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Forecasting The Stock Market Values Using Hidden Markov Model


Affiliations
1 Department of Statistics, Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu, India
     

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The financial market influences personal corporate financial lives and the economic health of a country. Price change of stock market is not a completely random model. The pattern of financial market has been observed by some economists, statisticians and computer scientists. This paper gives a detailed idea about the sequence and state prediction of stock market using Hidden Markov Model and also making inferences regarding stock market trend. The one day difference in close value of stock market value has been used for some period and the corresponding transition probability matrix and emission probability matrix are obtained. Seven optimal hidden states and three sequences are generated using MATLAB and then compared.

Keywords

Hidden Markov Model, Transition Probability Matrix, Emission Probability Matrix, Stock Market, States and Sequence.
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  • Forecasting The Stock Market Values Using Hidden Markov Model

Abstract Views: 344  |  PDF Views: 1

Authors

R. Sasikumar
Department of Statistics, Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu, India
A. Sheik Abdullah
Department of Statistics, Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu, India

Abstract


The financial market influences personal corporate financial lives and the economic health of a country. Price change of stock market is not a completely random model. The pattern of financial market has been observed by some economists, statisticians and computer scientists. This paper gives a detailed idea about the sequence and state prediction of stock market using Hidden Markov Model and also making inferences regarding stock market trend. The one day difference in close value of stock market value has been used for some period and the corresponding transition probability matrix and emission probability matrix are obtained. Seven optimal hidden states and three sequences are generated using MATLAB and then compared.

Keywords


Hidden Markov Model, Transition Probability Matrix, Emission Probability Matrix, Stock Market, States and Sequence.