Motivated by the industry practice of long/short equity strategies, we study an approach that combines statistical learning and optimization to construct portfolios with mean-reverting price dynamics. Our objectives are threefold:
- Design a portfolio that is well-represented by an Ornstein-Uhlenbeck process, with parameters estimated by maximum likelihood.
- Select portfolios with desirable characteristics, such as high mean reversion.
- Build a parsimonious portfolio, i.e. find a small subset from a larger collection of assets for long/short positions.
We present the full problem formulation, a specialized projected gradient algorithm to solve the constrained non-convex problem. Numerical examples using empirical price data are provided.
Presenter: Tim Leung
Dr. Tim Leung is the Boeing Endowed Chair Professor in the Department of Applied Mathematics and the director of the Computational Finance and Risk Management (CFRM) program and Quantitative Analytics Lab at University of Washington in Seattle. He previously taught at Johns Hopkins and Columbia universities. He has published more than 60 peer-reviewed articles and two books on the topics of mean reversion trading and ETFs; his research has attracted funding from the National Science Foundation. Prof. Leung is on the advisory board of the A.I. for Finance Institute and the editorial boards of multiple journals. He has served as chair of the INFORMS Finance Section, as well as vice chair for the SIAM Activity Group on Financial Mathematics & Engineering. He has a B.S. from Cornell University and a Ph.D. from Princeton University.
About this series
The Financial Engineering Seminar Series is a recurring event featuring thought leaders from industry and academia, who bring their experiences to a variety of important topics in this discipline.