An efficient fuzzy based neuro: genetic algorithm for stock market prediction

Authors:
K. G. Srinivasa;K. R. Venugopal;L. M. Patnaik
Affiliations:
Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bangalore, India and Department of Computer Science and Engineering, M S Ramaiah Institute of Techno ...;Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bangalore, India;Microprocessor Applications Laboratory, Indian Institute of Science, Bangalore, India
Venue:
International Journal of Hybrid Intelligent Systems
Year:
2006

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Abstract

Stock market prediction is a complex and tedious task that involves the processing of large amounts of data, that are stored in ever growing databases. The vacillating nature of the stock market requires the use of data mining techniques like clustering for stock market analysis and prediction. Genetic algorithms and neural networks have the ability to handle complex data. In this paper, we propose a fuzzy based neuro-genetic algorithm - Fuzzy based Evolutionary Approach to Self Organizing Map(FEASOM) to cluster stock market data. Genetic algorithms are used to train the Kohonen network for better and effective prediction. The algorithm was tested on real stock market data of companies like Intel, General Motors, Infosys, Wipro, Microsoft, IBM, etc. The algorithm consistently outperformed regression model, backpropagation algorithm and Kohonen network in predicting the stock market values.