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Stock Market Trend Analysis Using Hidden Markov Models

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  • 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.

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  • 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
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    File URL: http://arxiv.org/pdf/1311.4771
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    References listed on IDEAS

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    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.
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    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. Mikhail Goykhman & Ali Teimouri, 2017. "Machine learning in sentiment reconstruction of the simulated stock market," Papers 1708.01897, arXiv.org.
    4. Nguyet Nguyen & Dung Nguyen, 2020. "Global Stock Selection with Hidden Markov Model," Risks, MDPI, vol. 9(1), pages 1-18, December.

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