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Trading Strategy for Market Situation Estimation Based on Hidden Markov Model

Author

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  • Peng Chen

    (College of Liberal Arts and Sciences, National University of Defense Technology, Changsha 410073, China)

  • Dongyun Yi

    (College of Liberal Arts and Sciences, National University of Defense Technology, Changsha 410073, China
    School of Mathematics and Computing Science, Hunan First Normal University, Changsha 410205, China)

  • Chengli Zhao

    (College of Liberal Arts and Sciences, National University of Defense Technology, Changsha 410073, China)

Abstract

Determining states of the market and scientific laws of transfer between these states is an important subject in the field of financial mathematics. According to the results of market situation estimation, formulating corresponding trading strategies can gain profits in the market through machine trading. The market situation is mainly divided into three types: bull market, mixed market and bear market, and it can be further subdivided into multiple types. Using the hidden Markov model (HMM) to estimate the market situation is not restricted by linear conditions compared to the traditional use of linear models. In this paper, we first use HMM to model the market situation, perform feature analysis on the hidden state of the model input, and then estimate the three market situations, and propose the Markov situation estimation trading strategy. On this basis, we have made a more fine-grained division of the market situation and increased the number of hidden sequences in the model. Experiments verify that this method can improve the profitability of the strategy.

Suggested Citation

  • Peng Chen & Dongyun Yi & Chengli Zhao, 2020. "Trading Strategy for Market Situation Estimation Based on Hidden Markov Model," Mathematics, MDPI, vol. 8(7), pages 1-13, July.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:7:p:1126-:d:382308
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    References listed on IDEAS

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    1. Wen, Danyan & Ma, Chaoqun & Wang, Gang-Jin & Wang, Senzhang, 2018. "Investigating the features of pairs trading strategy: A network perspective on the Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 903-918.
    2. Thibaut Th'eate & Damien Ernst, 2020. "An Application of Deep Reinforcement Learning to Algorithmic Trading," Papers 2004.06627, arXiv.org, revised Oct 2020.
    3. Chiarella, Carl & He, Xue-Zhong & Hommes, Cars, 2006. "Moving average rules as a source of market instability," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 370(1), pages 12-17.
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    Cited by:

    1. Yongbo Pan & Xunlin Zhu, 2022. "Application of HMM and Ensemble Learning in Intelligent Tunneling," Mathematics, MDPI, vol. 10(10), pages 1-17, May.

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