EDHEC-Risk Partners with Coursera: Data Science for Investment Management

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EDHEC-Risk Institute has teamed up with the world's biggest online course provider, Coursera, to offer a specialisation in Data Science for Investment Management. In this month's interview, Lionel Martellini – Professor of Finance at EDHEC Business School and Director of EDHEC-Risk Institute, John Mulvey – Professor of Operations Research and Financial Engineering, ORFE Department, Princeton University, and EDHEC-Risk Research Associates Gideon Ozik – Founder, Managing Partner of MKT MediaStats and Vijay Vaidyanathan – CEO of Optimal Asset Management, discuss the new partnership with Coursera, unveil the list of massive open online courses (MOOCs) that will be covered and provide further details on the training process. They also reflect on the reasons behind their decision to join the e-learning adventure and share their objectives and initiatives for the future.




Why has EDHEC-Risk Institute decided to partner with Coursera?

Lionel Martellini: Since its creation in 2001, EDHEC-Risk Institute has had the ambition to provide professionals with educational programmes designed to make recent research advances and state-of- the-art practices accessible and usable in investment practice. Building on the exclusive and latest research advances developed within EDHEC-Risk Institute and beyond, our programmes are continually evolving, ensuring that they stay relevant and meet the needs of investment professionals. In order to manage increasingly tight time and budget constraints and to provide learners with more choice and flexibility, our portfolio of programmes has evolved, putting the emphasis on different entry pathway. This evolution in our offering has produced on-site courses, namely with the “Certificate in Risk and Investment Management”, which is jointly taught with the Yale School of Management (with a remote learning option). More recently, we seized the opportunity to create 100% online courses to further broaden the reach of our educational initiatives, notably with the EDHEC-Risk online course with our flagship “Advances in Asset Allocation” seminar. Our conviction that that education should be accessible and inclusive is precisely why we have decided to partner with Coursera, the world leader in e-learning with 31 million learners currently registered on their platform.


Participants in the specialisation will be able to acquire a certificate upon completion; could you provide us with more information about the training process?

Lionel Martellini: Given the current relevance and expected impact of this topic, we are currently developing a digital specialisation on Investment Management with Python and Machine Learning, which is expected to attract substantial attention from investment professionals and students aspiring to become investment professionals. This specialisation consists of four MOOCs. Each of these MOOCs consists of videos, recommended readings, discussion prompts and assignments that need to be completed to get a certificate. Participants who have successfully submitted the assignments for all four MOOCs will get the EDHEC-Risk Certificate for the Investment Management with Python and Machine Learning specialisation. All of these four courses will have a strong applied focus, with lectures complemented by computer lab sessions providing learners with an opportunity to implement machine learning techniques in real-world investment applications.


Who is the target audience for these online courses?

Vijay Vaidyanathan: It is our desire to address as broad a range of e-learners as possible. Financial health is second only to our physical health, and to that end, understanding how financial products are constructed and how financial problems can be solved are crucially important. At the risk of over-generalising, we hope to address the following broad categories of e-learners.

First, we hope to address the financial professional who already works in the industry and desires a deeper understanding of the science behind portfolio construction and risk management. This includes professionals who are considering a career transition within the industry to a position that requires a background in financial asset management.

Second, we hope to address students enrolled in undergraduate or graduate programs in finance and economics, and aspiring to start a career in investment management, who may find relevant material in our programme to complement their curriculum.

We also hope to address the non-financial professional who is curious about the industry and is considering a career transition to a position that might benefit from acquiring this background.

Finally, and more generally, we hope to address the intellectually curious individual investor who has little to no formal training in finance or computer programming but is intrigued by the use of systematic and scientific strategies to become a better investor.


What are the reasons behind your decision to join the e-learning adventure?

Vijay Vaidyanathan: There is much talk of a retirement crisis in our society. Although the extent of the crisis varies across nations, most of us live in societies that face an uncertain and precarious retirement landscape. If one believes, as I do, that our financial well-being is second only to our physical well-being, it seems to me that our society needs to do much more to raise its levels of financial literacy. To achieve this, we cannot limit ourselves to the relatively small subset of the population who can afford to invest the significant time and expense of attending a formal, full-time degree programme on a university campus. Therefore, we must find ways to elevate both the quality of financial conversation as well as access to financial education to ensure that all investors are fully equipped to make intelligent and well-informed investment and retirement decisions. I cannot think of a more efficient way to do so than through a platform like Coursera, which combines the ease of access of a web-based learning experience with the rigorous and academically-sound curriculum from the university that took the #1 spot in the Financial Times worldwide ranking of Masters in Finance programmes.


What are your thoughts regarding the increasing use of alternative data in investment management decisions? Would this be a welcome change?

Gideon Ozik: I am a strong advocate for using alternative data to inform investment management decisions. Having been a member of the industry for 15 years now, it has been hard to ignore the fact that most active managers rely on traditional and commonly used datasets (e.g. price, volume, accounting and analyst data) and just a handful of models to make most of their investment decisions. The concentration of data and ideas crowds trades and contributes, not surprisingly, to unattractive performance, increased return correlation among active investment managers and systemic risk due to a high level of overlapping positions.

On the other hand, alternative data (typically derived from diverse digital sources such as media, foot-traffic, transactions, satellite images, mobile devices, etc.) tend to be orthogonal to commonly-used traditional data and, if processed thoughtfully, could lead to less crowding, better performance, and lower correlations.

