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Global Stock Selection with Hidden Markov Model

Author

Listed:
  • Nguyet Nguyen

    (Department of Mathematics and Statistics, Youngstown State University, 31 Lincoln Ave., Youngstown, OH 44503, USA)

  • Dung Nguyen

    (Ned Davis Research Group, 600 Bird Bay Drive West, Venice, FL 34285, USA)

Abstract

Hidden Markov model (HMM) is a powerful machine-learning method for data regime detection, especially time series data. In this paper, we establish a multi-step procedure for using HMM to select stocks from the global stock market. First, the five important factors of a stock are identified and scored based on its historical performances. Second, HMM is used to predict the regimes of six global economic indicators and find the time periods in the past during which these indicators have a combination of regimes that is similar to those predicted. Then, we analyze the five stock factors of the All country world index (ACWI) in the identified time periods to assign a weighted score for each stock factor and to calculate the composite score of the five factors. Finally, we make a monthly selection of 10% of the global stocks that have the highest composite scores. This strategy is shown to outperform those relying on either ACWI, any single stock factor, or the simple average of the five stock factors.

Suggested Citation

  • Nguyet Nguyen & Dung Nguyen, 2020. "Global Stock Selection with Hidden Markov Model," Risks, MDPI, vol. 9(1), pages 1-18, December.
    • Handle: RePEc:gam:jrisks:v:9:y:2020:i:1:p:9-:d:472764
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    References listed on IDEAS

    as
    1. Dimitris Andriosopoulos & Michalis Doumpos & Panos M. Pardalos & Constantin Zopounidis, 2019. "Computational approaches and data analytics in financial services: A literature review," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(10), pages 1581-1599, October.
    2. Nguyet Nguyen & Dung Nguyen, 2015. "Hidden Markov Model for Stock Selection," Risks, MDPI, vol. 3(4), pages 1-19, October.
    3. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
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    6. Dimitris Andriosopoulos & Michalis Doumpos & Panos M. Pardalos & Constantin Zopounidis, 2019. "Computational approaches and data analytics in financial services," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(10), pages 1579-1580, October.
    7. Andreas Humpe & Peter Macmillan, 2009. "Can macroeconomic variables explain long-term stock market movements? A comparison of the US and Japan," Applied Financial Economics, Taylor & Francis Journals, vol. 19(2), pages 111-119.
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