Activist Funds

Activist Funds

Akshit Gupta

This article written by Akshit GUPTA (ESSEC Business School, Grande Ecole Program – Master in Management, 2019-2022) introduces activist funds which is a type of fund based on shareholder activism to influence a company’s board and top management decisions.

Introduction

Activist funds use an investment strategy where they buy shares in a publicly listed company with the aim to influence a company’s board and top management decisions. A large shareholding provides the activist fund with high power to influence the decision making of these firms at the management level. The aim of an active fund is to push for decisions or changes that would increase the share price and thus, the value of its portfolio.

Activist funds target companies which are poorly managed or have untapped value which if explored, can lead to significant increase in the stock price. They typically buy the equity shares of these companies which provides them with ownership and the rights to vote during the shareholders’ General Meetings to influence the board and top management decisions. Activist funds propose and help implement changes that favourably impact the stock prices and helps them to generate absolute market returns that are generally higher than the market benchmarks. These changes include changes in business strategy, operational decisions, capital structure, corporate governance and the day-to-day practices of the management.

Activist investors are normally seen operating either a private equity firm or a hedge fund and specialising in specific industries or businesses. High-net worth individuals and family offices are majorly involved in activist investing as they have access to huge investments and expertise.

Benefits of activist funds

Like other types of hedge funds and private equity firms, activist funds aim at providing their clients (investors) with investments managed in an efficient manner to optimize expected returns and risk. They try to generate alpha on the clients’ investment by actively participating in company’s board and top management decisions. So, activist funds are often acknowledged as the alternative funds in the asset management industry.

Concerns associated with activist funds

Although the investments in activist funds are handled by professionals and can generate absolute performance, they also come with some concerns for the investors. Some of the commonly associated concerns with activist fund investments are:

  • Narrow-sighted approach – Activist funds invest in companies with the aim to maximize the shareholder’s wealth. The approach has serious concerns as it doesn’t fully take into account the effects of the decision on the company’s workers and society.
  • Investment horizon – The investment horizon of activist funds is not very well defined as the changes propose d by the funds can either take shape immediately or may run over a couple of years before the effects are seen.

Example of activist fund

GameStop – Shareholder activism

The infamous GameStop stock rally that happened in 2021 drew people’s attention from around the world and it became the talk of the town. During the same time, the company also went through a change in its management. The event sheds light on the importance and impact of shareholder activism in today’s world.

Ryan Cohen is a famous activist investor who declared 10% stock ownership in GameStop through his investment firm, RC Ventures, in September 2020. This named him amongst the company’s biggest individual investor. He saw a huge opportunity for video games in the e-commerce market and wanted GameStop to evolve from a gaming company to a technology company and also change from traditional brick-and-mortar stores to online channels. To implement the changes, he made efforts to privately engage with the firm to review their strategic vision and change the company’s business model via . But the efforts yielded little success, following which he sent an open letter to the company’s Board of Directors (A copy of the letter can be seen below)

Ryan Cohen Letter to the Board of GameStop in November 2020

The letter was taken seriously by the company’s management and Ryan Cohen was appointed on the Board of Directors of the company in January 2021. Later, he was promoted as the Chairman of the Board to reshape the company’s strategic vision to become a technology-driven business rather than merely a gaming company.

Useful resources

Academic resources

Pedersen, L. H., 2015. Efficiently Inefficient: How Smart Money Invests and Market Prices Are Determined. Princeton University Press, Chapter 7, Discretionary Equity Investing.

Business resources

Business Insider Article on GameStop

Frick W. (2016) The Case for Activist Investors Harvard Business Review, 108–109.

Desjardine M., R. Durand (2021) Activist Hedge Funds: Good for Some, Bad for Others? Knowledge@HEC.

CNBC Article

Forbes Article

Related posts on the SimTrade blog

   ▶ Akshit GUPTA Asset management firms

   ▶ Akshit GUPTA Macro funds

   ▶ Akshit GUPTA Hedge funds

   ▶ Youssef LOURAOUI Introduction to hedge funds

About the author

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

Currency overlay

Jayati WALIA

In this article, Jayati WALIA (ESSEC Business School, Grande Ecole Program – Master in Management, 2019-2022) explains currency overlay which is a mechanism to effectively manage currency risk in asset portfolios.

Overview

Currency risk, also known as exchange-rate risk, forex exchange or FX risk, is a kind of market risk that is caused by the fluctuations in currency exchange rates.

Both individual and institutional investors are diversifying their portfolios through assets in international financial markets, but by doing so they also introduce currency risk in their portfolios.

Consider an investor in the US who decides to invest in the French equity market (say in the CAC 40 index). The investor is now exposed to currency risk due to the movements in EURUSD exchange rate. You can download the Excel file below which illustrates the impact of the EURUSD exchange rate on the overall performance of the investor’s portfolio.

Download the Excel file to illustrate the impact of currency risk on portfolio

This exercise demonstrates the importance of currency risk in managing an equity portfolio with assets dominated in foreign currencies. We can observe that over a one-month time-period (July 19 – August 19, 2022), the annual volatility of the American investor’s portfolio with FX risk included is 12.96%. On the other hand, if he hedges the FX risk (using a currency overlay strategy), the annual volatility of his portfolio is reduced to 10.45%. Thus, the net gain (or loss) on the portfolio is significantly reliant on the EURUSD exchange-rate.

Figure 1 below represents the hedged an unhedged returns on the CAC 40 index. The difference between the two returns illustrates the currency risk for an unhedged position of an investor in the US on a foreign equity market (the French equity market represented by the CAC 40 index.

Figure 1 Hedged and unhedged returns for a position on the CAC 40 index.
Hedged an unhedged return Source : computation by the author.

Currency overlay is a strategy that is implemented to manage currency exposures by hedging against foreign exchange risk. Currency overlay is typically used by institutional investors like big corporates, asset managers, pension funds, mutual funds, etc. For such investors exchange-rate risk is indeed a concern. Note that institutional investors often outsource the implementation of currency overlays to specialist financial firms (called “overlay managers”) with strong expertise in foreign exchange risk. The asset allocation and the foreign exchange risk management are then separated and done by two different persons (and entities), e.g., the asset manager and the overlay manager. This organization explains the origin of the world “overlay” as the foreign exchange risk management is a distinct layer in the management of the fund.

Overlay managers make use of derivatives like currency forwards, currency swaps, futures and options. The main idea is to offset the currency exposure embedded in the portfolio assets and providing hedged returns from the international securities. The implementation can include hedging all or a proportion of the currency exposure. Currency overlay strategies can be passive or active depending on portfolio-specific objectives, risk-appetite of investors and currency movement viewpoint.

Types of currency overlay strategies

Active currency overlay

Active currency overlay focuses on not just hedging the currency exposure, but also profiting additionally from exchange-rate movements. Investors keeps a part of their portfolio unhedged and take up speculative positions based on their viewpoint regarding the currency trends.

Passive currency overlay

A passive overlay focuses only on hedging the currency exposure to mitigate exchange-rate risk. Passive overlay is implemented through derivative contracts like currency forwards which are used to lock-in a specific exchange-rate for a fixed time-period, thus providing stability to asset values and protection against exchange-rate fluctuations.

Passive overlay is a simple strategy to implement and generally uses standardized contracts, however, it also eliminates the scope of generating any additional profits for the portfolio through exchange-rate fluctuations.

Implementing currency overlays

Base currency and benchmark

Base currency is generally the currency in which the portfolio is dominated or the investor’s domestic currency. A meaningful benchmark selection is also essential to analyze the performance and assess risk of the overlay. World market indices such as those published by MSCI, FTSE, S&P, etc. can be appropriate choices.

Hedge ratio

Establishing a strategic hedge ratio is a fundamental step in implementing a currency overlay strategy. It is the ratio of targeted exposure to be currency hedged by the overlay against the overall portfolio position. Different hedge ratios can have different impact on the portfolio returns and determining the optimal hedge ratio can depend on various factors such as investor risk-appetite and objectives, portfolio assets, benchmark selection, time horizon for hedging etc.

Cost of overlay

The focus of overlays is to hedge the fluctuations in foreign exchange rates by generating cashflows to offset the foreign exchange rate movements through derivatives like currency forwards, currency swaps, futures and options. The use of these derivatives products generates additional costs that impacts the overall performance of the portfolio strategy. These costs must be compared to the benefits of portfolio volatility reduction coming from the overlay implementation.

This cost is also an essential factor in the selection of the hedge ratio.

Note that passive overlays are generally cheaper than active overlays in terms of implementation costs.

Related posts on the SimTrade blog

   ▶ Jayati WALIA Credit risk

   ▶ Jayati WALIA Fixed income products

   ▶ Jayati WALIA Plain Vanilla Options

   ▶ Akshit GUPTA Currency swaps

Useful resources

Academic articles

Black, F. (1989) Optimising Currency Risk and Reward in International Equity Portfolios. Financial Analysts Journal, 45, 16-22.

Business material

Pensions and Lifetime Savings Association Currency overlay: why and how? video.

About the author

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

Reverse Convertibles

Reverse Convertibles

Shengyu ZHENG

In this article, Shengyu ZHENG (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023) explains reverse convertibles, which are a structured product with a fixed-rate coupon and downside risk.

Introduction

The financial market has been ever evolving, witnessing the birth and flourish of novel financial instruments to cater to the diverse needs of market participants. On top of plain vanilla derivative products, there are exotic ones (e.g., barrier options, the simplest and most traded exotic derivative product). Even more complex, there are structured products, which are essentially the combination of vanilla or exotic equity instruments and fixed income instruments.

Amongst the structured products, reverse convertible products are one of the most popular choices for investors. Reverse convertible products are non-principal protected products linked to the performance of an underlying asset, usually an individual stock or an index, or a basket of them. Clients can enter into a position of a reverse convertible with the over-the-counter (OTC) trading desks in major investment banks.

In exchange for an above-market coupon payment, the holder of the product gives up the potential upside exposure to the underlying asset. The exposure to the downside risks still remains. Reserve convertibles are therefore appreciated by the investors who are anticipating a stagnation or a slightly upward market trend.

Construction of a reverse convertible

This product could be decomposed in two parts:

  • On the one hand, the buyer of the structure receives coupons on the principal invested and this could be considered as a “coupon bond”;
  • On the other hand, the investor is still exposed to the downside risks of the underlying asset and foregoes the upside gains, and this could be achieved by a short position of a put option (either a vanilla put option or a down-and-in barrier put option).

Positions of the parties of the transaction

A reverse convertible involves two parties in the transaction: a market maker (investment bank) and an investor (client). Table 1 below describes the positions of the two parties at different time of the life cycle of the product.

Table 1. Positions of the parties of a reverse convertible transaction

t Market Maker (Investment Bank) Investor (Client)
Beginning
  • Enters into a long position of a put (either a vanilla put or a down-and-in barrier put)
  • Receives the nominal amount for the “coupon” part
  • Invests in the amount (nominal amount plus the premium of the put) in risk-free instruments
  • Enters into a short position of a put (either a vanilla put or a down-and-in barrier put)
  • Pays the nominal amount for the “coupon” part
Interim
  • Pays pre-specified interim coupons in respective interim coupon payment dates (if any)
  • Receives interest payment from risk-free investments
  • Receives the pre-specified interim coupons in respective interim coupon payment dates (if any)
End
  • Receives the payoff (if any) of the put option component
  • Pays the pre-specified final coupon in the final coupon payment date
  • Pays the payoff (if any) of the put option component
  • Receives the pre-specified final coupon in the final coupon payment date

Based on the type of the put option incorporated in the product (either plain vanilla put option or down-and-in barrier put option), reserve convertibles could be categorized as plain or barrier reverse convertibles. Given the difference in terms of the composition of the structured product, the payoff and pricing mechanisms diverge as well.

Here is an example of a plain reverse convertible with following product characteristics and market information.

Product characteristics:

  • Investment amount: USD 1,000,000.00
  • Underlying asset: S&P 500 index (Bloomberg Code: SPX Index)
  • Investment period: from August 12, 2022 to November 12, 2022 (3 months)
  • Coupon rate: 2.50% (quarterly)
  • Strike level : 100.00% of the initial level

Market data:

  • Current risk-free rate: 2.00% (annualized)
  • Volatility of the S&P 500 index: 13.00% (annualized)

Payoff of a plain reverse convertible

As is presented above, a reverse convertible is essentially a combination of a short position of a put option and a long position of a coupon bond. In case of the plain reverse convertible product with the aforementioned characteristics, we have the blow payoff structure:

  • in case of a rise of the S&P 500 index during the investment period, the return for the reverse convertible remains at 2.50% (the coupon rate);
  • in case of a drop of the S&P 500 index during the investment period, the return would be equal to 2.50% minus the percentage drop of the underlying asset and it could be negative if the percentage drop is greater than 2.5%.

