire, j’ai eu plusieurs missions bien différentes, certaines seulement temporairement et d’autres tout au long de mon stage.

Lorsque je suis arrivée chez Edgar Suites, il n’y avait pas encore de contrôle de gestion mis en place. L’entreprise avait juste quelques fichiers Excel avec lesquels elle faisait tant bien que mal les calculs de chiffres d’affaires (CA) et d’excédent brut d’exploitation (EBE) qui représente le bénéfice d’une société avant les intérêts, impôts, amortissement et provisions (EBITDA pour Earnings before interest, taxes, depreciation, and amortization)…

L’entreprise avait embauché un prestataire extérieur pour construire des fichiers de reporting financier et comptable grâce à un tableur (Excel) et une suite de logiciels qui permettent de transformer des données disparates en informations visuelles, immersives et interactives (Power BI). J’étais chargée de surveiller l’avancée du dossier, de superviser le respect des dates limites (deadlines) et surtout de vérifier la cohérence des fichiers envoyés (écarts, cohérences entre les grands livres, les résultats de l’entreprise (P&L pour Profit & Loss) et les budgets). Cela m’a ainsi permis d’apprendre à maîtriser un éditeur de requêtes de données (Power Query, un des logiciels de la suite Power BI) pour importer des données de l’entreprise dans le tableur Excel.

En plus de cette première responsabilité, j’ai été chargée d’améliorer les fichiers internes de suivi d’indicateurs utilisés pour l’aide à la décision et pour mesurer l’efficacité d’une mesure (KPI pour key performance indicator) notamment le coût au check-in et le coût par équivalent temps plein (ETP) qui est une unité de mesure permettant d’évaluer la charge de travail et la capacité d’un employé.

Réalisation des reportings mensuels

En plus de ces missions, j’étais responsable de la rédaction de tous les reportings mensuels pour BC Partners (le fonds d’investissement auprès duquel Edgar Suites a levé des fonds pour financer son développement) et pour les propriétaires d’immeuble à loyer variable (loyer calculé selon un certain pourcentage du chiffre d’affaires) où il s’agissait de calculer le chiffre d’affaires, les coûts fixes, les coûts variables du mois et ainsi les bénéfices du mois. Je devais aussi m’occuper des rapprochements bancaires (contrôle de la concordance entre les relevés des comptes bancaires et les comptes correspondant dans la comptabilité) et de la gestion des factures qui s’effectuaient à l’aide du logiciel Pennylane.

Réflexion sur la responsabilité sociétale des entreprises (RSE)

Enfin, j’ai également participé à la réflexion sur la responsabilité sociétale des entreprises (RSE) pour prendre en compte les enjeux environnementaux et sociaux d’Edgar Suites. Les dirigeants souhaitent, en effet, être labellisés B-Corp (Benefit Corporation). Une entreprise peut recevoir la certification B-Corp lorsque ses actions sont en adéquation avec les exigences sociales, environnementales et de gouvernance du public. Il s’agit d’une certification qui s’obtient après un long processus. J’ai donc effectué des recherches et conduit des entretiens pour trouver le cabinet de conseil adéquat pour nous accompagner tout au long de ce projet. J’ai également participé aux réunions de réflexion sur les actions d’Edgar Suites afin d’atténuer l’impact social et environnemental de l’activité.

Compétences et connaissances requises pour ce stage

Les principales compétences et connaissances techniques (hard skills) requises sont de maîtriser un tableur comme Excel et d’avoir de bonnes bases en comptabilité et en finance. En effet, pour réaliser les reportings, il était nécessaire de comprendre les données importantes de l’activité pour pouvoir les analyser et les synthétiser. Ces données importantes chez Edgar Suites étaient le coût par check-in, l’EBITDA, les coûts fixes et les coûts variables (notamment les loyers variables). De même, pour faire de la modélisation financière, il est essentiel d’avoir de bonnes connaissances financières afin de créer une logique et une présentation cohérente au sein du fichier.

Enfin, les compétences humaines et comportementales (soft skills) essentielles étaient principalement de savoir travailler en équipe ; cela permet de mettre à contribution les idées et les compétences de tous les membres du groupe pour améliorer le résultat du travail sur l’entreprise.

De façon générale, je suis très satisfaite de mon premier stage que j’ai effectué à la fin de ma première année à l’ESSEC. J’ai été responsabilisée et j’ai pu découvrir le fonctionnement comptable d’une entreprise ainsi que me familiariser avec la finance d’entreprise. En effet, j’ai pu manipuler les états financiers d’Edgar Suites pour me familiariser avec leur lecture et leur analyse. De plus, j’ai pu observer le fonctionnement des finances de l’entreprise : comment l’entreprise gérait ses coûts ; comment Edgar Suites essayait d’améliorer sa rentabilité ; quelles étaient les répercutions sur le plan financier des décisions de management …

Concepts clés

Je détaille ci-dessous quelques concepts clés qui m’ont été utile de maîtriser pendant mon stage :

Contrôle de gestion

Le contrôle de gestion est un service au sein d’une entreprise, chargé d’aider à la prise de décision. Il est responsable de l’élaboration des budgets, de la mise en place de procédures de gestion et de règles, du suivi des résultats, du choix des indicateurs clés dans les tableaux de bord et de la production et la diffusion d’outils de pilotage. L’objectif principal du contrôleur de gestion est d’optimiser les performances matérielles et financières de l’entreprise.

Chiffre d’Affaires

Le Chiffre d’Affaires (CA) correspond à la somme des ventes des produits ou services d’une entreprise. Il se calcule en multipliant les quantités vendues par leur prix de vente. Il s’agit donc d’un indicateur principal sur les performances de l’entreprise.

EBE ou Ebitda

L’Ebitda (Earnings before interest, taxes, depreciation, and amortization) correspond au bénéfice avant les intérêts, les impôts, les taxes, la dépréciation et l’amortissement. Il mesure donc la création de richesse avant toute charge. Il s’agit d’une notion assez proche de l’EBE (Excédent Brut d’Exploitation). Il existe deux formules pour calculer l’Ebitda :

Ebitda = Chiffres d’affaires – achats – autres charges externes – charges du personnel – autres charges

Ebitda = Résultat net + charges d’intérêts + charges d’impôts + amortissements et provisions

Lorsque l’Ebitda est positif, cela signifie que l’entreprise est rentable au niveau opérationnel mais pas forcément qu’elle est bénéficiaire (après la prise en compte d’autres éléments comme les charges financière).

Responsabilité sociétale des entreprises (RSE)

La responsabilité sociétale des entreprises (RSE) correspond à la contribution des entreprises aux enjeux du développement durable. Cela consiste à faire des efforts pour la protection de l’environnement et pour l’amélioration de la société. Ces efforts se font en collaboration avec toutes les parties prenantes (fournisseurs, clients, employés, actionnaires…). Il existe aujourd’hui de nombreuses certifications, comme la certification B-Corp, qui reconnaissent l’investissement des entreprises dans la RSE.

Articles à lire sur le blog SimTrade

   ▶ All posts about Professional experiences

   ▶ Anna BARBERO Career in finance

   ▶ Emma LAFARGUE Mon expérience en contrôle de gestion chez Chanel

   ▶ Ghali EL KOUHENE Asset valuation in the real estate sector

Ressources utiles

Edgar Suites

B-Corp France

A propos de l’auteure

Cet article a été écrit en mai 2022 par Chloé POUZOL (ESSEC Business School, Grande Ecole Program – Master in Management, 2022-2024). Vous pouvez me contacter via mon adresse mail ESSEC pour plus d’information sur mon stage.

Momentum Trading Strategy

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Momentum Trading Strategy

Akshit GUPTA

This article written by Akshit GUPTA (ESSEC Business School, Master in Management, 2019-2022) explains the momentum trading strategy.

Introduction

The momentum trading strategy is a strategy where a trader buys a security when its market price starts to rise and then sells it when its price seems to have reached a top. Similarly, a trader sells (or short sells) a security when its market price starts to fall and then buys it back when its price seems to have reached a bottom. In other words, if we observe a positive price change or return today, we are long tomorrow, and if we observe a negative price change or return today, we are short tomorrow.

This trading strategy is based on the direction of the price trend (up or down) in the market and its relative strength. The rationale behind the momentum trading strategy is that, for an upward trend, if there is enough buying force behind the rise in the price of an asset, it will keep on rising until a strong selling pressure is seen in the market to reverse the trend. Similarly, for a downward trend, if there is enough selling force behind the fall in the price of an asset, it will keep on falling until a strong buying pressure is seen in the market to reverse the trend.

Momentum trading is a trading strategy with a short-term horizon where traders try to capture and profit from the price trend. The period for implementing a momentum strategy can range from a trend forming within a day or over several days. Momentum traders try to identify the strength of an ongoing trend in a particular direction and take a position. The strength can measured by different technical indicators discussed below. Once the strength of the trend begins to fall, the trader exits the position at a profit.

Momentum traders are least concerned about the fundamentals of the company for which the stock is to be traded. They rather use various technical indicators to understand the trend in the stock price, especially its strength.

Implementation

Figure 1 below illustrates the implementation of the momentum trading strategy for Apple stock over the period from April 1, 2020 to March 31, 2021.

Figure 1. Implementation of the momentum trading strategy for Apple stock.
Implementation of the momentum trading strategy for Apple stock
Source: computation by the author (data source: Yahoo Finance).

In Figure 1, an upward trend can be seen forming in the period from November 22, 2020 to November 25, 2020 in the price of Apple stock. The trader following a momentum strategy will go long on the Apple stock till the momentum is in the upward direction. The right time to exit the long position is around December 2, 2020. By following this trend, the trader can capture a price movement of around $10 which is approximately 8%-9%, by going long on the Apple stock.

Momentum trading indicators

Momentum trading indicators help the trader to look for the formation of a trend and the signal of an entry/exit point, and also indicate the strength of that signal. We present below some of the most common indicators used to assess the strength of the trend: relative strength index (RSI), moving-average convergence-divergence (MACD) and Bollinger bands.

Relative Strength Index (RSI)

The RSI indicator is a technical indicator and is plotted on a chart which ranges from 0 to 100. It helps a trader in knowing the relative strength of a trend formation. The indicator is an oscillator which provides overbought or oversold signals based on the positioning of the line in the chart. During the uptrend, if the line crosses the 70 mark, an overbought signal is considered for the given security. Symmetrically, during a downtrend, if the line crosses the 30 mark, an oversold signal is considered. Momentum traders generally take a position in between in the indicator instead of waiting for a price reversal when the line crosses the given thresholds. For example, a trader can use the halfway mark of 50 to get an idea about the formation of a trend. If the RSI line crosses the 50 mark and is moving in an upward direction, it can show the high strength of the upward forming trend and the trader can take a long position in the respective stock.

Figure 2. Relative Strength Index of Apple stock.
Relative Strength Index of Apple stock
Source: computation by the author (data source: Yahoo Finance).

Moving-average convergence-divergence (MACD)

The moving-average convergence-divergence (MACD) is a technical indicator based on the moving averages of prices over a period of time. The indicator helps in understanding the direction and strength of a trend. It also helps in understanding the rate at which the change in trend is happening.

The indicator is shown by two lines namely, the MACD line and the signal line. The MACD line is the difference between two exponential moving-averages, a long-term moving-average like a 26-day moving average and a short-term moving-average like the 12-day moving average. The signal line is made up of the 9-day exponential moving-average of the MACD itself and is placed on the same graph. A bar graphs plotted on the zero-line (X axis) showing the difference by which the MACD line is below/above the signal line. Generally, the indicator is used to understand the degree of the bullish or bearish sentiments in the market. If the MACD line crosses the signal line from below the zero-level moving upwards, it indicates a bullish trend. In such a scenario, a trader practicing momentum strategy would take a long position in the market seeing the trend.

Figure 3. Moving-average convergence-divergence of Apple stock.
MACD of Apple stock
Source: computation by the author (data source: Yahoo Finance).

Bollinger bands

The Bollinger bands is a very popular technical indicator that represents the volatility in the prices of a financial asset. The indicator consist of three lines, namely, a simple moving-average (SMA), and an upper band and a lower band. The simple moving average is usually computed over a rolling period of 20 trading day (about a calendar month for the equity market). The upper and lower bands are usually set by default to two standard deviations away from the simple moving average.

The width between the upper and lower Bollinger bands provides a range for price changes in the market (an indicator of volatility). The bands help to identify the overbought or oversold situations in the market for an asset. They can be used by a trader to identify possible entry or exit prices to implement the momentum trading strategy.

