The selling process of funds

The selling process of funds

 David-Alexandre Blum

In this article, David-Alexandre BLUM (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2019-2023) explains about the selling process of funds.

The process of selling a fund involves several key steps and stakeholders. The fund is built by management teams that develop the strategy, allocation, and follow the macroeconomic scenario of economists. Let’s take the example of Lazard Frères Gestion.

Lazard Frères Gestion is characterized by active management based on fundamental analysis. Financial analysis and knowledge of companies are at the heart of the management process. All institutional management relies on the same macroeconomic scenario. Financial assets are the major source of financing for the real economy. The economy is cyclical. Financial assets are also cyclical. Volatility or bubble effects can cause the market price of an asset to diverge from its fair economic price in the short term, but not in the long term. Over time, it is the ability to capture the different phases of the cycle that creates outperformance.

Lazard Frères Gestion’s investment philosophy is based on both reading the economic cycle and evaluating companies. When products are launched and marketable, the distribution team prospects new clients (through seminars, contacts, or research), responds to tenders, or meets specific client requests.

The client meeting

Once the initial contact has been made, the sales team must prepare a specialized presentation for clients, gathering necessary documents (such as reports, management points, comparative studies, etc.). The preparation of such a presentation is crucial and should not be overlooked. It is important to choose the right arguments to highlight, taking into account the current economic environment and supporting the scenario put forward by the management team.

Selling a fund requires a thorough understanding of the management process, portfolio values, managers’ philosophy, as well as the benchmark performance and various risk and performance indicators of the presented fund.

Once the speech is prepared and the materials finalized, it is up to the seller to showcase the fund’s qualities. If the meeting goes well and negotiations are successful, the transaction can follow quickly or the client may request an entirely different service, such as a dedicated fund (customized fund), if it meets the criteria for accessing the service.

The team then needs to manage the contractual and administrative aspects to finalize the operation. The potential buyer conducts thorough due diligence on the fund, examining financial documents, contracts, internal procedures, and regulatory compliance to ensure there are no hidden issues or unforeseen risks.

Once due diligence is completed and the terms of the transaction are finalized, the parties draft and sign the sales contracts. In addition, they obtain the necessary approvals from the relevant regulatory authorities to transfer the fund’s management to the buyer.

After obtaining regulatory approvals, the client transfers the fund’s assets and liabilities to the buyer. This may include transferring securities, contracts with custodians and fund administrators, as well as communicating with the concerned investors.

Once the transfer is completed, the buyer integrates the fund into their own management structure and takes over the daily management of the fund, ensuring that investment objectives and applicable regulations are met.

Managing the relationship and the customer service

The team’s work does not stop at mere sales. Its role is much more important. It must ensure the proper receipt of financial and legal documents sent to clients periodically. It must also respond to all information requests about the subscribed products and ensure that the services subscribed by the client are performed. It is their duty to do everything possible to justify any underperformance and to nourish the relationship with information and explanations. To do this, they attend various committees and internal meetings to keep abreast of different movements and tactical bets. The sales team can encourage its client to invest more in its product and propose new products that seem to meet the client’s demand. In the event that the client chooses to withdraw from the fund, the relationship does not end there, and it is up to the sales team to work to potentially bring their client back.

From a technical standpoint, the sales team must master the knowledge of its products. The sales team must deeply understand the characteristics of the fund, including investment strategy, objectives, underlying assets, sectoral and geographical allocation, as well as fees and associated costs.

It is essential to know the historical performance of the fund, risk-adjusted returns (such as the Sharpe ratio), and comparisons with benchmark indices or similar funds. The sales team must be able to explain the main risks associated with the fund, such as market, credit, liquidity, and currency risks, as well as the measures taken by the fund manager to mitigate these risks.

Knowledge of regulations applicable to investment funds, such as UCITS or AIFMD directives in Europe, and disclosure and reporting requirements, is crucial to ensure compliance and client trust.

The sales team must be able to identify target investors for the fund, taking into account their risk profile, investment objectives, and liquidity needs.

Team members must master the procedures for subscribing and redeeming fund shares, including deadlines, fees, and specific conditions.

Communication and presentation: Communication and presentation skills are essential for clearly and convincingly explaining the benefits of the fund and addressing potential clients’ questions.

By mastering these technical aspects, the sales team will be able to effectively present the investment fund to potential clients, address their concerns, and assist them in making informed investment decisions.

Why should I be interested in this post?

The sales process of a fund helps to better understand the functioning of the investment fund market and the dynamics between asset management companies, investors, and financial intermediaries.

Investors who understand the sales process of a fund are better equipped to evaluate fund offerings and make informed investment decisions based on their objectives and risk tolerance. Professionals working or considering working in the financial industry, particularly in the areas of asset management, sales, and investment advisory, will benefit from a deep understanding of the fund sales process to enhance their skills and professional performance.

Furthermore, understanding the sales process of a fund can assist investors and financial advisors in comparing different investment products, such as mutual funds, exchange-traded funds (ETFs), and alternative investment funds, to determine the best solution for their specific needs and objectives.

Related posts on the SimTrade blog

   ▶ Louis DETALLE A quick presentation of the Asset Management field…

   ▶ Tanguy TONEL My experience as an Investment Specialist at Amundi Asset Management

Useful resources

Lazard Frères Gestion

Lazard Frères Gestion Les métiers de la gestion d’actifs (webinaire)

Lazard Frères Gestion Qu’est-ce que la gestion d’actifs ?

Lazard Frères Gestion Quelle allocation d’actifs pour un portefeuille diversifié ?

Hull J., P. Roger (2017) Options futures et autres actifs dérivés Pearson Education.

About the author

The article was written in May 2024 by David-Alexandre BLUM (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2019-2023).

My professional experience as an Institutional Sales Assistant with Lazard Frères Gestion

My professional experience as an Institutional Sales Assistant with Lazard Frères Gestion

 David-Alexandre Blum

In this article, David-Alexandre BLUM (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2019-2023) shares his professional experience as an Institutional Sales Assistant with Lazard Frères Gestion.

About the company

In 1848, Alexandre, Lazare and Simon Lazard, three French brothers from the Alsace region, founded Lazard Frères & Co in New Orleans as a dry goods merchant, having emigrated to the United States in the early 1840s.

Today, Lazard serves investors worldwide with a broad range of global investment solutions and asset management services. Lazard Asset Management operates in 19 countries across North America, Europe and Asia, as well as in Australia. The group focuses on strategies based on rigorous and detailed analysis and dynamic asset management.

Lazard Frères Gestion combines the power of a large global organization with the flexibility of a small entrepreneurial firm and focuses on asset management and advisory services for individual and institutional clients.

With offices in Paris, Lyon, Bordeaux, Nantes, Brussels and Luxembourg, Lazard Frères Gestion manages €30 billion of assets on behalf of institutional and retail clients.

Lazard Frères Gestion Logo
Logo of  Lazard Frères Gestion
Source: Lazard Frères Gestion

The department I joined at Lazard Frères Gestion is the Distribution France sales department. The team handles customer relations and organizes meetings to sell and inform about the company’s funds. The department works closely with the management and marketing teams. Financial analysis and company knowledge are at the heart of Lazard Frères Gestion’s management processes. The model is characterized by an “analyst-manager” approach: the entire team is involved in investment decisions, and each analyst-manager can contribute his or her own valuation and market expertise. In addition, all institutional management is based on the same macroeconomic scenario. Lazard Frères Gestion’s investment philosophy is based on both business cycle analysis and company valuation.

My internship

As an institutional sales assistant, I worked as part of the Distribution France sales team and was in constant contact with customers. Therefore, I took part in all our management committees, videoconferences and events. I worked with management, client servicing, reporting, risk control, legal and middle office.

My missions

  • Follow the sales activity of the Distribution team (multi-managers, private banks, insurance networks and independent asset managers)
  • Participate in specific responses to client requests
  • Help prepare client meetings
  • Prepare quarterly (or more frequent) management reports on flagship funds for clients
  • Summarize videoconferences organized on flagship funds and equity, fixed income, convertible and diversified fund management committees
  • Follow the performance of flagship funds and defined peer groups
  • Send specific documents from our tools (performance attribution, inventories, allocations, specific performance comments and fund positioning).

Required skills and knowledge

Firstly, the position I held required a broad knowledge of all asset classes. Lazard Frères Gestion offers a wide range of investment solutions: equities, bonds, mutual funds, structured products… It is therefore essential to know all the relevant vocabulary and the specific features of all the asset classes. I needed to know the different financial indicators and ratios in order to understand the different financial analyses carried out by the analyst teams.

Thoroughness and efficiency were the qualities that my superiors expected most of me. There was no room for error, even in an emergency, and in a sales department the unexpected was commonplace. When writing memos on funds intended for clients, it was essential to transpose the managers’ analysis correctly. It was also my responsibility to respond to clients as quickly and accurately as possible.

During this internship, I required to master some essential skills in order to be successful such as rigor, adaptability or efficiency. Adaptability was key as my role was cross-functional. I was in contact with most of the departments at Lazard Frères Gestion. I worked on projects with marketing in conjunction with the management teams, or I had to provide answers thanks to the reporting departments, which sent me the data I needed to make my calculations in response to clients.

What I learned

This experience was a real springboard for learning about the finance profession. I had the opportunity to apply the theoretical aspects to real projects and to work on various subjects under the guidance of experienced professionals. I also had the opportunity to perfect my knowledge of the financial market environment through daily contact with the Management, Reporting, Risk, Legal and Marketing teams. In particular, I improved my knowledge of asset management, understanding of macroeconomics and financial analysis.

In particular, I was able to learn a lot about the different asset classes, thanks to the discussions I had with professionals and the various presentations I attended. Additionally, I was able to follow a number of fund sales and look after a variety of clients. I worked on various client presentations and financial documents such as reports and prospectuses.

Financial concepts related to my internship

Sale of financial products

I learnt a lot about commercial sales techniques. It is important to know what elements to emphasize when prospecting or selling financial products, especially when dealing with professional and highly technical clients.

I was able to familiarize myself with macro-economic and financial indicators that helped me understand certain economic scenarios and management decisions.

I therefore carefully analyzed the financial markets and improved my understanding of the different types of financial markets, such as the stock, bond, foreign exchange (Forex) and derivatives markets.

Through various client meetings, I was able to familiarize and educate myself on the different financial products. I dealt with a variety of financial products, such as equities, bonds, currencies, derivatives (options, futures, swaps) and mutual funds.

Fixed income management

Before this internship, I was much more comfortable with equities than bonds. However, the various committees, training sessions and discussions I’ve had have taught me a lot about bond management. In particular, I was able to follow how the management teams manipulated the funds’ modified duration to take advantage of unprecedented market conditions following the crises at the US regional banks and Crédit Suisse.

I was also able to follow the launch of a bond fund and understand the entire portfolio construction process by following the strategy implemented by the management teams.

Macro-economics

During my internship I was able to acquire knowledge in macroeconomics by studying economic indicators, monetary and fiscal policies, international trade, business cycles, exchange rates, the relationship between financial markets and the economy, and sovereign debt. These skills enable me to better understand the global economic environment, assess investment opportunities and risks, and contribute to investment decision-making. At Lazard, a team of macro-economists gives analyses and predicts a scenario for the coming months, which are taken into account by all the managers in different proportions in order to respect the management process and the objective of their funds. It is therefore essential for the distribution team to know and master this scenario in order to explain the performance and strategies implemented.

Why should I be interested in this post?

If you are looking for a formative experience in finance with responsibilities and challenges combining financial expertise and sales, this internship is for you. You will be working on a wide range of assets and investment universes. Lazard Frères Gestion will require you to be rigorous and hard-working, but you’ll learn a lot about asset management.

Related posts on the SimTrade blog

   ▶ All posts about Professional experiences

   ▶ Louis DETALLE A quick presentation of the Asset Management field…

   ▶ Tanguy TONEL My experience as an Investment Specialist at Amundi Asset Management

Useful resources

Lazard Frères Gestion

Lazard Frères Gestion Les métiers de la gestion d’actifs (webinaire)

Lazard Frères Gestion Qu’est-ce que la gestion d’actifs ?

Lazard Frères Gestion Quelle allocation d’actifs pour un portefeuille diversifié ?