Figure 1. The payoff of a plain reverse convertible on the S&P 500 index
Payoff of a plain reverse convertible
Source: Computation by author.

Pricing of a plain reverse convertible

Since a reverse convertible is essentially a structured product composed of a put option and a coupon bond, the pricing of this product could also be decomposed into these two parts. In terms of the pricing a vanilla option, the Black–Scholes–Merton model could do the trick (see Black-Scholes-Merton option pricing model) and in terms of pricing a barrier option, two methods, analytical formula method and Monte-Carlo simulation method, could be of help (see Pricing barrier options with analytical formulas; Pricing barrier options with simulations and sensitivity analysis with Greeks).

With the given parameters, we can calculate, as follows, the margin for the bank with respect to this product. The calculated margin could be considered as the theoretical price of this product.

Table 2. Margin for the bank for the plain reverse convertible
Margin for the bank for the plain reverse convertible
Source: Computation by author.

Download the Excel file to analyze reverse convertibles

You can find below an Excel file to analyze reverse convertibles.
Download Excel file to analyze reverse convertibles

Why should I be interested in this post

As one of the most traded structured products, reverse convertibles have been an important instrument used to secure return amid mildly negative market prospect. It is, therefore, helpful to understand the product elements, such as the construction and the payoff of the product and the targeted clients. This could act as a steppingstone to financial product engineering and risk management.

Related posts on the SimTrade blog

   ▶ All posts about options

   ▶ Jayati WALIA Black-Scholes-Merton option pricing model

   ▶ Akshit GUPTA The Black Scholes Merton Model

   ▶ Shengyu ZHENG Barrier options

   ▶ Shengyu ZHENG Pricing barrier options with analytical formulas

   ▶ Shengyu ZHENG Pricing barrier options with simulations and sensitivity analysis with Greeks

Resources

Academic references

Broadie, M., Glasserman P., Kou S. (1997) A Continuity Correction for Discrete Barrier Option. Mathematical Finance, 7:325-349.

De Bellefroid, M. (2017) Chapter 13 (Barrier) Reverse Convertibles. The Derivatives Academy. Accessible at https://bookdown.org/maxime_debellefroid/MyBook/barrier-reverse-convertibles.html

Haug, E. (1997) The Complete Guide to Option Pricing. London/New York: McGraw-Hill.

Hull, J. (2006) Options, Futures, and Other Derivatives. Upper Saddle River, N.J: Pearson/Prentice Hall.

Merton, R. (1973). Theory of Rational Option Pricing. The Bell Journal of Economics and Management Science, 4:141-183.

Paixao, T. (2012) A Guide to Structured Products – Reverse Convertible on S&P500

Reiner, E. S. (1991) Breaking down the barriers. Risk Magazine, 4(8), 28–35.

Rich, D.R. (1994) The Mathematical Foundations of Barrier Option-Pricing Theory. Advances in Futures and Options Research: A Research Annual, 7, 267-311.

Business references

Six Structured Products. (2022). Reverse Convertibles et barrier reverse Convertibles

About the author

The article was written in August 2022 by Shengyu ZHENG (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023).

Macro Funds

Macro Funds

Akshit Gupta

This article written by Akshit GUPTA (ESSEC Business School, Grande Ecole Program – Master in Management, 2019-2022) explains marco funds which is a type of hedge fund based on the analysis of macroeconomic or political events.

Introduction

Macro funds, also known as global macro funds, are actively managed alternative investment vehicles (hedge funds) whose strategy profits from the broad market movements caused by macroeconomic (economic, fiscal and monetary) or geopolitical events. These funds typically invest in asset classes including equity, fixed income, currencies, and commodities. They invest in both the spot and derivatives markets. They use a mix of long and short positions in these asset classes to implement their market views to achieve superior returns (higher than a given benchmark).

Some key elements impacting the decisions taken by macro funds include:

  • Economic factors – Macro funds constantly monitor the economic data across different countries including interest rates, inflation rates, GDP growth, unemployment rates and industrial/retail growth rates to make investment decisions.
  • Mispricing – Macro funds try to arbitrage markets based on perceived mispricing.
  • Political situations – The political situations in different countries also play a major role in the investment decisions made by macro funds as unstable political situations can lead to low investor confidence and thus cause a decline in the financial markets.

Benefits of a macro funds

Like other types of hedge funds, macro funds aim at providing their clients (investors) with investments managed in an efficient manner to optimize expected returns and risk. Such funds are especially expected to diversify the clients’ portfolios. So, macro funds are often acknowledged as the alternative funds in the industry.

Other characteristics of macro funds

Other characteristics of macro funds (clients, fee structure, investment constraints) are similar to other types of hedge funds (see the posts Introduction to Hedge Funds and Hedge Funds).

Examples of macro funds strategies

A commonly used asset class in macro fund strategy includes currencies. Their exchange rates are affected by several factors including monetary and fiscal policies, economic factors like GDP growth and inflation and geopolitical situation. Black Wednesday is an example of an infamous event, where we can understand the different factors and use of macro fund strategies.

Black Wednesday

During the 1970s, an European Exchange Rate Mechanism (ERM) was set up to reduce exchange rate variability and stabilize the monetary policies across the continent. Also, a stage was being set to introduce a unified common currency named Euro. The United Kingdom joined ERM in 1990 due to political instability in the country raising fears of higher currency fluctuations.

The pound sterling shadowed the German mark but owing to challenges faced by Britain at that point in time, including lower interest rates, higher inflation rates and an unstable economy, the currency traders weren’t satisfied with the decision.

Seeing the economic situation, George Soros, one of the most famous investors, used the macro fund strategy during 1992 when he took a short position in the pound sterling for $10 billion and made a $1 billion profit from his position.

Related Posts

   ▶ Akshit GUPTA Asset management firms

   ▶ Akshit GUPTA Hedge Funds

   ▶ Youssef LOURAOUI Introduction to Hedge Funds

   ▶ Akshit GUPTA Portrait of George Soros: A famous investor

Useful resources

Academic resources

Pedersen, L. H., 2015. Efficiently Inefficient: How Smart Money Invests and Market Prices Are Determined. Princeton University Press, Chapter 11, Global macro Investing.

Business resources

JP. Morgan Asset Management

DeChesare Brian “Global Macro Hedge Funds: Living in an FX Traders’ Paradise?”

About the author

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

Initial and maintenance margins in stocks

Initial and maintenance margins in stocks

Akshit Gupta

This article written by Akshit GUPTA (ESSEC Business School, Grande Ecole Program – Master in Management, 2019-2022) explains the mechanisms of initial and maintenance margin used in stocks.

Introduction

In financial markets, margin requirements are present in leveraged positions in stock trading. They refer to a percentage of assets that an investor must put aside with his or her own cash or assets (collateral) as a means of protection against the risk exposure to its potential default for the other counterpart.

Margin requirements serve as a guarantee that the investor providing the margins will fulfill its trade obligations. Many exchanges across the world provide leverage facilities to investors for trading in different assets. For example, an investor can use leverage facilities for trading in equities, bonds, exchange rates, commodities, etc. It usually takes the form of derivatives contracts like futures and options. Whenever an investor buys or sells stocks using leverage, it is called buying or selling on margin.

Margin requirements can be categorized as initial and maintenance margin requirements.

Initial margin

Initial margin (or IM) refers to the initial deposit required when an investor opens a position in an underlying asset and amounts to a percentage of the nominal contract value. The amount for the initial margin requirement is calculated in accordance with approved margin models that are based on the market’s regulatory rules. The determination of the initial margin requirement is essentially based on the volatility of the asset being covered. The more volatile the asset, the higher the initial margin requirement.

You can download below the file to learn about the different initial margin requirements at Euronext Clearing used in stock trading (PDF document).

Maintenance margin

When an investor holds an underlying asset on margin, she is required to maintain a minimum margin amount of that asset position in her portfolio to keep her position open and this is known as the maintenance margin. Maintenance margin requirements aim to protect against excess losses and ensure the broker has enough capital to cover any losses the investor may incur. In case the investor is unable to fulfill the maintenance margin requirements, she receives a margin call initiated from the broker to deposit a further amount in order to keep her position open. If she fails to provide adequate maintenance margins, the broker has the power to close her position.

Mechanism of initial and maintenance margins

Now, we will see how initial and maintenance margins work in the financial markets with the concept of short selling used in equity trading. Since the short sell involves borrowing stock, the investor is required by its broker to post an initial margin at the time the trade is initiated. For instance, this initial margin is set to 50% of the value of the short sale. This money is essentially the collateral on the short sale to protect the lender of the stocks in the future against the default of the borrower (the investor).

Followed by this, a maintenance margin is required at any point of time after the trade is initiated. The maintenance is taken as 30% of the total value of the position. The short seller has to ensure that any time the position falls below this maintenance margin requirement, he will get a margin call and has to increase funds into the margin account.

Example

Here is an example of a typical case of short selling and its margin mechanism:

 Margin call on stocks

You can download below the Excel file for the computation of the Intial and Maintenance Margins for the stocks.

Download the Excel file to compute the initial and maintenance margins on stocks

Useful resources

Euronext Clearing

Maintenance margin

Initial Margin

Financial Industry Regulatory Authority (FINRA)

Related posts

   ▶Akshit GUPTA Initial and Maintenance margin in futures contracts

   ▶ Youssef LOURAOUI Introduction to Hedge Funds

   ▶ Akshit GUPTA Analysis of the Big Short movie

   ▶ Akshit GUPTA Analysis of the Margin call movie

   ▶ Akshit GUPTA Analysis of the Trading places movie

About the author

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

Initial and maintenance margins in futures contracts

Initial and maintenance margins in futures contracts

Akshit Gupta

This article written by Akshit GUPTA (ESSEC Business School, Grande Ecole Program – Master in Management, 2019-2022) explains the mechanisms of initial and maintenance margin used in futures contracts.

Introduction

In financial markets, margin requirements are present in leveraged positions in derivative products. They refer to a percentage of assets that an investor must pay for with his or her own cash or assets (collateral) as a means of protection against the risk exposure to its potential default for the other counterpart.

Margin requirements serve as a guarantee that the investor providing the margins will fulfil its trade obligations. Many exchanges across the world provide leverage facilities to investors for trading in different derivative assets. For example, an investor can use leverage facilities for trading in futures contracts across different asset classes like equities, bonds, currencies, interest rates, etc.

Margin requirements can be categorized as initial and maintenance margin requirements.

Initial margin

Initial margin (or IM) refers to the initial deposit required when an investor opens a position in a derivative product and amounts to a percentage of the nominal contract value. The amount for initial margin requirement is calculated in accordance with approved margin models that are based on the market’s regulatory rules. The determination of the initial margin requirement is essentially based on the volatility of the underlying asset of the derivative product being covered. The more volatile the underlying asset, the higher the initial margin requirement.

You can download below the file to learn about the different Euronext Clearing margin requirements used in derivatives trading.

Maintenance margin

When an investor holds an underlying asset on margin, she is required to maintain a minimum margin amount of that asset position in her portfolio to keep her position open and this is known as the maintenance margin. Maintenance margin requirements aim to protect against excess losses and ensures the broker has enough capital to cover any losses the investor may incur. Maintenance margin is generally calculated on a daily mark-to-market basis between the period starting from the trading date to the contract expiration date.

In case the investor is unable to fulfil the maintenance margin requirements, she receives a margin call initiated from the broker to deposit further amount in order to keep her position open. If she fails to provide adequate maintenance margins, the broker has the power to close her positions.

Mechanism of initial and maintenance margins

Now, we will see how initial and maintenance margins work in the financial markets using S&P 500 mini futures contract. Since the investor has bought the futures contract, he/she is required by its broker to post an initial margin at the time the trade is initiated. For instance, this initial margin is set to 40% of the nominal value of the contract. This money is essentially the collateral on the purchase to protect the seller of the contract in the future against the default of the buyer (the investor).

Followed by this, a maintenance margin is required at any point of time after the trade is initiated. The maintenance margin call is triggered when the value of the initial margin falls below the 30% threshold (i.e. 70% of the initial margin). The buyer has to ensure that any time the position falls below this maintenance margin requirements, he will get a margin call and has to increase funds into the margin account.