Figure 4 represents the Bollinger bands for Apple stocks. The price of the Apple stock is touching the lower band on November 2, 2020 and reverting just after that. This can be a signal for the momentum trader showing a trend reversal and the trader can take a long position in this stock till the price touches the 20-day SMA line which happens around November 5, 2020, thereby capturing a price movement of $8 approximately.

Figure 4. Bollinger bands of Apple stock.
Bollinger bands of Apple stock
Source: computation by the author (data source: Yahoo Finance).

Market conditions

Market liquidity and market volatility play a major role in the implementation of a momentum strategy.

A liquid market is generally preferred by traders in order to quickly enter and exit the market.

Stock price volatility is a major factor affecting a momentum trader’s decision to enter/exit a trade. A highly volatile stock can provide a good opportunity for a trader to earn high profits using this strategy as the asset prices can change dramatically in a short period of time. But a high stock volatility can also lead to huge losses if the prices move in an unfavorable direction.

The figure below represents the historical daily volatility (standard deviation of returns over rolling 10-day periods) of Apple stock over the period from April 1, 2020 to March 31, 2021.

Figure 5. Volatility of Apple stock.
Volatility of Apple stock
Source: computation by the author (data source: Yahoo Finance).

You can download below the Excel file for the computation of the different momentum trading indicators mentioned above.

Download the Excel file to compute the momentum trading indicators

Risks associated with momentum trading

Although momentum trading is a commonly used strategy, the risks associated with it are quite high. The trader using this strategy should be careful about:

  • Entering the position too early
  • Exiting the position too late
  • Relying on rumors and fake news
  • Missing the indication of a reversal in the direction of the trend
  • Not applying a strict stop loss rule

Link with market efficiency

Market efficiency refers to the degree to which all the relevant information about an asset is incorporated in the market prices of that asset. Fama (1970) distinguished three forms of market efficiency: weak, semi-strong, and strong according to the set of information considered (market data, public information, and private information).

In the weak form of the market efficiency hypothesis, the current market price of an asset incorporates all the historical market data (past transaction prices and volumes). The current market price of the asset is then the best predictor of its future price.

In a market efficient in the weak sense, the autocorrelation of asset price changes or returns is close to zero.

A positive autocorrelation coefficient would imply that after a price increase, we should likely observe another price increase, and symmetrically, after a price decrease, we should likely observe another price decrease, leading in both cases to price trends.

The implementation of a momentum strategy assumes that the autocorrelation of price changes is positive, which contradicts the efficient market hypothesis.

In a market which is efficient in the weak sense (implying an autocorrelation close to zero), momentum trading strategies should not exhibit extra profit as traders are not be able to beat the market on the long run.

Related posts

   ▶ Jayati WALIA Bollinger bands

   ▶ Jayati WALIA Moving averages

   ▶ Akshit GUPTA Growth investment strategy

Useful resources

Academic research

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

Fama E.F. (1991) Efficient Capital Markets II: A Review of Theory and Empirical Work, The Journal of Finance 46(5): 1575-1617.

Business analysis

Fidelity Learning center: Momentum trading strategy

About the author

Article written in May 2022 by Akshit GUPTA (ESSEC Business School, Grande Ecole – Master in Management, 2019-2022).

The WHO's news on the HPV vaccine caused the stock prices of Zhifei Bio and Wantai Bio to plunge

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The WHO’s news on the HPV vaccine caused the stock prices of Zhifei Bio and Wantai Bio to plunge

Pai LI

In this article, Pai LI (ESSEC Business School, Global BBA, 2021-2023) shares her insights on the event “The WHO’s news on the HPV vaccine caused the stock prices to plunge”.

The Brief Introduction of the event

On 2022 April 11, the World Health Organization (WHO) announced on its official website that the WHO convened a meeting of the Strategic Expert Group on Immunization (SAGE) from April 4 to 7 to vaccinate one dose of human papillomavirus (HPV) vaccine. The expert group considered that only 1 dose of HPV vaccine can produce the same immune effect as 2-3 doses, and can effectively prevent cervical cancer caused by HPV infection.

As soon as the news came out, the stock prices of HPV vaccine concept stocks Zhifei Bio and Wantai Bio plunged. As of the close, Zhifei Bio fell 14.19%, and Wantai Bio once fell by the limit, and as of the close, it fell 9.46% and approached the limit.

Stock chart of Zhifei Bio and Wantai Bio
Stock chart of Zhifei Bio and Wantai Bio
Source: Bloomberg.

Explanation of the Market reaction to the Event

The World Health Organization (WHO) website released information saying that from April 4 to April 7, the WHO Strategic Advisory Group of Experts on Immunization (SAGE) held a meeting, referring to the single dose of the HPV vaccine provides reliable protection, comparable to a 2- or 3-dose regimen.

Regarding the impact of the reduction in the number of HPV vaccination doses, at noon on April 14, After the stock market opened on the afternoon of April 14, the decline in the share price of related companies narrowed. As of the close, Zhifei Biological fell 14.19% to 116 yuan per share, with a market value of 185.6 billion yuan; Watson Bio fell 3.08% to 49.11 yuan / stock market value of 78.65 billion yuan; Wantai Bio fell 9.46% to 257.59 yuan / share , with a market value of 156.37 billion yuan.

The stocks of biology may be affected by the decline of three HPV vaccine companies. On April 14, many stocks in the A-share biological vaccine sector fell. For example, CanSino closed down 3.99%, and Kangtai Bio closed down 1.2%.

WHO press release
WHO press release
Source: WHO.

It is important to note that SAGE stated in the minutes of the meeting that it reviewed new evidence on the efficacy of single doses of HPV, and recommended that women aged 9-14 receive 1 or 2 doses, with a single dose providing comparable and high levels of protection. From a public health perspective, is more effective, less resource intensive and easier to implement. Likewise, 1 or 2 doses are also suitable for women between the ages of 15 and 20.

The current HPV vaccine policy in the world is 2 doses for girls aged 9-14 years, 3 doses for girls aged 15 years and above, and 3 doses for immunocompromised people of any age, including people with HIV.

Notably, SAGE emphasizes that more evidence is needed on whether reduced doses provide protection in immunocompromised groups.

The minutes also mentioned that WHO will conduct stakeholder consultations on these important policy changes before revising the position paper on HPV vaccination.

Predictions for the future

Regarding the recommendations of this WHO meeting on HPV vaccine, I believe that this meeting is only providing a recommendation and not implementing it, and that the current vaccination schedule is still dominated by 2-3 doses. In addition, WHO’s recommendations are mainly based on concerns about the slow introduction of HPV vaccine into immunization programs and low overall population coverage, especially in poorer countries, and the core is to address the huge gap between HPV vaccine supply and demand.

Even if the vaccination procedure is changed from three injections to two injections in the future, the improvement of industry penetration rate and accessibility is believed to effectively fill the market, which is expected to bring strong demand for HPV vaccination in relevant third world countries, and the export of Chinese domestic HPV vaccines is expected to accelerate. At the same time, for Merck’s nine-valent HPV vaccine, it is still in a stage of insufficient production capacity. Whether it is a two-shot or three-shot vaccination program, it is still in a stage of short supply. In conclusion, there is no need to worry too much about the impact on the performance of HPV-related companies.

About the author

The article was written in May 2022 by Pai LI (ESSEC Business School, Global BBA, 2021-2023).

My internship experience as a financial research analyst in Tianfeng Securities

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My internship experience as a financial research analyst in Tianfeng Securities

Pai LI

In this article, Pai LI (ESSEC Business School, Global BBA, 2021-2023) shares her internship experience as an assistant financial research analyst in Tianfeng Securities which is a Securities Research Institute in China.

The Company

Tianfeng Securities is a global full-license integrated financial securities service provider. Tianfeng Securities Research Institute is a high-end industry research think tank in China. It brings together more than 200 team members to build bridges and links between funds and industry and enhance the ability of financial services to serve the substantial economy.

Tianfeng Research Institute adheres to the “industry-oriented” driving force, creates a unique financial ecological alliance, forming a complete ecological chain that runs through the life cycle of enterprises and industries.

Logo of Tianfeng Securities
Logo of Tianfeng Securities
Source: Tianfeng Securities.

My Internship

My missions

The department I practiced for was the Securities Research Institute, and the position was financial research assistant. My work mainly consisted of two parts, the daily research work about industry and the related work of writing in-depth research reports about companies.

Daily work includes using a financial database called Wind (like Bloomberg but focused on mainland China) to find industry data, prospectus, company annual reports and other materials, doing market shares calculations, doing valuation models, collecting information for industry research topics, writing new stock purchase proposals, updating internal industry databases, modifying and improve the Powerpoint presentation of roadshow reports, operating social media for publishing weekly reports, comments, and in-depth reports.

In addition to the above routines, I also participated in the writing of the first draft of the Institute’s in-depth reports. At the beginning I wrote some simple company tracking reviews. These short reports were completed by referring to the relevant announcements and materials of the company. Next, I gradually participated in the writing of the in-depth reports. In the process of continuous maturity and improvement of the reports, I learned a lot of research skills.

Writing in-depth reports requires the collection of a large amount of financial and business data, and an overall overall grasp of the structure and context of the company. Not only did I improve my ability to understand the company’s business by collecting information from all parties, but I also learned to build a valuation model to predict the company’s future performance.

Required skills and knowledge

In terms of technical skills, you need to have financial knowledge, frameworks and insights for industry analysis and company analysis, and report writing skills. These professional abilities of mine have been greatly improved during this internship.
In terms of behavior skills, industry researchers need to have logical thinking ability to predict the future direction of companies and industries. In addition, interpersonal communication skills are also very important, through which research results can be presented to the buy-side clients in the best possible state.

What I have learnt

My biggest gain in this internship is that I learned how to write a professional report. I summarize the essential qualities of an extraordinary in-depth report into seven points:

  • The selection of the company is meaningful.
  • The core point of view about the company is highlighted.
  • The discussion about the business of the company is rigorous and logical.
  • The business and financial data are authentic and credible.
  • The business charts are clear and detailed.
  • The text is concise and straightforward.
  • The exhibit are exciting.

In addition, I also deepened my understanding of the industry of securities firm research. I realized that it is a highly homogenized industry, because the same teams research the same companies, and the companies provide the same type of information (announcements, financial and accounting data). This is an industry that pays great attention to timeliness. When a news comes out, investors expect to see relevant research results immediately, and will be swept away by other reports later. This is an industry with high barriers to entry. When looking for data, well-funded securities companies are equipped with sufficient database access qualifications, while small agencies can only search for free public information. Every year, many finance students try hard to get an internship in industry financial research, but few can get it. Therefore, in the face of such as intense competition, what we need to do is to maximize our core competitive advantages.

Three key financial concepts

Here are 3 useful valuation methods.

P/E Valuation Method

The Price-to-Earnings (P/E) valuation method is based on the price-earnings (P/E) ratio:

Price earnings ratio

EPS comes in two main varieties. TTM is a Wall Street acronym for “trailing 12 months”. This number signals the company’s performance over the past 12 months. The second type of EPS is found in a company’s earnings release, which often provides EPS guidance. This is the company’s best-educated guess of what it expects to earn in the future. These different versions of EPS form the basis of trailing and forward P/E, respectively.

The price-earnings ratio can be used to predict the stock price by the following calculation formula:

Stock price prediction based on the price-earnings ratio

P/B valuation method

The P/B valuation method is based on the price-to-book (P/B) ratio:

Price-book ratio

Generally speaking, stocks with low price-to-book ratios generally have relatively high investment value (in their balance sheet).
The price-to-book ratio can be used to predict the stock price by the following calculation formula:

Stock price prediction based on the price-to-book ratio

The P/B valuation method is suitable for companies with large and relatively stable net assets, such as steel, coal, construction and other traditional companies. However, it is not suitable for enterprises with light assets such as technology Internet and consulting services, which are small in scale and dominated by labor costs. The valuation should be based on the principle of “peer ratio and historical ratio”. Usually, the lower the price-to-book ratio, the safer the investment.

PEG valuation method

The PEG valuation method is based on the price-to-earnings growth (PEG) ratio:

Price-to-earnings growth ratio

In general, the smaller the PEG, the better and safer. But PEG>1 does not mean that the stock is overvalued. It must be measured according to the overall indicators of its peers. If the PEG is greater than 1, but its peers are higher than it, which also means that although the company’s PEG is already higher than 1, its value may also be is underrated.

Related posts on the SimTrade blog

   ▶ All posts about Professional experiences

   ▶ Anna BARBERO Career in finance

   ▶ Alexandre VERLET Classic brain teasers from real-life interviews

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

Useful resources

Tianfeng Securities

Wind Database

About the author

The article was written in May 2022 by Pai LI (ESSEC Business School, Global BBA, 2021-2023).