Hull J. and P. Roger (2017) Options futures et autres actifs dérivés Pearson Education.

About the author

The article was written in May 2024 by David-Alexandre BLUM (ESSEC Business School, Global Bachelor in Business Administration, 2019-2023).

My Experience as an Investment Intern at Eurazeo

My Experience as an Investment Intern at Eurazeo

Dante Marramiero

In this article, Dante MARRAMIERO (ESSEC Business School, Master in Strategy and Management of International Business (SMIB), 2020-2023) presents its professional experience in Euazeo, a European leading Private Equity based in Paris.

About Eurazeo

Eurazeo stands as a prominent European firm within the world of alternative investments, boasting a diversified portfolio within various investment strategies, including private debt, real Estate, venture, growth, small-mid buyout, and mid-large buyouts. Eurazeo was initially the family office of the Lazard Freres family, but in 2018 decided to merge with Idinvest in order to start fundraising capital from third parties. Following 2018, Eurazeo’s strategy has always been to reduce the balance sheet investments and to increase the third-party capital investments.

Logo of the company.
Logo of  Eurazeo
Source: the company.

Internship Overview

During my time as an Investment Intern at Eurazeo from January 2023 to June 2023, I had the privilege to immerse myself in the intricacies of private equity and alternative investments. My internship included a range of responsibilities aimed at supporting Eurazeo’s investment initiatives. My department was “Direct Transactions” and during my internship, I participated actively in three different activities:

Syndication of Co-Investment Opportunities

I actively participated in the syndication process of four co-investment opportunities across various investment strategies including private debt, growth, and mid-large buyout. This involved conducting comprehensive due diligence, financial analysis, and market research to assess the viability and potential returns of each opportunity. Together, these co-investment opportunities accounted for approximately €750 million in total investment value, underscoring Eurazeo’s commitment to strategic partnerships and collaborative investment initiatives. Co-investments, theoretically speaking can be cataloged under direct transactions as SPV (Special Purpose Vehicle) are created specifically for one single transaction and you are not making the investment for the limited partners but you are making it with the LPs (Limited Partners).

Strategic SPV Structures Analysis

I was tasked with examining strategic Special Purpose Vehicle (SPV) structures solutions for potential investment opportunities. This entailed analyzing, comparing, and developing alternative fundraising structures such as Collateral Fund Obligation and Rated Feeder Fund, focusing on optimizing capital deployment and mitigating risk. The main reason why we were evaluating new financial structures was to attract a category investor that, at the time, was not willing to invest in our funds: American insurance companies. 2023 has been generically speaking a rough year for fundraising capital and for this reason, we decided to implement this kind of solution. A collateral fund obligation is a structure composed by certified debt and equity; this structure will invest in different funds (all managed by Eurazeo) and will have the advantage of using the leverage raised as certified debt to enhance the return on the investment and the Cash on Cash. Therefore, by evaluating various SPV structures, we aimed to enhance our flexibility in structuring investments and optimizing returns for our investors, by using the right amount of leverage.

Evaluation of Secondary Transactions Advisors

I had the opportunity to participate in two competitive selection processes for secondary transaction advisors, tasked with choosing the financial advisor to support us in executing a single asset continuation vehicle. The evaluation process included analyzing and comparing proposed solutions, assessing current market conditions, and evaluating alignment with Eurazeo’s investment strategy and objectives.

Furthermore, this project included evaluation and due diligence, intending to identify strategic partners capable of delivering value-added solutions and maximizing returns for our investors. Single asset continuation vehicles are specialized structures tailored for investments held within the portfolio of a current fund of the firm. These investments require divestment as limited partners seek liquidity. However, recognizing the potential upside, the firm decides to establish these vehicles.

What did I learn during this experience?

My internship at Eurazeo provided invaluable opportunities for skills and knowledge development across various areas:

  • Financial Analysis: I honed my skills in financial modeling, valuation techniques, and investment analysis through hands-on experience with real-world investment opportunities.
  • Due Diligence: I gained practical insights into the due diligence process, including a thorough examination of financial statements, market trends, and competitive landscapes.
  • Strategic Thinking: I developed a strategic mindset by evaluating investment opportunities within the broader context of Eurazeo’s investment thesis and long-term objectives.
  • Communication and Collaboration: I enhanced my communication and collaboration skills through interaction with cross-functional teams and external stakeholders, fostering effective teamwork and decision-making.

This internship therefore offered a unique opportunity to gain firsthand experience in the dynamic and fast-paced world of private equity and alternative investments. As an aspiring finance professional, this experience has equipped me with the skills, knowledge, and insights necessary to thrive in the competitive landscape of the investment industry. Moreover, it has reaffirmed my passion for finance and deepened my understanding of the critical role played by alternative investment firms in driving economic growth and value creation.

As a newcomer to the finance industry, I had not anticipated the level of intricacy and competition inherent within the environment of Eurazeo. The depth of analysis, the meticulous attention to detail, and the relentless pursuit of excellence underscored the caliber of professionals operating within the firm. Despite the initial surprise, I found myself invigorated by the intellectual rigor and spirited competition that permeated every facet of Eurazeo’s operations.

Central to my experience at Eurazeo was the discovery of a challenging yet remarkably cohesive team—a team that demanded nothing short of excellence yet fostered an environment of camaraderie and mutual support. The intensity of our collaborative efforts forged bonds that transcended professional boundaries, culminating in a shared sense of purpose and accomplishment. Indeed, within the crucible of challenging assignments and tight deadlines, I discovered that the true measure of an internship lies not merely in the tasks accomplished but, in the relationships, forged and the personal growth attained.

Long But Fulfilling Working Hours

While the demands of the internship necessitated long hours and unwavering dedication, I found solace in the gratifying pursuit of knowledge and skill refinement. On average, my workday extended until around 10:30 in the evening, with occasional instances requiring weekend office visits. Despite the rigors of the schedule, the sense of fulfilment derived from contributing to meaningful projects and engaging with industry experts mitigated the challenges posed by extended working hours.

A game-changing internship

My internship at Eurazeo stands as a transformative chapter in my professional journey, characterized by unexpected challenges, profound growth, and enduring camaraderie. Through immersion in the fast-paced realm of private equity, I have gained invaluable insights, honed essential skills, and cultivated enduring relationships that will undoubtedly shape my future endeavours. As I reflect on my time at Eurazeo, I am reminded that true growth emerges from embracing adversity, fostering meaningful connections, and steadfastly pursuing excellence—lessons that will continue to guide me on the path toward personal and professional fulfillment.

Why should I be interested in this post?

I aspire that this experience might aid other students intrigued by Private Equity in gaining deeper insights into the internal dynamics and the range of exposure one can encounter within a private equity firm. Often, when students hear about private equity, their minds jump straight to financial analysis and modeling, overlooking the broader scope. My aim is for this article to spark curiosity among students about this sector, encouraging them to explore the private equity market further.

Related posts on the SimTrade blog

   ▶ All posts about Professional experiences

   ▶ Chloé ANIFRANI My experience as an Asset Management Sales Assistant for Amplegest

   ▶ Alexandre VERLET Classic brain teasers from real-life interviews

   ▶ Matisse FOY Key participants in the Private Equity ecosystem

   ▶ Lilian BALLOIS Discovering Private Equity: Behind the Scenes of Fund Strategies

Useful resources

Eurazeo

Bain Bain private Equity Report 2023

About the author

The article was written in April 2024 by Dante MARRAMIERO (ESSEC Business School, Master in Strategy and Management of International Business (SMIB), 2020-2023)

My experience as an Asset Management Sales Assistant for Amplegest

My experience as an Asset Management Sales Assistant for Amplegest

Chloé ANIFRANI

In this article, Chloé ANIFRANI (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2019-2024) shares her professional experience as Asset Management Development Assistant for Amplegest.

About the company

Amplegest was established in 2007 and operates across three main business segments: Private Wealth Management, Family Office, and Asset Management.

Private Wealth Management

Wealth Engineering: A team of wealth engineers provides personalized advice to clients, addressing evolving issues related to wealth, taxation, and family matters over time.

Discretionary Portfolio Management: The firm offers the market expertise of its fund managers, developed through extensive experience in banks or asset management firms. Amplegest actively seeks new investment opportunities across all asset classes and geographical regions, employing a short and collaborative decision-making process for responsiveness.

Profiled Portfolio Management: The firm offers specific profiled portfolios to retail investors, who manage their own clients’ portfolios.

Family Office

Amplegest serves high-net-worth individuals in France and internationally through a dedicated department, Canopée FO, offering fully customized services.

Asset Management

Amplegest’s Asset Management division offers three expertise:

  • Equity: with 5 funds, the firm covers many thematics (such as technological innovation, pricing power), capitalizations (large, mid and small caps) and regions (global, US and Eurozone),
  • Diversified portfolios: with its Latitude range, the firm offers diversified funds with precise return objectives and risk allocation, with an offer for each profile of investor,
  • Fixed Income: the firm distributes Octo AM’s funds, a company specialized in bonds funds, with a Value management style.

Key Facts and Figures

  • Assets under Management (AuM): 3bn€
  • A large product range of more than 13 funds
  • Diverse clientele: institutional, retail, funds selectors…
  • All activities of Amplegest are approved by the AMF (Autorité des Marchés Financiers).

Logo of the company.
Logo of Amplegest
Source: Amplegest.

My internship

My internship was in the sales department of Amplegest Asset Management. With a team of five sellers, I learned about the different distribution channels of funds in a B2B model (the team I was in did not work with final clients). The focus of the team is on institutional and retail clients. In 2023, we mainly worked on distributing Octo AM’s bonds funds, which have met a great success following the interest rates’ raises. The firm’s fixed income’s AuM went from €350m in 2022 to €800m in 2023.

My missions

Over the course of six months, I supported the team with customer relationship management and enhancing our understanding of the firm’s competitive landscape.

One of my primary responsibilities involved diligently preparing for client appointments. This entailed creating comprehensive briefs on Amplegest funds and conducting in-depth analyses of their competitive environments. Whether addressing global competition or specific funds selected by clients, my aim was to highlight the differentiating aspects of our offerings.

In addition to client-focused tasks, I took charge of producing documents containing technical information about the funds, ensuring compliance with our customers’ regulatory requirements such as “étude de transparisation”, KYC, and Due Diligence. Monthly, I managed the dispatching of these documents, tailoring the frequency to the individual needs of each client.

Collaborating closely with both the Asset Management and Marketing teams, I actively contributed to the planning and execution of numerous B2B events. This encompassed the coordination of trade fairs such as Patrimonia, organizing large-scale professional lunches and presentations, facilitating webinars, and orchestrating engaging professional afterwork events.

Furthermore, I dedicated efforts to augment the firm’s understanding of its funds’ positions in the market. Collaborating with dedicated tools designed to gather real-time information on competitors’ performance and track records, I systematically compared these metrics against our own. This included the creation of specific peer groups tailored to each fund, providing valuable insights into their relative standing within the market.

Required skills and knowledge

In Asset Management firms, the role of Sales Assistants requires a multifaceted skill set that encompasses technical expertise and strong interpersonal skills. B2B clients expect sales professionals to possess an in-depth understanding of the market and its dynamics, coupled with the ability to articulate a fund’s management process, recent market movements, and current values with the same proficiency as a portfolio manager.

Upon assuming the role, I prioritized enhancing my knowledge of current events, particularly those related to the stock market and global financial trends. Each day commenced with a thorough review of newsletters, and I highly recommend daily publications by Bloomberg for comprehensive insights. This proactive approach allowed me to respond swiftly when clients sought information about the prevailing market conditions and how they correlated with Amplegest’s product offerings.

A good knowledge of the regulatory environment of Asset Management firms is also essential. The rules that govern this profession are numerous and constantly updated. This means that a great interest for current events (suits and convictions in other firms, general recommendations…) will be beneficial, as well as a good understanding of the guidelines provided by the Compliance department.

A proficiency in Excel is paramount, serving as a vital tool for data analysis, reporting, and decision-making within the asset management landscape. Additionally, financial analysis skills are crucial for interpreting complex financial data and providing comprehensive insights to clients.

In terms of soft skills, effective communication is fundamental—both verbal and written—enabling the clear and concise articulation of complex financial concepts. Strong client relationship management skills are essential for building and maintaining long-term partnerships, understanding client needs, and providing excellent customer service.