Example with initial margin

Here is an example of a typical case of buying a futures contract and its margin mechanism:

The characteristics of the contract and market data include:

 Margin call on futures

 Margin call on long futures

The final value of the investor’s brokerage account is equal to $253,000. At the end of the contract, the investor can get back its initial margin of $158,000 leaving $95,000 on its account. The gain is equal to $10,000 which is the amount left on the account ($95,000) minus the sum of the margin calls ($85,000).

Here is an example of a typical case of selling a futures contract and its margin mechanism using the same characteristics and market data:

 Margin call on short futures

The final value of the investor’s brokerage account is equal to $178,000. At the end of the contract, the investor can get back its initial margin of $158,000 leaving $20,000 on its account. The loss is equal to $10,000 which is the amount left on the account ($20,000) minus the sum of the margin calls ($30,000).

You can download below the Excel file for the computation of the Intial and Maintenance Margins for the futures contracts.

Download the Excel file to compute the initial margins for futures

Related posts in the SimTrade blog

   ▶ Akshit GUPTA Initial and Maintenance margin in stocks

   ▶ Akshit GUPTA Analysis of the Big Short movie

   ▶ Akshit GUPTA Analysis of the Margin call movie

   ▶ Akshit GUPTA Analysis of the Trading places movie

Useful resources

Maintenance margin

Initial Margin

Financial Industry Regulatory Authority (FINRA)

Prof. Longin’s website Margin Call mechanism for a futures contract (in French).

About the author

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

VICOBA

VICOBA

Louise Pizon

In this article, Louise PIZON (ESSEC Business School, Master in Strategy & Management of International Business (SMIB), 2020-2022) explains the concept of Village Community Bank (VICOBA) which is a type of micro funding used in non-governmental organizations (NGOs).

Village Community Bank (VICOBA)

Village Community Bank (VICOBA) is a savings and loan fund for members who have joined together and formed a group for economic improvement purposes. The system started in Tanzania twenty years ago and has shown great success for its members in being able to lend to each other, helping each other to solve various problems as well set up joint economic projects.

What is micro funding? It is the funding of projects that are too small to gain support from mainstream venture capital firms. Through micro funding, such projects can be linked to a group of investors willing to take a chance on the project. Micro funding allows entrepreneur to network with investors and managers to grow your business to the next level.

Purpose of VICOBA group and activities

A VICOBA group is a group of people who have agreed to gather their strengths and resources together to eradicate poverty and bring development in their household. Group members will participate to all group activities as weekly meetings and general meetings, elect a steering committee and establish group rules to guide them.

It is better for groups to meet weekly to increase intimacy and help the loan’ fund to grow faster. Previous experiences have shown that the growth of a group that meet weekly is faster from the growth of a group that meet once a month. Weekly meeting helps people to repay their loans on time and keep the cash flow within the group stable.

All VICOBA group activities are run by the group members themselves through volunteerism. By experience, group members can effectively carry out their activities after receiving leadership training and management teamwork from VICOBA experts, which are AJISO members.

AJISO is a legal aid provision organization in Tanzania who help women to empowered themselves economically, in pushing them to be part of a VICOBA group and giving them some entrepreneurship trainings to allows them to be engage in economic activities and improve their livelihood.

Steps to create a VICOBA group

A VICOBA group is created following the steps:

  • Persons with the idea of starting a group to meet (not less than fifteen and not more than thirty)
  • Members collect admission fees usually 10,000 TZS per member. The money is used to buy equipment such as ledgers, passbooks, etc.; Admission fees are also used for group registration costs.
  • Members start the training supervised by an AJISO trainer.
  • Members formulate a constitution and rules that will include the level of shares, the social fund, and the date of the meeting.

Details rules of VICOBA

VICOBA groups are made up of 15 to 30 members who are self-selected. These groups come from small groups of five people whose members select and assemble themselves. Members begin to buy shares in the group and after a delay of twelve weeks the share fund begins to be lent to its members.

Membership of VICOBA groups is open to women and men and at least two of the five members of the steering committee must be women. A person who is heavily indebted to other such groups will not be allowed to join the scheme until he has paid off his debts. Religious and government leaders will be allowed to join the program but will not be allowed to lead the group, these leaders are not allowed to be leaders due to their positions and responsibilities they have in society, instead they will be group advisors.

VICOBA groups elect their leaders who are a chairperson, a secretary, a treasurer and two accountants. The members of the steering committee are elected annually and may be removed at any time if 2/3 of the members of the general assembly decide so. VICOBA groups formulate their own rules of governance, and these groups are self-reliant. Each group has its own rules, and these rules give authority to the group leadership to show direction for the conduct of group activities as well as the resolution of group conflicts.

Each participant needs to know all the group rules and to follow them accordingly. VICOBA groups met in a specific order: group members determine the best time to meet. This system emphasizes the group meeting weekly but may consider meeting monthly but will depend on the activities carried out by the members themselves. The group will agree on the date of the meeting, the time of the meeting and the place of meeting. However, the group is important to consider what this program suggests to going further strengthen collaboration and communication to be closer among members. Meeting weekly will allow some members who want to take out loans to make it easier and those who want to take out emergency loans as well.

Fundraising process

The fundraising process involves banking transactions and group deposits using a share system. Shares are funds that are injected by a group member into the group for the purpose of making profit and becoming the owner of the group. Group members contribute financially each week in buying one to five shares. The value of one share will be based on the agreement of the members of the group and it is recommended that the rate take into account the economic potential of its members.

This system requires that each group members buy shares in loudly stating the number of shares that they are buying and the amount of social fund they are investing. The member will submit the money to the accountants and the book is handed over to the treasurer for replenishing the shares and social fund in which the member has invested.

Procedures for repaying loans

Loans given to VICOBA members should be repaid with a small interest which is used to fund the group. This supplement is distributed as a benefit to members based on the number of shares, they have each deposited. Initial loans that are usually smaller than subsequent loans are required to be repaid for a period of 3 to 6 months.

Benefits

VICOBA loans are small loans granted to the poor and low-income households for their microenterprises and small businesses to enable them to raise their income levels and improve their living standards.

This micro lending model has ensured women to be empowered and independent enough through loans taken from VICOBA, using this loan to develop their economic activities. This system promotes the integration of the poor into the process of economic growth, people who do not have access to the formal labor market can often benefit from pooling resources and working in these groups. VICOBA has cultivated the culture of saving, now members have some money to curter their daily expenses and savings. Most of them have changed from poor life to a better life.

Example of success story

Mary CHARLES (37 years old) lives in Usseri division tell her story :

“I am able to provide to my family all the necessities they need, including food, clothing, and medical treatment. I can educate my two children, one is in seventh grade and the other one in third grade, thanks to my dress hair salon business. I obtained the capital to start my business after joining a VICOBA group. I was able to borrow six hundred thousand shillings (600,000 TZS) and so far, I have been able to repay four and half thousand shillings (450,000 TZS) through the profit I get from my business. I aimed to have another salon in the Tara kea Division at the marketplace. Corona’s disease has pushed me back because the business was so volatile, customers were afraid of getting an infection.”

Mary Charles in her shop
 Mary Charles
Source: Mary Charles

Why should I be interested in this post?

If you are interesting to work for a NGO or helping low income people to going out of the poverty through micro funding this post is for you. In this post, I explain the principle of micro funding named VICOBA using by the association AJISO headquarter in Tanzania to help the population to live in better conditions and more particularly empowered women.
This system can be used in all countries, it only needs a good and devoted team to train future members to be autonomous and teach them some basic business knowledges.

Useful resources

VICOBA Micro funding (ICD)

Related posts on the SimTrade blog

   ▶ All posts about professional experiences

   ▶ Louise PIZON My professional experience as a business developer at AJISO

About the author

The article was written in August 2022 by Louise PIZON (ESSEC Business School, Master in Strategy & Management of International Business (SMIB), 2020-2022).

Moving averages

Moving averages

Jayati WALIA

In this article, Jayati WALIA (ESSEC Business School, Grande Ecole Program – Master in Management, 2019-2022) explains the concept of moving averages and its implementation in financial markets as an indicator in technical analysis of stock price movements.

What is a moving average?

A moving average is a technique to analyze a time-series of data points by taking subsets of data and computing their averages. The subsets of data can explicitly be of a fixed size like simple moving averages or implicitly take into account all past points like exponential moving averages. These averages computed on rolling windows constitute a new time series. The aim of this exercise is essentially to filter noise and smoothen out the data in order to identify an overall trend in the data.

In financial markets, moving averages are one of the most popular indicators used in technical analysis. A moving average is used to interpret the current trend of a stock price (or any asset). It basically shows the price fluctuations in a stock as a single curve and is calculated using previous prices. Hence, a moving average is a lagging indicator.

Moving averages can be computed for different time periods such as 10 days, 20 days or 200 days. The greater the length of the time period (the lag in the trend), the greater the degree of smoothness in the moving average, however, the lower the price sensitivity of the moving average.

To measure the direction and strength of a trend, moving averages involve price averaging to establish a baseline. For instance, if the price moves above the average, the indicated trend is bullish and if it moves below the average, the trend is bearish. Moving average crossovers are also used commonly in trading strategies to identify trends. It then involves two moving averages: one computed on a short-term period and another one computed over a long-term period. When a shorter period moving average crosses above a longer period moving average, the trend is identified as bullish and indicates a buy signal. When a shorter period moving average crosses below a longer period moving average, the trend is identified as bearish and indicates a sell signal.

Moving averages are also used in development of other indicators such as Bollinger’s bands and Moving Average Convergence Divergence (MACD).

Types of moving averages

The moving average indicator can be of many types. Two basic types of moving averages and their interpretation are explained below: simple moving average and exponential-weighted moving average.

Simple moving average

Simple moving average (SMA) is the easiest type of moving average to compute. An n-period SMA is simply calculated by taking the sum of the closing prices of an asset for the past ‘n’ time-periods divided by ‘n’.

The formula to compute the SMA at time t is given by:

Simple moving average formula

Where Pi represents the asset price at time i (i indicating any time between the interval [t-n, t]).

If the current asset price is greater than the SMA value, the viewpoint for trend is established as bullish and similarly, if the current asset price is less than the SMA value, the viewpoint for trend is established as bearish.

Figure 1 below illustrates the 20-day and 50-day SMA for Amazon stock price.

Figure 1. 20-day and 50-day simple moving averages for Amazon stock price.
20-day and 50-day SMA for Amazon stock price Source: Computation by author.

We can observe from the above figure that when the price is going down, the SMA also is going downwards (as expected from the formula). It can also be seen that the movement of the SMA curve lags the change in price movements. The greater is the chosen time-period for SMA, the greater is the lag observed. Thus, while a 50-day SMA maybe smoother compared to a 20-day SMA, the lag observed will also be greater.

Exponential-weighted moving average

Exponential-weighted moving average (EWMA), also known as exponential moving average (EMA) is an improvisation of moving average over the SMA. It assigns weights to moving averages such that the recent data points are assigned greater weight factors than older data points. Thus, EWMA is more sensitive to recent price changes and the line is smoother than that of SMA.

The formula to compute the value of the EWMA at time t is given by:

Exponential-weighted moving average formula

Where Pt represents the stock price at time t, and α is a smoothing (or weighting) factor.

The series is initialized as: EWMA0 = P0.

The smoothing factor, α, is a constant value which lies between 0 and 1. The higher the value of α, the greater the weight assigned to the recent data, and the less smooth the EWMA curve.

How to set alpha for an exponential-weighted moving average?

α can be varied by a trader using EWMA based on how heavily he or she wants the recent data to be weighted. If a single EWMA is being considered, an optimal value for alpha can be chosen by minimizing the mean-squared errors (MSE).

A rule of thumb sometimes by traders is specified as:
Alpha for EWMA

For instance, for a short-term EWMA with the lookback period, n = 20, and alpha is equal to 2/21 = 0.095. For a long-term EWMA with n = 50, and alpha is equal to 0.039. Note that n is not related to a meaningful number of days like for the SMA.

When α=2/(n+1), the weights of an SMA and EWMA have the same center of mass.

A more sophisticated method is to relate alpha to the ‘half-life’ concept, meaning how long it takes for the weight to become half of the weight of the most recent data.

If the formula of EWMA is expanded for k days, we get the following:

EWMA formula expanded

For α=2/(n+1), the idea is that for a sufficiently large value of n, the sum of weights assigned to last n days is around 86%.