Returns

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Jayati WALIA

In this article, Jayati WALIA (ESSEC Business School, Grande Ecole Program – Master in Management, 2019-2022) explains how returns of financial assets are computed and their interpretation in the world of finance.

Introduction

The main focus of any investment in financial markets is to make maximum profits within a coherent risk level. Returns in finance is a metric that inherently refers to the change in the value of any investment. Positive values of returns are interpreted as gains whereas negative values are interpreted as losses.

Returns are generally computed over standardized frequencies such as daily, monthly, yearly, etc. They can also be computed for specific time periods such as the holding period for ease of comparison and analysis.

Computation of returns

Consider an asset for a time period [t -1, t] with an initial price Pt-1 at time t-1 and final price Pt at time t (one period, two dates). Different forms of defining returns for the asset over period [t -1, t] are discussed below.

Arithmetic (percentage) returns

This is the simplest way for computation of returns.

The return over the period [t -1, t], denoted by Rt, is expressed as:

Arithmetic returns

Logarithmic returns

Logarithmic returns (or log returns) are also used commonly to express investment returns. The log return over the period [t-1, t], denoted by Rt is expressed as:

Logarithmic returns

Log returns provide the property of time-additivity to the returns which essentially means that the log returns over a given period can be simply added together to compute the total return over subperiods. This feature is particularly useful in statistical analysis and reduction of algorithmic complexity.

Logarithmic returns additivity

Log returns are also known as continuously compounded returns because the rate of log returns is equivalent to the continuously compounding interest rate for the asset at price P0 and time period t.

img_SimTrade_compounded_returns

Link between arithmetic and logarithmic returns

The arithmetic return (Rari) and the logarithmic return (Rlog) are linked by the following formula:

Relation between arithmetic and logarithmic returns

Components of total returns

The total return on an investment is essentially composed of two components: the yield and the capital gain (or loss). The yield refers to the periodic income or cash-flows that may be received on the investment. For example, for an investment in stocks, the yield corresponds to the revenues of dividends while for bonds, it corresponds to interest payments.

On the other hand, capital gain (or loss) refers to the appreciation (or depreciation) in the price of the investment. Thus, the capital gain (or loss) for any asset is essentially the price change in the asset.

Total returns for a stock over the period [t -1, t], denoted by Rt, can hence be expressed as:

Total returns

Where
   Pt: Stock price at time t
   Pt-1: Stock price at time t-1
   Dt-1,t: Dividend obtained over the period [t -1, t]

Price changes and returns

Consider a stock with an initial price of 100€ at time t=0. Suppose the stock price drops to 50€ at time t=1. Thus, there is a change of -50% (minus sign representing the decrease in price) in the initial stock price.

Now for the stock price to reach back to its initial price (100€ in this case) at time t=2 from its price of 50€ at time t=1, it will require an increase of (100€-50€)/50€ = 100%. With arithmetic returns, the increase (+100%) has to be higher than the decrease (-50%).

Similarly, for a price drop of -25% in the initial stock price of 100€, we would require an increase of 33% in the next time period to reach back the initial stock price. Figure 1 illustrates this asymmetry between positive and negative arithmetic returns.

Figure 1. Evolution of price change as a measure of arithmetic returns.
img_SimTrade_price_change_evolution
Source: computation by the author.

If the return is defined as a logarithmic return, there is a symmetry between positive and negative logarithmic returns as illustrated in Figure 2.

Figure 2. Evolution of price change as a measure of logarithmic returns.
img_SimTrade_price_change_evolution
Source: computation by the author.

You can also download below the Excel file for computation of arithmetic returns and visualise the above price change evolution.

Download the Excel file to compute required returns to come back to the initial price

Internal rate of return (IRR)

Internal rate of return (IRR) is the rate at which a project undertaken by the firm break’s even. It is a financial metric used by financial analysts to compute the profitability from an investment and is calculated by equating the initial investment and the discounted value of the future cashflows i.e., making the net present value (NPV) equal to zero. The IRR is the sprecail value of the discount rate which makes the NPV equal to zero.

The IRR for a project can be computed as follows:

IRR formula

Where,
   CFt : Cashflow for time period t

The higher the IRR from a project, the more desirable it is to pursue with the project.

Ex ante and ex post returns

Ex ante and ex post are Latin expressions. Ex ante refers to “before an event” while ex post refers to “after the event”. In context of financial returns, the ex ante return corresponds to prediction or estimation of an asset’s potential future return and can be based on a financial model like the Capital Asset Pricing Model (CAPM). On the other hand, the ex post return corresponds to the actual return generated by an asset historically, and hence are lagging or backward-looking in nature. Ex post returns can be used to forecast ex ante returns for the upcoming period and together, both are used to make sound investment decisions.

Related posts on the SimTrade blog

   ▶ Jayati WALIA Standard deviation

   ▶ Raphaël ROERO DE CORTANZE The Internal Rate of Return

   ▶ Jérémy PAULEN The IRR function in Excel

   ▶ Léopoldine FOUQUES The IRR, XIRR and MIRR functions in Excel

About the author

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

Supply and Demand

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Supply and Demand

Diana Carolina SARMIENTO PACHON

In this article, Diana Carolina SARMIENTO PACHON (ESSEC Business School, Master in Strategy & Management of International Business (SMIB), 2021-2022) explains the economic concept of supply and demand, which is key to understand the way markets work.

Supply and demand are the fundamental concepts that shape the way we make business and operate in the world. They construct both simple transactions such as the purchase of coffee or more compounded transactions such as the operations in the financial world. For this reason, it’s crucial to understand and uncover them deeper.

The basic concepts

Supply is referred to the amount available of a product that firms offer, whereas demand is the amount desired by consumers or households. When these quantities are equal, an equilibrium is reached and consequently a transaction takes place, leading to the well-known law of supply & demand which shapes the behavior of daily transactions and shifts in the economy. If price increases, then supply also increases; nonetheless, demand decreases as it’s more expensive for consumers to a buy good; on the contrary, if prices decline then supply also decline since producers would make less revenue whereas demand goes up as it is cheaper to buy. This dynamic takes place until the quantities of supply and demand are equal so that the optimum equilibrium is found.

Figure 1. Supply and demand.
img_Simtrade_risk_reduction_stocks
Source: computation by the author.

From another perspective, if demand escalates then price rises due to the high desirability of the good, meanwhile when demand drops it can create a surplus of supply which can drag the price down. Likewise, this scenario can be applied in financial markets e.g., in the case of a bullish sentiment in the market, there can be a positive speculation which creates a higher desirability for certain stock resulting in a decrease in price; nevertheless, when demand is low the price may drop because of a low or negative speculation on a specific stock.

Furthermore, the fundamental law of supply and demand can also explain the price movements seen in the financial markets. To illustrate, for a commodity such as coffee, if the surface of cultivation expands or if the harvest is good, it is very likely that the coffee price will sink as its supply will be abundant. Therefore, it is essential to consider the information about the market regularly as it can have a significant influence on the speculation of investors which will eventually define their demands and so the price of a stock. Consequently, it is very important to be able to determine how an announcement or any kind of information can affect the demand or even the supply of a stock, commodity, or financial instrument since this will define how markets will behave.

Special cases

However, it’s also important to mention that there are industries and situations in which the law of supply and demand does not apply. An instance of this is the luxury industry, in which the higher the product price set by firms, the higher the demand from consumers. This may be due to the value that costumers perceive by purchasing such items. Alternatively, oil is another example to be mentioned as its price has a low-price sensitivity which means that any change in its price won’t result in any significant demand changes, this could be due to the high necessity of oil in all industries which makes it crucial for daily operations.

Useful resources

Krugman, P. & Wells, R. (2012) Economics. 3rd edition. United States: Macmillan Learning.

Mankiw, G. (2016) The Market Forces of Supply and Demand (table of content) Principles of Economics. 8th edition. Boston: Cengage Learning.

Mankiw, G. (2016) The Market Forces of Supply and Demand (slides) Principles of Economics. 8th edition. Boston: Cengage Learning.

Deskara Supply and Demand: Law, Curves, and Examples

International Energy Agency (IEA) Supply and demand for oil

Sabiou M. Inoua and Vernon L. Smith The Classical Theory of Supply and Demand

About the author

The article was written in April 2022 by Diana Carolina SARMIENTO PACHON (ESSEC Business School, Master in Strategy & Management of International Business (SMIB), 2021-2022)

Risk Aversion

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Risk Aversion

Diana Carolina SARMIENTO PACHON

In this article, Diana Carolina SARMIENTO PACHON (ESSEC Business School, Master in Strategy & Management of International Business (SMIB), 2021-2022) explains the economic concept of risk aversion, which is key to understand the behavior of participants in financial markets.

Risk Aversion refers to the level of reluctance that an individual possesses towards risk. Specifically, it refers to the attitude of investors towards the risk underlying investments which will directly determine how portfolios are allocated or even how a stock may behave depending on market conditions. To elaborate, when market participants have higher risk aversion due to unfavorable market shocks e.g., natural disasters, bad news or scandals that affect a company or a security, this situation will cause a perception of higher risk leading to many selling, and thus decreasing prices. Therefore, risk aversion should be analyzed carefully.

Risk aversion and investor’s characteristics

It’s important to note that risk aversion can be highly variable over time as this notion changes along with investor profile, in other words with age, income, culture and other key factors, making it even more complex to evaluate than it appears in the traditional economics literature. To illustrate more accurately some of the factors that define an investor profile are:

Age

The older the person is, the more risk averse he or she is. On the contrary, younger individuals tend to be less risk averse which may be due to their high expectations and eagerness to attempt something new as well as the longer timeframe they have, whereas older people prefer safety and stability in their lives.

Income

Individuals with a smaller budget tend to have a higher risk aversion since they have fewer resources, and a loss would make a greater impact on them than a wealthy individual.

Past Losses

When an individual has already experienced some loss, she or he will be more wary of it since it’s now too costly to bear another loss; therefore, risk aversion will be significantly higher. An example of this is the post-crisis, as people have lost so much and this has had a negative impact on their lives, they tend to become more cautious of risk.

Investment Objective

For crucial events such as retirement or education, risk version tends to be higher as the individual cannot bear to risk for such a fundamental matter of his or her life.

Investment Horizon

Investors focused on short-term horizon tend to be more risk averse as they cannot take too much risk due to the short timeline.

Risk aversion and financial investments

Furthermore, risk aversion also takes into account more factors apart from those mentioned above, for this reason most of the time before creating the respective portfolio for an investor, financial advisors shape their client’s risk preferences in order to adjust the portfolio allocation to them. Many times, these can be conducted by questionnaires and tests that will accordingly assign a risk profile concluding with certain risk categories:

  • A Conservative profile refers to more risk averse individuals, the portfolios assigned for this type are mainly composed by both more secure & less volatile securities such as bonds, meanwhile stocks have a minimal participation.
  • A Moderate profile is attributed to more risk averse individuals who are willing to take more risk, however he or she does not want to step too much further. These portfolios are usually more diversified as they contain more types of securities in different percentages such as government & corporate bonds, and stocks.
  • An aggressive profile which is allocated to portfolios mainly composed in the highest percentage by the risky securities. For instance, the main securities could be stocks, specifically growth stocks or even crypto.

Due to all sensitive and private information used by financial institutions, financial regulatory entities are important to ensure the protection and transparency of information, thereby the Mifid (The Markets in Financial Instruments Directive) has been created in the European Union to fulfill such task through the use of rules and general standards.

Measure of risk of financial assets

Additionally, there are other mathematical metrics that can interfere in the risk profile, and depending on these the portfolio may be constructed:

Standard Deviation

It refers to the volatility of historical data, in other words how dispersed the data is over time which illustrates how risky the security may be. The higher the standard deviation, the higher the risk since this is suggesting that the stock is more variable and there is more uncertainty, thus a risk averse individual prefers a lower standard deviation.

Beta

It is linked with the systematic risk that comes with a stock, that is to say it illustrates the volatility compared to the market. Firstly, a beta equal to 1 indicates a volatility and movement equalizing the market, secondly a beta higher than 1 is referred to a security that is more volatile than the market, to illustrate B= 1.50 specifies 50% more volatility than the market. Thirdly, a beta less than 1 stipulates less volatility than the market. Therefore, the lower the beta the less risk exposure is found.

Modern Portfolio Theory & Risk

Introduced by Harry Markowitz in 1950s, the Modern Portfolio Theory illustrates the optimum portfolio allocation that maximizes return given a specific level of risk, in which risk is measured by the standard deviation and the return by the average mean of the portfolio. This explanation also leads to the one- single period mean-variance theory which suggests various portfolio allocations depending on the trade-off between return and risk. However, there are more advance models which explain this scenario in a multiperiod by rebalancing or diversifying further.

