Factor Momentum and Regime-Switching Overlay Strategy

Investors are faced with challenges in diversifying risks and protecting capital during crash periods. In this article, the authors incorporate regime information in the portfolio optimization context by identifying regimes for historical time periods using an ℓ1-trend filtering algorithm and exploring different machine learning techniques to forecast the probability of an upcoming stock market crash. They then apply a regime-based asset allocation to nominal risk parity strategy. Investors can further improve their investment performance by implementing a dollar-neutral factor momentum strategy as an overlay in conjunction with the core portfolio. The authors demonstrate that the time-series factor momentum strategy generates high risk-adjusted returns and exhibits pronounced defensive characteristics during market crashes. A volatility scaling approach is employed to manage the risk and further magnify the benefits of factor momentum. Empirical results suggest that the approach improves risk-adjusted returns by a substantial amount over the benchmark from both the standalone perspective and the contributory perspective. Key Findings ▪ The authors identify historical regimes with ℓ1-trend filtering and implement a regime-switching risk parity strategy with supervised learning methods to optimize the core portfolio allocation. ▪ By adding a long–short factor momentum strategy on top of the core diversified portfolios, the authors are able to further enhance the portfolio’s risk-adjusted return. ▪ The factor momentum strategy exhibits defensive characteristics during crashes, and its risks can be further managed by scaling the leverage based on the realized volatility.