Figure 2 below illustrates the weights of each day for a EWMA with α equal to 3.92% (corresponding to n equal to 50 with the rule of thumb used by traders). It can be observed that the weights are decreasing in an exponential fashion and lower values are assigned as weights to the least recent days. The sum of the weights assigned to the first 10 days is 35.60 %, the first 50 days 86.47%, and the first 100 days 98.24%.

Figure 2. Weights of each day for an EWMA
EWMA day weights
Source: Computation by author.

Crossovers

EWMA is typically used in crossovers, which is a common strategy used by traders wherein two or more moving averages can help determine a more long-term trend. Basically, if a short-term EWMA crosses above a long-term EWMA, the crossover indicates an uptrend and similarly, if a short-term EWMA crosses below a long-term EWMA, the crossover indicates a downtrend. Traders can utilize it to establish their position in the stock.

Figure 3. below illustrates short-term and long-term EWMA curves for Amazon stock prices.

Figure 3. Short-term and long-term EWMA for Amazon stock price.
img_SimTrade_EWMA_Amazon_stock
Source: Computation by author.

We can observe in the figure above that the short-term EWMA follows the price movements in Amazon stock more closely than the long-term EWMA does. We can also see that a crossover of the two EWMA curves is followed by a change in trend. For instance, in April 2022, the short-term EWMA crosses below the long-term EWMA and there is an evident downtrend observed post the crossover.

You can also download below the Excel file for computation of SMA and EWMA for Amazon stock price and visualize the above graphs.

Download the Excel file to compute SMA and EWMA for Amazon stock price

Related posts on the SimTrade blog

   ▶ Jayati WALIA Trend analysis and trading signals

   ▶ Jayati WALIA Bollinger bands

   ▶ Akshit GUPTA Momentum trading strategy

Useful resources

Hunter, J. S. (1986). The exponentially weighted moving average. Journal of Quality Technology, 18:203–210.

Wikipedia Moving averages

National Institute of Standards and Technology (NIST) US Department of Commerce Single Exponential Smoothing

About the author

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

Pricing barrier options with simulations and sensitivity analysis with Greeks

Pricing barrier options with simulations and sensitivity analysis with Greeks

Shengyu ZHENG

In this article, Shengyu ZHENG (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023) explains the pricing of barrier options with Monte-Carlo simulations and the sensitivity analysis of barrier options from the perspective of Greeks.

Pricing of discretely monitored barrier options with Monte-Carlo simulations

With the simulation method, only the pricing of discretely monitored barrier options can be handled since it is impossible to simulate continuous price trajectories with no intervals. Here the method is illustrated with a down-and-out put option. The general setup of economic details of the down-and-out put option and related market information are presented as follows:

General setup of simulation for barrier option pricing

Similar to the simulation method for pricing standard vanilla options, Monte Carlo simulations based on Geometric Brownian Motion could also be employed to analyze the pricing of barrier options.

Figure 1. Trajectories of 600 price simulations.

With the R script presented above, we can simulate 6,000 times with the simprice() function from the derivmkts package. Trajectories of 600 price simulations are presented above, with the black line representing the mean of the final prices, the green dashed lines 1x and 2x standard deviation above the mean, the red dashed lines 1x and 2x derivation below the mean, the blue dashed line the strike level and the brown line the knock-out level.

The simprice() function, according to the documentation, computes simulated lognormal price paths with the given parameters.

With this simulation of 6,000 price paths, we arrive at a price of 0.6720201, which is quite close to the one calculated from the formulaic approach from the previous post.

Analysis of Greeks

The Greeks are the measures representing the sensitivity of the price of derivative products including options to a change in parameters such as the price and the volatility of the underlying asset, the risk-free interest rate, the passage of time, etc. Greeks are important elements to look at for risk management and hedging purposes, especially for market makers (dealers) since they do not essentially take these risks for themselves.

In R, with the combination of the greeks() function and a barrier pricing function, putdownout() in this case, we can easily arrive at the Greeks for this option.

Barrier option R code Sensitivity Greeks

Table 1. Greeks of the Down-and-Out Put

Barrier Option Greeks Summary

We can also have a look at the evolutions of the Greeks with the change of one of the parameters. The following R script presents an example of the evolutions of the Greeks along with the changes in the strike price of the down-and-out put option.

Barrier option R code Sensitivity Greeks Evolution

Figure 2. Evolution of Greeks with the change of Strike Price of a Down-and-Out Put

Evolution Greeks Barrier Price

Download R file to price barrier options

You can find below an R file (file with txt format) to price barrier options.

Download R file to price barrier options

Why should I be interested in this post?

As one of the most traded but the simplest exotic derivative products, barrier options open an avenue for different applications. They are also very often incorporated in structured products, such as reverse convertibles. It is, therefore, important to be equipped with knowledge of this product and to understand the pricing logics if one aspires to work in the domain of market finance.

Simulation methods are very common in pricing derivative products, especially for those without closed-formed pricing formulas. This post only presents a simple example of pricing barrier options and much optimization is needed for pricing more complex products with more rounds of simulations.

Related posts on the SimTrade blog

   ▶ All posts about Options

   ▶ Shengyu ZHENG Barrier options

   ▶ Shengyu ZHENG Pricing barrier options with analytical formulas

Useful resources

Academic articles

Broadie, M., Glasserman P., Kou S. (1997) A Continuity Correction for Discrete Barrier Option. Mathematical Finance, 7:325-349.

Merton, R. (1973) Theory of Rational Option Pricing. The Bell Journal of Economics and Management Science, 4:141-183.

Paixao, T. (2012) A Guide to Structured Products – Reverse Convertible on S&P500

Reiner, E.S., Rubinstein, M. (1991) Breaking down the barriers. Risk Magazine, 4(8), 28–35.

Rich, D. R. (1994) The Mathematical Foundations of Barrier Option-Pricing Theory. Advances in Futures and Options Research: A Research Annual, 7:267-311.

Wang, B., Wang, L. (2011) Pricing Barrier Options using Monte Carlo Methods, Working paper.

Books

Haug, E. (1997) The Complete Guide to Option Pricing. London/New York: McGraw-Hill.

Hull, J. (2006) Options, Futures, and Other Derivatives. Upper Saddle River, N.J: Pearson/Prentice Hall.

About the author

The article was written in June 2022 by Shengyu ZHENG (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023).

Asset Allocation

Asset Allocation

Akshit Gupta

This article written by Akshit GUPTA (ESSEC Business School, Grande Ecole Program – Master in Management, 2019-2022) explains asset allocation, a much-discussed topic in asset management.

Introduction

Asset allocation refers to the process of dividing an investment among different assets and, at a more integrated level, asset classes, sectors of the economy and geographical areas.

The allocation of an investor’s money across different assets can be analyzed according to different dimensions: investment objective, risk profile, and time horizon. The allocation process helps in finding a right balance between these dimensions and ultimately generates optimal returns in terms of expected return and risk. A key concept underlying asset allocation is diversification.

There are several assets in financial markets that the investor can use in his/her asset allocation. These asset classes include traditional assets like equities, bonds and cash, and alternative assets like real estates, commodities, and cryptocurrencies. Investors may also use combinations of such basic assets like mutual funds, exchange trade funds and more complex products like structured products.

Basics of asset allocation

Characteristics of investors

The characteristics of asset allocation for investors comes from its significant impact on the portfolio performance. Asset allocation decisions rely on input of the process: investment objective, risk profile, and time horizon.

Investment objective

The process of asset allocation impacts the financial objectives of the investor. If the investor has a low-risk appetite, he/she might be exposed to high degree of risk by investing in equities. Thus, such an investor should invest in safer assets such as bonds and fixed deposits to have a low-risk portfolio.

Risk Profile

The risk appetite of an investor determines the mix of different asset classes in a portfolio. Investors aiming for low risk should include a comparatively higher mix of risk less assets like bonds and real estate than equities.

Time horizon

The time horizon of an investment is also an important characteristic of the asset allocation process. Investors can either invest for a long-term time horizon or a short term depending on their investment objective.

Characteristics of assets

The characteristics of asset allocation comes from its significant impact on the portfolio performance. Asset allocation decisions can also rely on asset’s features such as: Expected returns, risk, and correlation.

Expected returns

The main focus of any investment in financial markets is to make maximum profits (returns) within a coherent risk level. Different asset classes have traditionally offered different returns, determined by their risk levels and market correlation. Generally, bonds have offered a lower long-term return as compared to the equity markets. Thus, investors aiming for higher returns should include an higher mix of these high return asset classes like equities than bonds.

Risk

Different asset classes have different characteristics and thus, different risk levels. The bonds market is generally considered less risky as compared to the equity markets. Thus, investment in bonds exposes the investor to a lower degree of risk than investing in equities.

Correlation

Different asset classes differ in their correlation which is also an important factor while deciding the optimal portfolio mix. It is possible that one asset class might be increasing in value whereas the other may be decreasing in value. For example, if the bonds markets are trending upwards, it is possible that the equity markets might be falling. Thus, by having an optimal mix of these asset, the investor can be compensated for the losses in equity markets with gains in the bond markets. Degree of correlation plays an important role in protecting the investor from downfalls in one asset class by compensating the losses with gains in other asset class.

Asset allocation processes

The asset allocation processes can be divided into two types: strategic asset allocation and tactical asset allocation.

Strategic asset allocation

Strategic asset allocation is a long-term investment strategy driven by long term market outlook and fundamental trends in the market. The strategy follows a top-down approach, and the investor generally looks at the macro level trends followed by trends in different asset classes to take the investment decisions. The investor following this allocation type generally has a pre-defined return expectation and risk tolerance levels and practices diversification to lower the risk. These investments are made in traditional assets like equities, bonds and cash assets but can also include alternative assets.

The investor follows a fixed objective which remains unchanged throughout the investment horizon. This can include a policy mix of investing 40% of portfolio in equities, 30% in bonds, 10% in real estate and remaining 20% in cash. As opposed to the tactical asset allocation, strategic asset allocation involves periodical rebalancing of the portfolio to get higher returns. If the investor diverges from the fixed objective, he/she must rebalance the portfolio to unify it with the original mix.

This strategy is suited to new or irregular investors who seek to generate returns at par with the market returns. The standard asset class suited for this strategy includes mutual funds, ETFs, blue-chip equities, bonds, fixed deposits, and real estate.

Tactical asset allocation

Tactical asset allocation involves actively investing in asset and securities to enhance portfolio returns by constantly rebalancing the portfolio and exploiting market anomalies. Even though the investor is following strategic asset allocation, the financial markets often present attractive buying or selling opportunities which can be exploited by tactical asset allocation to attain even higher returns. These opportunities can involve cyclical deviations in businesses, momentum trends and exploiting under valuations. However, these deviations from strategic allocation are often done carefully so as not to hinder the long-term objective.

The investment horizon in this strategy can be short or long depending on the investor’s preferences. However, the investor tries to generate higher returns and constantly rebalances the portfolio to achieve these returns by exploiting the market inefficiencies. Tactical asset allocation requires good understanding of the financial markets and is generally practiced by experienced investors with moderate to high risk tolerance.

Asset allocation over time

The investors deciding on the asset allocation process over time can follow different approaches, which includes:

Passive management: the buy-and-hold approach

In a passive asset management, the aim of the investor is to replicate the performance of a benchmark index. These investors can have lower risk appetite; thus, replications help to reduce the risk exposure for them. The investors following a passive approach can buy the individual components of the index by applying similar weights and invest with a moderate to long term time horizon in mind. The suitable asset classes for such investors can include mutual funds, exchange traded funds, index funds, etc.

Active management: dynamic asset allocation

In active asset management, the aim of the investor is to maximize the returns on the portfolio by actively investing in asset classes. The portfolio mix is frequently adjusted to capitalize on the short-term trends across different asset classes. The rebalancing decisions are based on business and economic cycles, momentum trends, relative valuations across different asset classes and macro factors like inflation, GDP growth, etc. The investor tries to beat the benchmark indices by dynamically trading in different asset classes and exploiting the market inefficiencies. They generally have high risk appetite and good knowledge about different asset classes. The suitable asset classes for such investors can include equities, commodities, and bonds.

Useful resources

US Securities and Exchange Commission (SEC) Asset Allocation

Related Posts

   ▶ Youssef LOURAOUI Systematic risk and specific risk

   ▶ Youssef LOURAOUI Portfolio

About the author

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

Pricing barrier options with analytical formulas

Pricing barrier options with analytical formulas

Shengyu ZHENG

As is mentioned in the previous post, the frequency of monitoring is one of the determinants of the price of a barrier option. The higher the frequency, the more likely a barrier event would take place.