Adaptability is key in navigating evolving market conditions, client preferences, and organizational changes. Problem-solving skills come into play in identifying challenges and proposing effective solutions to address client inquiries and concerns.

Negotiation skills are valuable in securing mutually beneficial agreements with clients, while team collaboration is essential for working effectively with colleagues across different departments, fostering a cooperative and supportive work environment. Effective organization and multitasking are necessary for managing multiple tasks and projects simultaneously, while analytical thinking is crucial for making data-driven decisions and providing valuable insights to clients.

Furthermore, networking skills contribute to building a professional network within the industry, attending relevant events, and staying informed about industry trends. Finally, strong time management ensures efficient task prioritization, meeting deadlines, and delivering results in a fast-paced environment. Together, these skills collectively contribute to the effectiveness of an Asset Management Sales Assistant in navigating the complexities of the financial industry and delivering value to clients and the organization.

What I learned

In terms of knowledge, I learned a lot about the organization of an Asset Management firm, and its funds. In this internship, I gained practical knowledge of the regulatory landscape governing the financial sector. I also learned about fund organization and shares, exploring the nuances of fund structures, issuance of shares, and compliance with legal frameworks. Moreover, I developed a perspective on the distinctions between the back, middle, and front office specific functions within an asset management firm. This exposure allowed me to appreciate the integral roles each department plays in the overall operational efficiency and success of the organization.

In this role, I was also able to use skills developed in previous internships. Time management was one of them, which, as explained earlier, revealed itself to be a crucial component to a good experience in this field. Indeed, some requests from clients and coworkers needed to be tended to in a matter of minutes or may make the firm lose millions (a bit extreme, but sometimes realistic). Therefore, my other missions needed to be done as soon as possible, to allow time for the more pressing ones. I learned to organize my work to optimize my efficiency on this matter.

In terms of technical skills, I learned funds analysis, with the ability to evaluate their performance, risk profiles and underlying strategies thanks it their allocation and communications. This involved a systematic examination of the firm’s competitive market and its key players and trends.

Thanks to this in-depth benchmark, a sales team is able to prepare clients’ briefs, but also to offer new strategies and product offerings to their managers, identifying market opportunities and specific needs for the clients.

This experience has not only enhanced my analytical capabilities but also deepened my understanding of the intricate dynamics within the financial markets.

As a Sales Assistant, I also developed my VBA skills, and learned the power of this tool, especially used in finance firms. Excel VBA helped me to automate and streamline numerous tasks related to data analysis, reporting, and client communication, thereby significantly enhancing my efficiency and productivity. By developing proficiency in Excel VBA, I could create customized macros and scripts tailored to the specific needs of our team, automating repetitive processes and allowing me to focus more on strategic aspects of sales and client relationship management.

Overall, this experience not only broadened my knowledge and skills base but also equipped me with practical insights crucial for navigating the complex and highly regulated landscape of asset management.

Financial concepts related my internship

Fixed income

As explained earlier, 2023 was the year of fixed income. Because of this, understanding the inner workings of a bond funds was essential, as those funds are more complex than equity funds.

In order to give the clients the information they required and work adequately with the provided documents, this knowledge was a real necessity.

Diversified Asset Allocation

In preparing briefings for clients and partners, I often had to summarize the recent movements made on the firm’s diversified funds. Those funds invest in ETFs, bonds, monetary funds structured products in order achieve their expected annual return and respect their risk budget. Therefore, this type of product is, once again, more complex than equity funds, and require a deep understanding of active asset allocation and market movements.

Return on Investment

In order to have more insight on Amplegest’s clients’ satisfaction, I had to compute their total RoI, taking into account every movement they made over the course of their investment in the firm (subscription/redemption), in different funds at different times and with different net asset value of the shares they bought. This required a good understanding of Return on Investment.

Why should I be interested in this post?

As ESSEC students, we often think of working in Asset Management firms as working as a portfolio manager. However, there are many other functions in this field, and sales is one of them. If you are looking to expend your knowledge on the field and your potential future job inquiries, this post will teach you more about a very exciting position!

Related posts on the SimTrade blog

   ▶ All posts about Professional experiences

   ▶ Louis DETALLE A quick interview with an Asset Manager at Vontobel

   ▶ Akshit GUPTA Asset management firms

Useful resources

Asset management markets in Europe size & share analysis – growth trends & forecast

Amplegest

About the author

The article was written in February 2024 by Chloé ANIFRANI (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2019-2024).

My professional experience as a marketing assistant at Auris Gestion

My professional experience as a marketing assistant at Auris Gestion

Ines ILLES MEJIAS

In this article, Ines ILLES MEJIAS (ESSEC Business School, Global BBA, 2020-2024) shares her experience as a marketing assistant at Auris Gestion (France).

About the company

Auris gestion is an asset management company created in 2004 which currently manages 3 billion euros. At the beginning, Auris Gestion focused on assisting only private clients with a worldwide offer by providing tailor-made solutions to their investment needs and demands. However, it decided to merge along with Salamandre AM in June 2020 to improve its positioning with financial advisor and Family Office partners. As a result, Auris Gestion makes its expertise and institutional management approaches available to a larger clientele, including individual consumers, asset management advisers, and institutions.

Logo of Auris Gestion.
 Logo of Auris Gestion
Source: Auris Gestion.

One of Auris Gestion main strengths is their close relationship with their clients and the fact that it places a high importance on developing this one by providing excellent tailored management, advising, and reporting solutions. To accomplish this successfully, they have decided to dive their team into two independent business divisions: the Private Banking and the Corporate Banking.

My internship

My internship lasted a total of three months and took place in Paris.

What I enjoyed the most about my internship is that I could work in different areas of business and not only finance, which I was quite scared of considering that jobs in finance are known to be very intense and I didn’t consider myself having a wide financial knowledge after my first year at ESSEC. However, I really enjoyed being able to do some marketing. This helped me to get to know inside out. Additionally, it really helped me to improve my finance vocabulary and knowledge in French.

My missions

I was in charge of creating customer master records and separating them into the two different clients and divisions which Auris works for: Private Banking/management and Institutional/Corporate finance.

In addition, I also worked as an assistant in the Marketing department. In this one, some of my tasks included:

  • Editing and writing up content for the new Auris and Salamander websites that they were working on to update their information and highlight their partnership with Salamander. Also, their aim was to create a more visually attractive website for customers to understand better the information and improve Auris’ positioning in terms of modern in technology and marketing.
  • Also, I was in charge of adjusting the website’s vocabulary according the two client divisions: Private Banking (simpler financial language) and Institutional/Corporate finance (advanced financial language and more precise information regarding the services offered).
  • Finally, within the marketing department, I had to create information documents and “poster style” documents about ESG. These documents had as a purpose informing and inciting clients to invest in this growing and important, although dangerous sector (greenwashing) funds. Also, to highlight Auris’ implications and contribution in green finance.

Using excel was one of my competences, which is why they also charged me to create and complete fund factsheets and reports. For successful completion, I had to search in Websites such as Morning Star, or the main company/fund websites, information about the funds which then I needed to update or fill in the Due Diligence factsheet template.

Another of my many jobs, which I found interesting and in which I got to learn loads was by attending and representing Auris during fund presentation and pitches. Here, I got to meet different company representatives and got to see how people pitched a fund. One of my other roles after these meetings, was not only making notes about what I learnt, but also summarizing pros and cons from the funds pitched and presented.

Required skills and knowledge

During my internship some of the skills which I most made use of and I believe were required to do my job were teamwork, adaptability, creativity, critical thinking, communication skills and Analytical skills, considering I worked for the financial department which is very quantitative, and the marketing department which is a bit more qualitative and visual.

Also, autonomy was very important, especially since the manager asked me what would be something that I’ve seen in the company that I’m interested in working on, and then I would go ahead and work on that and do research on my own.

Then, I would say one of the most important required skills is being an advanced user of the Pack Office. This is because I was required to use daily either Microsoft teams, Word, PowerPoint, and Excel.

To continue, some finance and asset management knowledge and vocabulary are obviously essential. However, if you are pursuing a business degree you should have a good base of knowledge already. The same goes for marketing and strategic marketing knowledge.

Finally, knowledge and skills on how to use of marketing tools for the creation of digital content such as Canvas and Adobe Photoshop/Photopea were needed.

What I learned

During my internship I was lucky to learn and expand my knowledge in few areas.
First, I learned how to elaborate and fill Due Diligence factsheets to evaluate different funds from different companies. For this, I had to become familiar with the company websites, Morning Star, understand fund rating, etc.

Then, I got to further improve my knowledge on finance thanks to personal research that I did as well as to some of the employees which were eager in teaching me new financial terms and concepts. Some examples of new things I learnt are structured products, ESG, greenwashing, different bond ratings, etc. One of the workers was very kind and once every few days he would sit with me and explain me concepts which I came across that I didn’t understand or was struggling with.

Moreover, the weekly “Rendez-vous de Lundi”, which is a concept set up by the company which consisted of weekly newsletters sent to workers and published publicly, helped me stay informed about the main the weekly performance of markets through the overview of the markets with charts showing their risk, inflation, and summaries regarding their importance and other news, etc.

Finally, the “Point macro”, which was done every two weeks by the company with the aim of keeping up to date all the workers with the main macroeconomic factors affecting today’s investing world. This really got me to improve my macroeconomics knowledge as well as got me to learn a lot about the diversity and importance of this one in the asset management and investing world.

Key financial concepts related to your work

During my internship, I came across the following key financial concepts: structured products, ESG funds, and bond rating.

Structured Products

Structured products are investments that normally include assets linked to interest plus one or more derivatives. These are sometimes attached to an index or group of assets and create extremely specific risk-return objectives. The package is known to be composed of: a bond, some underlying assets, and the derivatives strategy.

ESG Funds

ESG Funds are bands which carry Environmental, Societal and Governance principles investments. This means that the bonds have gone through a test which determines how sustainable the company or government is in terms of the ESG criteria. This index has pushed a lot of companies to become more responsible. Also, they’re important today due to the importance sustainability, and they’re becoming more popular as investors want to be seen as contributors of stopping global warming and contributing to human development without impacting their returns.

Bond Ratings

Bond ratings are letters which define and judge the quality and creditworthiness of a bond. Normally starts with triple letter A (“AAA” for Standard & Poor’s, and “Aaa” for Moody’s), and starting from BB bonds are known as “Junk bonds” due to their low ratings. What we must remember is that the higher a bond’s rating, the lower the interest rate it will carry, and the lower the risk, etc.

Why should I be interested in this post?

This post might interest you if you plan on working in a future in an asset management company or in this sector as a marketing or asset management assistant. You will be able to see what tasks you might be asked to do, the skills that you must have to perform successfully during your internship, as well as terms you might come across that you will need to learn about.

Related posts on the SimTrade blog

   ▶ All posts about Professional experiences

   ▶ Anna BARBERO Career in finance

   ▶ Alexandre VERLET Classic brain teasers from real-life interviews

   ▶ Jayati WALIA My experience as a credit analyst at Amundi Asset Management

Useful resources

Auris Gestion

Standard & Poor’s

Moody’s

About the author

The article was written in December 2022 by Ines ILLES MEJIAS (ESSEC Business School, ESSEC Business School, Global BBA, 2020-2024).

Asset allocation techniques

Youssef LOURAOUI

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

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

Introduction

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

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

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

The top-down approach

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

The bottom up approach

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

Types of asset allocations

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

Policy asset allocation

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

Dynamic asset allocation

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

Tactical asset allocation

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

Asset allocation application: an example

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

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

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

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

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

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

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

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

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

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

Excel file for asset allocation

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

 Download the Excel file for asset allocation

Why should I be interested in this post?

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

Related posts on the SimTrade blog

   ▶ Youssef LOURAOUI Markowitz Modern Portfolio Theory

   ▶ Youssef LOURAOUI Optimal portfolio

   ▶ Youssef LOURAOUI Portfolio

Useful resources

Academic research

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

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

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

About the author

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

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).

My experience as a credit analyst at Amundi Asset Management

My experience as a credit analyst at Amundi Asset Management

Jayati WALIA

In this article, Jayati WALIA (ESSEC Business School, Grande Ecole Program – Master in Management, 2019-2022) shares her apprenticeship experience as an assistant credit analyst in Amundi which is a leading European asset management firm.

