IDEAS home Printed from https://ideas.repec.org/a/gam/jrisks/v9y2020i1p9-d472764.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-9091/9/1/9/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-9091/9/1/9/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nguyet Nguyen & Dung Nguyen, 2015. "Hidden Markov Model for Stock Selection," Risks, MDPI, vol. 3(4), pages 1-19, October.
    2. 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.
    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.
    4. G. Kavitha & A. Udhayakumar & D. Nagarajan, 2013. "Stock Market Trend Analysis Using Hidden Markov Models," Papers 1311.4771, arXiv.org.
    5. 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.
    6. George Hondroyiannis & Evangelia Papapetrou, 2001. "Macroeconomic influences on the stock market," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 25(1), pages 33-49, March.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Doumpos, Michalis & Zopounidis, Constantin & Gounopoulos, Dimitrios & Platanakis, Emmanouil & Zhang, Wenke, 2023. "Operational research and artificial intelligence methods in banking," European Journal of Operational Research, Elsevier, vol. 306(1), pages 1-16.
    2. 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.
    3. Edouard Ribes, 2022. "Financial planning & optimal retirement timing for physically intensive occupations," Working Papers hal-03219182, HAL.
    4. repec:ipg:wpaper:2014-080 is not listed on IDEAS
    5. Edouard A. Ribes, 2022. "Financial planning and optimal retirement timing for physically intensive occupations," SN Business & Economics, Springer, vol. 2(8), pages 1-28, August.
    6. Rania Jammazi & Duc Khuong Nguyen, 2015. "Responses of international stock markets to oil price surges: a regime-switching perspective," Applied Economics, Taylor & Francis Journals, vol. 47(41), pages 4408-4422, September.
    7. 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.
    8. Julio Cezar Soares Silva & Adiel Teixeira de Almeida Filho, 2023. "A systematic literature review on solution approaches for the index tracking problem in the last decade," Papers 2306.01660, arXiv.org, revised Jun 2023.
    9. Wang, Lu & Wu, Jiangbin & Cao, Yang & Hong, Yanran, 2022. "Forecasting renewable energy stock volatility using short and long-term Markov switching GARCH-MIDAS models: Either, neither or both?," Energy Economics, Elsevier, vol. 111(C).
    10. Reetam Majumder & Qing Ji & Nagaraj K. Neerchal, 2023. "Optimal Stock Portfolio Selection with a Multivariate Hidden Markov Model," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 177-198, May.
    11. Stancu Mirela Simina & Duţescu Adriana, 2021. "The impact of the Artificial Intelligence on the accounting profession, a literature’s assessment," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 15(1), pages 749-758, December.
    12. C. A. Valle & J. E. Beasley, 2019. "A nonlinear optimisation model for constructing minimal drawdown portfolios," Papers 1908.08684, arXiv.org.
    13. repec:ipg:wpaper:2014-085 is not listed on IDEAS
    14. Nguyet Nguyen, 2018. "Hidden Markov Model for Stock Trading," IJFS, MDPI, vol. 6(2), pages 1-17, March.
    15. Huidong Sun & Mustafa Raza Rabbani & Muhammad Safdar Sial & Siming Yu & José António Filipe & Jacob Cherian, 2020. "Identifying Big Data’s Opportunities, Challenges, and Implications in Finance," Mathematics, MDPI, vol. 8(10), pages 1-19, October.
    16. Milan Kumar Das & Anindya Goswami, 2019. "Testing of binary regime switching models using squeeze duration analysis," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 6(01), pages 1-20, March.
    17. Carstensen, Kai & Heinrich, Markus & Reif, Magnus & Wolters, Maik H., 2020. "Predicting ordinary and severe recessions with a three-state Markov-switching dynamic factor model," International Journal of Forecasting, Elsevier, vol. 36(3), pages 829-850.
    18. Chkili, Walid & Nguyen, Duc Khuong, 2014. "Exchange rate movements and stock market returns in a regime-switching environment: Evidence for BRICS countries," Research in International Business and Finance, Elsevier, vol. 31(C), pages 46-56.
    19. Manuela Goretti, 2005. "The Brazilian currency turmoil of 2002: a nonlinear analysis," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 10(4), pages 289-306.
    20. David Andolfatto & Paul Gomme, 2003. "Monetary Policy Regimes and Beliefs," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 44(1), pages 1-30, February.
    21. Valentina Aprigliano & Danilo Liberati, 2021. "Using Credit Variables to Date Business Cycle and to Estimate the Probabilities of Recession in Real Time," Manchester School, University of Manchester, vol. 89(S1), pages 76-96, September.
    22. DAVID E. ALLEN & MICHAEL McALEER & ROBERT J. POWELL & ABHAY K. SINGH, 2018. "Non-Parametric Multiple Change Point Analysis Of The Global Financial Crisis," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 13(02), pages 1-23, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jrisks:v:9:y:2020:i:1:p:9-:d:472764. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.