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Hidden Markov Model for Stock Trading

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  • Nguyet Nguyen

    (Department of Mathematics & Statistics at Youngstown State University, 1 University Plaza, Youngstown, OH 44555, USA)

Abstract

Hidden Markov model (HMM) is a statistical signal prediction model, which has been widely used to predict economic regimes and stock prices. In this paper, we introduce the application of HMM in trading stocks (with S&P 500 index being an example) based on the stock price predictions. The procedure starts by using four criteria, including the Akaike information, the Bayesian information, the Hannan Quinn information, and the Bozdogan Consistent Akaike Information, in order to determine an optimal number of states for the HMM. The selected four-state HMM is then used to predict monthly closing prices of the S&P 500 index. For this work, the out-of-sample R OS 2 , and some other error estimators are used to test the HMM predictions against the historical average model. Finally, both the HMM and the historical average model are used to trade the S&P 500. The obtained results clearly prove that the HMM outperforms this traditional method in predicting and trading stocks.

Suggested Citation

  • Nguyet Nguyen, 2018. "Hidden Markov Model for Stock Trading," IJFS, MDPI, vol. 6(2), pages 1-17, March.
    • Handle: RePEc:gam:jijfss:v:6:y:2018:i:2:p:36-:d:138097
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    Cited by:

    1. Pohle, Jennifer & Adam, Timo & Beumer, Larissa T., 2022. "Flexible estimation of the state dwell-time distribution in hidden semi-Markov models," Computational Statistics & Data Analysis, Elsevier, vol. 172(C).
    2. Mojtaba Sedighi & Hossein Jahangirnia & Mohsen Gharakhani & Saeed Farahani Fard, 2019. "A Novel Hybrid Model for Stock Price Forecasting Based on Metaheuristics and Support Vector Machine," Data, MDPI, vol. 4(2), pages 1-28, May.
    3. Vrontos, Spyridon D. & Galakis, John & Vrontos, Ioannis D., 2021. "Modeling and predicting U.S. recessions using machine learning techniques," International Journal of Forecasting, Elsevier, vol. 37(2), pages 647-671.
    4. 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.
    5. Danisman, Ozgur & Uzunoglu Kocer, Umay, 2021. "Hidden Markov models with binary dependence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 567(C).
    6. 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.
    7. Lennart Oelschlager & Timo Adam, 2020. "Detecting bearish and bullish markets in financial time series using hierarchical hidden Markov models," Papers 2007.14874, arXiv.org.
    8. 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.
    9. 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.
    10. Guglielmo D’Amico & Philippe Regnault & Stefania Scocchera & Loriano Storchi, 2018. "A Continuous-Time Inequality Measure Applied to Financial Risk: The Case of the European Union," IJFS, MDPI, vol. 6(3), pages 1-16, June.

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