About Amundi

Amundi is a French asset management firm with currently over €2 trillion asset under management (AUM). It ranks among the top 15 asset managers in the world (see Table 1 below). Amundi is a public company quoted on Euronext with the highest market capitalization in Europe among asset management firms (€10.92 billion as of May 20, 2022). Amundi was founded in 2010 following a merger between Crédit Agricole Asset management and Société Générale Asset management.

Table 1. Rank of asset management firms by asset under management (AUM).
Top asset management firms rankings Source: www.advratings.com

Amundi has over 100 million clients (retail, institutional and corporate) and it offers a range of savings and investment solutions, services, advice, and technology in active and passive management, in both traditional and real assets.

Amundi logo Source: Amundi

My apprenticeship

My team at Amundi, Fixed Income Solutions, works in coordination with all the teams of the firm’s global bond management platform. The team’s work revolves majorly around product development on Amundi’s Fixed Income offerings including technological work, generating new investment ideas, and bringing them to clients both institutional and distributors. My position in the team is Assistant Credit Analyst.

Missions

My work primarily involves setting up tools and procedures linked to various investment solutions and portfolios handled by team. The tools are developed through algorithms in programming languages (mainly Python) and their functionalities range from analysis of market signals for investment, pricing of securities, risk monitoring and reporting. I worked on fixed-income portfolio construction and optimization algorithms implementing modern portfolio theory.

My daily responsibilities include report production related to daily fund activity such as monitoring fund balance and calculation of regulatory financial ratios to check for alignment against specific risk constraints. Additionally, I also participate in market research for new investment ideas through analysis of various fixed-income securities and derivatives.

Required skills and knowledge

The work and missions involved in my role require technical knowledge especially programming skills in Python, quantitative modelling and an understanding of financial markets, products and concepts of valuation, various types of risks and financial data analysis. Other behavioral skills such as project management, autonomy and interpersonal communication are also essential.

Three key financial concepts

The following are three key concepts that are used regularly in my work at Amundi:

Credit ratings

Credit ratings are extensively used in fixed income. They reflect the creditworthiness of a borrower entity such as a company or a government, which has issued financial debt instruments like loans and bonds.

Credit risk assessment for companies and governments is generally performed by rating agencies (such as S&P, Moody’s and Fitch) which analyze the internal and external, qualitative and quantitative attributes that drive the economic future of the entity.
Bonds can be grouped into the following categories based on their credit rating:

  • Investment grade bonds: These bonds are rated Baa3 (by Moody’s) or BBB- (by S&P and Fitch) or higher and have a low rate of default.
  • Speculative grade bonds: These bonds are rated Ba1 (by Moody’s) or BB+ (by S&P and Fitch) or lower and have a higher rate of default. They are thus riskier than investment grade bonds and issued at a higher yield. Speculative grade bonds are also referred to “high yield” and “junk bonds”.

Often, some bonds are designated “NR” (“not rated”) or “WR” (“withdrawn rating”) if no rating is available for them due to various reasons, such as lack of credible information.

Credit spreads

Credit spread essentially refers to the difference between the yields of a debt instrument (such as corporate bonds) and a benchmark (government or sovereign bond) with similar maturities but contrasting credit ratings. It is measured in basis points and is indictive of the premium of a risky investment over a risk-free one.

Credit spreads can tighten or widen over time depending on economic and market conditions. For instance, times of financial stress cause an increase in credit risk which leads to spread widening. Similarly, when markets rally, and credit risk is low, spreads tighten. Thus, credit spreads are an indicator of current macro-economic and market conditions.

Credit spreads are used by market participants for investment analysis and bond valuations.

Duration and convexity

Bond prices and interest rates share an inverse relationship, i.e., if interest rates go up, bond prices move down and similarly if interest rates go down, bond prices move up. Duration measures this price sensitivity of bonds with respect to interest rates and helps analyze interest-rate risk for bonds. Bonds with higher duration are more sensitive to interest rate changes and hence more volatile. Duration for a zero-coupon bond is equal to its time to maturity.

While duration is linear measure of bond price-interest rates relationship, in real life, the curve of bond prices against interest rates is convex i.e., the duration of the bonds also changes with change in interest-rates. Convexity measures this duration sensitivity of bonds with respect to interest rates.

Related posts on the SimTrade blog

   ▶ All posts about Professional experiences

   ▶ Alexandre VERLET Classic brain teasers from real-life interviews

   ▶ Louis DETALLE My professional experience as a Credit Analyst at Société Générale.

   ▶ Jayati WALIA Credit risk

   ▶ Jayati WALIA Fixed-income products

Useful resources

Amundi

About the author

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

A quick presentation of the Asset Management field…

A quick presentation of the Asset Management field…

Louis DETALLE

In this article, Louis DETALLE (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023) explains what does an Asset Management company consists in.

What does Asset Management consist in?

Asset management is a financial activity whose objective is to create, manage, grow and maximize the benefits of financial products or investments entrusted by companies or individual investors. Asset management therefore consists in managing a client portfolio and increasing its profitability by balancing expected returns and risks in order to achieve previously defined objectives.

When thinking about asset management, companies such as Allianz, Amundi, AVIVA or Natixis Investment Managers could be quoted as examples of Asset Management companies.

What are the main clients of Asset Managers?

The main clients of asset management companies are :

– Companies wishing to invest their cash surpluses;
– Pension funds and mutual insurance companies;
– Financial institutions investing for their own account;
– Banks and insurance companies that distribute financial products to their clients (retail, private and corporate banking).

Two main types of management

Management under mandate

The company manages the account of a single client or a group of clients who have delegated the management of the fund to it. All of the fund’s assets belong to one person or to a small number of people,

Collective management

A fund with a large number of investors and units. It is managed according to the same strategic orientation corresponding to the profile adapted to these investors.

What does an asset manager work on?

The day-to-day work consists mainly of assessing how the previous day’s transactions and market movements have affected the portfolio’s risk profile in terms of liquidity, credit and market.

Another key aspect of this job is the development, adaptation and improvement of quantitative portfolio risk analysis tools. Other tools to assist investment decisions, to monitor developments in financial research in terms of risk and to analyze macroeconomic news require more specific attention and are therefore more complex to implement.

Useful resources

Thinking ahead Institute The world’s largest asset managers – 2021

Related posts on the SimTrade blog

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

About the author

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

Portfolio

Youssef_Louraoui

In this article, Youssef LOURAOUI (Bayes Business School, MSc. Energy, Trade & Finance, 2021-2022) elaborates on the concept of portfolio, which is a basic element in asset management.

This article is structured as follows: we introduce the concept of portfolio. We give the basic modelling to define and characterize a portfolio. We then expose the different types of portfolios that investors can rely on to meet their financial goals.

Introduction

An investment portfolio is a collection of assets that an investor owns. These assets can be individual assets such as bonds and stocks or baskets of assets such as mutual funds or exchange-traded funds (ETFs). In a nutshell, this refers to any asset that has the potential to increase in value or generate income. When building a portfolio, investors usually consider the expected return and risk. A well-balanced portfolio includes a variety of investments.

Modelling of portfolios

Portfolio weights

At a point of time, a portfolio is fully defined by the weights (w) of the assets of the universe considered (N assets).

Portfolio weights

The sum of the portfolio weights adds up to one (or 100%):

Sum of the portfolio weights

The weight of a given asset i can be positive (for a long position in the asset), equal to zero (for a neutral position in the asset) or negative (for a short position in the asset):

Asset weight for a long position

Asset weight for a neutral position

Asset weight for a short position

Short selling is the process of selling a security without owning it. By definition, a short sell occurs when an investor borrows a stock, sells it, and then buys it later back to repay the lender.

The equally-weighted portfolio is defined as the portfolio with weights that are evenly distributed across the number of assets held:

Equally-weigthed portfolio

Portfolio return: the case of two assets

Over a given period of time, the returns on assets 1 and 2 are equal to r1 and r2. In the two-asset portfolio case, the portfolio return rP is computed as

Return of a 2-asset portfolio

The expected return of the portfolio E(rP) is computed as

Expected return of a 2-asset portfolio

The standard deviation of the portfolio return, σ(rP) is computed as

Standard deviation of a 2-asset portfolio return

where:

  • σ1 = standard deviation of asset 1
  • σ2 = standard deviation of asset 2
  • σ1,2 = covariance of assets 1 and 2
  • ρ1,2 = correlation of assets 1 and 2

Investing in asset classes with low or no correlation to one another can help you increase portfolio diversification and reduce portfolio volatility. While diversification cannot guarantee a profit or eliminate the risk of investment loss, the ideal scenario is to have a mix of uncorrelated asset classes in order to reduce overall portfolio volatility and generate more consistent long-term returns. Correlation is depicted mathematically as the division of the covariance between the two assets by the individual standard deviation of the asset. Correlation is a more interpretable metric than covariance because it’s measurable within a defined rank. Correlation is measured between -1 and 1, with a high positive correlation showing that the assets move in tandem, while negative correlation depicts securities that have contrary price movements. The holy grail of investing is to invest in securities that offer a low correlation of the portfolio as a whole.

Rho_correlation_2_asset

where:

  • σ1,2 = covariance of assets 1 and 2
  • σ1 = standard deviation of asset 1
  • σ2 = standard deviation of asset 2

Correlation is a more interpretable metric than covariance because it’s measurable within a defined rank. Correlation is measured between -1 and 1, with high positive correlation showing that the assets move in tandem, while negative correlation depicts securities that have contrary price movements. The holy grail of investing is to invest in securities that offer a low correlation of the portfolio as a whole.

You can download an Excel file to help you construct a portfolio and compute the expected return and variance of a two-asset portfolio. Just introduce the inputs in the model and the calculations will be performed automatically. You can even draw the efficient frontier to plot the different combinations of portfolios that optimize the risk-return trade-off (to minimize the risk for a given level of expected return or to maximize the expected return for a given level of risk).

Download the Excel file to construct 2-asset portfolios

Portfolio return: the case of N assets

Over a given period of time, the return on asset i is equal to ri. The portfolio return can be computed as

Portfolio return

The expression of the portfolio return is then used to compute two important portfolio characteristics for investors: the expected performance measured by the average return and the risk measured by the standard deviation of returns.

The expected return of the portfolio is given by

Expected portfolio return

Because relying on multiple assets can get extremely computationally heavy, we can refer to the matrix form for more straightforward use. We basically compute the vector of weight with the vector of returns (NB: we have to pay attention to the dimension and to the properties of matrix algebra).

Matrix_calculus_PF_Er

  • w = weight vector
  • r = returns vector

The standard deviation of returns of the portfolio is given by the following equivalent formulas:

Standard deviation of portfolio return

  • wi = weight of asset i
  • wj = weight of asset j
  • σi = standard deviation of asset i
  • σj = standard deviation of asset j
  • ρi,j = correlation of asset i,j

Standard deviation of portfolio return

where:

  • wi2 = squared weight of asset I
  • σi2 = variance of asset i
  • wi = weight of asset i
  • wj = weight of asset j
  • σi = standard deviation of asset i
  • σj = standard deviation of asset j
  • ρi,j = correlation of asset i,j

We can use the matrix form for a more straightforward application due to the computational burden associated with relying on multiple assets. Essentially, we multiply the vector of weights with the variance-covariance matrix and the transposed weight vector (NB: we must pay attention to the dimension and to the properties of matrix algebra).

Matrix_calculus_PF_stdev

  • w = weight vector
  • ∑ = variance-covariance matrix
  • w’ = transpose of weight vector

You can get an Excel file that will help you build a portfolio and calculate the expected return and variance of a three-asset portfolio. Simply enter the data into the model, and the calculations will be carried out automatically. You can even use the efficient frontier to plot the various portfolio combinations that best balance risk and reward (to minimize the risk for a given level of expected return or to maximize the expected return for a given level of risk).

