How machine learning will reshape the future of investment management

Data science applied to asset management, and education in the field, is expected to affect not only investment professionals but also individuals

Published: Feb 11, 2020 04:12:00 PM IST
Updated: Feb 12, 2020 02:14:58 PM IST

machine learningImage: Shutterstock

The 2020 outlook for Asset Management re-affirms impact of globalization and outperformance of private equity. While the developed world’s economy has sent mixed signals, all eyes are now on Asia and especially India, to drive the next phase of growth. The goal is to provide Investment Solutions for its mix of young as well as senior population. Its diversity – cultural, economic, regional & regulatory, will pose the next challenge.

The application of Data Science & Machine Learning has delivered value for portfolio managers through quick and uniform decision-making. Strategic Beta Funds which have consistently generated added value, rely heavily on the robustness of their portfolio creation models which are excruciatingly data driven. Deploying Machine Learning algorithms helps assess credit worthiness of firms and individuals for lending and borrowing. Data Science and Machine Learning solutions eliminate human bias and calculation errors while evaluating investments in an optimum period.

Vinayak Singh – Sr. Vice-President at Centrum Capital Ltd. Mumbai and EDHEC Alumnus 2008-batch reckons “Using Data Science for Weight Allocation is the latest trend in Portfolio Analytics. While most Global Financial Institutions are expanding their knowledge-base, this trend has just arrived in Indian Markets”.

Investment management is justified as an industry only to the extent that it can demonstrate a capacity to add value through the design of dedicated investor-centric investment solutions, as opposed to one-size-fits-all manager-centric investment products. After several decades of relative inertia, the much needed move towards investment solutions has been greatly facilitated by a true industrial revolution taking place in investment management, triggered by profound paradigm changes with the emergence of novel approaches such as factor investing, liability-driven and goal-based investing, as well as sustainable investing. Data science is expected to play an increasing role in these transformations.

This trend poses a critical challenge to global academic institutions: educating a new breed of young professionals and equipping them with the right skills to address the situation, and who could seize the fast-developing new job opportunities in this field. Continuous education gives the opportunity to meet with new challenges of this ever-changing world, especially in the investment industry.

As recently emphasized by our colleague Vijay Vaidyanathan, CEO, Optimal Asset Management, former EDHEC Business School PHD student, and online course instructor at EDHEC Business School, our financial well-being is second only to our physical well-being, and one of the key challenges we face is to enhance financial expertise. 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 the quality of financial professional financial education to ensure that all asset managers and asset owners are fully equipped to make intelligent and well-informed investment decisions.   

Data science applied to asset management, and education in the field, is expected to affect not only investment professionals but also individuals. On this topic, we would like to share insights from Professor John Mulvey, Princeton University, who is also one of EDHEC on-line course instructors.
John believes that machine learning applied to investment management is a real opportunity to assist individuals with their financial affairs in an integrated manner. Most people are faced with long-term critical decisions about saving, spending, and investing to achieve a wide variety of goals.

These decisions are often made without much professional guidance (except for wealthier clients), and without much technical training. Current personalized advisors are reasonable initial steps. Much more can be done in this area with modern data science and decision-making tools. Plus, younger people are more willing to trust fully automated computational systems. This domain is one of the most relevant and significant areas of development for future investment management.   

By Nilesh Gaikwad, EDHEC Business School country manager in India, and Professor Lionel Martellini, EDHEC-Risk Institute Director.

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