Risk aversion and economic conditions

Risk aversion does not only shape the portfolio allocation and its diversification, but it also may have a significant impact on the market as a result of expectations. When there are booming economic times, individuals usually feel more confident and thus less risk averse as a consequence of positive expectations of future cash flows; however, when a recession is coming investors may shift to a more risk averse behavior making them feel afraid of the future which influences them to sell certain stocks and, in this way, making the price plump. Although it may be seen as a simple emotion that defines the fear of risk, it still impacts in a very large extent the financial market as it dictates the roles and strategies behind investing, and thereby it is crucial analyze it carefully.

Related posts

   ▶ Youssef LOURAOUI Markowitz Modern Portfolio Theory

   ▶ Youssef LOURAOUI Implementing Markowitz asset allocation model

   ▶ Jayati WALIA Capital Asset Pricing Model (CAPM)

Useful resources

Díaz A and Esparcia C (2019) Assessing Risk Aversion From the Investor’s Point of View Frontiers in Psychology, 10:1490

Desjardins Online brokerage The Risk Aversion Coefficient

Coursera course Investment management

Crehana course Trading: How to invest in stocks (Trading: Como invertir en Bolsa)

About the author

The article was written in April 2022 by Diana Carolina SARMIENTO PACHON (ESSEC Business School, Master in Strategy & Management of International Business (SMIB), 2021-2022)

My experience as a junior market research analyst at Procolombia

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My experience as a junior market research analyst at Procolombia

Diana Carolina SARMIENTO PACHON

In this article, Diana Carolina SARMIENTO PACHON (ESSEC Business School, Master in Strategy & Management of International Business (SMIB), 2021-2022) shares her experience as a junior market research analyst in the investment department at Procolombia.

The company: Procolombia

Procolombia is the Colombian government entity that promotes direct investment, exports, and tourism into Colombia. The entity has several offices both in Colombia and in different countries around the world in order to reach to other governments, and thus facilitate negotiations. Examples of the locations of these offices are Paris, New York, London, Tokyo, Beijing, Dubai, Mexico, among others.

Procolombia is divided into the three departments: investment, exports and tourism.

Investment

Investment department whose main mission is to promote and bring into Colombia direct foreign investment. Examples of investments executed by the promotion of Procolombia are Amazon, Softbank, and Harley-Davidson.

Exports

Exports department with the main objective of promoting Colombian good across the world, and its main mission is accompanying and support exporters as well as contacting different public and private entities interested in Colombian products.

Tourism

Tourism department main focus is promote tourists into Colombia and expand the market share of the country in the Latin-American tourism.

The investment department organization and execution

Firstly, the investment department is divided into four regional hubs: North-America & the Caribbean, Latin-America, Europe-Middle East & Asia. Each hub is specialized in its respective region and market. Secondly, each hub has a general manager and usually 4/5 advisors specialized in a specific industry (Chemicals, Industries 4.0 which refers to AI/IoT/digitalization, Investment & Real Estate, Agro, Energy, etc.) which would facilitate the operations of the department so that every person is assigned with a specific region and industry.

The process of bringing investment

  • First of all, the investment advisors from Procolombia contact the respective firm/investor to create a very first contact, or the investor may contact Procolombia to obtain the very first information.
  • In the second place, if the investor is interested, he or she will ask for further information and probably require the specific opportunities available. For this purpose, the Colombians firms or projects looking for investing usually provide their basic financial information such as EBITDA, debt ratios, and the amount of money required, so that investors can have the primary financial information.
  • Once the investor shows more interest after having analyzed the basic financial metrics, there will be some factories and free-trade visits alongside meeting with the respective companies in order to gain deeper insight, and if they finally decide to invest, Procolombia will be supporting them in legal and tax matters to facilitate their investment journey in Colombia.

My internship Experience

I was specifically an intern of the North America management team. My main mission was supporting the team by providing market research of the potential investment opportunities as well as the possible investors that could be reached in order to promote the country in North America (US, Canada, and Caribbean countries). Additionally, I provided the consolidation of financial data about different Colombian companies and consolidated such information in such a way that it was understandable by potential investors.

Furthermore, I also had to support the logistics of the various events in which Procolombia looked to promote the country usually with very important high-level guests such as ambassadors, officials or investors looking for large investments, experience that showed how negotiation among different countries were conducted and how was the planning of such plans executed.

Skills needed

This internship required computational abilities with the purpose of comprehending the data and financial information of companies along with rapid analytical skills that can synthetize and summarize such information efficiently.

Regarding soft skills: team oriented and adaptability are crucial as operations are most of the time executed by sharing diverse opinions and agreeing with others which requires the wiliness to work and listen carefully. Besides, confronting different situation which may be one’s out of comfort zone is also a very common situation in the workplace, thereby it’s essential to be open to different challenges and situations as new issues can arise at any moment.

Financial Concepts

Even though my internship was more focused on the promotion of foreign direct investments in Colombia, I was still able to have direct contact with some financial concepts that were used regularly in the running of the entity, such as Ebitda and Debt Ratio.

Foreign direct investment (FDI)

Foreign direct investment (FDI) indicates the transfer of foreign capital into an entity or organization with a long-term vision. For instance, when an American or European multinational corporation invests in Colombia with the aim of opening facilities in this country in order to facilitate the operations in the region, and probably improve profitability. An example of this is P&G, Henkel or L’Oréal, companies that invested in the country in such a way that the performance both in Colombia and Latin America becomes more efficient in addition to providing employment, development and technology .

EBITDA

EBITDA refers to Earnings Before Interest, Taxes, Depreciation & Amortization. In other words, it’s the operating income however the non-cash costs such depreciation & amortization are added. It is usually used because it reflects the earnings from operations and the efficiency of them.

Net debt

All the debt (long-term + shot-term) – all cash & equivalents which would indicate what they company still owed in case of liquidation

Solvency ratios

Solvency ratios were usually used so that the investor could know the debt state of the company such as debt ratio = Total obligations/ Total Liabilities indicating how much financial leveraged the company has. The higher it is, the riskier the company may be, e.g., a 0.5 debt ratio indicated that 50% of the firm assets are financed by debt.

Thus, this experience also helped to shape some basic financial knowledge in real life situations and even taught me the importance of understanding financial concept as even if they are not directly in our expertise, they will always be the base of discussions in the business world.

Related posts on the SimTrade blog

   ▶ All posts about Professional experiences

   ▶ Anna BARBERO Career in finance

   ▶ Akshit GUPTA Green bonds

Useful resources

Procolombia

About the author

The article was written in April 2022 by Diana Carolina SARMIENTO PACHON (ESSEC Business School, Master in Strategy & Management of International Business (SMIB), 2021-2022)

A quick overview of the Bloomberg terminal…

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A quick overview of the Bloomberg terminal…

Louis DETALLE

In this article, Louis DETALLE (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023) explains everything there is to know about the Bloomberg terminal which is a must-know in finance.

How to use the main functions of the Bloomberg terminal?

One may notice that the keyboard of the Bloomberg terminal is a little strange. Indeed, this keyboard called Starboard, and contains red, blue, green and yellow keys for specific functions in addition to your regular keys.

Functions are unique Bloomberg applications that provide analysis and information on securities,
sectors, regions and more. Each function is accessed by typing in its unique mnemonic (a short, memorable name) and then pressing the key located in the lowest-right sided area of the keyboard.

Let’s review together the different functions of the buttons:

The HELP button is perhaps the most useful button for those just starting out. If you have questions about anything on the terminal, simply press the button once and a Bloomberg specialist will be there to start a live chat with you to resolve your questions.

In order to benefit from the latest news, users can simply type NEWS and press enter to get the latest information on market trends, movements and other relevant news.

Those in the finance industry chat via Bloomberg Messaging, which is essentially equivalent to Facebook Messenger but on Bloomberg. It enables you to send a message to anyone on the device. This means that anyone in the industry can technically contact each other instantly. No need to ask for someone else’s number or find out the best way to get in touch.

Main users of the Bloomberg terminal

Traders, brokers, analysts, portfolio managers, investors and executives are the Terminal’s primary consumer base as they need to access the data provided by Bloomberg easily in order to do their job.

A subscription to the Bloomberg Terminal costs approximately $20,000 a year, but that does not stop its customers from renewing their subscriptions because of its usefulness.

Training webinars

First and foremost, the Bloomberg beginner should work on the document available on Bloomberg website, Getting started on the Bloomberg Terminal, which will give you the main information on the keys and their function.

The best next step to get used to the Bloomberg Terminal is to complete the certification
course: Bloomberg Market Concepts (BMC). BMC is an 8-hour e-learning course that will
provide a visual introduction to the financial markets and covers nearly 70 Terminal functions which is enough for whoever wants to start using Bloomberg.

Related posts on the SimTrade blog

   ▶ Louis DETALLE The importance of data in finance

   ▶ Louis DETALLE Reuters

   ▶ Louis DETALLE Bloomberg

Useful resources

Bloomberg’s website

Capital Markets (BMC) Certification’s website

About the author

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

Bloomberg

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Bloomberg

Louis DETALLE

In this article, Louis DETALLE (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023) explains everything there is to know about Bloomberg LP, the international leader in the data-providing market…

Quick presentation of the company

Bloomberg LP is an American financial group specialized in services to financial market professionals and in economic and financial information. Bloomberg operates as a news agency and via numerous media such as TV, radio, press, internet, and books. The company was founded in 1981 by Michael Bloomberg, former mayor of New York City.

In its early days, Bloomberg LP’s activities were only based on the exploitation of a historical database of US Treasury yield curves, bought by the founder to its former employer, the investment bank Salomon Brothers. After that, the company added on its terminals a messaging system, retransmissions of financial assets’ prices and developed financial news flows long before the watershed of Internet.
In 1990, Michael Bloomberg installed his 1,000th terminal.

Type of people working at Bloomberg (types of jobs)

The careers available at Bloomberg LP are numerous and very diverse. The Board’s needs in terms of employees mainly consist of software designers to help design the Bloomberg’s terminals, sectorial financial specialists in order to provide precise and adequate analysis.
Finally, the last kind of profiles that Bloomberg needs are journalists and more broadly, people with great writing abilities since Bloomberg LP produces a huge flow of written articles every day. Bloomberg News for instance (one of many Bloomberg LP’s subsidiaries) has over 10 000 employees which gives an idea of the written flow emitted by the company.

Main competitors

As Bloomberg’s activities are very diverse, we will classify the main competitors of the American firm in respect to the activities.
For Bloomberg’s core business, which is the terminals, Thomson Reuters is the most natural competitor in this space (with products like Kobra, Eikon, D3000). The terminal business is built on a fantastic technology platform that provides comprehensive financial information. There are other competitors, such as Dow Jones Industrial Average FX Trader, which have specialized in one type of industry whereas Bloomberg remains a generalist.

Bloomberg’s editorial branch’s main competitors would be Reuters, the FT, the Wall Street Journal, and other traditional financial news companies. The same goes for their TV/radio operation (their competitor would be CNBC).

Use of data in financial markets

The explosion of financial data, enabled by the Internet tremendous potential, caused an explosion of demand for financial data. As evidenced in 2006 by the British mathematician and Tesco marketing mastermind Clive Humby’s quote, “Data is the new oil”, the data market seems to be limitless.

In addition, as Bloomberg acquires many of his competitors, such as BNA and BusinessWeek, this contributes to curbing the number of data providers and improving the monopoly of Bloomberg on the data-providing market.

Useful resources

Bloomberg

Thomson Reuters

Related posts on the SimTrade blog

   ▶ Louis DETALLE Understand the importance of data providers and how they influence global finance…

   ▶ Louis DETALLE Reuters

About the author

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

Specific risk

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Youssef_Louraoui

In this article, Youssef LOURAOUI (Bayes Business School, MSc. Energy, Trade & Finance, 2021-2022) explains the specific risk of financial assets, a key concept in asset pricing models and asset management in practice.

This article is structured as follows: we start with a reminder of portfolio theory and the central concept of risk in financial markets. We then introduce the concept of specific risk of an individual asset and especially its sources. We then detail the mathematical foundation of risk. We finish with an insight of the relationship between diversification and risk reduction with a practical example to test this concept.

Portfolio Theory and Risk

Markowitz (1952) and Sharpe (1964) created a framework for risk analysis based on their seminal contributions to portfolio theory and capital market theory. All rational profit-maximizing investors attempt to accumulate a diversified portfolio of risky assets and borrow or lend to achieve a risk level consistent with their risk preferences given a set of assumptions. They established that the key risk indicator for an individual asset in these circumstances is its correlation with the market portfolio (the beta).