Download the Excel file to construct 3-asset portfolios

Basic principles on portfolio construction

Diversify

Diversification, a core principle of Markowitz’s portfolio selection theory, is a risk-reduction strategy that entails allocating assets among a variety of financial instruments, sectors, and other asset classes (Markowitz, 1952). In more straightforward terms, it refers to the concept “don’t put all your eggs in one basket.” If the basket is dropped, all eggs are shattered; if many baskets are used, the likelihood of all eggs being destroyed is significantly decreased. Diversification may be accomplished by investments in a variety of companies, asset types (e.g., bonds, real estate, etc.), and/or commodities such as gold or oil.

Diversification seeks to enhance returns while minimizing risk by investing in a variety of assets that will react differently to the same event(s). Portfolio diversification methods should include not just diverse stocks inside and outside of the same industry, but also diverse asset classes, such as bonds and commodities. When there is an imperfect connection between assets (lower than one), the diversification effect occurs. It is a critical and successful risk mitigation method since risk mitigation may be accomplished without jeopardizing profits. As a result, any prudent investor who is cautious (or ‘risk averse’) will diversify to a certain extent.

Portfolio Asset Allocation

The term “asset allocation” refers to the proportion of stocks, bonds, and cash in a portfolio. Depending on your investing strategy, you’ll determine the percentage of each asset type in your portfolio to achieve your objectives. As markets fluctuate over time, your asset allocation is likely to go out of balance. For instance, if Tesla’s stock price increases, the percentage of your portfolio allocated to stocks will almost certainly increase as well.

Portfolio Rebalancing

Rebalancing is a term that refers to the act of purchasing and selling assets in order to restore your portfolio’s asset allocation to its original state and avoid disrupting your plan.

Reduce investment costs as much as possible

Commission fees and management costs are significant expenses for investors. This is especially important if you frequently purchase and sell stocks. Consider using a discount brokerage business to make your investment. Clients are charged much lesser fees by these firms. Also, when investing for the long run, it is advisable to avoid making judgments based on short-term market fluctuations. To put it another way, don’t sell your stocks just because they’ve taken a minor downturn in the near term.

Invest on a regular basis

It is critical to invest on a regular basis in order to strengthen your portfolio. This will not only build wealth over time, but it will also develop the habit of investing discipline.

Buying in the future

It’s possible that you have no idea how a new stock will perform when you buy it. To be on the safe side, avoid putting your entire position to a single investment. Start with a little investment in the stock. If the stock’s performance fulfils your expectations, you can gradually increase your investments until you’ve covered your entire position.

Types of portfolio

We detail below the different types of portfolios usually proposed by financial institutions that investors can rely on to meet their financial goals.

Aggressive Portfolio

As the name implies, an aggressive portfolio is one of the most frequent types of portfolio that takes a higher risk in the pursuit of higher returns. Stocks in an aggressive portfolio have a high beta, which means they present more price fluctuations compared to the market. It is critical to manage risk carefully in this type of portfolio. Keeping losses to a minimal and taking profits are crucial to success. It is suitable for a high-risk appetite investor.

Defensive Portfolio

A defensive portfolio is one that consists of stocks with a low beta. The stocks in this portfolio are largely immune to market swings. The goal of this type of portfolio is to reduce the risk of losing the principal. Fixed-income securities typically make up a major component of a defensive portfolio. It is suitable for a low-risk appetite investor.

Income Portfolio

Another typical portfolio type is one that focuses on investments that generate income from dividends (for stocks), interests (for bonds) or rents (for real estate). An income portfolio invests in companies that return a portion of their profits to shareholders, generating positive cash flow. It is critical to remember that the performance of stocks in an income portfolio is influenced by the current economic condition.

Speculative Portfolio

Among all portfolio types, a speculative portfolio has the biggest risk. Speculative investments could be made of different assets that possess inherently higher risks. Stocks from technology and health-care companies that are developing a breakthrough product, junk bonds, distressed investments among others might potentially be included in a speculative portfolio. When establishing a speculative portfolio, investors must exercise caution due to the high risk involved.

Hybrid Portfolio

A hybrid portfolio is one that includes passive investments and offers a lot of flexibility. The cornerstone of a hybrid portfolio is typically made up of blue-chip stocks and high-grade corporate or government bonds. A hybrid portfolio provides diversity across many asset classes while also providing stability by combining stocks and bonds in a predetermined proportion.

Socially Responsible Portfolio

A socially responsible portfolio is based on environmental, social, and governance (ESG) criteria. It allows investors to make money while also doing good for society. Socially responsible or ESG portfolios can be structured for any level of risk or investment aim and can be built for growth or asset preservation. The important thing is that they prefer stocks and bonds that aim to reduce or eliminate environmental impact or promote diversity and equality.

Why should I be interested in this post?

Portfolio management’s objective is to optimize the returns on the entire portfolio, not just on one or two stocks. By monitoring and maintaining your investment portfolio, you can accumulate a sizable capital to fulfil a variety of financial objectives, including retirement planning. This article helps to understand the grounding fundamentals behind portfolio construction and investing.

Related posts on the SimTrade blog

   ▶ Youssef LOURAOUI Markowitz Modern Portfolio Theory

   ▶ Jayati WALIA Capital Asset Pricing Model (CAPM)

   ▶ Youssef LOURAOUI Beta

   ▶ Youssef LOURAOUI Alpha

   ▶ Youssef LOURAOUI Systematic and specific risk

   ▶ Jayati WALIA Value at Risk (VaR)

   ▶ Anant JAIN Social Responsible Investing (SRI)

Useful resources

Academic research

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

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

Business analysis

Edelweiss, 2021.What is a portfolio?

Forbes, 2021.Investing basics: What is a portfolio?

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

About the author

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

Black-Litterman Model

Youssef_Louraoui

In this article, Youssef LOURAOUI (Bayes Business School, MSc. Energy, Trade & Finance, 2021-2022) presents the Black-Litterman model, used to determine optimal asset allocation in a portfolio. The Black-Litterman model takes the Markowitz model one step further: it incorporates an investor’s own views in determining asset allocations.

This article is structured as follows: we introduce the Black-Litterman model. We then present the mathematical foundations of the model to understand how the method is derived. We finish with an example to illustrate how we can implement a Black-Litterman asset allocation in practice.

Introduction

The Black-Litterman asset allocation model, developed by Fischer Black and Robert Litterman in the early 1990’s, is a complex method for dealing with unintuitive, highly concentrated, input-sensitive portfolios produced by the Markowitz model. The most likely reason why more portfolio managers do not employ the Markowitz paradigm, in which return is maximized for a given level of risk, is input sensitivity, which is a well-documented problem with mean-variance optimization.

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

The purpose of the Black-Litterman model is to develop stable, mean-variance efficient portfolios based on an investor’s unique insights that overcome the problem of input sensitivity. According to Lee (2000), the Black-Litterman Model “essentially mitigates” the problem of estimating error maximization (Michaud, 1989) by dispersing errors throughout the vector of expected returns.

The vector of expected returns is the most crucial input in mean-variance optimization; yet, Best and Grauer (1991) demonstrate that this input can be very sensitive in the final result. Black and Litterman (1992) and He and Litterman (1999) investigate various potential projections of expected returns in their search for a fair starting point: historical returns, equal “mean” returns for all assets, and risk-adjusted equal mean returns. They demonstrate that these alternate forecasts result in extreme portfolios, which have significant long and short positions concentrated in a small number of assets.

Mathematical foundation of Black-Litterman model

It is important to introduce the Black-Litterman formula and provide a brief description of each of its elements. In the formula below, the integer k is used to represent the number of views and the integer n to express the number of assets in the investment set (NB: the superscript ’ indicates the transpose and -1 indicates the inverse).

BL_formula

Where:

  • E[R] = New (posterior) vector of combined expected return (n x 1 column vector)
  • τ = Scalar
  • Σ = Covariance matrix of returns (n x n matrix)
  • P = Identifies the assets involved in the views (k x n matrix or 1 x n row vector in the special case of 1 view)
  • Ω = Diagonal covariance matrix of error terms in expressed views representing the level of confidence in each view (k x k matrix)
  • П = Vector of implied equilibrium expected returns (n x 1 column vector)
  • Q = Vector of views (k x 1 column vector)

Traditionally, personal views are used for prior distribution. Then observed data is used to generate a posterior distribution. The Black-Litterman Model assumes implied returns as the prior distribution and personal views alter it. The basic procedure to find the Black-Litterman model is: 1) Find implied returns 2) Formulate investor views 3) Determine what the expected returns are 4) Find the asset allocation for the optimal portfolio.

Black-Litterman asset allocation in practice

An investment manager’s views for the expected return of some of the assets in a portfolio are frequently different from the the Implied Equilibrium Return Vector (Π), which represents the market-neutral starting point for the Black-Litterman model. representing the uncertainty in each view. Such views can be represented in absolute or relative terms using the Black-Litterman Model. Below are three examples of views stated in the Black and Litterman model (1990).

  • View 1: Merck (MRK) will generate an absolute return of 10% (Confidence of View = 50%).
  • View 2: Johnson & Johnson (JNJ) will outperform Procter & Gamble (PG) by 3% (Confidence of View = 65%).
  • View 3: GE (GE) will beat GM (gm), Wal-Mart (WMT), and Exxon (XOM) by 1.5 percent (Confidence of View = 30%).

An absolute view is exemplified by View 1. It instructs the Black-Litterman model to set Merck’s return at 10%.

Views 2 and 3 are relative views. Relative views are more accurate representations of how investment managers feel about certain assets. According to View 2, Johnson & Johnson’s return will be on average 3 percentage points higher than Procter & Gamble’s. To determine if this will have a good or negative impact on Johnson & Johnson in comparison to Procter & Gamble, their respective Implied Equilibrium returns must be evaluated. In general (and in the absence of constraints and other views), the model will tilt the portfolio towards the outperforming asset if the view exceeds the difference between the two Implied Equilibrium returns, as shown in View 2.

View 3 shows that the number of outperforming assets does not have to equal the number of failing assets, and that the labels “outperforming” and “underperforming” are relative terms. Views that include several assets with a variety of Implied Equilibrium returns are less intuitive, generalizing more challenges. In the absence of constraints and other views, the view’s assets are divided into two mini-portfolios: a long and a short portfolio. The relative weighting of each nominally outperforming asset is proportional to that asset’s market capitalization divided by the sum of the market capitalization of the other nominally outperforming assets of that particular view. Similarly, the relative weighting of each nominally underperforming asset is proportional to that asset’s market capitalization divided by the sum of the market capitalizations of the other nominally underperforming assets. The difference between the net long and net short positions is zero. The real outperforming asset(s) from the expressed view may not be the mini-portfolio that receives the good view. In general, the model will overweight the “outperforming” assets if the view is greater than the weighted average Implied Equilibrium return differential.

Why should I be interested in this post?

Modern Portfolio Theory (MPT) is at the heart of modern finance and its core foundations are structuring the modern investing panorama. MPT has established itself as the foundation for modern financial theory and practice. MPT’s premise is that beating the market is difficult, and those that do it by diversifying their portfolios appropriately and accepting higher-than-average investment risks.

MPT has been around for almost sixty years, and its popularity is unlikely to wane anytime soon. Its theoretical contributions have laid the groundwork for more theoretical research in the field of portfolio theory. Markowitz’s portfolio theory, however, is vulnerable to and dependent on continuing ‘probabilistic’ development and expansion. This article shed light on an enhancement of the initial Markowitz work by going a step further: to incorporate the views of the investors in the asset allocation process.

Related posts on the SimTrade blog

   ▶ Youssef LOURAOUI Portfolio

   ▶ Youssef LOURAOUI Alpha

   ▶ Youssef LOURAOUI Factor Investing

   ▶ Youssef LOURAOUI Origin of factor investing

   ▶ Youssef LOURAOUI Markowitz Modern Portfolio Theory

   ▶ Jayati WALIA Capital Asset Pricing Model (CAPM)

Useful resources

Academic research

Best, M.J., and Grauer, R.R. 1991. On the Sensitivity of Mean-Variance-Efficient Portfolios to Changes in Asset Means: Some Analytical and Computational Results.The Review of Financial Studies, 315-342.

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

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

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

He, G. and Litterman, R. 1999. The Intuition Behind Black-Litterman Model Portfolios. Goldman Sachs Investment Management Research, working paper.

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

Lee, W., 2000, Advanced theory and methodology of tactical asset allocation. Fabozzi and Associates Publications.

