EASOM: An Efficient Soft Computing Method for Predicting Share Values

摘要

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 and are immune to noise in the input. In this paper, we propose an algorithm Evolutionary Approach to Self Organizing Map(EASOM) to cluster stock market data. Genetic algorithms are used to train the Kohonen network for better and effective prediction. Fuzzy logic is used to fix learning rate for faster convergence. 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.