GA BASED STOCHASTIC OPTIMIZATION FOR STOCK PRICE FORECASTING USING FUZZY TIME SERIES HIDDEN MARKOV MODEL

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IJPAM: Volume 117, No. 1 (2017)

Title

GA BASED STOCHASTIC OPTIMIZATION FOR STOCK
PRICE FORECASTING USING FUZZY TIME SERIES
HIDDEN MARKOV MODEL

Authors

G. Kavitha

, A. Udhayakumar

$^{1,2}$ Department of Mathematics
Hindustan Institute of Technology and Science
Tamil Nadu, 603103, INDIA

Abstract

This paper presents GA based stochastic optimization for Hidden Markov Model (HMM) combined with Fuzzy Time Series (FTS) for forecasting stock price with accuracy. The proposed model is adopted to realize the probabilistic state transition involving time evolution in a probabilistic system. The parameters of the HMM model are calculated in the first phase for initialization. In the second phase, the initial parameters are fed into HMM in MATLAB for estimation and they are optimized by the GA optimization technique in MATLAB. The method is tested on the datasets of several stocks of the National Stock Exchange of India Limited (NSE) and New York Stock Exchange (NYSE). The different forecasting errors namely, Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) for various stocks of NSE and NYSE are found out. The results of directional accuracy average for various NSE stock indices from January to August 2014 shows the prediction quality with a best MAPE of 1.3% for INFOSYS, 1.71% for TCS, 1.16% for HCLTECH and 1.09% for WIPRO. Furthermore, the performance of NYSE stocks was tested for validation and compared to some of the existing methods. It was found that the model provides a good prediction performance with a best MAPE of 0.7629% for IBM, 1.8% for APPLE, 0.9110% for DELL stocks getting satisfactory quality solutions showing forecasting efficiency.

History

Received: 2017-08-04
Revised: 2017-10-30
Published: November 29, 2017

AMS Classification, Key Words

AMS Subject Classification: 60Gxx
Key Words and Phrases: hybrid intelligent systems, computational linguistics, parameter estimation, genetic algorithms, forecast uncertainty

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How to Cite?

DOI: 10.12732/ijpam.v117i1.13 How to cite this paper?

Source: International Journal of Pure and Applied Mathematics
ISSN printed version: 1311-8080
ISSN on-line version: 1314-3395
Year: 2017
Volume: 117
Issue: 1
Pages: 143 - 171

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