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Global Asset Allocation Strategy Using a Hidden Markov Model

Author

Listed:
  • Eun-chong Kim

    (Department of Industrial Engineering, Yonsei University, Seoul 03722, Korea)

  • Han-wook Jeong

    (School of Economics and Trade, Kyungpook National University, Daegu 41566, Korea)

  • Nak-young Lee

    (Department of Industrial Engineering, Yonsei University, Seoul 03722, Korea)

Abstract

This study uses the hidden Markov model (HMM) to identify the phases of individual assets and proposes an investment strategy using price trends effectively. We conducted empirical analysis for 15 years from January 2004 to December 2018 on universes of global assets divided into 10 classes and the more detailed 22 classes. Both universes have been shown to have superior performance in strategy using HMM in common. By examining the change in the weight of the portfolio, the weight change between the asset classes occurs dynamically. This shows that HMM increases the weight of stocks when stock price rises and increases the weight of bonds when stock price falls. As a result of analyzing the performance, it was shown that the HMM effectively reflects the asset selection effect in Jensen’s alpha, Fama’s Net Selectivity and Treynor-Mazuy model. In addition, the strategy of the HMM has positive gamma value even in the Treynor-Mazuy model. Ultimately, HMM is expected to enable stable management compared to existing momentum strategies by having asset selection effect and market forecasting ability.

Suggested Citation

  • Eun-chong Kim & Han-wook Jeong & Nak-young Lee, 2019. "Global Asset Allocation Strategy Using a Hidden Markov Model," JRFM, MDPI, vol. 12(4), pages 1-15, November.
    • Handle: RePEc:gam:jjrfmx:v:12:y:2019:i:4:p:168-:d:284029
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    References listed on IDEAS

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    Cited by:

    1. Anton Gerunov, 2023. "Stock Returns Under Different Market Regimes: An Application of Markov Switching Models to 24 European Indices," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 1, pages 18-35.
    2. Perry Sadorsky, 2021. "Predicting Gold and Silver Price Direction Using Tree-Based Classifiers," JRFM, MDPI, vol. 14(5), pages 1-21, April.
    3. 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.
    4. Lolea Iulian Cornel & Stamule Simona, 2021. "Trading using Hidden Markov Models during COVID-19 turbulences," Management & Marketing, Sciendo, vol. 16(4), pages 334-351, December.

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