Abstract
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.
Original language | English (US) |
---|---|
Pages (from-to) | 101-129 |
Number of pages | 29 |
Journal | Journal of Financial Data Science |
Volume | 3 |
Issue number | 4 |
DOIs | |
State | Published - Sep 1 2021 |
Externally published | Yes |
All Science Journal Classification (ASJC) codes
- Information Systems and Management
- Artificial Intelligence
- Information Systems
- Business, Management and Accounting (miscellaneous)
- Business and International Management
- Finance
- Computational Theory and Mathematics
- Strategy and Management