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

Michaud, R.O. 1989. The Markowitz Optimization Enigma: Is Optimized Optimal?. Financial Analysts Journal, 31-42.

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

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

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

About the author

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

Passive Investing

Youssef_Louraoui

In this article, Youssef LOURAOUI (Bayes Business School, MSc. Energy, Trade & Finance, 2021-2022) elaborates on the concept of passive investing.

This article will offer a concise summary of the academic literature on passive investment. After that, we’ll discuss the fundamental principles of passive investment. The article will finish by establishing a link between passive strategies and the Efficient Market Hypothesis.

Review of academic literature on passive investing

We can retrace the foundations of passive investing to the theory of portfolio construction developed by Harry Markowitz. For his theoretical implications, Markowitz’s work is widely regarded as a pioneer in financial economics and corporate finance. For his contributions to these disciplines, which he developed in his thesis “Portfolio Selection” published in The Journal of Finance in 1952, Markowitz received the Nobel Prize in economics in 1990. His ground-breaking work set the foundation for what is now known as ‘Modern Portfolio Theory’ (MPT).

William Sharpe (1964), John Lintner (1965), and Jan Mossin (1966) separately developed the Capital Asset Pricing Model (CAPM). The CAPM was a huge evolutionary step forward in capital market equilibrium theory because it enabled investors to appropriately value assets in terms of their risk. The asset management industry intended to capture the market portfolio return in the late 1970s, defined as a hypothetical collection of investments that contains every kind of asset available in the investment universe, with each asset weighted in proportion to its overall market participation. A market portfolio’s expected return is the same as the market’s overall expected return. But as financial research evolved and some substantial contributions were made, new factor characteristics emerged to capture some additional performance.

Core principles of passive investing

Positive outlook: The core element of passive investing is that investors can expect the stock market to rise over the long run. A portfolio that mimics the market will appreciate in lockstep with it.

Low cost: A passive strategy has low transaction costs (commissions and market impact) due to its steady approach and absence of frequent trading. While management fees required by funds are unavoidable, most exchange traded funds (ETFs) – the vehicle of choice for passive investors – charge much below 1%.

Diversification: Passive strategies automatically provide investors with a cost-effective method of diversification. This is because index funds diversify their risk by investing in a diverse range of securities from their target benchmarks.

Reduced risk: Diversification almost usually results in lower risk. Investors can also diversify their holdings more within sectors and asset classes by investing in more specialized index funds.

Passive investing and Efficient Market Hypothesis

The Efficient Market Hypothesis (EMH) asserts that markets are efficient, meaning that all information is incorporated into market prices (Fama, 1970). The passive investing strategy is built on the concept of “buy-and-hold,” or keeping an investment position for a lengthy period without worrying about market timing. This latter technique is frequently implemented through the purchase of exchange-traded funds (ETF) that aim to closely match a given benchmark to produce a performance that is comparable to the underlying index or benchmark. The index might be broad-based, such as the S&P500 index in the US equity market for instance, or more specialized, such as an index that monitors a specific sector or geographical zone.

A study from Bloomberg on index funds suggests that passive investments lead 11.6 trillion $ in the US domestic equity-fund market. Passive investing accounts for approximately 54% of the market, owing largely to the growth of funds tracking the S&P 500, the total US stock market, and other broad US indexes. Large-cap stocks in the United States are widely recognized as the world’s most efficient equity market, contributing to passive investing’s dominance. The $6.2 trillion in passive assets represents less than a sixth of the US stock market, which currently has a market capitalization of approximately $40.4 trillion (Bloomberg, 2021).

Figure 1 depicts the historical monthly returns of the S&P500 highlighting the contraction periods in orange. It is considered as a key benchmark that is heavily tracked by passive instruments like Exchange Traded Funds and Mutual Funds. In a two-decade timeframe analysis, the S&P managed to offer an annualised 5.56% return on average coupled with a 15.16% volatility.

Figure 1. S&P500 historical returns (Jan 2000 – November 2021).

img_SimTrade_S&P500_analysis

Source: Computation by the author (data source: Thomson Reuters).

Estimation of the S&P500 return

You can download an Excel file with data for the S&P500 index returns (used as a representation of the market).

Download the Excel file to compute S&P500 returns

Why should I be interested in this post?

If you are a business school or university undergraduate or graduate student, this content will help you in grasping the concept of passive investing, which is in practice key to investors, and which has attracted a lot of attention in academia.

Related posts on the SimTrade blog

   ▶ Youssef LOURAOUI Portfolio

   ▶ Youssef LOURAOUI Alpha

   ▶ Youssef LOURAOUI Factor Investing

   ▶ Youssef LOURAOUI Origin of factor investing

   ▶ Youssef LOURAOUI Alternatives to market-capitalisation weighted indexes

   ▶ Youssef LOURAOUI Markowitz Modern Portfolio Theory

   ▶ Jayati WALIA Capital Asset Pricing Model (CAPM)

Useful resources

Academic research

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

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

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

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

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

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

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

Business analysis

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

Bloomberg, 2021. Passive likely overtakes active by 2026, earlier if bear market

About the author

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

Beta

Youssef_Louraoui

In this article, Youssef LOURAOUI (Bayes Business School, MSc. Energy, Trade & Finance, 2021-2022) explains the concept of beta, one of the most fundamental concepts in the financial industry, which is heavily used in asset management to assess the risk of assets and portfolios.

This article is structured as follows: we introduce the concept of beta in asset management. Next, we present the mathematical foundations of the concept. We finish with an interpretation of beta values for risk analysis.

Introduction

The (market) beta represents the sensitivity of an individual asset or a portfolio to the fluctuations of the market. This risk measure helps investors to predict the movements of their assets according to the movements of the market overall. It measures the asset risk in comparison with the systematic risk inherent to the market.

In practice, the beta for a portfolio (fund) in respect to the market M represented by a predefined index (the S&P 500 index for example) indicates the fund’s sensitivity to the index. Essentially, the fund’s beta to the index attempts to capture the amount of money made (or lost) when the index increases (or decreases) by a specified amount.

Graphically, the beta represents the slope of the straight line through a regression of data points between the asset return in comparison to the market return for different time periods. It is a traditional risk measure used in the asset management industry. To give a more insightful explanation, a regression analysis has been performed using data for the Apple stock (APPL) and the S&P500 index to see how the stock behaves in relation to the market fluctuations (monthly data for the period July 2018 – June 2020). Figure 1 depicts the regression between Apple stock and the S&P500 index (excess) returns. The estimated beta is between zero and one (beta = 0.3508), which indicates that the stock price fluctuates less than the market index.

Figure 1. Linear regression of the Apple stock return on the S&P500 index return.
Beta analysis for Apple stock return
Source: Computation by the author (data source: Thomson Reuters).

Mathematical derivation of Beta

Use of beta

William Sharpe, John Lintner, and Jan Mossin separately developed key capital markets theory as a result of Markowitz’s previous works: the Capital Asset Pricing Model (CAPM). The CAPM was a huge evolutionary step forward in capital market equilibrium theory since it enabled investors to appropriately value assets in terms of systematic risk, defined as the market risk which cannot be neutralized by the effect of diversification.

The CAPM expresses the expected return of an asset a function of the risk-free rate, the beta of the asset, and the expected return of the market. The main result of the CAPM is a simple mathematical formula that links the expected return of an asset to these different components. For an asset i, it is given by:

CAPM risk beta relation

Where:

  • E(ri) represents the expected return of asset i
  • rf the risk-free rate
  • βi the measure of the risk of asset i
  • E(rm) the expected return of the market
  • E(rm)- rf the market risk premium.

The risk premium for asset i is equal to βi(E(rm)- rf), that is the beta of asset i, βi, multiplied by the risk premium for the market, E(rm)- rf.

In this model, the beta (β) parameter is a key parameter and is defined as:

CAPM beta formula

Where:

  • Cov(ri, rm) represents the covariance of the return of asset i with the return of the market
  • σ2(rm) the variance of the return of the market.

Excel file to compute the beta

You can download below an Excel file with data for Apple stock returns and the S&P500 index returns (used as a representation of the market). This Excel file computes the beta of apple with the S&P500 index.

Download the Excel file to estimate the beta of Apple stock

Interpretation of the beta

Beta helps investors to explain how the asset moves compared to the market. More specifically, we can consider the following cases for beta values:

  • β = 1 indicates a fluctuation between the asset and its benchmark, thus the asset tends to move at a similar rate than the market fluctuations. A passive ETF replicating an index will present a beta close to 1 with its associated index.
  • 0 < β < 1 indicates that the asset moves at a slower rate than market fluctuations. Defensive stocks, stocks that deliver consistent returns without regarding the market state like P&G or Coca Cola in the US, tend to have a beta with the market lower than 1.
  • β > 1 indicates a more aggressive effect of amplification between the asset price movements with the market movements. Call options tend to have higher betas than their underlying asset.
  • β = 0 indicates that the asset or portfolio is uncorrelated to the market. Govies, or sovereign debt bonds, tend to have a beta-neutral exposure to the market.
  • β < 0 indicates an inverse effect of market fluctuation impact in the asset volatility. In this sense, the asset would behave inversely in terms of volatility compared to the market movements. Put options and Gold typically tend to have negative betas.

Why should I be interested in this post?

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

Related posts on the SimTrade blog

   ▶ Youssef LOURAOUI Systematic and specific risks

   ▶ Youssef LOURAOUI Portfolio

   ▶ Youssef LOURAOUI Alpha

   ▶ Jayati WALIA Capital Asset Pricing Model (CAPM)

Useful resources

Academic research

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

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

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

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

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

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

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

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

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

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

Business analysis

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

Man Institute, 2021. How to calculate the Beta of a portfolio to a factor

Nasdaq, 2021. Beta

About the author

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

Alpha

Youssef_Louraoui

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

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

Introduction

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

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

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

Figure 1. Alpha and the Security Market Line

Estimation of alpha

Source: Computation by the author.

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

Download the Excel file to compute the Jensen's alpha

Academic Literature

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

Mathematical derivation of Jensen’s alpha

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

Equation for Jensen's alpha

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

Why should I be interested in this post?

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

Related posts on the SimTrade blog

   ▶ Youssef LOURAOUI Portfolio

   ▶ Youssef LOURAOUI Systematic risk and specific risk

   ▶ Youssef LOURAOUI Beta

   ▶ Youssef LOURAOUI Markowitz Modern Portfolio Theory

   ▶ Jayati WALIA. Capital Asset Pricing Model (CAPM)

Useful resources

Academic research

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

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

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

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

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

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

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

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

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

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

Business analysis

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

About the author

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

Capital Market Line (CML)

Youssef_Louraoui

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

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

Capital Market Line

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

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

Risky assets

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

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

Risky assets and a risk-free asset

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

Figure 2. Efficient frontier with risky assets and a risk-free asset.
img_Simtrade_CML_graph_0
Source: Computation by the author.

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

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

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

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

Figure 3. Efficient frontier with different points.
img_Simtrade_CML_graph_2
Source: Computation by the author.

Mathematical representation

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

img_SimTrade_CML_equations_0

Where:

  • σP: the volatility of portfolio P
  • Rf: the risk-free interest rate
  • E(RM): the expected return of the market M
  • σM: the volatility of the market M
  • E[RM– Rf]: the market risk premium.

The expected return of the portfolio can be computed as:

img_SimTrade_CML_equations_1

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

img_SimTrade_CML_equations_2

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

img_SimTrade_CML_equations_3

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

Why should I be interested in this post?

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

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

Related posts on the SimTrade blog

   ▶ Youssef LOURAOUI Portfolio

   ▶ Youssef LOURAOUI Systematic and Specific risk

   ▶ Jayati WALIA Capital Asset Pricing Model (CAPM)

   ▶ Youssef LOURAOUI Markowitz Modern Portfolio Theory

   ▶ Youssef LOURAOUI Alpha

   ▶ Youssef LOURAOUI Factor Investing

   ▶ Youssef LOURAOUI Origin of factor investing

   ▶ Youssef LOURAOUI Security Market Line (SML)

Useful resources

Academic research

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

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

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

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

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

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

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

About the author

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

Active Investing

Youssef_Louraoui

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

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

Introduction

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

Stock picking

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

Market timing

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

Review of academic literature on active investing

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

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

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

Core principles of active investing

First principle: market efficiency varies between asset classes.