In this article, Shengyu ZHENG (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023) explains the pricing of continuously and discretely monitored barrier options with analytical formulas.

Pricing of standard continuously monitored barrier options

For pricing standard barrier options, we cannot simply apply the Black-Sholes-Merton Formula for the particularity of the barrier conditions. There are, however, several models available developed on top of this theoretical basis. Among them, models developed by Merton (1973), Reiner and Rubinstein (1991) and Rich (1994) enabled the pricing of continuously monitored barrier options to be conducted in a formulaic fashion. They are concisely put together by Haug (1997) as follows:

Knock-in and knock-out barrier option pricing formula

Knock-in barrier option pricing formula

Knock-in barrier option pricing formula

Pricing of standard discretely monitored barrier options

For discretely monitored barrier options, Broadie and Glasserman (1997) derived an adjustment that is applicable on top of the pricing formulas of the continuously monitored counterparts.

Let’s denote:

Knock-in barrier option pricing formula

The price of a discretely monitored barrier option of a certain barrier price equals the price of a continuously monitored barrier option of the adjusted price plus an error:

Knock-in barrier option pricing formula

The adjusted barrier price, in this case, would be:

Knock-in barrier option pricing formula

Knock-in barrier option pricing formula

It is also worth noting that the error term o(·) grows prominently when the barrier approaches the strike price. A threshold of 5% from the strike price should be imposed if this approach is employed for pricing discretely monitored barrier options.

Example of pricing a down-and-out put with R with the formulaic approach

The general setup of economic details of the Down-and-Out Put and related market information is presented as follows:

Knock-in barrier option pricing formula

There are built-in functions in the “derivmkts” library that render directly the prices of barrier options of continuous monitoring, such as calldownin(), callupin(), calldownout(), callupout(), putdownin(), putupin(), putdownout(), and putupout (). By incorporating the adjustment proposed by Broadie and Glasserman (1997), all barrier options of both monitoring methods could be priced in a formulaic way with the following function:

Knock-in barrier option pricing formula

For example, for a down-and-out Put option with the aforementioned parameters, we can use this function to calculate the prices.

Knock-in barrier option pricing formula

For continuous monitoring, we get a price of 0.6264298, and for daily discrete monitoring, we get a price of 0.676141. It makes sense that for a down-and-out put option, a lower frequency of barrier monitoring means less probability of a knock-out event, thus less protection for the seller from extreme downside price trajectories. Therefore, the seller would charge a higher premium for this put option.

Download R file to price barrier options

You can find below an R file (file with txt format) to price barrier options.

Download R file to price barrier options

Why should I be interested in this post?

As one of the most traded but the simplest exotic derivative products, barrier options open an avenue for different applications. They are also very often incorporated in structured products, such as reverse convertibles. It is, therefore, important to understand the elements having an impact on their prices and the closed-form pricing formulas are a good presentation of these elements.

Related posts on the SimTrade blog

   ▶ All posts about options

   ▶ Shengyu ZHENG Barrier options

   ▶ Shengyu ZHENG Pricing barrier options with simulations and sensitivity analysis with Greeks

Useful resources

Academic research articles

Broadie, M., Glasserman P., Kou S. (1997) A Continuity Correction for Discrete Barrier Option. Mathematical Finance, 7:325-349.

Merton, R. (1973) Theory of Rational Option Pricing. The Bell Journal of Economics and Management Science, 4:141-183.

Paixao, T. (2012) A Guide to Structured Products – Reverse Convertible on S&P500

Reiner, E.S., Rubinstein, M. (1991) Breaking down the barriers. Risk Magazine, 4(8), 28–35.

Rich, D. R. (1994) The Mathematical Foundations of Barrier Option-Pricing Theory. Advances in Futures and Options Research: A Research Annual, 7:267-311.

Wang, B., Wang, L. (2011) Pricing Barrier Options using Monte Carlo Methods, Working paper.

Books

Haug, E. (1997) The Complete Guide to Option Pricing. London/New York: McGraw-Hill.

Hull, J. (2006) Options, Futures, and Other Derivatives. Upper Saddle River, N.J: Pearson/Prentice Hall.

About the author

The article was written in July 2022 by Shengyu ZHENG (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023).

Barrier options

Barrier options

Shengyu ZHENG

In this article, Shengyu ZHENG (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023) explains barrier options which are the most traded exotic options in derivatives markets.

Description

Barrier options are path dependent. Their payoffs are not only a function of the price of the underlying asset relative to the option strike, but also depend on whether the price of the underlying asset reached a certain predefined barrier during the life of the option.

The two most common kinds of barrier options are knock-in (KI) and knock-out (KO) options.

Knock-in (KI) barrier options

KI barrier options are options that are activated only if the underlying asset attains a prespecified barrier level (the “knock-in” event). With the absence of this knock-in event, the payoff remains zero regardless of the trajectory of the price of the underlying asset.

Knock-out (KO) barrier options

KO barrier options are options that are deactivated only if the underlying asset attains a prespecified barrier level (the “knock-out” event). In the presence of this knock-out event, the payoff remains zero regardless of the trajectory of the price of the underlying asset.

Observation

The determination of the occurrence of a barrier event (KI or KO conditions) is essential to the ultimate payoff of the barrier option. In practice, the details of the KI or KO conditions are precisely defined in the contract (called “Confirmations” by the International Swaps and Derivatives Association (ISDA) for over-the counter (OTC) traded options).

Observation period

The observation period denotes the period where a barrier event (KI or KO) can be observed, that is to say, when the price of the underlying asset is monitored. There are three styles of observation period: European style, partial-period American style, and full-period American style.

  • European style: The observation period is only the expiration date of the barrier option.
  • Partial-period American style: The observation period is part of the lifespan of the barrier option.
  • Full-period American style: The observation period spans the whole period from the effective date to the expiration date of the barrier option.

Monitoring method

There are two typical types of monitoring methods in terms of the determination of a knock-in/knock-out event: continuous monitoring and discrete monitoring. The monitoring method is one of the key factors in determining the premium of a barrier option.

  • Continuous monitoring: A knock-in/knock-out event is deemed to take place if, at any time in the observation period, the knock-in/knock-out condition is met.
  • Discrete monitoring: A knock-in/knock-out event is deemed to occur if, at pre-specific times in the observation period, usually the closing time of each trading day, the knock-in/knock-out condition is met.

Barrier Reference Asset

For the most cases, the Barrier Reference Asset is the underlying asset itself. However, if specified in the contract, it can be another asset or index. It can also be other calculatable properties, such as the volatility of the asset. In this case, the methodology of calculating such properties should be clearly defined in the contract.

Rebate

For knock-out options, there could be a rebate. A rebate is an extra feature and it corresponds to the amount that should be paid to the buyer of the knock-out option in case of the occurrence of a knock-out event.

In-out parity relation for barrier options

Analogous to the call-put parity relation for plain vanilla options, there is an in-out parity relation for barrier options stating that a long position in a knock-in option plus a long position in a knock-out option with identical strikes, barriers, monitoring methods and maturity is equivalent to a long position in a comparable vanilla option. It could be stated as follows:

Knock-in knock-out barrier option parity relation

Where K denotes the strike price, T the maturity, and B the barrier level.

It is worth noting that this parity relation is valid only when the two KI and KO options are identical, and there is no rebate in case of a knock-out option.

Basic barrier options

There are four types of basic barrier options traded in the market: up-and-in option, up-and-out option, down-and-in option, and down-and-out option. “Up” and “down” denotes the direction of surpassing the barrier price. “In” and “out” depict the type of barrier condition, i.e. knock-in or knock-out. These four types of barrier features are available for both call and put options.

Up-and-in option

An up-and-in option is a knock-in option whose barrier condition is achieved if the underlying price arrives higher than the barrier level during the observation period.

Figure 1 illustrates the occurrence of an up-and-in barrier event for a barrier option with full-period American style and discrete monitoring (the closing time of each trading day).

Figure 1. Illustration of an up-and-in barrier option
Example of an up-and-in call option

Up-and-out option

An up-and-out option is a knock-out option whose barrier condition is achieved if the underlying price arrives higher than the barrier level during the observation period.

Figure 2. Illustration of an up-and-out option

Example of an up-and-out call option

Down-and-in option

A down-and-in option is a knock-in option whose barrier condition is achieved if the underlying price arrives lower than the barrier level during the observation period.

Figure 3. Illustration of a down-and-in option
Example of a down-and-in call option

Down-and-out option

A down-and-out option is a knock-out option whose barrier condition is achieved if the underlying price arrives lower than the barrier level during the observation period.

Figure 4. Illustration of a down-and-out option
Example of a down-and-out call option

Download R file to price barrier options

You can find below an R file to price barrier options.

Download R file to price barrier options

Trading of barrier options

Being the most popular exotic options, barrier options on stocks or indices have been actively traded in the OTC market since the inception of the market. Unavailable in standard exchanges, they are less accessible than their vanilla counterparts. Barrier options are also commonly utilized in structured products.

Why should I be interested in this post?

As one of the most traded but the simplest exotic derivative products, barrier options open an avenue for different applications. They are also very often incorporated in structured products, such as reverse convertibles. Knock-in/knock out conditions are also common features in other types of more complicated exotic derivative products.

It is, therefore, important to be equipped with knowledge of this product and to understand the pricing logics if one aspires to work in financial markets.

Related posts on the SimTrade blog

   ▶ All posts about options

   ▶ Shengyu ZHENG Pricing barrier options with analytical formulas

   ▶ Shengyu ZHENG Pricing barrier options with simulations and sensitivity analysis with Greeks

References

Academic research articles

Broadie, M., Glasserman P., Kou S. (1997) A Continuity Correction for Discrete Barrier Option. Mathematical Finance, 7:325-349.

Merton, R. (1973) Theory of Rational Option Pricing. The Bell Journal of Economics and Management Science, 4:141-183.

Paixao, T. (2012) A Guide to Structured Products – Reverse Convertible on S&P500

Reiner, E.S., Rubinstein, M. (1991) Breaking down the barriers. Risk Magazine, 4(8), 28–35.

Rich, D. R. (1994) The Mathematical Foundations of Barrier Option-Pricing Theory. Advances in Futures and Options Research: A Research Annual, 7:267-311.

Wang, B., Wang, L. (2011) Pricing Barrier Options using Monte Carlo Methods, Working paper.

Books

Haug, E. (1997) The Complete Guide to Option Pricing. London/New York: McGraw-Hill.

Hull, J. (2006) Options, Futures, and Other Derivatives. Upper Saddle River, N.J: Pearson/Prentice Hall.

About the author

The article was written in July 2022 by Shengyu ZHENG (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023).

Fama-MacBeth two-step regression method

Youssef_Louraoui

In this article, Youssef Louraoui (Bayes Business School, MSc. Energy, Trade & Finance, 2021-2022) presents the Fama-MacBeth two-step regression method used to test asset pricing models.

This article is structured as follow: we introduce the Fama-MacBeth testing method. Then, we present the mathematical foundation that underpins their approach. We conclude with a practical case study followed by a discussion on econometric issues.

Introduction

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

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

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

Mathematical foundations

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

Step 1: time-series analysis of returns

The model considers the following inputs:

  • The return of N assets denoted by Ri for asset i observed over the time period [0, T].
  • The risk factors denoted by F1 for the market factor influencing the asset returns.

For each asset i from 1 to N, we estimate the following parameters:

img_SimTrade_Fama_MacBeth_time_series

From this model, we obtain a series of coefficients: αi which is the risk premium for asset i and the βi, F1 associated to the market risk factor.

Figure 1 represents for a given asset the regression of a return with respect to the market factor (as in the CAPM). The slope of the regression line corresponds to the market beta of the regression.

Figure 1 Time-series regression.
 Time-series regression Source : computation by the author.

The econometric issues (estimation bias, heteroscedasticity, and autocorrelation) related to the model are discussed in more details in the econometric limitation section.

Step 2: cross-sectional analysis of returns

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

img_SimTrade_Fama_MacBeth_cross_section

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

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

Figure 2. Cross-sectional regression.
 Time-series regression Source: computation by the author.

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

Empirical study by Fama and MacBeth (1973)

Fama-MacBeth performed a second time the cross-sectional regression of monthly stock returns on the equity betas computed on the initial workings to account for the dynamic nature of stock returns, which help to compute a robust standard error to gauge the level of error and assess if there is any heteroscedasticity in the regression. The conclusion of the seminal paper suggests that the beta is “dead”, in the sense that it cannot explain returns on its own (Fama and MacBeth, 1973).