The variance of returns of an individual asset can be decomposed as the sum of systematic risk and specific risk. Systematic risk refers to the proportion of the asset return variance that can be attributed to the variability of the whole market. Specific risk refers to the proportion of the asset return variance that is unconnected to the market and reflects the unique nature of the asset. Specific risk is often regarded as insignificant or irrelevant because it can be eliminated in a well-diversified portfolio.

Sources of specific risk

Specific risk can find its origin in business risk (in the assets side of the balance sheet) and financial risk (in the liabilities side of the balance sheet):

Business risk

Internal or external issues might jeopardize a business. Internal risk is directly proportional to a business’s operational efficiency. An internal risk would include management neglecting to patent a new product, so eroding the company’s competitive advantage.

Financial risk

This pertains to the capital structure of a business. To continue growing and meeting financial obligations, a business must maintain an ideal debt-to-equity ratio.

Mathematical foundations

Following the Capital Asset Pricing Model (CAPM), the return on asset i, denoted by Ri can be decomposed as

img_SimTrade_return_decomposition

Where:

  • Ri the return of asset i
  • E(Ri) the risk premium of asset i
  • βi the measure of the risk of asset i
  • RM the return of the market
  • E(RM) the risk premium of the market
  • RM – E(RM) the market factor
  • εi represent the specific part of the return of asset i

The three components of the decomposition are the expected return, the market factor and an idiosyncratic component related to asset only. As the expected return is known over the period, there are only two sources of risk: systematic risk (related to the market factor) and specific risk (related to the idiosyncratic component).

The beta of the asset with the market is computed as:

Beta

Where:

  • σi,m : the covariance of the asset return with the market return
  • σm2 : the variance of market return

The total risk of the asset measured by the variance of asset returns can be computed as:

Decomposition of total risk

Where:

  • βi2 * σm2 = systematic risk
  • σεi2 = specific risk

In this decomposition of the total variance, the first component corresponds to the systematic risk and the second component to the specific risk.

Decomposition of returns

We analyze the decomposition of returns on Apple stocks. Figure 1 gives for every month of 2021 the decomposition of Apple stock returns into three parts: expected return, market factor (systematic return) and an idiosyncratic component (specific return). We used historical price downloaded from the Bloomberg terminal for the period 1999-2022.

Figure 1. Decomposition of Apple stock returns:
expected return, systematic return and specific return.
Decomposition of asset returnsComputation by the author (data: Bloomberg).

You can download below the Excel file which illustrates the decomposition of returns on Apple stocks.

Download the Excel file for the decomposition of Apple stock returns

Why should I be interested in this post?

Investors will be less influenced by single incidents if they possess a range of firm stocks across several industries, as well as other types of assets in a number of asset classes, such as bonds and stocks. 

An investor who only bought telecommunication equities, for example, would be exposed to a high amount of unsystematic risk (also known as idiosyncratic risk). A concentrated portfolio can have an impact on its performance. This investor would spread out telecommunication-specific risks by adding uncorrelated positions to their portfolio, such as firms outside of the telecommunication market.

Related posts on the SimTrade blog

   ▶ Louraoui Y. Systematic risk and specific risk

   ▶ Louraoui Y. Systematic risk

   ▶ Louraoui Y. Beta

   ▶ Louraoui Y. Portfolio

   ▶ Louraoui Y. Markowitz Modern Portfolio Theory

   ▶ Walia J. Capital Asset Pricing Model (CAPM)

Useful resources

Academic research

Evans, J.L., Archer, S.H. 1968. Diversification and the Reduction of Dispersion: An Empirical Analysis. The Journal of Finance, 23(5): 761–767.

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.

Tole T.M. 1982. You can’t diversify without diversifying. The Journal of Portfolio Management. Jan 1982, 8 (2) 5-11.

About the author

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

Systematic risk

by

Youssef_Louraoui

In this article, Youssef LOURAOUI (Bayes Business School, MSc. Energy, Trade & Finance, 2021-2022) presents the systematic risk of financial assets, a key concept in asset pricing models and investment management theories more generally.

This article is structured as follows: we introduce the concept of systematic risk. We then explain the mathematical foundation of this concept. We present an economic understanding of market risk on recent events.

Portfolio Theory and Risk

Markowitz (1952) and Sharpe (1964) developed a framework on risk based on their significant work in portfolio theory and capital market theory. All rational profit-maximizing investors seek to possess a diversified portfolio of risky assets, and they borrow or lend to get to a risk level that is compatible with their risk preferences under a set of assumptions. They demonstrated that the key risk measure for an individual asset is its covariance with the market portfolio under these circumstances (the beta).

The fraction of an individual asset’s total variance attributable to the variability of the total market portfolio is referred to as systematic risk, which is assessed by the asset’s covariance with the market portfolio. Systematic risk can be decomposed into the following categories:

Interest rate risk

We are aware that central banks, such as the Federal Reserve, periodically adjust their policy rates in order to boost or decrease the rate of money in circulation in the economy. This has an effect on the interest rates in the economy. When the central bank reduces interest rates, the money supply expands, allowing companies to borrow more and expand, and when the policy rate is raised, the reverse occurs. Because this is cyclical in nature, it cannot be diversified.

Inflation risk

When inflation surpasses a predetermined level, the purchasing power of a particular quantity of money reduces. As a result of the fall in spending and consumption, overall market returns are reduced, resulting in a decline in investment.

Exchange Rate Risk

As the value of a currency reduces in comparison to other currencies, the value of the currency’s returns reduces as well. In such circumstances, all companies that conduct transactions in that currency lose money, and as a result, investors lose money as well.

Geopolitical Risks

When a country has significant geopolitical issues, the country’s companies are impacted. This can be mitigated by investing in multiple countries; but, if a country prohibits foreign investment and the domestic economy is threatened, the entire market of investable securities suffers losses.

Natural disasters

All companies in countries such as Japan that are prone to earthquakes and volcanic eruptions are at risk of such catastrophic calamities.

Following the Capital Asset Pricing Model (CAPM), the return on asset i, denoted by Ri can be decomposed as

img_SimTrade_return_decomposition

Where:

  • Ri the return of asset i
  • E(Ri) the risk premium of asset i
  • βi the measure of the risk of asset i
  • RM the return of the market
  • E(RM) the risk premium of the market
  • RM – E(RM) the market factor
  • εi represent the specific part of the return of asset i

The three components of the decomposition are the expected return, the market factor and an idiosyncratic component related to asset only. As the expected return is known over the period, there are only two sources of risk: systematic risk (related to the market factor) and specific risk (related to the idiosyncratic component).

The beta of the asset with the market is computed as:

Beta

Where:

  • σi,m : the covariance of the asset return with the market return
  • σm2 : the variance of market return

The total risk of the asset measured by the variance of asset returns can be computed as:

Decomposition of total risk

Where:

  • βi2 * σm2 = systematic risk
  • σεi2 = specific risk

In this decomposition of the total variance, the first component corresponds to the systematic risk and the second component to the specific risk.

Systematic risk analysis in recent times

The volatility chart depicts the evolution of implied volatility for the S&P 500 and US Treasury bonds – the VIX and MOVE indexes, respectively. Implied volatility is the price of future volatility in the option market. Historically, the two markets have been correlated during times of systemic risk, like as in 2008 (Figure 1).

Figure 1. Volatility trough time (VIX and MOVE index).
Volatility trough time (VIX and MOVE index)
Sources: BlackRock Risk and Quantitative Analysis and BlackRock Investment Institute, with data from Bloomberg and Bank of America Merrill Lynch, October 2021 (BlackRock, 2021).

The VIX index has declined following a spike in September amid the equity market sell-off. It has begun to gradually revert to pre-Covid levels. The periodic, albeit brief, surges throughout the year underscore the underlying fear about what lies beyond the economic recovery and the possibility of a wide variety of outcomes. The MOVE index — a gauge of bond market volatility – has remained relatively stable in recent weeks, despite the rise in US Treasury yields to combat the important monetary policy to combat the effect of the pandemic. This could be a reflection of how central banks’ purchases of government bonds are assisting in containing interest rate volatility and so supporting risk assets (BlackRock, 2021).

The regime map depicts the market risk environment in two dimensions by plotting market risk sentiment and the strength of asset correlations (Figure 2).

Figure 2. Regime map for market risk environment.
Regime map for market risk environment
Source: BlackRock Risk and Quantitative Analysis and BlackRock Investment Institute, October 2021 (BlackRock, 2021).

Positive risk sentiment means that riskier assets, such as equities, are outperforming less risky ones. Negative risk sentiment means that higher-risk assets underperform lower-risk assets.

Due to the risk of fast changes in short-term asset correlations, investors may find it challenging to guarantee their portfolios are correctly positioned for the near future. When asset correlation is higher (as indicated by the right side of the regime map), diversification becomes more difficult and risk increases. When asset prices are less correlated (on the left side of the map), investors have greater diversification choices.

When both series – risk sentiment and asset correlation – are steady on the map, projecting risk and return becomes easier. However, when market conditions are unpredictable, forecasting risk and return becomes substantially more difficult. The map indicates that we are still in a low-correlation environment with a high-risk sentiment, which means that investors are rewarded for taking a risk (BlackRock, 2021). In essence, investors should use diversification to reduce the specific risk of their holding coupled with macroeconomic fundamental analysis to capture the global dynamics of the market and better understand the sources of risk.

Why should I be interested in this post?

Market risks fluctuate throughout time, sometimes gradually, but also in some circumstances dramatically. These adjustments typically have a significant impact on the right positioning of a variety of different types of investment portfolios. Investors must walk a fine line between taking enough risks to achieve their objectives and having the proper instruments in place to manage sharp reversals in risk sentiment.

Related posts on the SimTrade blog

   ▶ Louraoui Y. Systematic risk and specific risk

   ▶ Youssef LOURAOUI Specific risk

   ▶ Youssef LOURAOUI Beta

   ▶ Youssef LOURAOUI Portfolio

   ▶ Youssef LOURAOUI Markowitz Modern Portfolio Theory

   ▶ Jayati WALIA Capital Asset Pricing Model (CAPM)

Useful resources

Academic research

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

BlackRock, 2021. Market risk monitor

About the author

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

Financial products marketing in neobanks

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Financial products marketing in neobanks

Cynthia LIN

In this article, Cynthia LIN (ESSEC Business School, Global BBA, 2018-2022) explains how neobanks market their financial products.

Introduction

Marketing of financial products in neobanks has become an integral part of their daily tasks because of the increased awareness of the benefits of financial products and services to consumers. Also, the marketing has been defined as the act of persuading a potential customer to purchase a financial product, service, or service in a neobank.

Neobanks

A Neobank is a kind of digital bank without any physical branches. These are completely operated online. Consumers don’t have to be physically present at a specific location. They are usually mobile operated, leveraging technology to minimize operating costs and offer a customer-friendly interface.

Neobanks can be called fintech firms that provide digital and mobile-first financial solutions payments and money transfers, money lending, and more. The neobank tends to work as a tech-savvy decision-making model and handle cash more easily and openly. These banks gather and assess data, understand the trends, try to quantify the behavior of their customers, and then come out with predictions/results. They are cheaper, quicker, and can leverage a single network with the entire financial portfolio.

Neobanks are quick, transparent, and more reliable than megabanks, although neobanks prefer collaborating with them for financial products. Neobanks are also known as challenger banks. They have transformed banking sectors and made them appealing to millions of customers. For example, the U.S. confirmed that chime had over 11 million customers (Corander, 2021). The previous year, they had eight million customers; in response, other banking sectors are working extra hard to improve their products and services to survive the competition. They make money by using business models different from those used in banking institutions.

Financial product marketing

Financial product marketing refers to a range of marketing solutions tailored to the needs of financial services companies. Highly effective financial product marketing uses digital channels to promote new financial products and increase brand awareness.

Here are 5 strategies for financial product marketing:

  1. Go mobile with your financial product marketing initiatives: your mobile experience should provide the same ease-of-use and functionality as your website.
  2. Make social media an important tool: understand your target audience and post the right content on the right channel to maximize engagement.
  3. Create educational content: simple educational content that is clear and easily understood by people who may not have prior knowledge about finances.
  4. Invest in emerging technologies: disruptive technologies like blockchain, chatbots and artificial intelligence (AI) help financial companies reduce operational costs and gain new clients.
  5. Bet on transparency: customers who believe your company is transparent are more likely to recommend your services or leave positive online reviews.

