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A GA-optimized SAX-ANN based Stock Level Prediction System

Publication Type : Journal Article

Publisher : International Journal of Computer Applications

Source : International Journal of Computer Applications, Foundation of Computer Science, Volume 106, Number 15 (2014)

Url : http://search.proquest.com/openview/5480b8fd8ba336eb1b2d93ec2b0ce7a5/1?pq-origsite=gscholar

Keywords : Artificial Neural Network, Genetic algorithms, prediction, Stock, Symbolic Aggregate Approximation, wavelet

Campus : Coimbatore

School : School of Engineering

Department : Electronics and Communication

Year : 2014

Abstract : Forecasting stock price movements is of immense importance to any stock trader. However, traditionally, this has been accomplished using technical analysis tools. In this study, an attempt has been made to employ data mining to identify the one-day-ahead stock price levels. Two different approaches are considered. The two approaches are empirically validated on twelve stock price datasets, with the stocks drawn from the Indian, US and UK stock markets. Results indicate that both the approaches proposed in the present study are capable of successfully forecasting the one-day-ahead stock price levels.

Cite this Research Publication : Dr. Binoy B. Nair, Xavier, N., Mohandas, V. P., Sathyapal, A., Anusree, E. G., Kumar, P., and Ravikumar, V., “A GA-optimized SAX-ANN based Stock Level Prediction System”, International Journal of Computer Applications, vol. 106, 2014.

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