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Momentum Investment Strategy Using a Hidden Markov Model

Author

Listed:
  • Hosun Ryou

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

  • Han Hee Bae

    (Department of Investment Information Engineering, Yonsei University, Seoul 03722, Korea)

  • Hee Soo Lee

    (Department of Business Administration, Sejong University, Seoul 05006, Korea)

  • Kyong Joo Oh

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

Abstract

There has been a growing demand for portfolio management using artificial intelligence (AI). To sustain a competitive advantage for portfolio management, stock market investors require a strategic investment decision that can realize better returns. In this study, we propose a momentum investment strategy that employs a hidden Markov model (HMM) to select stocks in the rising state. We construct an HMM momentum portfolio that includes 890 Korean stocks and analyze the performance of the stocks over the period of January 2000 to December 2018. By identifying states of stocks, sectors, and markets through HMM, our strategy buys shares in the rising state and proceeds with rebalancing after the holding period. The HMM momentum portfolio is determined to earn higher returns than traditional momentum portfolios and to achieve the best performance under the conditions of a short holding period (one week) and a short formation period (one month). In addition, our strategy exhibits competitive performance in market and sector index investment compared with market returns. This study implies that the momentum investment strategy using HMM is useful in the Korean stock market. Based on our HMM momentum strategy, future research can be enriched by applying the HMM to developing a new AI momentum strategy that can be utilized for other portfolios containing various types of financial assets on the global market.

Suggested Citation

  • Hosun Ryou & Han Hee Bae & Hee Soo Lee & Kyong Joo Oh, 2020. "Momentum Investment Strategy Using a Hidden Markov Model," Sustainability, MDPI, vol. 12(17), pages 1-16, August.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:17:p:7031-:d:405619
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    References listed on IDEAS

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    Cited by:

    1. Fotis Kitsios & Maria Kamariotou, 2021. "Artificial Intelligence and Business Strategy towards Digital Transformation: A Research Agenda," Sustainability, MDPI, vol. 13(4), pages 1-14, February.
    2. 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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