Artikel

Regime-switching factor investing with hidden Markov models

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.

Language
Englisch

Bibliographic citation
Journal: Journal of Risk and Financial Management ; ISSN: 1911-8074 ; Volume: 13 ; Year: 2020 ; Issue: 12 ; Pages: 1-15 ; Basel: MDPI

Classification
Wirtschaft
Subject
factor models
hidden Markov model
market regime

Event
Geistige Schöpfung
(who)
Wang, Matthew
Lin, Yi-Hong
Mikhelson, Ilya
Event
Veröffentlichung
(who)
MDPI
(where)
Basel
(when)
2020

DOI
doi:10.3390/jrfm13120311
Handle
Last update
10.03.2025, 11:42 AM CET

Data provider

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Object type

  • Artikel

Associated

  • Wang, Matthew
  • Lin, Yi-Hong
  • Mikhelson, Ilya
  • MDPI

Time of origin

  • 2020

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