IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1311.4771.html
   My bibliography  Save this paper

Stock Market Trend Analysis Using Hidden Markov Models

Author

Listed:
  • G. Kavitha
  • A. Udhayakumar
  • D. Nagarajan

Abstract

Price movements of stock market are not totally random. In fact, what drives the financial market and what pattern financial time series follows have long been the interest that attracts economists, mathematicians and most recently computer scientists [17]. This paper gives an idea about the trend analysis of stock market behaviour using Hidden Markov Model (HMM). The trend once followed over a particular period will sure repeat in future. The one day difference in close value of stocks for a certain period is found and its corresponding steady state probability distribution values are determined. The pattern of the stock market behaviour is then decided based on these probability values for a particular time. The goal is to figure out the hidden state sequence given the observation sequence so that the trend can be analyzed using the steady state probability distribution( ) values. Six optimal hidden state sequences are generated and compared. The one day difference in close value when considered is found to give the best optimum state sequence.

Suggested Citation

  • G. Kavitha & A. Udhayakumar & D. Nagarajan, 2013. "Stock Market Trend Analysis Using Hidden Markov Models," Papers 1311.4771, arXiv.org.
    • Handle: RePEc:arx:papers:1311.4771
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1311.4771
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jyoti Badge, 2012. "Forecasting of Indian Stock Market by Effective Macro- Economic Factors and Stochastic Model," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 1(2), pages 1-4.
    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. Eugene W. Park, 2023. "Principal Component Analysis and Hidden Markov Model for Forecasting Stock Returns," Papers 2307.00459, arXiv.org.
    2. Danisman, Ozgur & Uzunoglu Kocer, Umay, 2021. "Hidden Markov models with binary dependence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 567(C).
    3. Nguyet Nguyen & Dung Nguyen, 2020. "Global Stock Selection with Hidden Markov Model," Risks, MDPI, vol. 9(1), pages 1-18, December.
    4. Mikhail Goykhman & Ali Teimouri, 2017. "Machine learning in sentiment reconstruction of the simulated stock market," Papers 1708.01897, arXiv.org.

    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.

      More about this item

      NEP fields

      This paper has been announced in the following NEP Reports:

      Statistics

      Access and download statistics

      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:arx:papers:1311.4771. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

      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.