Factor Momentum and Regime-Switching Overlay Strategy

Junhan Gu, John M. Mulvey

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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 languageEnglish (US)
Pages (from-to)101-129
Number of pages29
JournalJournal of Financial Data Science
Issue number4
StatePublished - Sep 1 2021

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Information Systems
  • Finance
  • Business and International Management
  • Strategy and Management
  • Business, Management and Accounting (miscellaneous)
  • Information Systems and Management


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