New empirical study

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

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

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

Figure 3. Computation of average returns and betas of the stocks.
img_SimTrade_Fama_MacBeth_method_4 Source: computation by the author.

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

Figure 4. Step 1 of the regression: Time-series analysis of returns
img_SimTrade_Fama_MacBeth_method_1 Source: computation by the author.

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

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

Figure 5. Step 2: cross-sectional analysis of return.
img_SimTrade_Fama_MacBeth_method_2 Source : computation by the author.

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

Figure 6. Hypothesis testing of the cross-sectional regression.
img_SimTrade_Fama_MacBeth_method_1 Source: computation by the author.

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

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

 Download the Excel file to perform a Fama-MacBeth two-step regression method

Econometric issues

Errors in data measurement

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

Asset return heteroscedasticity

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

Asset return autocorrelation

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

Limitation of CAPM

Roll: selection of the appropriate market index

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

Furthermore, the coefficients are sensitive to the market index chosen for the study. These shortcomings must be taken into account when assessing other CAPM studies.

Fama-MacBeth: Stability of the coefficients

The stability of the beta across time is difficult. Fama-MacBeth attempted to address this shortcoming by implementing its innovative approach. However, some points need to be addressed:

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

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

Why should I be interested in this post?

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

Related posts on the SimTrade blog

▶ Jayati WALIA Capital Asset Pricing Model (CAPM)

▶ Youssef LOURAOUI Security Market Line (SML)

▶ Youssef LOURAOUI Origin of factor investing

▶ Youssef LOURAOUI Factor Investing

Useful resources

Academic research

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

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

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

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

Business Analysis

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

About the author

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

Introduction to Hedge Funds

Youssef_Louraoui

In this article, Youssef LOURAOUI (Bayes Business School, MSc. Energy, Trade & Finance, 2021-2022) elaborates on the concept of Hedge Funds. Hedge funds are a type of asset class that differs from standard fixed-income and equities investments in terms of risk/return profile.

The structure of this article is as follows: First, we will define a hedge fund. Second, we provide a historical perspective on the first known hedge fund. Third, we will discuss hedge fund fees. Fourth, we discuss the conventional long-short strategy and provide an overview of the major hedge fund strategies. And finally, we end by discussing the economic importance of hedge funds.

Introduction

There is no straightforward definition of a hedge fund. Simply said, a hedge fund is an investment vehicle that aims to create performance by employing a variety of complex trading strategies. When the first hedge fund was introduced, the term “hedge” referred to lowering risk by investing in both long and short positions at the same time.

Hedge funds are exempted from the financial regulations that apply to other investment vehicles such as mutual funds. On the one hand, hedge funds have a lot of freedom to implement their investment strategy and face minimal disclosure rules. Hedge funds have the freedom to utilize leverage using derivatives products. On the other hand, hedge funds are restricted in the way they raise money from investors. Hedge fund investors must be “accredited investors,” which means they must have a particular amount of financial wealth and/or financial education to invest. Hedge funds have also been subject to a non-solicitation restriction, which means they are not allowed to advertise or aggressively seek individuals for investment.

According to the Security Exchange Commission (SEC, ), the governmental branch for regulated financial markets in the US, a hedge fund can be defined as follows:

“Hedge fund’ is a general, non-legal term used to describe private, unregistered investment pools that traditionally have been limited to sophisticated, wealthy investors. Hedge funds are not mutual funds and, as such, are not subject to the numerous regulations that apply to mutual funds for the protection of investors – including regulations requiring a certain degree of liquidity, regulations requiring that mutual fund shares be redeemable at any time, regulations protecting against conflicts of interest, regulations to assure fairness in the pricing of fund shares, disclosure regulations, regulations limiting the use of leverage, and more.” (SEC)

The first hedge fund: Jones

In 1949, Alfred Winslow Jones is said to have founded the first professional hedge fund and is regarded as the “father of the hedge fund industry”. He set up the fund as a limited partnership, with the hedge fund manager providing significant initial capital and a few significant investors. The fund’s principal strategy was to use a long/short method, the fund being long on undervalued securities and short on overvalued securities. Jones based his investment approach on stock picking (he believed he lacked market timing skills). Hedge funds’ main idea is that they can use leverage to boost returns in both directions.

From 1955 to 1965, Jones is reported to have achieved a 670% return on his hedge fund by taking both long and short positions. Before Jones, short selling had been popular for a long time, but he realized that by balancing long and short positions, he could be relatively immune to overall market changes while benefiting from the relative outperformance of his long positions against his short positions. The performance of Jones’s fund is shown in Figure 1 about the Dow Jones Industrials index used as a benchmark and Fidelity’s highest performing mutual fund. Over the 1960-65 period, the fund managed to multiply its return by a factor of four, which is higher than the best performing mutual fund (Fidelity Trend Fund) and the Dow-Jones industrials.

Figure 1. Alfred Winslow Jones’s hedge fund performance between 1960-65.
img_SimTrade_jones_performance
Source: “The Jones Nobody Keeps Up With” (Fortune, 1966).

Development of hedge funds

Interest in hedge funds grew after Fortune magazine published Jones’s results in 1966, and the Securities and Exchange Commission (SEC) listed 140 hedge funds in 1968. As institutional investors began to embrace hedge funds in the 1990s, the hedge fund industry saw a huge spike in interest. Hedge funds with billions of dollars under management were typical in the 2000s, with total hedge fund assets reaching a peak of nearly $2 trillion before the global financial crisis of 2008, dropping during the crisis, and recently reached a new peak.

Hedge funds’ aggregate positions are much larger than their assets under management due to their leverage, and their trading volume is a much larger part of the aggregate trading volume than their relative position sizes due to their high turnover, so hedge fund trading now accounts for a significant portion of all trading. Given a limited demand for liquidity, there is a limited amount of profit to be made and a limited requirement for active investment in an optimally inefficient market, the quantity of capital committed to hedge funds cannot keep expanding.

Hedge funds fees

Among the most frequent fees in the hedge fund industry, we can name the following:

Management fee

Management fee represents the fees that the hedge funds collect to run their operations (salaries, infrastructure, etc.). The management fee is usually about 3%

Performance fee

The performance fee is a compensation when the hedge fund achieves a certain level of performance. This threshold, called the hurdle rate, represents the minimum performance that a hedge fund has to achieve to charge an incentive fee. This motivates the hedge fund manager to perform and to align its interest with its clients’ interests. Beyond the hurdle rate, the outperformance is shared between the hedge fund manager (20%) and the clients (80%).

The high water mark (HWM) provision is a mechanism where the hedge fund will only charge performance fees if it manages to deliver returns above the returns of the previous period. If the hedge fund is down 50%, the performance achieved to recover the losses (100% won’t be subject to performance fees). Only after recovering entirely from the drawdown, the hedge fund can be entitled to earn the performance fee.

A classic hedge fund strategy: the long-short strategy

The long-short strategy is the strategy implemented by the first hedge fund (Alfred Winslow Jones fund). According to Credit Suisse, long-short equity funds engage in both the long and short sides of the equity markets, to diversify or hedge across sectors, regions, and market capitalizations. Managers can switch from value to growth, from small to medium to large capitalization equities, and from net long to net short positions. Managers can also trade stock futures and options, as well as equity-related instruments and debt, and form more concentrated portfolios than classic long-only equity funds.

To illustrate a long-short strategy, we create a hedge fund portfolio based on two stocks from the US equity market. We pick one overvalued stock and one undervalued stock based on their price-to-earnings (P/E) ratio. We chose for this purpose Twitter (overvalued) and Pfizer (undervalued). We download a time series of three-month worth of data for two stocks (Twitter and Pfizer) and the S&P500 index.

Figure 2 represents the regression of the returns of the simulated hedge fund portfolio on the S&P500 index. We can appreciate a null slope (0.0936) of the regression indicating the low correlation of the hedge fund with the market represented by the S&P500 index. This strategy is market-neutral, meaning that the portfolio is not correlated directly with the market fluctuations. The performance of a zero-beta portfolio would be derived from the alpha, a key metric in the portfolio management industry.

Figure 2. Regression of the hedge fund return on the S&P500 market index.
Hedge fund portfolio regression
Source: computation by the author (data: Bloomberg).

We compute the return and volatility of each security and the market index as a starting point. We also determine the correlation of the stocks to the market index. For the short position (Twitter), the sign of the correlation inverts of the sign. We compute an equally-weighted portfolio composed of two stocks: a long position on Pfizer and a short position on Twitter. This portfolio delivered a return of 0.27%, which is better than the broader stock index return over the same period (-0.22%).

Figure 3 depicts the return of the hedge fund portfolio relative to the market index return. From the analysis, the long-short strategy managed to outperform the S&P500 market index by 49 basis points. Even if the market is in a bearish setting, the strategy managed to deliver positive returns as the short position helps to be uncorrelated the return of the hedge fund from the market return.

Figure 3. Return of the hedge fund relative to the S&P500 market index.
Long short strategy performance
Source: computation by the author (data: Bloomberg).

You can download below the Excel file below which gives the details of the computation of the long-short strategy example.

Excel file for the long-short startegy example

Hedge fund role in economy

Hedge funds, for example, are frequently criticized in the media. Companies, for example, dislike seeing their shares shorted because it indicates a belief that the company’s share price will fall. Short sellers, including hedge funds, are sometimes blamed for a company’s problems, even though the stock price is usually falling due to the company’s poor financial condition, not because of any other source.

Hedge funds, in general, serve several important functions in the economy. First, they improve market efficiency by gathering information about businesses and incorporating it into prices through their trades. Because the capital market is the tool used to allocate resources in the economy, increased efficiency can improve real economic outcomes. Companies with good growth prospects see their share prices rise when markets are efficient, allowing them to raise capital and fund new projects. Companies that produce goods and services that are no longer required to see their share prices fall and the factories may be repurposed for more productive purposes, possibly leading to a merger. Furthermore, when share prices reflect more information and are more efficient, CEO decisions may improve, and they may be more prudent if active investors are monitoring them. Hedge funds also serve as a source of liquidity for other investors who need to buy or sell (e.g., to smooth out their consumption), hedge or buy insurance, or simply enjoy certain types of securities. Finally, hedge funds offer investors another source to diversify their returns.

Why should I be interested in this post?

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

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

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

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

Useful resources

Academic research

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

Business Analysis

Wikipedia Alfred Winslow Jones

Fortune (2015) The Jones Nobody Keeps Up With (Fortune, 1966).

SEC Mutual Funds and Exchange-Traded Funds (ETFs) – A Guide for Investors.

SEC Selected Definitions of “Hedge Fund”

Credit Suisse Hedge fund strategy

Credit Suisse Hedge fund performance

Credit Suisse Long-short strategy

Credit Suisse Long-short performance benchmark

Related posts on the SimTrade blog

   ▶ Shruti CHAND Financial leverage

   ▶ Akshit GUPTA Initial and maintenance margins in futures contracts

   ▶ Akshit GUPTA Hedge funds

About the author

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

Branding and marketing in the financial services sector

Branding and marketing in the financial services sector

Samantha MARCUS Branding and Marketing in Financial Services

In this article, Samantha MARCUS (ESSEC Business School, Semester Exchange BBA, 2022) explains the changes and trends in branding and marketing in the financial services sector.

While generally overlooked, the messaging and branding of a financial institution is becoming increasingly more important, and financial institutions are facing pressure to act.

Financial service marketing uses various strategies and branding techniques to drive awareness and create brand loyalty. Unlike traditional tangible products, financial service providers must plant their brand in their consumers’ mind in any way they can because it is generally not a tangible product they are selling. The customer experience is extremely important in the financial services sector, and this is where branding and marketing play in. While the marketing approaches of financial products is oftentimes different than that of retail or consumer packaged goods, the marketing of financial services primarily utilizes two methods: traditional marketing and digital marketing

Traditional marketing

In the past, financial institutions have relied on marketing tactics such as word of mouth advertising, TV ads, radio and print marketing. As the consumer in the financial services sector is changing, these tactics are not as effective in this industry as they once were.

Digital marketing

In 2022, financial service providers are being pushed to step up their digital marketing efforts. Whether it’s through optimizing your firm’s SEO, creating more personalized algorithms or being active on social media platforms, there are various efforts in digital marketing happening in the financial services industry. The financial services sector is leaning more into digital marketing and this strategy is beginning to show more returns in terms of reaching new consumers and driving brand loyalty.