Marketing of financial products by neobanks

The marketing of financial products by neobanks include advertising and marketing strategies to promote financial products and services that neobanks offer to the market. The marketing of financial products and services in neobanks has increased the interest of consumers in financial products and services offered by neobanks. The increased adoption of these marketing techniques has also increased the neobank’s adoption rate of financial products and services (Zoi, 2021). Moreover, adopting marketing strategies has enabled neobanks to use various techniques to market financial products and services to consumers.

Neobank’s marketing strategies includes television advertisement, radio advertisement, newspaper advertisement, direct advertising, online advertising, sales promotion, sales promotion, direct selling, and personal selling. This have been felt through the increased interest, awareness, and patronage toward banks and products offered by neobanks.

Let’s take the example of Revolut:

  • Customer is King: Revolut are aiming to be very transparent and open by sharing a lot of company insights on their blog
  • Solving a real issue: Focus on building a state-of-art platform from the ground up with design, functionality, and speed at its core.
  • Unique buying experience: Provide a very distinctive and innovative product packaging.
  • Hassle-free onboarding process: Revolut focused on eliminating all unnecessary touch points and providing a fast connection with minimal data collection.
  • Turn clients into fans and salesmen: Revolut launched a merch line of branded products including t-shirts, hats, hoodies etc. to turn clients into fans.

Conclusion

In conclusion, neobanks use various marketing techniques, usually more digital-oriented in order to market their financial products and services. They use marketing strategies to develop new products, services, and strategies to improve their brand image and awareness. Furthermore, neobanks should be able to use a marketing strategy to attract new customers and maintain the loyalty of existing customers for them to be able to compete with other banks.

Ressources

Zoi, S. (2021). FinTech and digital transformation in financial services: a new digital financial world (Master’s thesis, Πανεπιστήμιο Πειραιώς).

Corander, B. (2021). Neobanks: Challenges, Risks and Opportunities.

Revolut

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   ▶ Ashima MALIK Financial products marketing

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About the author

The article was written in March 2022 by Cynthia LIN (ESSEC Business School, Global BBA, 2018-2022).

Financial communication in family firms

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Financial communication in family firms

Cynthia LIN

In this article, Cynthia LIN (ESSEC Business School, Global BBA, 2018-2022) presents the importance of financial communication in family firms.

Introduction

Financial communication is mainly used by companies that publicly trade their shares on a stock exchange, to interface with actual and potential financial stakeholders such as retail and institutional investors and financial analysts. The prevailing belief is that unlisted, family-owned companies do not need to publish financial information, except for their statutory accounts. This leads us to ask to what extent this is the case.

Communication within the firm is an integral part of communication in a family, as the family is the primary unit that makes up the firm. Also, family businesses can only be operated by a direct relative. While family firms are usually successful, they are still family businesses, and therefore, the family is not likely to share the fruits of its success.

Thus, family firms need to have a clear communication strategy in place. The purpose of a family business communication strategy is to ensure that the core members of the company stay in communication. The family firm communication strategy is also crucial because each firm has different communication needs. Some may need more face-to-face communication, while others may need more written communication or phone or email communication.

Financial communication

In general, the framework that organizes the financial reporting process has several elements. The most important element of financial reporting is the content of the information. There is a natural tension between the desire of managers to provide complete information and the need to be cautious about the extent of disclosure. Historical quantitative financial data is generally not a concern, as companies are legally required to provide such data in their financial statements. Despite increasing attempts to limit companies’ discretion, the regulations still give them considerable leeway in reporting their results. Therefore, companies should go beyond the basics and provide useful supplements and interpretations.
Family businesses are generally more reluctant to disclose forward-looking information, which can help to highlight the impact of a new strategic decision on financial data, because they need to protect themselves from litigation in case, they provide ammunition for stakeholders to take legal action, or because they fear that information will leak to competitors.

Another important element that has a significant impact on financial reporting is the composition of stakeholders. Companies interact with many stakeholders, and the objective of financial communication is to inform these stakeholders about the economic situation of the company. The optimal method of communication depends on the composition of the stakeholders, although equity investors are often considered to be the main recipients of such communication.

Two other factors can potentially influence financial communication. These factors are the visibility of the company and the credibility of the management. Low visibility of the company, due to low awareness of its existence, limits the group that can be influenced by a disclosure package. Even a perfectly designed information package is ineffective if it does not reach the target group. On the other hand, a lack of credibility on the part of management can reduce the response to financial communication, even if the problems of information content and visibility have been overcome. When outsiders have confidence in management, they are much more likely to accept management’s interpretation of the company’s current situation.

Why communication is important in family firms?

Good communication is not just a matter of transmitting information between interested parties. It starts with a clear understanding of the family business’ objectives, strategies and roles, relayed through the most acceptable and effective channels to convey the desired message, and follow-up to ensure that understanding.

The family narrative, for example, is an important, relatively formal communication tool that links family history, culture, goals and strategy.

The unique characteristics of family businesses, including concentrated ownership in the hands of a controlling family and family involvement in the management or running of the business, limit the need for detailed financial disclosure. Influential shareholders (the controlling family) have all the information they need to assess the risk and return of their investment in the company and to make investment decisions. Family-controlled companies usually provide little external information, as the main investor (the family) already has this information. In addition, family shareholders generally act like entrepreneurs who want to keep their competitive advantage secret.

Family business: the case of Auchan

The Auchan holding company, for example, is an unlisted company and is heavily owned by the family. The company must regularly refinance itself on the debt markets and has a credit rating from Standard & Poor’s. Financial communication at Auchan is done regularly on a voluntary basis in order to give the financial community a better view of the company and its plans.

In general, management has a wealth of information on the company’s activities and economic situation. They must decide what information should be included in the financial communication and how to communicate it in the most transparent way to a wide range of internal and external stakeholders. In family businesses, concern for the reputation and long-term viability of the business requires particular attention to financial reporting. It is therefore worth examining whether the components of financial communication in family businesses differ from those in non-family businesses.

Conclusion

In conclusion, family businesses can benefit from a communication strategy, and family members must be made aware of the strategy. Additionally, to be effective, the communication strategy must be communicated, and family members must adopt the strategy simultaneously. Also, family firms’ communication strategies must consider the communication needs of the different family members. The information must be delivered to the right people, in the right way, at the right time, with the appropriate level of detail.

Useful resources

Ferramosca, S. and Ghio, A., 2018. Accounting choices in family firms. An Analysis of Influences and Implications. Cham: Springer International Publishing.

Campden FB (Family in Business)

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About the author

The article was written in March 2022 by Cynthia LIN (ESSEC Business School, Global BBA, 2018-2022).

My experience as a junior financial analyst at ACE

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My experience as a junior financial analyst at ACE

William LONGIN

In this article, William LONGIN (EDHEC Business School, Global BBA, 2020-2024) shares his experience as a junior financial analyst at ACE Finance et Conseil, which is a wealth management firm specialized in financial investments.

ACE Finance et Conseil

First, let me present ACE Finance et Conseil. It is a wealth management firm created by Gabriel Eschbach in 2002. It is located in Strasbourg in the East of France. ACE Finance et Conseil currently manages a portfolio of 230 clients who are individual investors. The profile of these investors varies in terms of wealth and investment objectives. Most of the clients of ACE Finance et Conseil are living in the East of France, especially in the Strasbourg area. The ambition of the company is to expand its base of clients at the national and even international level.

Logo of ACE Finance et Conseil.
Logo of ACE Finance et Conseil
Source: ACE Finance et Conseil.

The founder of the company, Gabriel Eschbach, is a graduate student from the University of Strasbourg. Gabriel also attended a program in wealth management at ESSEC Business School. Building on his past professional experience in large financial institutions and insurance companies, he has developed extensive skills and knowledge on financial markets and asset management.

My personal experience at ACE Finance et Conseil

My job was to find relevant information on the firms of interest for ACE. To find such information, I used the Bloomberg Terminal. Beyond the search of information about companies, I also spent time on building a portfolio based on our current knowledge of the market conditions. During my internship, the stock market was bullish (Summer 2021). ACE’s strategy was to find the most interesting stocks based on the risk level that the firm was willing to take on behalf of its clients.

Bloomberg – Terminal and keyboard
Bloomberg terminal and keyboard
Source: Bloomberg.

My most valuable experience in the firm was to be able to understand the investment philosophy of the firm, which relied on a rigorous analysis of the relationship between risk and (expected) return on the one hand, and on a clear understanding of the investors profile of its clients on the other hand.

Everything is planned! And what I came to realize is that investing has nothing to do with gambling. No technical analysis, no gibberish, only careful analysis of companies through the fundamental analysis of their financial accounts (balance sheet and income statement), financial ratios and company news. As we are unable to predict the future, ACE has an investment philosophy based on the rigorous investment process combining the analysis of the relationship between risk and (expected) return of financial assets and a clear understanding of the risk profile of its clients on the other hand.

The ACE Finance Conseil team.
The ACE Finance Conseil team
Source: ACE Finance et Conseil.

Core missions and duties

During my internship I had to do research on companies and create short presentations for ACE clients. For example, I prepared presentations on Chinese companies for a new client who was not familiar with the Chinese stock market. The Chinese companies involved were the so-called BATX that stands for Baidu, Alibaba, Tencent and Xiaomi. My presentations’ focus was on Tencent and Alibaba, two companies that stroked our interest at that time. The Bloomberg Terminal gave information about the profits made by each business units of the company, and its future estimates. Unlike other sources of information, Bloomberg standardizes information about the different drivers that generate revenue in a company. This gives an excellent overview of the current state of the company in addition to the existing important financial indicators such as the P/E and EPS ratios, the working capital, and the quick ratio (these financial indicators are defined below).

At ACE everyday was a different day, I had many types of missions. Every morning, I prepared a morning briefing. This allowed me to learn many things on the link between political news and companies. I really enjoyed the diversified aspect in my work, and I hope to find a job where I can thrive the same way I did at ACE Finance et Conseil.

About the skills and knowledge

For this type of internship, the prerequisites were to know how to read financial statements as well as knowing what the key financial indicators are, how they are calculated, and how they can be interpreted. Being able to browse the internet with ease and to be familiar with financial tools like the Bloomberg Terminal were important to be efficient in the job. Have an interest in the geopolitical field was an advantage to be able to interpret the news and extract the important information that would affect the economic world and the value of companies.

At ACE I understood that there is a whole other side to the iceberg, companies that are focused on b2b sales (business to business) that play a major role in the economy. These companies in the shadows are mostly part of a supply chain for major b2c (business to consumer) whose brand is known by the public. b2b businesses rarely make it on to the front pages of mainstream news medias but a lot of information is available on media for investors.

Unfamiliar with the region of the East of France I learned many things on the culture and way of living in an anchored European city. Strasbourg is considered as a capital of Europe as it hosts major European institutions such as the European Parliament and the Council of Europe. Because of its ties to both Germany and France after World War II, Strasbourg served as a symbol of reconciliation between peoples.

Key concepts

I present below some key concepts that are useful to understand the internship that I did at ACE Finance et Conseil.

Asset management

Asset management consists in managing capital in the best way by respecting the level risk decided to be taken by the manager, with respect to an estimated rate of return. The responsibility of asset management’s firm is to know how to invest and manage assets correctly and accurately.

ACE Finance provides private investors with more comprehensive advice as part of their investment advisory services and fully documents discussions. The objective is to create transparency regarding the costs and risks associated with their investments. With ACE, clients can module their portfolios and are able to express their preferences after receiving advise from the firm based on fundamental research.

Asset allocation

Asset allocation is a step-in asset management which consists in defining the weight to be given to each category of assets within an investment portfolio. Allocation is generally made by sector (cyclical, defensive, sensitive), by profile (growth, value), by geography and/or by asset class (equities, bonds, real estate, commodities, etc.)

In determining the best asset allocation, the key is to be able to balance between the expected return on assets and the riskiness associated with each of them. Asset allocation depends on the time the investor is intending to invest his/her assets, his/her tolerance for risk and the volatility of the various assets.

As mentioned earlier ACE accompanies clients in their investment and gives them the opportunity to have a say on the way of allocating assets. The level of risk, the geographical or sectoral distribution of the portfolios and the type of products used, or the time horizon of the investments is different specific to each client.

Example of equity portfolio.
Example of equity portfolio
Source: ACE Finance et Conseil.

Active and passive asset allocation

There are two types of asset allocation management styles: passive and active. Passive management is management based on a buy-and-hold strategy. Active management is based on rebalancing of the portfolio via discretionary decisions or decisions based on quantitative models. Stock picking and market timing are key to a successful active management.

