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Regime-Switching Factor Investing with Hidden Markov Models

Author

Listed:
  • Matthew Wang

    (Electrical and Computer Engineering, Northwestern University, Evanston, IL 60208, USA)

  • Yi-Hong Lin

    (Media IC & System Lab, National Taiwan University, Taipei 10617, Taiwan)

  • Ilya Mikhelson

    (Electrical and Computer Engineering, Northwestern University, Evanston, IL 60208, USA)

Abstract

This study uses the hidden Markov model (HMM) to identify different market regimes in the US stock market and proposes an investment strategy that switches factor investment models depending on the current detected regime. We first backtested an array of different factor models over a roughly 10.5 year period from January 2007 to September 2017, then we trained the HMM on S&P 500 ETF historical data to identify market regimes of that period. By analyzing the relationship between factor model returns and different market regimes, we are able to establish the basis of our regime-switching investing model. We then back-tested our model on out-of-sample historical data from September 2017 to April 2020 and found that it both delivers higher absolute returns and performs better than each of the individual factor models according to traditional portfolio benchmarking metrics.

Suggested Citation

  • Matthew Wang & Yi-Hong Lin & Ilya Mikhelson, 2020. "Regime-Switching Factor Investing with Hidden Markov Models," JRFM, MDPI, vol. 13(12), pages 1-15, December.
    • Handle: RePEc:gam:jjrfmx:v:13:y:2020:i:12:p:311-:d:457386
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    References listed on IDEAS

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    Cited by:

    1. Lim Hao Shen Keith, 2024. "Covariance Matrix Analysis for Optimal Portfolio Selection," Papers 2407.08748, arXiv.org.

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