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

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

Second principle: market efficiency varies across asset classes.

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

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

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

Why should I be interested in this post?

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

Related posts on the SimTrade blog

   ▶ Youssef LOURAOUI Portfolio

   ▶ Youssef LOURAOUI Systematic and specific risk

   ▶ Youssef LOURAOUI Alpha

   ▶ Youssef LOURAOUI Factor Investing

   ▶ Youssef LOURAOUI Origin of factor investing

   ▶ Youssef LOURAOUI Markowitz Modern Portfolio Theory

   ▶ Jawati WALIA Capital Asset Pricing Model (CAPM)

Useful resources

Academic research

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

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

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

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

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

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

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

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

Business analysis

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

JP Morgan Asset Management, 2021. Investing

Morgan Stanley, 2021. Active vs Passive management

About the author

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

Smart Beta industry main actors

Youssef_Louraoui

In this article, Youssef LOURAOUI (ESSEC Business School, Global Bachelor of Business Administration, 2017-2021) presents the main actors of the smart beta industry, which is estimated to represent a cumulative market value of $1.9 trillion as of 2017 and is projected to grow to $3.4 trillion by 2022 (BlackRock, 2021).

The structure of this post is as follows: we begin by presenting an overview of the smart beta industry actors. We will then discuss the case of BlackRock, the 10 trillion dollar powerhouse of the asset management industry, which is the main actor in the smart beta industry segment.

Overview of the market

The asset management sector, which is worth 100 trillion dollars worldwide, is primarily divided into active and passive management (BCG, 2021). While active management continues to dominate the market, passive management’s proportion of total assets under managed (AUM) increased by 4 percentage points between 2008 and 2019, reaching 15%. This market transition is even more dramatic in the United States, where passive management accounted for more than 40% of the total market share in 2019. A new category has arisen and begun to acquire market share over the last decade. Smart beta exchange-traded funds (ETFs) are receiving fresh inflows and are the industry’s fastest-growing ETF product. Various players are entering the market by developing and releasing new products (Deloitte, 2021).

Active funds have demonstrated divergent returns when compared to passive funds, making the cost difference increasingly difficult to justify (Figure 1). The growing market share of passive funds in both the United States and the European Union is putting further pressure on active managers’ fees. When it comes to meeting the demands of investors, both active and passive management has shown shortcomings. Active management funds often fail to outperform their benchmarks because they lack clear indicators, charge expensive fees, and don’t always have clear indicators. As seen in Figure 1, active funds struggle to deliver consistent returns over a prolonged timeframe, as depicted in the European market. In this sense, the active funds success rate is divided by more than half between year one and year three (Deloitte, 2021). Concentration is a problem for passive funds that are weighted by market capitalization.. These limits have prepared the ground for smart beta funds to emerge (Figure 1).

Figure 1. Active funds success rates (% of funds beating their index over X years)
Active funds success rates
Source: Deloitte (2021).

The smart beta market is dominated by several players who have a strategic position with a large volume of assets under management. Figure 2 compares smart beta actors based on percentage of asset under management (%AUM), one the most important metric in the asset management industry. Some key elements can be drawn for the first figure. BlackRock is the provider with the largest market share, with over 40% of the smart beta industry in the analysis, followed by Vanguard and State Street Global Advisors with 30.66% and 18.44% respectively in this benchmark study underpinning nearly $1 trillion (Figure 2).

Figure 2. % AUM of the biggest Smart Beta ETF providers
Smart_Beta_benchmark_analysis
Source: etf.com (2021).

BlackRock dominance

The main powerhouses of the passive investing industry, BlackRock and Vanguard, are poised to capture the lion’s share of assets in the rapidly rising world of actively managed exchange-traded funds. The conclusion is likely to dissatisfy active fund managers, who have been squeezed by the fast development of passive ETFs in recent years and may have seen the introduction of active ETFs as a chance to fight back and get a piece of the lucrative pie (Financial Times, 2021).

According to a study of 320 institutional investors with a combined $12.9 trillion in assets, institutional investors prefer BlackRock and Vanguard to handle their active ETF investments. The juggernauts were expected to provide the best performance as well as the best value for money. With over a third of the global ETF market capitalization, BlackRock remains the dominant player (The Financial Times, 2021). BlackRock is unquestionably a major force in the ETF business, with an unparalleled market share in both the US and European ETF markets. BlackRock has expanded to become the world’s largest asset manager, managing funds for everyone from pensioners to oligarchs and sovereign wealth funds. It is now one of the largest stockholders in practically every major American corporation — as well as a number of overseas corporations. It is also one among the world’s largest lenders to businesses and governments.

Aladdin, the company’s technological platform, provides critical wiring for large portions of the worldwide investing industry. By the end of June this year, BlackRock was managing a stunning $9.5 trillion in assets, a sum that would be hardly understandable to most of the 35 million Americans whose retirement accounts were managed by the business in 2020. If the current rate of growth continues, BlackRock’s third-quarter reports on October 13 might disclose that the company’s market capitalization has surpassed $10 trillion. It’s expected to have surpassed that mark by the end of the year (FT, 2021). To put this in perspective, it is about equivalent to the worldwide hedge fund, private equity, and venture capital industries combined.

Industry-wide fee cuts had helped BlackRock maintain its dominance in the ETF sector. Its iShares brand is the industry’s largest ETF provider for both passive and actively managed products (CNBC, 2021).

Why should I be interested in this post?

If you are a business school or university undergraduate or graduate student, this content will help you in understanding the various evolutions of asset management throughout the last decades and in broadening your knowledge of finance.

Smart beta funds have become a trending topic among investors in recent years. Smart beta is a game-changing invention that addresses an unmet need among investors: a higher return for lower risk, net of transaction and administrative costs. In a way, these investment strategies create a new market. As a result, smart beta is gaining traction and influencing the asset management industry.

Related posts on the SimTrade blog

Factor investing

   ▶ Youssef LOURAOUI Factor Investing

   ▶ Youssef LOURAOUI Origin of factor investing

   ▶ Youssef LOURAOUI MSCI Factor Indexes

   ▶ Youssef LOURAOUI Smart beta 1.0

   ▶ Youssef LOURAOUI Smart beta 2.0

Factors

   ▶ Youssef LOURAOUI Size Factor

   ▶ Youssef LOURAOUI Value Factor

   ▶ Youssef LOURAOUI Yield Factor

   ▶ Youssef LOURAOUI Momentum Factor

   ▶ Youssef LOURAOUI Quality Factor

   ▶ Youssef LOURAOUI Growth Factor

   ▶ Youssef LOURAOUI Minimum Volatility Factor

Useful resources

Business analysis

BlackRock, 2021.What is factor investing?

BCG, 2021.The 100$ Trillion Machine: Global Asset Management 2021

CNBC, 2021. What Blackrock’s continued dominance means for other ETF issuers.

Deloitte, 2021. Will smart beta ETFs revolutionize the asset management industry? Understanding smart beta ETFs and their impact on active and passive fund managers

Etf.com, 2021.Smart Beta providers

Financial Times (13/09/2020) BlackRock and Vanguard look set to extend dominance to active ETFs

Financial Times (07/10/2021) The ten trillion dollar man: how Larry Fink became king of Wall St

About the author

The article was written in October 2021 by Youssef LOURAOUI (ESSEC Business School, Global Bachelor of Business Administration, 2017-2021).

MSCI Factor Indexes

Youssef_Louraoui

In this article, Youssef LOURAOUI (ESSEC Business School, Global Bachelor of Business Administration, 2017-2021) presents the MSCI Factor Indexes. MSCI is one of the most prominent actors in the indexing business, with approximately 236 billion dollars in assets benchmarked to the MSCI factor indexes.

The structure of this post is as follows: we begin by introducing MSCI Factor Indexes and the evolution of portfolio performance. We then delve deeper by describing the MSCI Factor Classification Standards (FaCS). We finish by analyzing factor returns over the last two decades.

Definition

Factor

A factor is any component that helps to explain the long-term risk and return performance of a financial asset. Factors have been extensively used in portfolio risk models and in quantitative investment strategies, and documented in academic research. Active fund managers use these characteristics while selecting securities and constructing portfolios. Factor indexes are a quick and easy way to get exposure to several return drivers. Factor investing aims to obtain greater risk-adjusted returns by exposing investors to stock features in a systematic way. Factor investing isn’t a new concept; it’s been utilized in risk models and quantitative investment techniques for a long time. Factors can also explain a portion of fundamental active investors’ long-term portfolio success. MSCI Factor Indexes use transparent and rules-based techniques to reflect the performance characteristics of a variety of investment types and strategies (MSCI Factor Research, 2021).

Performance analysis

Understanding portfolio returns is crucial to determining how to evaluate portfolio performance. It may be traced back to Harry Markowitz’s pioneering work and breakthrough research on portfolio design and the role of diversification in improving portfolio performance. Investors did not discriminate between the sources of portfolio gains throughout the 1960s and 1970s. Long-term portfolio management was dominated by active investment. The popularity of passive investment as an alternative basis for implementation was bolstered by finance research in the 1980s. Through passive allocation, investors began to effectively capture market beta. Investors began to perceive factors as major determinants of long-term success in the 2000s (MSCI Factor Research, 2021). Figure 1 presents the evolution of portfolio performance analysis over time: until the 1960s, based on the CAPM model, returns were explain by one factor only: the market return. Then, the market model was used to assess active portfolio with the alpha measuring the extra performance of the fund manager. Later on in the 2000s, the first evaluation model based on the market factor was augmented with other factors (size, value, etc.).

Figure 1. Evolution of portfolio performance analysis.
Evolution_portfolio_performance
Source: MSCI Research (2021).

MSCI Factor Index

MSCI Factor Classification Standards (FaCS) establishes a standard vocabulary and definitions for factors so that they may be understood by a wider audience. MSCI FaCS is comprised of 6 Factor Groups and 14 factors and is based on MSCI’s Barra Global Equity Factor Model (MSCI Factor Research, 2021) as shown in Table 1.

Table 1 Factor decomposition of the different factor strategies.
MSCI_FaCS
Source: MSCI Research (2021).

The MSCI Factor Indexes are based on well-researched academic studies. The MSCI Factor Indexes were identified and developed based on academic results, creating a unified language to describe risk and return via the perspective of factors (MSCI Factor Research, 2021).

Performance of factors over time

Figure 2 compares the MSCI factor indexes’ performance from 1999 to May 2020. All indexes are rebalanced on a 100-point scale to ensure consistency in performance and to facilitate factor comparisons. Over a two-decade period, smart beta factors have all outperformed the MSCI World index, with the MSCI World Minimum Volatility Index as the most profitable factor which has consistently provided excess profits over the long run while (MSCI Factor research, 2021).

Figure 2. Performance of MSCI Factor Indexes during the period 1999-2017.
MSCI_performance
Source: MSCI Research (2021).

Individual factors have consistently outperformed the market over time. Figure 2 represents the performance of the MSCI Factor Indexes for the last two decades compared to the MSCI ACWI, which is MSCI’s flagship global equity index and is designed to represent the performance of large- and mid-cap stocks across 23 developed and 27 emerging markets.

It is possible to make some conclusions regarding the performance of the investment factor over the previous two decades by dissecting the performance of the various factorial strategies. The value factor was the one that drove performance in the first decade of the 2000s. This outperformance is characterized by a movement towards more conservative investment in a growing market environment. The dotcom bubble crash resulted in a bear market, with the minimal volatility approach helping to absorb market shocks in 2002. When it comes to the minimal volatility approach, it is evident that it is highly beneficial during moments of high volatility, acting as a viable alternative to hedging one’s stock market exposure and moving into more safe-haven products. Several times of extreme volatility may be recognized, including the dotcom boom, the US subprime crisis, and the European debt crisis as shown in Figure 3.

Figure 3. Table of performance of MSCI Factor Indexes from 1999-2017.
MSCI_historical_performance
Source: MSCI Research (2021).

Why should I be interested in this post?

If you are a business school or university undergraduate or graduate student, this content will help you in understanding the evolution of asset management throughout the last decades and in broadening your knowledge of finance.

