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An Analysis and Implementation of the Hidden Markov Model to Technology Stock Prediction

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  • Nguyet Nguyen

    (Faculty of Mathematics and Statistics, Youngstown State University, 1 University Plaza, Youngstown, OH 44555, USA)

Abstract

Future stock prices depend on many internal and external factors that are not easy to evaluate. In this paper, we use the Hidden Markov Model, (HMM), to predict a daily stock price of three active trading stocks: Apple, Google, and Facebook, based on their historical data. We first use the Akaike information criterion (AIC) and Bayesian information criterion (BIC) to choose the numbers of states from HMM. We then use the models to predict close prices of these three stocks using both single observation data and multiple observation data. Finally, we use the predictions as signals for trading these stocks. The criteria tests’ results showed that HMM with two states worked the best among two, three and four states for the three stocks. Our results also demonstrate that the HMM outperformed the naïve method in forecasting stock prices. The results also showed that active traders using HMM got a higher return than using the naïve forecast for Facebook and Google stocks. The stock price prediction method has a significant impact on stock trading and derivative hedging.

Suggested Citation

  • Nguyet Nguyen, 2017. "An Analysis and Implementation of the Hidden Markov Model to Technology Stock Prediction," Risks, MDPI, vol. 5(4), pages 1-16, November.
    • Handle: RePEc:gam:jrisks:v:5:y:2017:i:4:p:62-:d:120204
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    References listed on IDEAS

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    1. Andrew Ang & Geert Bekaert, 2002. "International Asset Allocation With Regime Shifts," The Review of Financial Studies, Society for Financial Studies, vol. 15(4), pages 1137-1187.
    2. Nguyet Nguyen & Dung Nguyen, 2015. "Hidden Markov Model for Stock Selection," Risks, MDPI, vol. 3(4), pages 1-19, October.
    3. Guidolin, Massimo & Timmermann, Allan, 2007. "Asset allocation under multivariate regime switching," Journal of Economic Dynamics and Control, Elsevier, vol. 31(11), pages 3503-3544, November.
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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. Hayet Soltani & Mouna Boujelbène Abbes, 2023. "The Predictive Power of Financial Stress on the Financial Markets Dynamics: Hidden Markov Model," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 47(1), pages 94-115, March.
    3. 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.
    4. De Gooijer, Jan G. & Henter, Gustav Eje & Yuan, Ao, 2022. "Kernel-based hidden Markov conditional densities," Computational Statistics & Data Analysis, Elsevier, vol. 169(C).
    5. Albert Cohen, 2018. "Editorial: A Celebration of the Ties That Bind Us: Connections between Actuarial Science and Mathematical Finance," Risks, MDPI, vol. 6(1), pages 1-3, January.

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