6:00 PM Seminar Begins
7:30 PM Reception
Hybrid Event:
Fordham University
McNally Amphitheater
140 West 62nd Street
New York, NY 10023
Free Registration!
For Virtual Attendees: Please select Virtual instead of member type upon registration.
Abstract:
Asset-specific factors are commonly used to forecast financial returns and quantify asset-specific risk premia. Using various machine learning models, we demonstrate that the information contained in these factors leads to even larger economic gains in terms of forecasts of sector returns and the measurement of sector-specific risk premia. To capitalize on the strong predictive results of individual models for the performance of different sectors, we develop a novel online ensemble algorithm that learns to optimize predictive performance. The algorithm continuously adapts over time to determine the optimal combination of individual models by solely analyzing their most recent prediction performance. This makes it particularly suited for time series problems, rolling window backtesting procedures, and systems of potentially black-box models. We derive the optimal gain function, express the corresponding regret bounds in terms of the out-of-sample R-squared measure, and derive optimal learning rate for the algorithm. Empirically, the new ensemble outperforms both individual machine learning models and their simple averages in providing better measurements of sector risk premia. Moreover, it allows for performance attribution of different factors across various sectors, without conditioning on a specific model. Finally, by utilizing monthly predictions from our ensemble, we develop a sector rotation strategy that significantly outperforms the market. The strategy remains robust against various financial factors, periods of financial distress, and conservative transaction costs. Notably, the strategy's efficacy persists over time, exhibiting consistent improvement throughout an extended backtesting period and yielding substantial profits during the economic turbulence of the COVID-19 pandemic.
Bio:
Dr. Pawel Polak is an Assistant Professor in the Department of Applied Mathematics and Statistics and an Affiliated Faculty at the Institute for Advanced Computational Science at Stony Brook University. He is specializing in statistical learning and machine learning with applications in quantitative finance, medicine, and engineering. Before Stony Brook, he served as a Lecturer at Stevens Institute of Technology, and an Assistant Professor in Columbia University's Statistics Department. He completed his Ph.D. (summa cum laude) at the Swiss Finance Institute and University of Zurich in 2014, and holds a Postgraduate Diploma in Economics from the Institute for Advanced Studies, Vienna, as well as Master and Bachelor degrees in Mathematics from the University of Warsaw in Poland.