Smart beta funds have become a trending topic among investors in recent years. Smart beta is a game-changing invention that addresses an unmet need among investors: a higher return for lower risk, net of transaction and administrative costs. In a way, these investment strategies create a new market. As a result, smart beta is gaining traction and influencing the asset management industry.

Related posts on the SimTrade blog

Factor investing

   ▶ Youssef LOURAOUI Factor Investing

   ▶ Youssef LOURAOUI Origin of factor investing

   ▶ Youssef LOURAOUI Smart beta 1.0

   ▶ Youssef LOURAOUI Smart beta 2.0

Factors

   ▶ Youssef LOURAOUI Size Factor

   ▶ Youssef LOURAOUI Value Factor

   ▶ Youssef LOURAOUI Yield Factor

   ▶ Youssef LOURAOUI Momentum Factor

   ▶ Youssef LOURAOUI Quality Factor

   ▶ Youssef LOURAOUI Growth Factor

   ▶ Youssef LOURAOUI Minimum Volatility Factor

Useful resources

Business analysis

MSCI Factor Research, 2021.MSCI Factor Indexes

MSCI Factor Research, 2021. MSCI Factor Classification Standards (FaCS)

About the author

The article was written in October 2021 by Youssef LOURAOUI (ESSEC Business School, Global Bachelor of Business Administration, 2017-2021).

Smart beta 2.0

Youssef_Louraoui

In this article, Youssef LOURAOUI (ESSEC Business School, Global Bachelor of Business Administration, 2017-2021) presents the concept of Smart beta 2.0, an enhancement of the first generation of smart beta strategies.

The structure of this post is as follows: we begin by defining smart beta 2.0 as a topic. We then discuss then the characteristics of smart beta 2.0.

Definition

“Smart beta 2.0” is an expression introduced by Amenc, Goltz and Martellini (2013) from the EDHEC-Risk Institute. This new vision of smart beta investment intends to empower investors to maximize the performance of their smart beta investments while managing their risk. Rather than offering solely pre-packaged alternatives to equity market-capitalization-weighted indexes, the Smart beta 2.0 methodology enables investors to experiment with multiple smart beta indexes to create a benchmark that matches their own risk preferences, and by extension increase their portfolio diversification overall.

Characteristics of smart beta 2.0 strategies

The main characteristic of smart beta 2.0 strategies compared to smart beta 1.0 strategies is portfolio diversification.

If factor-tilted strategies (i.e., portfolios with a part specifically invested in factor strategies) do not consider a diversification-based goal, they may result in very concentrated portfolios in order to achieve their factor tilts. Investors have lately started to integrate factor tilts with diversification-based weighting methods to create well-diversified portfolios using a flexible strategy known as Smart beta 2.0 (EDHEC-Risk Institute, 2016).

This method, in particular, enables the creation of factor-tilted indexes that are also adequately diversified by using a diversification-based weighting scheme. Because it combines the smart weighting scheme with the explicit factor tilt (Amenc et al., 2014), this strategy is also known as “smart factor investment”. In order to achieve extra value-added, investors are increasingly focusing on allocation choices across factor investing techniques.

The basic foundation for the smart beta has been substantially outstripped by its success with institutional investors. It is clear that market-capitalization-weighted indices have no counterpart when it comes to capturing market fluctuations (Amenc et al., 2013). Even the harshest detractors of market-capitalization-weighted, in the end, use market-capitalization-weighted indices to assess the success of their own new indexes (Amenc et al., 2013). In fact, because smart beta strategies outperform market-capitalization-weighted indexes, the great majority of investors are likely to pick them. While everyone believes cap-weighted indexes provide the most accurate representation of the market, they do not always provide an efficient benchmark that can be used as a reference for a strategic allocation. It’s worth noting that smart beta 2.0 seeks to close the gap in terms of exposure to factors from the first generation, but it doesn’t guarantee outperformance over market-capitalization-weighted strategies (Amenc et al., 2013).

Why should I be interested in this post?

If you are a business school or university undergraduate or graduate student, this content will help you in understanding the evolution of asset management during the last decades and in broadening your knowledge of finance.

Smart beta funds have become a hot issue among investors in recent years. Smart beta is a game-changing invention that addresses an unmet need among investors: a higher return for lower risk, net of transaction and administrative costs. In a way, these strategies (smart beta 1.0 and then smart beta 2.0) have created a new market. As a result, smart beta is gaining traction and influencing the asset management industry.

Related posts on the SimTrade blog

Factor investing

   ▶ Youssef LOURAOUI Factor Investing

   ▶ Youssef LOURAOUI Origin of factor investing

   ▶ Youssef LOURAOUI Smart beta 1.0

   ▶ Youssef LOURAOUI Alternatives to market-capitalization weighting strategies

Factors

   ▶ Youssef LOURAOUI Size Factor

   ▶ Youssef LOURAOUI Value Factor

   ▶ Youssef LOURAOUI Yield Factor

   ▶ Youssef LOURAOUI Momentum Factor

   ▶ Youssef LOURAOUI Quality Factor

   ▶ Youssef LOURAOUI Growth Factor

   ▶ Youssef LOURAOUI Minimum Volatility Factor

Useful resources

Amenc, N., F., Goltz, F., Le Sourd, V., 2016. Investor perception about Smart beta ETF. EDHEC-Risk Institute working paper.

Amenc, N., F., Goltz, F., Martellini, L., 2013. Smart beta 2.0. EDHEC-Risk Institute working paper.

Amenc, N., F., Goltz, F., Martinelli, L., Deguest, R., Lodh, A., Shirbini, E., 2014. Risk Allocation, Factor Investing and Smart Beta: Reconciling Innovations in Equity Portfolio Construction. EDHEC-Risk Institute working paper.

About the author

The article was written in September 2021 by Youssef LOURAOUI (ESSEC Business School, Global Bachelor of Business Administration, 2017-2021).

Smart Beta 1.0

Youssef_Louraoui

In this article, Youssef LOURAOUI (ESSEC Business School, Global Bachelor of Business Administration, 2017-2021) presents the concept of the smart beta 1.0, the first generation of alternative indexing investment strategies that created a new approach in the asset management industry.

This post is structured as follows: we start by defining smart beta 1.0 as a topic. Finally, we discuss an empirical study by Motson, Clare and Thomas (2017) emphasizing the origin of smart beta.

Definition

The “Smart Beta” expression is commonly used in the asset management industry to describe innovative indexing investment strategies that are alternatives to the market-capitalization-weighted investment strategy (buy-and-hold). In terms of performance, the smart beta “1.0” approach outperforms market-capitalization-based strategies. According to Amenc et al. (2016), the latter have a tendency for concentration and unrewarded risk, which makes them less appealing to investors. In finance, “unrewarded risk” refers to taking on more risk without receiving a return that is commensurate to the increased risk.

When smart beta techniques were first introduced, they attempted to increase portfolio diversification over highly concentrated and capitalization-weighted, as well as to capture the factor premium available in equity markets, such as value indices or fundamentally weighted indices which aim to capture the value premium. While improving capitalization-weighted indices is important, concentrating just on increasing diversity or capturing factor exposure may result in a less than optimal outcome. The reason for this is that diversification-based weighting systems will always result in implicit exposure to certain factors, which may have unintended consequences for investors who are unaware of their implicit factor exposures. Unlike the second generation of Smart Beta, the first generation of Smart Beta are integrated systems that do not distinguish between stock selection and weighting procedures. The investor is therefore required to be exposed to certain systemic risks, which are the source of the investor’s poor performance.

Thus, the first-generation Smart Beta indices are frequently prone to value, small- or midcap, and occasionally contrarian biases, since they deconcentrate cap weighted indices, which are often susceptible to momentum and large growth risk. Furthermore, distinctive biases on risk indicators that are unrelated to deconcentration but important to the factor’s objectives may amplify these biases even further. Indexes that are fundamentally weighted, for example, have a value bias because they apply accounting measures that are linked to the ratios that are used to construct value indexes.

Empirical study: monkeys vs passive mangers

Andrew Clare, Nick Motson, and Steve Thomas assert that even monkey-created portfolios outperform cap-weighted benchmarks in their study (Motson et al., 2017). A lack of variety in cap-weighting is at the foundation of the problem. The endless monkey theory states that a monkey pressing random keys on a typewriter keyboard for an unlimited amount of time will almost definitely type a specific text, such as Shakespeare’s whole works. For 500 businesses, there is an infinite number of portfolio weighting options totaling 100%; some will outperform the market-capitalization-weighted index, while others will underperform. The authors of the study take the company’s ticker symbol and use the following guidelines to create a Scrabble score for each stock:

  • A, E, I, O, U, L, N, S, T, R – 1 point. D and G both get two points.
  • B, C, M, P – 3 points ; F, H, V, W, Y – 4 points ; K – 5 points.
  • J, X – 8 points ; Q, Z – 10 points

The scores of each company’s tickers are then added together and divided by this amount to determine each stock’s weight in the index. As illustrated in Figure 1, the results obtained are astonishing, resulting in a clear outperformance of the randomly generated portfolios compared to the traditional market capitalization index by 1.5% premium overall.

Figure 1. Result of the randomly generated portfolio with the Cass Scrabble as underlying rule compared to market-capitalization portfolio performance.
Scrabble_performance
Source: Motson et al. (2017).

In the same line, the authors produced 500 weights that add up to one using this technique, with a minimum increase of 0.2 percent. The weights are then applied to a universe of 500 equities obtained from Bloomberg in December 2015 (Motson et al., 2017). The performance of the resultant index is then calculated over the next twelve months. This technique was performed ten million times. As illustrated in Figure 2, the results are striking, with smart beta funds outperforming nearly universally in the 10 million simulations run overall, and with significant risk-adjusted return differences (Motson et al., 2017).

Figure 2. 10 million randomly generated portfolios based on a portfolio construction of 500 stocks
Scrabble_performance
Source: Motson et al. (2017).

For performance analysis, the same method was employed, but this time for a billion simulation. This means they constructed one billion 500-stock indexes with weights set at random or as if by a monkey. Figure 9 suggests that the outcome was not accidental. The black line shows the distribution of 1 billion monkeys’ returns in 2016, while the grey line shows the cumulative frequency. 88 percent of the monkeys outperformed the market capitalization benchmark, according to the graph. The luckiest monkey returned 27.2 percent, while the unluckiest monkey returned just 3.83 percent (Motson et al., 2017) (Figure 3).

FFigure 3. Result of one billion randomly simulated portfolios based on a portfolio construction of 500 stocks.
Scrabble_performance
Source: Motson et al. (2017).

Why should I be interested in this post?

If you are a business school or university undergraduate or graduate student, this content will help you in understanding the various evolutions of asset management throughout the last decades and in broadening your knowledge of finance.

If you’re an investor, you’re probably aware that smart beta funds have become a popular topic. Smart beta is a game-changing development that fills a gap in the market for investors: a better return for a reduced risk, net of transaction and administrative costs. These strategies, in a sense, establish a new market. As a result, smart beta is gaining traction and having an impact on asset management.

Related posts on the SimSrade blog

Factor investing

   ▶ Youssef LOURAOUI Factor Investing

   ▶ Youssef LOURAOUI Origin of factor investing

   ▶ Youssef LOURAOUI Smart beta 2.0

   ▶ Youssef LOURAOUI Alternatives to market-capitalisation weighted indexes

Factor

   ▶ Youssef LOURAOUI Size Factor

   ▶ Youssef LOURAOUI Value Factor

   ▶ Youssef LOURAOUI Yield Factor

   ▶ Youssef LOURAOUI Momentum Factor

   ▶ Youssef LOURAOUI Quality Factor

   ▶ Youssef LOURAOUI Growth Factor

   ▶ Youssef LOURAOUI Minimum Volatility Factor

Useful resources

Academic research

Amenc, N., F., Goltz, F. and Le Sourd, V., 2016. Investor perception about Smart beta ETF. EDHEC Risk Institute working paper.

Amenc, N., F., Goltz, F. and Martinelli, L., 2013. Smart beta 2.0. EDHEC Risk Institute working paper.

Motson, N., Clare, A. & Thomas, S., 2017. Was 2016 the year of the monkey?. Cass Business School research paper.

About the author

The article was written in September 2021 by Youssef LOURAOUI (ESSEC Business School, Global Bachelor of Business Administration, 2017-2021).