Therefore, alternative data has the potential to improve the quality of investment management by enhancing return predictability, portfolio construction and risk management. In my class, I intend to discuss these opportunities and provide learners with useful tools to seize them.


You will teach one of the four MOOCs for the specialisation on machine learning for investment management. Can you give us more details on the content of your course and what your hopes for the course are?

John Mulvey: The title “Investment Management with Python and Machine Learning” highlights the main course concerns. This topic presents several challenges. In most machine learning (ML) domains, we assume stability of the underling population distribution. However, economic markets rarely display stability. Alas, most investors are subject to behavioural biases and tendencies, which hinders widespread usage of even the most efficient strategies and tools. To addresses these limitations, we focus on hybrid approaches – combining traditional statistical methods with modern ML -- and cover a carefully chosen set of target problems including: factor investing, economic regimes, graphical networks, and feature selection to forecast economic crashes. Students will learn the basic concepts via carefully constructed Jupyter notebooks. Our goal is to give students both a summary of fundamentals as well as existing and likely future ML applications in an intuitive and interactive setting.


What are the future projects with Coursera?

John Mulvey: Lionel and I are committed to helping individuals manage and improve their financial affairs. In this context, we envision future online courses that cover topics such as deriving lessons from institutional investing to the individual domain, and extensions of goal-based investing to easy-to-implement dynamic strategies in a simple investment wrapper. These topics aim to improve financial literacy and thus potentially reduce systemic risks for the overall economy. This ambitious goal is our passion.

Lionel Martellini: As indicated by John, we envision launching subsequent courses in the area of investment solutions for institutions and individuals. After several decades of relative inertia, the much-needed move towards investor-centric investment solutions, as opposed to manager-centric investment products, has been greatly facilitated by the emergence of liability-driven investing and goal-based investing strategies, as well as the rise of smart factor indices as meaningful building blocks. We believe that more education is needed in these areas and we expect to launch a number of related online courses and programmes over the next few years. More precisely, we expect to launch courses in the area of asset-liability management for institutional investors, retirement investing for individual investors, as well as factor investing in equity and fixed-income markets. In the longer run, we envision the launch of a fully online graduate degree programme in the area of financial markets, where EDHEC-Risk Institute and EDHEC Business School have acquired a strong international reputation for excellence and expertise. Having said this, the development of the first courses and specialisation programmes, as well as the market feedback on these courses, will help us decide on and fine-tune the content of our future online education initiatives.


More details on our web page: https://risk.edhec.edu/investment-management-python-and-machine-learning


About Lionel Martellini

Lionel Martellini is Professor of Finance at EDHEC Business School and Director of EDHEC-Risk Institute. He has graduate degrees in economics, statistics, and mathematics, as well as a PhD in finance from the University of California at Berkeley. Lionel is a member of the editorial board of the Journal of Portfolio Management and the Journal of Alternative Investments. An expert in quantitative asset management and derivatives valuation, his work has been widely published in academic and practitioner journals and he has co-authored textbooks on alternative investment strategies and fixed-income securities.

About John M. Mulvey

John M. Mulvey is a Professor in the Operations Research and Financial Engineering Department and a founding member of the Bendheim Center for Finance at Princeton University. His specialty is financial optimization and advanced portfolio theory. For over thirty-five years, he has implemented asset-liability management systems for numerous organizations, including PIMCO, Towers Perrin/Tillinghast, AXA, Siemens, Munich Re-Insurance, and Renaissance Re-Insurance. His current research addresses regime identification and factor approaches for long-term investors, including family offices, and pension plans, with an emphasis on optimizing performance and protecting investor wealth (and surplus wealth). He has published over 150 articles and edited 5 books. He is current a senior consultant for Ant Financial (Alibaba) in Hangzhou China and for First Republic Bank in San Francisco.

About Gideon Ozik

Dr. Gideon Ozik is founder and managing partner of MKT MediaStats, LLC, a technology and data analytics company, which combines expertise in data science and financial economics to extract financial markets insights from unique sets of untapped big data sources. Prior to MKT Gideon was head of investment solutions at Nexar Capital (acquired by UBP). Previously, he was a fund manager and head of hedge fund solutions at Société Générale Asset Management and quantitative derivatives trader at NISA Investment Advisors. His prior academic experience includes teaching big data analytics, financial derivatives, investments theory and quantitative research at HEC Paris, Dauphine University and EDHEC Business School where he is currently an affiliate professor and research associate. Dr. Ozik earned B.Sc from the Technion - Israel Institute of Technology (cum laude), MBA from Washington University (first graduate in finance), and PhD in Finance from EDHEC Business School, France.

About Vijay Vaidyanathan

Optimal Asset Management's founder and lead Portfolio Manager Vijay Vaidyanathan has a PhD in Finance and an MS (Risk & Asset Management) from the EDHEC Business School, France. He also has an MS in Computer Science from SUNY Albany and a MSc (Tech) from BITS Pilani, India and is an alumnus of IMD Lausanne, Switzerland. Previously, Vijay was the President, EDHEC-Risk Indices and Benchmarks North America where his research interests included smart beta and the role of factors and risk premia in equity markets. He holds several patents in financial micro-transactions in Digital Markets.