IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v8y2020i7p1126-d382308.html
   My bibliography  Save this article

Trading Strategy for Market Situation Estimation Based on Hidden Markov Model

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/8/7/1126/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/8/7/1126/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Thibaut Th'eate & Damien Ernst, 2020. "An Application of Deep Reinforcement Learning to Algorithmic Trading," Papers 2004.06627, arXiv.org, revised Oct 2020.
    2. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    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. Antonio Briola & Jeremy Turiel & Riccardo Marcaccioli & Alvaro Cauderan & Tomaso Aste, 2021. "Deep Reinforcement Learning for Active High Frequency Trading," Papers 2101.07107, arXiv.org, revised Aug 2023.
    2. Eric Benhamou & David Saltiel & Sandrine Ungari & Abhishek Mukhopadhyay & Jamal Atif, 2020. "AAMDRL: Augmented Asset Management with Deep Reinforcement Learning," Papers 2010.08497, arXiv.org.
    3. He, Xue-Zhong & Li, Youwei, 2015. "Testing of a market fraction model and power-law behaviour in the DAX 30," Journal of Empirical Finance, Elsevier, vol. 31(C), pages 1-17.
    4. Hermsen, Oliver & Witte, Björn-Christopher & Westerhoff, Frank, 2010. "Disclosure requirements, the release of new information and market efficiency: new insights from agent-based models," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 4, pages 1-26.
    5. Thibaut Th'eate & S'ebastien Mathieu & Damien Ernst, 2020. "An Artificial Intelligence Solution for Electricity Procurement in Forward Markets," Papers 2006.05784, arXiv.org, revised Dec 2020.
    6. Gupta, Kartikay & Chatterjee, Niladri, 2020. "Selecting stock pairs for pairs trading while incorporating lead–lag relationship," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    7. Fabio Della Rossa & Lorenzo Giannini & Pietro DeLellis, 2020. "Herding or wisdom of the crowd? Controlling efficiency in a partially rational financial market," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-16, September.
    8. Gündüz, Güngör & Gündüz, Yalin, 2010. "Viscoelastic behavior of stock indices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(24), pages 5776-5784.
    9. Franke, Reiner, 2010. "On the specification of noise in two agent-based asset pricing models," Journal of Economic Dynamics and Control, Elsevier, vol. 34(6), pages 1140-1152, June.
    10. Anufriev, Mikhail & Dindo, Pietro, 2010. "Wealth-driven selection in a financial market with heterogeneous agents," Journal of Economic Behavior & Organization, Elsevier, vol. 73(3), pages 327-358, March.
    11. Chiarella Carl & Di Guilmi Corrado, 2015. "The limit distribution of evolving strategies in financial markets," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(2), pages 137-159, April.
    12. Agliari, Anna & Naimzada, Ahmad & Pecora, Nicolò, 2018. "Boom-bust dynamics in a stock market participation model with heterogeneous traders," Journal of Economic Dynamics and Control, Elsevier, vol. 91(C), pages 458-468.
    13. Kantar, Ersin & Deviren, Bayram & Keskin, Mustafa, 2014. "Hierarchical structure of the European countries based on debts as a percentage of GDP during the 2000–2011 period," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 414(C), pages 95-107.
    14. Kyrtsou, Catherine, 2008. "Re-examining the sources of heteroskedasticity: The paradigm of noisy chaotic models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(27), pages 6785-6789.
    15. Wen, Danyan & Wang, Yudong, 2021. "Volatility linkages between stock and commodity markets revisited: Industry perspective and portfolio implications," Resources Policy, Elsevier, vol. 74(C).
    16. Chiarella, Carl & He, Xue-Zhong & Zheng, Min, 2011. "An analysis of the effect of noise in a heterogeneous agent financial market model," Journal of Economic Dynamics and Control, Elsevier, vol. 35(1), pages 148-162, January.
    17. Eric Benhamou & David Saltiel & Sandrine Ungari & Abhishek Mukhopadhyay, 2020. "Bridging the gap between Markowitz planning and deep reinforcement learning," Papers 2010.09108, arXiv.org.
    18. Tatiana de Macedo Nogueira Lima, 2022. "Documento de Trabalho 03/2022 - Aprendizado de máquina e antitruste," Documentos de Trabalho 2022030, Conselho Administrativo de Defesa Econômica (Cade), Departamento de Estudos Econômicos.
    19. Bischi, Gian Italo & Merlone, Ugo & Pruscini, Eros, 2018. "Evolutionary dynamics in club goods binary games," Journal of Economic Dynamics and Control, Elsevier, vol. 91(C), pages 104-119.
    20. Chiarella, Carl & He, Xue-Zhong & Pellizzari, Paolo, 2012. "A Dynamic Analysis Of The Microstructure Of Moving Average Rules In A Double Auction Market," Macroeconomic Dynamics, Cambridge University Press, vol. 16(4), pages 556-575, September.

    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:jmathe:v:8:y:2020:i:7:p:1126-:d:382308. 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.