Why are branding and marketing important in financial services?

Retail banks, investment banks, brokerages, credit card companies, and many more financial service institutions can benefit from branding and marketing. Now more than ever, the younger generations are not trusting financial service institutions with about 92% of millennials claiming they do not trust banks at all (Financeography.com, 2016). Branding and marketing in this sector are so important as they can build this trust with consumers. Traditionally, financial service providers did not have to focus as much on branding and marketing because their services were deemed a necessity and consumers would normally approach them. This is all changing now for a variety of reasons that make this outdated mindset ineffective and dangerous. One of the largest reasons that it’s important for financial service providers to utilize marketing is the commoditization of financial products; standardization has made it harder for providers to differentiate their products as there are now more options than ever before. In addition, disruptive financial technology (like blockchains/cryptos) is changing the financial services sector and decentralizing the control, therefore marketing and grasping consumer awareness is more vital than ever. Lastly, as our world becomes increasingly more digitized, consumers are expecting personalized, digitized experiences regardless of the industry.

Trends for financial services marketing

The Rise of Personalization

As the financial service industry becomes increasingly more saturated and decentralized, personalization within marketing strategies offers a way for firms to differentiate themselves and shift the focus to a customer centric approach. Traditionally, banking and financial services have been more focused on their products rather than their consumers, however this is all changing. As within any marketing approach, when financial service providers have a grasp on their audience and demographic, they are better able to appeal to the wants and needs of this consumer and it even begins to become part of their brand. This approach is becoming increasingly more popular, and it helps create more personalized relationships which facilitate customer loyalty which is crucial in the financial services industry.

Digitalization

As there is an emphasis on mobile technology and e-commerce now more than ever, firms are adjusting their marketing strategies to be more digitalized and more customer centric as mentioned above. Many consumers now prefer to manage their finances online, so it only makes sense that digitalization is a priority. Consumers want to manage their money, pay their bills, and buy the things that they need to on their own terms, so it is important that financial brands message this in their marketing strategies as well as process the right technology to cater to this need. Whether it is through marketing techniques such as pay-per-click advertising, email marketing, search engine optimization or activity on social media, financial institutions must push to develop their brand online in order to be noticed amongst competitors.

Intriguing social media content

In the financial service industry, creating impactful content is now being discussed. Video content can be utilized as a great marketing took and form of content as firms can utilize videos to create video courses or webinars to help their target audience understand their products and the more complex financial concepts. It is also a trend to utilize content by showing in your social media posts your firms services and your specialties.

Using personal stories to build a brand

Since the modern consumer has changed especially in the financial sector, product-focused, cold, and impersonal branding does not cut it anymore. Consumers are less trusting of financial service providers so financial brands must find a way to capture their consumer’s attention and create a brand that they trust. While traditionally financial services did not place much emphasis on the human element, there is increasing pressure for financial brands to let this side shine through in their marketing. Is there an interesting story within your company? Is there a personable employee to tell a compelling finance story or explain events in the industry? The question is now: how can a firm use marketing to create trust with their consumers?

Key concepts

Branding

Branding is the personality of a brand. Branding can include everything from your logo to your mission statement; branding is how you define your business. Branding goes beyond just the color of your website or the style of your font, but rather it is how you tell a story and how you draw the attention of your target audience. Branding is how you make a connection with your target customers.

Marketing

While oftentimes people get branding and marketing get confused, there are fundamental differences between the two. Marketing is how a company positions their product based on their brand strategy. Marketing identifies a target market, uses the optimal tactics and segment markets to win over a bigger market share. Branding is all about knowing your company’s story while marketing is more focused on knowing who your target customers are.

Relevance to SimTrade Certificate

The SimTrade certificate allows students to increase their knowledge of financial markets, but it is also important to look at the business behind how financial institutions are able to thrive. Marketing and branding are a large part in this and how firms stay competitive.

Final thoughts

Marketing in financial institutions’ sector is very interesting because it is a quickly evolving area and institutions are experiencing pressure to act in order to sustain their businesses and keep their customers. Financial service marketing is so different from what we know as traditional marketing and advertising as it is not a tangible product, but it is peoples’ financials and essentially their life; this adds a lot of importance on winning over consumers loyalty and trust. As the modern consumer has more options now than ever before, financial firms are placing more importance on marketing and shifting their strategies from a product-obsessed mindset to a customer-focused mindset. Financial firms and their marketing teams must look into getting the right marketing technology to support their consumer centric marketing initiatives. There is so much opportunity to build trustworthy brands and improve customer’s experience through carefully crafted messaging and the right technology. The correct marketing provides a unique opportunity for financial institutions to differentiate themselves in this evolving marketing.

Related posts on the SimTrade blog

▶ Cynthia LIN Financial products marketing in neobanks

▶Ashima MALIK Financial products marketing

Useful resources

Academic books

Heding, T., Knudtzen, C. F., & Bjerre, M. (2009). Brand management: Research, theory and practice. Routledge.

Kapferer, J. N. (2012). The new strategic brand management: Advanced insights and strategic thinking. Kogan Page Publishers.

Business resources

Financeography.com (November 30, 2016) 92% of Millennials Do Not Trust Financial Institutions with Money Matters

O8 Agency for Marketing Financial Services Marketing: Everything You Need to Know

Templafy blog Industry Branding Series: Branding Financial Services

Purpose brand A corporate ESG content strategy puts brands at a competitive advantage.

About the author

The article was written in May 2022 by Samantha MARCUS (ESSEC Business School, Semester Exchange BBA, 2022).

Implied Volatility

Jayati WALIA

In this article, Jayati WALIA (ESSEC Business School, Grande Ecole Program – Master in Management, 2019-2022) explains how implied volatility is computed from option market prices and a option pricing model.

Introduction

Volatility is a measure of fluctuations observed in an asset’s returns over a period of time. The standard deviation of historical asset returns is one of the measures of volatility. In option pricing models like the Black-Scholes-Merton model, volatility corresponds to the volatility of the underlying asset’s return. It is a key component of the model because it is not directly observed in the market and cannot be directly computed. Moreover, volatility has a strong impact on the option value.

Mathematically, in a reverse way, implied volatility is the volatility of the underlying asset which gives the theoretical value of an option (as computed by Black-Scholes-Merton model) equal to the market price of that option.

Implied volatility is a forward-looking measure because it is a representation of expected price movements in an underlying asset in the future.

Computation methods for implied volatility

The Black-Scholes-Merton (BSM) model provides an analytical formula for the price of both a call option and a put option.

The value for a call option at time t is given by:

 Call option value

The value for a put option at time t is given by:

Put option value

where the parameters d1 and d2 are given by:,

call option d1 d2

with the following notations:

St : Price of the underlying asset at time t
t: Current date
T: Expiry date of the option
K: Strike price of the option
r: Risk-free interest rate
σ: Volatility of the underlying asset
N(.): Cumulative distribution function for a normal (Gaussian) distribution. It is the probability that a random variable is less or equal to its input (i.e. d₁ and d₂) for a normal distribution. Thus, 0 ≤ N(.) ≤ 1

From the BSM model, both for a call option and a put option, the option price is an increasing function of the volatility of the underlying asset: an increase in volatility will cause an increase in the option price.

Figures 1 and 2 below illustrate the relationship between the value of a call option and a put option and the level of volatility of the underlying asset according to the BSM model.

Figure 1. Call option value as a function of volatility.
Call option value as a function of volatility
Source: computation by the author (BSM model)

Figure 2. Put option value as a function of volatility.
Put option value as a function of volatility
Source: computation by the author (BSM model)

You can download below the Excel file for the computation of the value of a call option and a put option for different levels of volatility of the underlying asset according to the BSM model.

Excel file to compute the option value as a function of volatility

We can observe that the call and put option values are a monotonically increasing function of the volatility of the underlying asset. Then, for a given level of volatility, there is a unique value for the call option and a unique value for the put option. This implies that this function can be reversed; for a given value for the call option, there is a unique level of volatility, and similarly, for a given value for the put option, there is a unique level of volatility.

The BSM formula can be reverse-engineered to compute the implied volatility i.e., if we have the market price of the option, the market price of the underlying asset, the market risk-free rate, and the characteristics of the option (the expiration date and strike price), we can obtain the implied volatility of the underlying asset by inverting the BSM formula.

Example

Consider a call option with a strike price of 50 € and a time to maturity of 0.25 years. The market risk-free interest rate is 2% and the current price of the underlying asset is 50 €. Thus, the call option is ‘at-the-money’. If the market price of the call option is equal to 2 €, then the associated level of volatility (implied volatility) is equal to 18.83%.

You can download below the Excel file below to compute the implied volatility given the market price of a call option. The computation uses the Excel solver.

Excel file to compute implied volatility of an option

Volatility smile

Volatility smile is the name given to the plot of implied volatility against different strikes for options with the same time to maturity. According to the BSM model, it is a horizontal straight line as the model assumes that the volatility is constant (it does not depend on the option strike). However, in practice, we do not observe a horizontal straight line. The curve may be in the shape of the alphabet ‘U’ or a ‘smile’ which is the usual term used to refer to the observed function of implied volatility.

Figure 3 below depicts the volatility smile for call options on the Apple stock on May 13, 2022.

Figure 3. Volatility smile for call options on Apple stock.
Apple volatility smile
Source: Computation by author.

Excel file for implied volatility from Apple stock option

We can also observe that the for a specific time to maturity, the implied volatility is minimum when the option is at-the-money.

Volatility surface

An essential assumption of the BSM model is that the returns of the underlying asset follow geometric Brownian motion (corresponding to log-normal distribution for the price at a given point in time) and the volatility of the underlying asset price remains constant over time until the expiration date. Thus theoretically, for a constant time to maturity, the plot of implied volatility and strike price would be a horizontal straight line corresponding to a constant value for volatility.

Volatility surface is obtained when values for implied volatilities are calculated for options with different strike prices and times to maturity.

CBOE Volatility Index

The Chicago Board Options Exchange publishes the renowned Volatility Index (also known as VIX) which is an index based on the implied volatility of 30-day option contracts on the S&P 500 index. It is also called the ‘fear gauge’ and it is a representation of the market outlook for volatility for the next 30 days.

Related posts on the SimTrade blog

   ▶ All posts about Options

   ▶ Akshit GUPTA Options

   ▶ Jayati WALIA Brownian Motion in Finance

   ▶ Jayati WALIA Brownian Motion in Finance

   ▶ Youssef LOURAOUI Minimum Volatility Factor

   ▶ Youssef LOURAOUI VIX index

Useful resources

Academic articles

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

Dupire B. (1994). “Pricing with a Smile” Risk Magazine 7, 18-20.

Merton R.C. (1973) “Theory of Rational Option Pricing” Bell Journal of Economics, 4, 141–183.

Business

CBOE Volatility Index (VIX)

CBOE VIX tradable products

About the author

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

A quick presentation of the M&A field…

A quick presentation of the M&A field…

Louis DETALLE

In this article, Louis DETALLE (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023) explains what does an M&A daily life looks like.

What does M&A consist in?

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

What does an analyst work on?

The tasks of an M&A analyst are diverse and include, for example, drawing up a business plan, modelling different scenarios and strategies in Excel, and drafting information memorandums (IMs) on the various deals in progress. All these skills are then widely used for the mergers and acquisitions of companies, in the development of their external strategy, in their financial evaluation or in the analysis of databases. Overall, M&A allows you to move into any sector of finance and this is part of the reason why it is so attractive.

Why does M&A jobs appeal so much to students?

First of all, it is the dynamic working atmosphere that investment banking enjoys that also attracts young graduates. M&A is indeed marked by a culture of high standards and maximum commitment, with highly responsive teams and extremely competent colleagues. Working in a quality team is very stimulating, and often makes it possible to approach the workload with less apprehension and to rapidly increase one’s competence. The remuneration is also much higher than in other professions at the beginning of a professional career for a young graduate and it progresses rapidly. Finally, it is also the exit hypotheses that attract young M&A analysts.

What are the main exits for M&A?

Most professionals who started out in M&A move on to other types of activities where experience in this sector is required. This is particularly the case in private equity. After advising companies on their growth and expansion projects, the young investment banker has all the tools needed to work in investment funds. The skills are indeed transposable to the financial and strategic questions that private equity funds ask themselves in order to obtain a return on investment.

