The JOIM Conference Series showcases high quality presentations and a platform for interactive discussions of current topics in the investment management arena.
October 5 – 7, 2020 / Virtual Conference
Applications of Data Science in Investment Management
COVID-19 Investment Implications
We are pleased to announce we are having a three day Virtual JOIM Conference hosted by Scheller College of Business, Georgia Institute of Technology. There will be two themes:
The first theme will present the views of thought leaders who will discuss the short and long term implications of the pandemic and identify fundamental changes which may occur. The second theme will focus on Artificial Intelligence in the investment management industry. Topics include machine learning methods for investment management as a complement to standard econometric approaches. This conference will feature papers that are early exemplars of this methodological shift, covering supervised learning, unsupervised learning, and reinforcement learning.
Panel Discussion on COVID-19 Investment Implications
Robert Merton, MIT; Campbell Harvey, Duke University
Moderator: Sanjiv Das, Santa Clara University
Panel Discussion II – Epidemiology of the Pandemic
Moderator: Andrew Lo, MIT
Xiao-Li Meng, Harvard University; Peter Hale, Vaccine Foundation; Tomas Philipson, University of Chicago
Sudheer Chava, Georgia Tech
Doing Well by Doing Good: Corporate Social Responsibility and Downside Risk
Tucker Balch, JP Morgan
AI Research at J.P. Morgan
Time-Series Variation in Factor Premia: the Influence of the Business Cycle
heterogeneity can be exploited to motivate dynamic rotation strategies among established factors: size, value, quality, low volatility and momentum. A timely and realistic identification of business cycle regimes, using leading economic indicators and global risk appetite, can be used to construct long-only factor rotation strategies with information ratios nearly 70% higher than static multifactor strategies. Results are statistically and economically significant across regions and market segments, also after accounting for transaction costs, capacity and turnover.
Vasant Dhar, New York University
The Stability of Machine Learning Models in Finance
I report results from a controlled study that measures model variance as a function of (1) the inherent predictability of a problem and (2) the frequency of the occurrence of the class of interest. The results provide important guidelines for what we should expect from machine learning methods for the range of problems that vary across different levels of predictability and base rates.
The results are of general scientific interest, and explain when and why we trust machine learning models in various domains including vision, language, finance, and healthcare.
Bryan Kelly, Yale University and AQR Capital Management
Text Data in Asset Management
Ananth Madhavan, BlackRock
What Happens with More Funds than Stocks?
Deep Srivastav and Jennifer Ball, Franklin Templeton
Data Science in Retirement
Risk Managers, Portfolio Managers, Pension Managers, Senior Executives of Financial Firms such as Plan Sponsors and Endowments and Academics would benefit from attending.