Avoiding momentum crashes using stochastic mean-CVaR optimization with time-varying risk aversion

Abstract

In occasions called momentum crashes, the usually effective cross-sectional momentum strategy for financial asset allocation produces drastically negative returns. We develop a stochastic mean-risk optimization model featuring CVaR to control the risk, dynamically adjusted CVaR tail probability and objective function weight, and return scenarios generated by hybrid moment-matching. In a 95-year backtest, portfolios rebalanced by our method provide higher returns and lower risk than those rebalanced by a cross-sectional momentum heuristic, while avoiding momentum crashes.