Switching to alternative portfolio management (hedge funds) is also a possibility. Hedge funds can invest in different types of assets such as commodities, currencies, corporate or government bonds, real estate or others. As a former M&A analyst, you have the skills to analyse the market and determine the assets that seem to be the most appropriate and profitable.

Finally, some former M&A bankers switch to corporate M&A, which involves determining which companies or subsidiaries the company should buy or sell. This can be a very interesting area as you have the opportunity to follow the acquisition of a company from start to finish and therefore take a long-term view of the company’s strategy.

Related posts on the SimTrade blog

   ▶ Suyue MA Analysis of synergy-based theories for M&A

   ▶ Louis DETALLE How does a takeover bid work & how is it regulated?

   ▶ Raphaël ROERO DE CORTANZE In the shoes of a Corporate M&A Analyst

   ▶ Basma ISSADIK My experience as an M&A Analyst Intern at Oaklins Atlas Capital

   ▶ Antoine PERUSAT A New Angle in M&A E-Commerce

Useful resources

Décideurs magazine Rankings for M&A banks in France (league tables)

About the author

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

Warren Buffet and his basket of eggs

Warren Buffet and his basket of eggs

Rayan AKKAWI

In this article, Rayan AKKAWI (ESSEC Business School, Master in Strategy & Management of International Business (SMIB), 2021-2022) analyzes the two following quotes “Do not put all eggs in one basket” and “Put all your eggs in one basket and watch that basket” often used by Warren Buffet to describe his investment strategy.

“Do not put all eggs in one basket”

I particularly liked this quote first because it is said by the world’s greatest investor and one of the richest people on the planet, Warren Buffet. I aspire this man due to his great investment philosophy which is to invest in great businesses at value for money prices and then by using the “buy and hold strategy” keep the stocks over the long term. He has bought great brands such as Coca Cola, Microsoft, and American Express. Second, I like this quote particularly because it is dedicated to any person who has little or no knowledge in investment, so it is easy to implement.

Analysis

If we analyze the wealthiest people in the world, they are entrepreneurs who have created companies that grew exponentially in value. For example, Bill gates who is the founder of Microsoft (1975), Jeff Bezos who is the founder of Amazon (1994), and Mark Zuckerberg who is the founder of Facebook (2004). And as we continue to analyze these founders, we come to realize that they have made their wealth by putting all their eggs in one basket at least early in their lives. However, not all of us have this entrepreneurial spirit and business success such as these brilliant men. Thus, when Warren Buffet said “do not put all eggs in one basket” he was referring to an average person who has little knowledge in investments. Therefore, he advocates investment into index tracker or passive funds which have the benefit of low charges, better performance, and large diversification than most active managed funds. This involves a buy and hold strategy which keeps share dealing charges low. Thus, it is always recommended to have 80% of investments in passive funds which are low cost, predictable, and conservative funds and 20% of investments in satellite which usually involve higher charges with greater volatility and greater returns.

Another way of looking at it is the following. One might decide to invest a certain number of personal wealth in a new business or in crypto. This would be a risky type of investment because another competitor might release a better and more attractive or even more affordable version of the product or service. Eventually, this might put you out of business if a customer writes a bad review of your product or business or if the bitcoin value drops.

So before you invest more time and money in your business, consider how you can manage your risk. First, you must think about your risk tolerance which depends on your age and current financial obligations. Second, you need to keep sufficient liquidity in your portfolio by setting aside an emergency fund that should be equal to 6 to 8 months’ expenses. For ensuring that there is easy accessibility to emergency funds, you should have low-risk investment options like Liquid Funds and Overnight Funds in your accounts. Then you need to determine an asset allocation strategy that works which refers to investing in more than one asset class for reducing the investment risks and this strategy also provides you with optimal returns. You can invest in a perfect mix of key asset classes like Equity, Debt, Mutual Funds, real estate, etc. One of the asset allocation strategies is to invest in a combination of asset classes that are inversely correlated to each other. After you have found the best mix of asset classes for your portfolio, you can reduce the overall investment risk by diversifying your investment in the same asset class. Think about diversifying by offering more than one product or service. To avoid liquidity risk, it is always better to stay invested in blue chip stock or fund. Investors should check the credit rating of debt securities to avoid default risk.

“Put all your eggs in one basket and watch that basket”

At the same time, Warren Buffet believes that diversification makes little sense if a person doesn’t know exactly what he or she is doing. Diversification is a protection against ignorance and is for people who do not know how to analyze businesses. Sometimes it is enough to invest in two or three companies that are resistant to competition rather than fifty average companies due to less risk. That is why it is as critical for a person to invest in a company where its values and vision are similar to that of the investor and to be able to watch closely the performance of that business and its stocks.

Thus, Warren Buffet believes that it is extremely crucial to be able to “watch your basket” or your stocks closely to better understand the stock market. For example, when the stock market is going down, it is the best way to start buying stocks because businesses will be selling at a discount.

Why should I be interested in this post?

One would be interested to read this post because it introduces the basics of investing in stock markets for an average person who has little knowledge in investments or for a student studying business. As a student, it is crucial and important to be able to have at least a general idea of the basic rules of investments and especially those stated by one of the most famous investors in the world such as Mr. Warren Buffet. Whether you are interested in buying stocks yourself or whether you are not, as a business student, you might be asked about investments and the financial market one time in your life and knowing some useful information about investments will be impressive for you. It will allow you to understand the bigger picture of financial markets, give some recommendations for your family and friends, and help you invest yourself in the safest and most successful way.

Related posts on the SimTrade blog

   ▶ Youssef LOURAOUI Portfolio

   ▶ Youssef LOURAOUI Passive Investing

   ▶ Youssef LOURAOUI Active Investing

   ▶ Youssef EL QAMCAOUI The Warren Buffett Indicator

Useful resources

Berkshire Hathaway

About the author

The article was written in May 2022 by Rayan AKKAWI (ESSEC Business School, Master in Strategy & Management of International Business (SMIB), 2021-2022).

Big data in the financial sector

Big data in the financial sector

Rayan AKKAWI

In this article, Rayan AKKAWI (ESSEC Business School, Master in Strategy & Management of International Business (SMIB), 2021-2022) explains the role of big data in the financial sector.

Big data is a term used for contemporary technologies and methodologies that are used to collect, process, and analyze complex data. Today, data is being created at an exponential rate. In fact, and according to a 2015 IBM study, 90% of the data in the world has been created in the past two years. As big data gets bigger, it becomes even more important and essential for executives in the financial sector to stay ahead of the curve. Also, it is expected that data creation will continue to grow moving forward in time.

Big Data in The Financial Sector

For decades, financial analysts have relied on data to extract insights. Today, with the rise of data science and machine learning, automated algorithms and complex analytical tools are being used hand in hand to get a head of the curve in diferetn areas of the financial sector.

Fraud prevention

First, data has helped with fraud prevention such as identity theft and credit card schemes. Abnormally high transactions from conservative spenders and out of region purchases often signal credit card fraud. Whenever this happens, the card is automatically blocked, and a notification is sent out to the card owner. This protects users, insurance companies, and banks from huge financial loses in a small period. This also made things even easier and more practical avoiding the hassle of having to call and cancel the card. Data science comes in the form of tool like random forests that can detect a certain suspicion. In addition, and to lower the chance of identity theft, data has helped ease this process through 3D passwords, text messages, and PINT code which have backed up the safety of online transactions.

Anomaly detection

Second, data has helped the financial sector through anomaly detection. Data analysis is not only created to avoid a problem but also to detect it. For example, data today helps with catching illegal insider traders. To do so, data analysts created anomaly detection algorithms that can analyze history in trading patterns and thus detect and catch abnormal transactions of illegal traders.

Customer analytics

Third, data has helped with improving customer analytics. Data analyzes previous behavioral trends of consumers based on historical transactions and then makes future predictions of how consumers are likely to act. With the help of socioeconomic characteristics, we can create clusters of consumers and group customers based on how much money we expect to gain or lose from each client in the future. Following that, we can come up with decisions to focus on a certain type of clients to make profits and cut on other customers to make savings. Thus, financial institutions minimize human errors by utilizing data science. To achieve that, first, by identifying uncertain interactions and then monitor them going forward. Finally, prioritizing the investments most vulnerable at a given time. For example, banks use this approach to create adaptive real risk score time models to identify risky clients and those who are suitable for a mortgage or a loan.

Algorithmic trading

Fourth and most importantly data has created algorithmic trading. Machines make trading based on algorithms multiple times every second with no need for approval by a stand-by analyst. These trades can be in any market and even in multiple markets simultaneously. Thus, algorithmic trading has mitigated opportunity costs. Thus, there are algorithmic rules that can help in identifying if there is a need to trade or not to trade and reinforces business models where errors are highly penalized and then adjust hyper parameters. We can see algorithms that exploit arbitrage opportunities where they can find inconsistencies and make trades which can cause problems. The huge upside is that it is high frequency trading; whenever it will find an opportunity to make a trading, it will. However, the downside is that imprecision could lead to huge losses due to lack of human supervision. That is why sometimes human interventions are needed.

Conclusion

Thus, we can say that data has become the hottest commodity that results in getting an edge over competition. Financial institutions spend a huge amount of money to get exclusive rights to data. By having more information, they can construct better models. The most valuable commodities are not analysts but the data itself. That is how the data science has revolutionized finance.

Characteristics of Big Data

When talking about Big Data, four main characteristics need to be considered to understand the why Big Data plays a transformational role in the financial sector: volume, variety, velocity, and value.

Volume

First, the amount also known as volume of data being produced on daily basis by users has been increasing exponentially by users. This large output of data has helped create Zettabytes (1012 Gigabyte) and Yottabytes (1015 Gigabyte) of datasets in which companies can benefit by extracting knowledge and insights out of it. However, this amount of data cannot be processed using regular computers and laptops. Since they would require a lot of processing power.

Variety

Second, as the massive amount of data is being generated by multiple sources, the output of this data is unstructured making it hard to organize the data extract insights. Raw data extracted from the source without being processed does not provide any value to business as it does provide stakeholders with the ability to analyze it.

Velocity

Third, to address the issue of processing technological advancements have brought us to the tipping point where technologies such as cloud computing have enabled companies to process this large amount of data by utilizing the ability to share computational power. Furthermore, cloud platforms have not only helped in the processing part of data but by the emergence or cloud solution such as data lakes and data warehouses. Businesses are able to store this data in its original from to make sure that they can benefit from it.

Value

Finally, this brings us to the most important aspect of Big Data and that in being able to extract insights and value out of the data to understand what it is telling us. This process is tedious and time consuming however with ETL tool (Extract Transform Load) the data in its raw format is transformed so that standardized data sets can be produced. Insights can be extracted through Business Intelligence (BI) tools to create visualization that help business decisions. As well as predictive artificial intelligence models that help business predict when to take a strategic decision. In the case of financial markets, these decisions are when to buy or sell assets, and how much to invest.

Challenges Solved by Big Data in the Financial Industry

Utilizing Big Data in the finance industry presents a lot of benefits and helps the industry to overcome multiple challenges.

Data Quality

As previously mentioned, the multiple data sources present a huge challenge from a data management standpoint. Making it an ongoing and a tedious effort to maintain the integrity and the reliability of the records collected. Therefore, adding information processing systems and standardizing the data gathering and transformation processes helps improve the accuracy of the decision-making process, especially in financial services companies where real-time data enables fast decision making and elevates the performance of companies.

Data Silos

Since financial data comes from multiple sources (applications, emails, documents, and more), the use of data integration tools help simplifies and consolidate the data of the institution. These technologies facilitate processes and make them faster and more agile, which are important characteristics in the financial markets.

Robo-Advisory

Big Data and analytics have had a huge impact on the financial advisory sector. Where financial advisors are being replaced by machine learning algorithms and AI models to manage portfolio and provide customers with personalized advice and without human intervention.

Why should I be interested in this post?

This article is just an eye opener on the trends and the future state of the financial industry.

Like many other industries, the financial sector is becoming one of the most data driven field. Therefore, as future leaders it is vital to keep track and push towards data driven solutions to excel and succeed within the financial sector.

Related posts on the SimTrade blog

   ▶ All posts about Financial techniques

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

   ▶ Louis DETALLE The importance of data in finance

   ▶ Louis DETALLE Reuters

   ▶ Louis DETALLE Bloomberg

Useful resources

The Future of Cognitive Computing

Five Ways to Use RPA in Finance

About the author

The article was written in May 2022 by Rayan AKKAWI (ESSEC Business School, Master in Strategy & Management of International Business (SMIB), 2021-2022).

Momentum Trading Strategy

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).