ACE is mostly focused on active management of assets. The goal in active management of assets is to be able “beat the market”, the benchmark. The work done by ACE is to select the assets, using various analysis tools, the mostly likely assets that are likely to grow faster than the benchmark and market in general. This management method, as opposed to passive management, concerns all funds and portfolios that do not aim to reproduce the performance of a reference market, but to do better than the reference market.

Stock picking

Stock picking is a methodic process were an investor searches for stocks that are likely to bring future cash flows. The analyst’s or investor’s view for the price of the stock will determine whether the position is long or short.

When it comes to stock picking ACE does research on various companies and keeps track of the news. The financial statements (balance sheet, income statement and cash flow statement) with the focus on key business indicators (sales and profits) are important to understand the structural investments in the company. ACE also pays great attention to key financial ratios such as: P/E, EPS, working capital, quick ratio, and the EBITDA.

ACE also has partner companies such as JP Morgan and Gemway Equity that collaborate with ACE in this process. Getting insight and trying to understand other people point of view is part of the culture at ACE and how it has done so well for these past 20 years.

Financial indicators

EPS ratio

The Earnings Per Share (EPS) ratio is a financial ratio that shows the amount of net profit that a stock can generate. To calculate the EPS, we divide the total earnings (net income) of the company by the number of outstanding shares issued by the company (or average of outstanding shares).

Earnings per share formula

Note that if the company issued common and preferred shares, the EPS ratio is adjusted to take into account the preferred dividends. The EPS can be positive or negative based on the positive or negative earnings (profits or loss). In case of a negative EPS the company in question does not present a profitable overall activity. However, having a negative EPS is not as rare as you might think. As firms are not always making a profit due to heavy investment (start-ups for example). A company which presents a very fluctuating EPS from one year to another or an EPS which does not stop decreasing from year to year, could cause the downfall of the stock price.

At ACE, the EPS is a ratio that we looked at as an indicator of where the wind was blowing but did not base our decisions uniquely on this ratio since it does not look at the investments made by the firm that could generate important future cash flows.

P/E ratio

The price to earnings ratio (P/E or PER) is an indicator used in stock market analysis. The calculation of PER is very straightforward, divide the market capitalization by the net earnings or by dividing the market price of a share by the earnings er share (EPS). Another way of calculating it is by dividing the individual price of a share by the net income per share. You can calculate PER based on quarterly and yearly results and even projected results which would give the expected PER ratio.

Price earnings ratio (PER)

The PER represents the number of years it would take for a company to buy all its stocks. For example, a PER of 20 means that a company would take 20 years to “redeem” all its floating capital with constant profits.

This indicator can be used to evaluate a company to its competitors despite their differences in size as it looks at firm valuation according to their profits. A lower PER indicates a cheap stock, a higher PER an expensive stock.

Analysts may consider two types of PER: the trailing PER and forward PER. Simply put, the trailing PER looks at historical earnings to calculate PER. The forward PER considers expected earnings.

Bloomberg Terminal – Relative value function (RV) – Baidu – 14.06.2021
Bloomberg RV function
Source: Bloomberg.

The RV function on the Bloomberg Terminal gives us indications on the relative value of the firm. At ACE when doing some research on Baidu, the PER was one of lowest amongst its competitors. The value of the PER is important as it reflects investors’ expectations. Thus, the PER can reveal the speculations of investors, who anticipate a strong increase in future profits: in which case, the higher the PER, the greater the expected increase in profits. So it is important to monitor and the progress of the PER.

Working capital

Working capital is an accounting concept which represents the amount the business has available to pay total operating expenses such as suppliers and employees. This indicator gives information on the company’s ability to cover its expenses.

Working capital

Quick ratio or Acid test

The quick ratio, or acidity test, is used to determine short-term liquidity in a company. To calculate this ratio, the value of the company’s current assets, excluding inventory, is divided by the company’s current liabilities (see formula). The goal of an acid test is to estimate the financial stability of a firm by measuring the company’s ability to immediately pay its debts using cash.

Acid test ratio

Assets used to calculate the quick ratio include cash and other very liquid assets such as marketable securities and accounts receivables. Inventory is also excluded from the quick ratio formula because it cannot be sold immediately to generate cash flow.

EBITDA

EBITDA (earnings before interest, taxes, depreciation, and amortization) is an indicator that is used to compare companies on their potential ability to generate wealth regardless of the balance sheet differences. EBITDA does not consider the investment and financing policy and the impact of taxes. On the contrary, a negative EBITDA means that the company is not profitable. The EBITDA is computed as follows:

EBITDA

EBITDA is a financial indicator that measures a company’s revenue before subtracting interest, taxes, depreciation and amortization charges and provisions on fixed assets.

Why should you be interested in this post?

If you are looking at getting an internship in an investment firm, this post will surely be interesting to you. This post provides a little reminder of the basics of asset management. There are plenty of investment firms in the world however ACE is unique by its approach to understanding the markets and counselling its clients. In this post I detail some of the core missions that I had as a newcomer to the professional investing field.

Word of conclusion

As my first internship inside of an asset management firm, this initiation to the financial world was exactly what I was looking for before applying at ACE Finance et Conseil. ACE Finance et Conseil differentiates itself from other companies by its simplicity in functioning and the richness of its experience. This unique experience has made me want to explore the financial world even more.

Related posts on the SimTrade blog

   ▶ All posts about Professional experiences

   ▶ Alexandre VERLET Classic brain teasers from real-life interviews

   ▶ William LONGIN How to compute the present value of an asset?

Useful resources

ACE Finance et Conseil

Bloomberg terminal

Bloomberg market concepts

About the author

Article written in March 2022 by William LONGIN (EDHEC Business School, Global BBA, 2020-2024).

Understand the importance of data providers and how they influence global finance…

by

Understand the importance of data providers and how they influence global finance…

Louis DETALLE

In this article, Louis DETALLE (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023) explains the importance of data providers and how they influence global finance…

What are data providers?

A data provider is an intermediary between data and data users. Indeed, a data provider provides market data to financial firms, traders, and investors. The data distributed is previously gathered, organized and presented in an understandable way. Data providers collect the data from sources such as stock exchange feeds, brokers’ notes and dealer desks or regulatory filings. Some names will definitely ring a bell, such as Bloomberg, Thomson Reuters whereas some others will be less known as Moody’s Analytics.

The different types of data that are exchanged for financial purposes

When it comes to data used in finance, trading rooms are the best example as they contain almost nothing but data. Indeed, transaction prices, traded volumes of stocks and bonds are displayed at all times. But trading rooms are only one specific of example of data’s use in finance.

As mentioned, there are many different types of instruments (e.g., stocks, bonds, currencies, funds, options, futures, etc.) and hundreds of financial markets for investment, which leads to an extremely large flow of data exchanged.

The types of data offered vary by data provider. Generally, they cover information about companies and financial instruments (options, shares, bonds, treasury bonds and currencies) which companies might trade or issue.

The data can be updated every day or several times a day! Intraday data for instance are prices provided throughout the day and are usually released on a continuous basis.

The main dynamics of the Data Providers’ market

The explosion of financial data, enabled by the Internet tremendous potential, caused an explosion of demand for financial data. As evidenced in 2006 by the British mathematician and Tesco marketing mastermind Clive Humby’s quote, “Data is the new oil”, the data providers enjoy a market that seems to be limitless. Indeed, as data provider raw material’s amount is ever-increasing, it appears they will thrive for decades.

In addition, the market seems to be detained by only a few actors among which Bloomberg that acquired BNA and BusinessWeek. This contributes to curbing the number of data providers and improving the monopoly of Bloomberg on the data-providing market. Let’s review the market shares of the 4 major data providers: Bloomberg enjoys a comfortable 33,4% market share, Refinitiv Eiken follows with a 19,6% share of the market, Capital IQ has a 6,2% market share when FactSet closes the ranking with 4,5% of the market. (source:https://www.wallstreetprep.com/knowledge/bloomberg-vs-capital-iq-vs-factset-vs-thomson-reuters-eikon/)

Useful resources

Bloomberg

Refinitiv

Capital IQ

FactSet

Thomson Reuters

Related posts on the SimTrade blog

   ▶ Louis DETALLE The importance of data in finance

   ▶ Louis DETALLE Reuters

   ▶ Louis DETALLE Bloomberg

About the author

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

The importance of data in finance

by

The importance of data in finance

Louis DETALLE

In this article, Louis DETALLE (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023) explains the importance of data-management for corporations and how they are used to improve profitability.

According to a study published by CapGemini untitled: The data-powered enterprise: Why organizations must strengthen their data mastery, it is estimated that the gain from efficient data-management would represent 22% in terms of firm profitability.

Why is data used?

The use of data in finance can also be very useful in finance for various reasons.

Indeed, the multitude of data available allows for a deeper understanding of the market in terms of risks and opportunities. This knowledge is accompanied by an important consideration of political, social and economic factors.

As early as 2006, British mathematician and Tesco marketing mastermind Clive Humby stated “Data is the new oil.” The companies with the largest market capitalizations also bear witness to this importance of data. The ranking shows of tradingstat shows a podium of Apple, Microsoft and Google: the predominance of data-driven companies is clearly observable here.

In which finance-related fields is data used?

In finance, it is especially in the trading rooms that data has become an absolutely indispensable tool. Indeed, it is thanks to Big Data – i.e. increasingly exhaustive data, at an ever faster pace – that high frequency trading has been developed. In short, high-frequency trading makes it possible to place several thousand buy and/or sell orders in a few seconds, or even milliseconds, while optimizing risk management in order to adapt the strategy to market responses. This trading strategy allows for buying and selling in a sufficiently short period of time to avoid a potentially negative market movement during the operation.

On the other hand, retail banks (i.e. banks for individuals) are also confronted with the challenges of data-management. The development of online services offers them a better knowledge of their customers, which leads to a change in the bank’s relationship with its customers. In doing so, banks improve their ability to adapt their offer to the customer profile. Big Data also enables banks to fight fraud. Banks are now able to monitor all bank card transactions and be alerted when a user makes a payment (particularly in terms of amount, time or geographical area). For investment banks, whether it is the implementation of a more reliable scoring of credit files, the pooling of data between banks, analysis of the “sentiment” of investors for traders or the compliance of data and its processing, the indispensable character of data is no longer to be proven.

The importance of data regulation though

The use of data in finance is very useful but can be problematic when the data concerns the personal data of users or customers. In this context, financial actors are subject to ever increasing regulation and the adoption of the EU’s GDPR, in 2016, seems to be a step in this direction.

Useful resources

BlackRock L’utilisation du Big Data dans un processus d’investissement

Related posts on the SimTrade blog

   ▶ Louis DETALLE Understand the importance of data providers and how they influence global finance…

   ▶ Louis DETALLE Reuters

   ▶ Louis DETALLE Bloomberg

About the author

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

The Monte Carlo simulation method for VaR calculation

by

Jayati WALIA

In this article, Jayati WALIA (ESSEC Business School, Grande Ecole – Master in Management, 2019-2022) explains the Monte Carlo simulation method for VaR calculation.

Introduction

Monte Carlo simulations are a broad class of computational algorithms that rely majorly on repeated random sampling to obtain numerical results. The underlying concept is to model the multiple possible outcomes of an uncertain event. It is a technique used to understand the impact of risk and uncertainty in prediction and forecasting models.

The Monte Carlo simulation method was invented by John von Neumann (Hungarian-American mathematician and computer scientist) and Stanislaw Ulam (Polish mathematician) during World War II to improve decision making under uncertain conditions. It is named after the popular gambling destination Monte Carlo, located in Monaco and home to many famous casinos. This is because the random outcomes in the Monte Carlo modeling technique can be compared to games like roulette, dice and slot machines. In his autobiography, ‘Adventures of a Mathematician’, Ulam mentions that the method was named in honor of his uncle, who was a gambler.

Calculating VaR using Monte Carlo simulations

The basic concept behind the Monte Carlo approach is to repeatedly run a large number of simulations of a random process for a variable of interest (such as asset returns in finance) covering a wide range of possible scenarios. These variables are drawn from pre-specified probability distributions that are assumed to be known, including the analytical function and its parameters. Thus, Monte Carlo simulations inherently try to recreate the distribution of the return of a position, from which VaR can be computed.

Consider the CAC40 index as our asset of interest for which we will compute the VaR using Monte Carlo simulations.

