Title: Performance-based regularization in mean-CVaR portfolio optimization
Speaker: Prof. Gah-Yi Vahn
Affiliation: Management Science and Operations – London Business School
Location: Room 145 Huxley
Time: 2:00pm

Abstract. Regularization is a technique widely used to improve the stability of solutions to statistical problems. We propose a new regularization concept, performance-based regularization (PBR), for data-driven stochastic optimization. The goal is to improve upon Sample Average Approximation (SAA) in finite-sample performance while maintaining minimal assumptions about the data. We apply PBR to mean-CVaR portfolio optimization, where we penalize portfolios with large variability in the constraint and objective estimations, which effectively constrains the probabilities that the estimations deviate from the respective true values. This results in a combinatorial optimization problem, but we prove its convex relaxation is tight. We show via simulations that PBR substantially improves upon SAA in finite-sample performance for three different population models of stock returns. We also prove that PBR is asymptotically optimal, and further derive its first-order behavior by extending asymptotic analysis of M-estimators. This is joint work with Noureddine El Karoui (UC Berkeley Statistics) and Andrew EB Lim (NUS Business School)

About the speaker. Gah-Yi Vahn is an Assistant Professor of Management Science and Operations at London Business School. She has a BSc (1st Class Hons. with Univ. Medal) from the University of Sydney (2007), an MA in Statistics (2011) and a PhD in Operations Research (2012) from the University of California, Berkeley. Gah-Yi’s research interest is data-driven decision-making, in particular optimization with complex, high dimensional, and/or highly uncertain data, with applications to finance and operations management.