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Detecting bearish and bullish markets in financial time series using hierarchical hidden Markov models

Author

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  • Lennart Oelschlager
  • Timo Adam
Abstract
Financial markets exhibit alternating periods of rising and falling prices. Stock traders seeking to make profitable investment decisions have to account for those trends, where the goal is to accurately predict switches from bullish towards bearish markets and vice versa. Popular tools for modeling financial time series are hidden Markov models, where a latent state process is used to explicitly model switches among different market regimes. In their basic form, however, hidden Markov models are not capable of capturing both short- and long-term trends, which can lead to a misinterpretation of short-term price fluctuations as changes in the long-term trend. In this paper, we demonstrate how hierarchical hidden Markov models can be used to draw a comprehensive picture of financial markets, which can contribute to the development of more sophisticated trading strategies. The feasibility of the suggested approach is illustrated in two real-data applications, where we model data from two major stock indices, the Deutscher Aktienindex and the Standard & Poor's 500.

Suggested Citation

  • Lennart Oelschlager & Timo Adam, 2020. "Detecting bearish and bullish markets in financial time series using hierarchical hidden Markov models," Papers 2007.14874, arXiv.org.
    • Handle: RePEc:arx:papers:2007.14874
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    File URL: http://arxiv.org/pdf/2007.14874
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    References listed on IDEAS

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    1. Tobias Rydén & Timo Teräsvirta & Stefan Åsbrink, 1998. "Stylized facts of daily return series and the hidden Markov model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 13(3), pages 217-244.
    2. Nguyet Nguyen, 2018. "Hidden Markov Model for Stock Trading," IJFS, MDPI, vol. 6(2), pages 1-17, March.
    3. Bulla, Jan & Bulla, Ingo, 2006. "Stylized facts of financial time series and hidden semi-Markov models," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2192-2209, December.
    4. Vianey Leos-Barajas & Eric J. Gangloff & Timo Adam & Roland Langrock & Floris M. Beest & Jacob Nabe-Nielsen & Juan M. Morales, 2017. "Multi-scale Modeling of Animal Movement and General Behavior Data Using Hidden Markov Models with Hierarchical Structures," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(3), pages 232-248, September.
    5. Cohen, Lauren & Diether, Karl & Malloy, Christopher, 2013. "Legislating stock prices," Journal of Financial Economics, Elsevier, vol. 110(3), pages 574-595.
    6. 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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    Cited by:

    1. 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.
    2. Tiago Monteiro, 2024. "AI-Powered Energy Algorithmic Trading: Integrating Hidden Markov Models with Neural Networks," Papers 2407.19858, arXiv.org, revised Aug 2024.
    3. Adam, Timo & Mayr, Andreas & Kneib, Thomas, 2022. "Gradient boosting in Markov-switching generalized additive models for location, scale, and shape," Econometrics and Statistics, Elsevier, vol. 22(C), pages 3-16.

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