The first step in the simulation is choosing a stochastic model for the behavior of our random variable (the return on the CAC 40 index in our case).
A common model is the normal distribution; however, in this case, we can easily compute the VaR from the normal distribution itself. The Monte Carlo simulation approach is more relevant when the stochastic model is more complex or when the asset is more complex, leading to difficulties to compute the VaR. For example, if we assume that returns follow a GARCH process, the (unconditional) VaR has to be computed with the Monte Carlo simulation methods. Similarly, if we consider complex financial products like options, the VaR has to be computed with the Monte Carlo simulation methods.

In this post, we compare the Monte Carlo simulation method with the historical method and the variance-covariance method. Thus, we simulate returns for the CAC40 index using the GARCH (1,1) model.
Figure 1 and 2 illustrate the GARCH simulated daily returns and volatility for the CAC40 index.

Figure 1. Simulated GARCH daily returns for the CAC40 index.
img_SimTrade_CAC40_GARCH_ret
Source: computation by the author.

Figure 2. Simulated GARCH daily volatility for the CAC40 index.
img_SimTrade_CAC40_GARCH_vol
Source: computation by the author.

Next, we sort the distribution of simulated returns in ascending order (basically in order of worst to best returns observed over the period). We can now interpret the VaR for the CAC40 index in one-day time horizon based on a selected confidence level (probability).

For instance, if we select a confidence level of 99%, then our VaR estimate corresponds to the 1st percentile of the probability distribution of daily returns (the bottom 1% of returns). In other words, there are 99% chances that we will not obtain a loss greater than our VaR estimate (for the 99% confidence level). Similarly, VaR for a 95% confidence level corresponds to bottom 5% of the returns.

Figure 3 below represents the unconditional probability distribution of returns for the CAC40 index assuming a GARCH process for the returns.

Figure 3. Probability distribution of returns for the CAC40 index.
img_SimTrade_CAC40_MonteCarloVaR
Source: computation by the author.

From the above graph, we can interpret VaR for 99% confidence level as -3% i.e., there is a 99% probability that daily returns we obtain in future are greater than -3%. Similarly, VaR for 95% confidence level as -1.72% i.e., there is a 95% probability that daily returns we obtain in future are greater than -1.72%.

You can download below the Excel file for computation of VaR for CAC40 stock using Monte Carlo method involving GARCH(1,1) model for simulation of returns.

Download the Excel file to compute the Monte Carlo VaR

Advantages and limitations of Monte Carlo method for VaR

The Monte Carlo method is a very powerful approach to VAR due its flexibility. It can potentially account for a wide range of scenarios. The simulations also account for nonlinear exposures and complex pricing patterns. In principle, the simulations can be extended to longer time horizons, which is essential for risk measurement and to model more complex models of expected returns.

This approach, however, involves investments in intellectual and systems development. It also requires more computing power than simpler methods since the more is the number of simulations generated, the wider is the range of potential scenarios or outcomes modelled and hence, greater would be the potential accuracy of VaR estimate. In practical applications, VaR measures using Monte Carlo simulation often takes hours to run. Time requirements, however, are being reduced significantly by advances in computer software and faster valuation methods.

Related posts on the SimTrade blog

   ▶ Jayati WALIA Quantitative Risk Management

   ▶ Jayati WALIA Value at Risk

   ▶ Jayati WALIA The historical method for VaR calculation

   ▶ Jayati WALIA The variance-covariance method for VaR calculation

   ▶ Jayati WALIA Brownian Motion in Finance

Useful resources

Jorion P. (2007) Value at Risk, Third Edition, Chapter 12 – Monte Carlo Methods, 321-326.

About the author

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

Implementing Black-Litterman asset allocation model

by

Youssef_Louraoui

In this article, Youssef Louraoui (Bayes Business School, MSc. Energy, Trade & Finance, 2021-2022) presents an implementation of the Black-Litterman model, used to determine the expected return of a portfolio by integrating investor’s views regarding the performance of the underlying assets selected in the investment portfolio.

This article follows the following structure: first, we introduce the Black-Litterman model. We then present the mathematical foundations of this model. We conclude with an explanation of the methodology to build the spreadsheet with the results obtained. You will find in this post an Excel spreadsheet which implement the model.

Introduction

The Black-Litterman asset allocation model, established for the first time in the early 1990’s by Fischer Black and Robert Litterman, is a sophisticated strategy for dealing with unintuitive, highly concentrated, and input-sensitive portfolios. The most likely reason that more portfolio managers do not use the Markowitz model, which maximises return for a given degree of risk, is input sensitivity, a well-documented issue with mean-variance optimization.

The Black-Litterman Model employs a Bayesian technique to integrate an investor’s subjective views of expected returns on one or more assets with the market equilibrium vector (prior distribution) of expected returns to obtain a new, mixed estimate of expected returns. The new vector of returns (the posterior distribution) is a weighted complex average of the investor’s views and market equilibrium.

Mathematical foundation

The idea of the Black Litterman estimates is not to find the optimum portfolio weights as in the Markowitz optimization framework, but instead to find the expected return that would be used as an input to compute the optimum portfolio weights. This approach is referred as reversion portfolio optimization technique. The idea behind is that optimum weights are already observed in the market and captured in the market portfolio. We can approach the reasoning by maximizing the following utility function adjusted to the risk:

img_SimTrade_mathematical_foundation_Black_Litterman_6

  • wT = transposed of portfolio weights
  • Π = Implied equilibrium excess return vector
  • A = price of risk or risk aversion factor
  • Σ = variance-covariance matrix

We take the partial derivative of U in terms of weights (w) and we derive the following expression:

img_SimTrade_mathematical_foundation_Black_Litterman_5

By setting the partial derivative equal to zero, we can maximize the utility function in term of weights. The proposed approach in the Black Litterman approach is that instead of seeking the optimal weights, which are incorporated in the market portfolio and thus computable via the market capitalization of the equities in the portfolio, we’ll isolate the Π (implied equilibrium excess return) to obtain the optimal expected returns for the portfolio:

img_SimTrade_mathematical_foundation_Black_Litterman_4

We can deconstruct the Black-Litterman model as

img_SimTrade_mathematical_foundation_Black_Litterman_3

  • τ= scalar
  • P = Linking matrix
  • ∑ = Variance-covariance matrix
  • Π= implied equilibrium excess return
  • A = Price of risk
  • w = weight vector
  • Ω = uncertainty of views

The first term of the formula is introduced in order to respect the constraint that the portfolio weights should be equal to one:

img_SimTrade_mathematical_foundation_Black_Litterman_2

The second term of the formula is to compute a weighted average of the implied equilibrium excess return adjusted to the uncertainty of the returns by the view vector weighted with the uncertainty of views:

img_SimTrade_mathematical_foundation_Black_Litterman_1

The final output E(R) is a vector of return n x 1 that represent the equilibrium returns of the market adjusted to investors views.

Implementation of the Black-Litterman asset allocation model in practice

To model a Black-Litterman portfolio allocation, we obtained a large time series to obtain useful results by downloading the equivalent of 23 years of market data from a data provider (in this example, Bloomberg). We generate the variance-covariance matrix after obtaining all necessary statistical data, which includes the expected return and volatility indicated by the standard deviation of the returns for each stock during the provided period.

The data is derived from the Bloomberg terminal. The first step is to calculate the logarithmic returns and excess returns on the selected assets (returns minus the risk-free rate). After calculating the logarithmic returns on each asset, we can estimate the capital asset pricing model’s returns (CAPM) expected returns. This information will be used to calculate the Black-Litterman expected returns on a comparative basis.

1. The first input for the model is the price of risk A, which represents the risk aversion of investor and is obtained by subtracting the expected return of the market the risk-free rate and divided by the variance of the market:

img_SimTrade_Black_Litterman_formulas_for_spreadsheet_1

  • E(rm)= expected market returns
  • rf = risk-free rate
  • σ2m = variance of market

2. We extract the respective market capitalization of each security to obtain their market weights in the portfolio. Given that our investable universe is made of five stocks, we can retrieve their respective market capitalization and compute the weights of each stock in relation to the sum of total market-capitalization in the portfolio.

img_SimTrade_Black_Litterman_formulas_for_spreadsheet_2

Table 1 depicts the optimal weights obtained from their respective market capitalisation, coupled with the respective expected return and volatility.

Table 1. Asset characteristics of Apple, Amazon, Microsoft, Goldman Sachs, and Pfizer.

img_SimTrade_Black_Litterman_spreadsheet_2

Source: computation by the author.

3. We compute the variance-covariance matrix of logarithmic returns using the data analysis tool pack available in Excel (Table 2).

Table 2. Variance-covariance matrix of asset returns

img_SimTrade_Black_Litterman_spreadsheet_5

Source: computation by the author.

4. We compute the implied equilibrium excess return (Π) as the matrix calculation of the price of risk (A) times the matrix multiplication of the weights computed in step 4 times the variance-covariance matrix computed in step 3.

img_SimTrade_Black_Litterman_formulas_for_spreadsheet_3

  • Π= implied equilibrium excess return
  • A = Price of risk
  • w = weight vector

5. The views are incorporated into the model. To achieve this, we provide three views to include into the model. If there are no views, the values will correspond to the market portfolio. The investment manager’s views for the expected return on certain of the portfolio’s assets regularly diverge from the Implied Equilibrium Return Vector (), which serves as the market-neutral starting point for the Black-Litterman model that quantifies the uncertainty associated with each view. The Black-Litterman Model can be used to depict such views in absolute or relative terms. As an illustration, let us suppose that the real and simulated portfolio will have the same views:

  • View 1: Apple will outperform Microsoft by .05 percent
  • View 2: Amazon will outperform Microsoft by .1 percent
  • View 3: Apple will outperform Amazon by .05 percent

To incorporate the vector Q of views, we create a link matrix P where the rows sum to zero. Figure 3 depicts the workings done in the spreadsheet.

Table 3. Views vector and Link Matrix (P)

img_SimTrade_Black_Litterman_spreadsheet_1

Source: computation by the author.

6. We compute omega to determine the degree of uncertainty associated with the views. While Black-Litterman paper used a value of tau equal to 0.25, an important number of academics went for calculating the tau equal to one. For the sake of simplifying the model, consider tau to be equal to one. This input is obtained by multiplying the Linking matrix by the variance-covariance matrix and transposing the Linking matrix (P).

img_SimTrade_Black_Litterman_formulas_for_spreadsheet_4

  • τ= scalar
  • P = Linking matrix
  • ∑ = Variance-covariance matrix

7. We integrate all the values computed previously in the Black-Litterman model. Table 4 depicts the results obtained via the Black-Litterman allocation model.

Table 4. Results of the Black-Litterman allocation

img_SimTrade_Black_Litterman_spreadsheet_4

Source: computation by the author.

We can see that the results converge slightly to those from CAPM. Additionally, we can see that the views are reflected in the Black-Litterman expected returns. As a result, we can determine whether or not each view is satisfied. Indeed, Apple outperforms Amazon and Microsoft, while Amazon outperforms Microsoft.

You can download an Excel file to help you construct a portfolio via the Black-Litterman allocation model.

Download the Excel file to construct a portfolio with the Black-Litterman allocation model

Why should I be interested in this post?

Modern Portfolio Theory is at the heart of modern finance, shaping the modern investing landscape. MPT has become the cornerstone of current financial theory and practice. MPT’s thesis is that winning the market is difficult and requires diversification and taking higher-than-average risks.

MPT has been around for nearly sixty years and shows no signs of slowing down. His theoretical contributions paved the way for more portfolio theory study. But Markowitz’s portfolio theory is sensitive to and depends on further ‘probabilistic’ expansion. This paper expanded on Markowitz’s previous work by incorporating investor views into the asset allocation process.

Related posts on the SimTrade blog

   ▶ Youssef LOURAOUI Implementation of the Markowitz allocation model

   ▶ Youssef LOURAOUI Black-Litterman Model

   ▶ Youssef LOURAOUI Markowitz Modern Portfolio Theory

   ▶ Youssef LOURAOUI Origin of factor investing

   ▶ Youssef LOURAOUI Portfolio

   ▶ Youssef LOURAOUI Alpha

   ▶ Youssef LOURAOUI Factor Investing

   ▶ Jayati WALIA Capital Asset Pricing Model (CAPM)

Useful resources

Academic research

Black, F. and Litterman, R. 1990. Asset Allocation: Combining Investors Views with Market Equilibrium. Goldman Sachs Fixed Income Research working paper

Black, F. and Litterman, R. 1991. Global Asset Allocation with Equities, Bonds, and Currencies. Goldman Sachs Fixed Income Research working paper

Black, F. and Litterman, R. 1992. Global Portfolio Optimization.Financial Analysts Journal, 28-43.

Idzorek, T.M. 2002. A step-by-step guide to Black-Litterman model. Incorporating user-specified confidence levels. Working Paper, 2-11.

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

About the author

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