Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting

  • Authors:

  • Esmaeil Hadavandi;Hassan Shavandi;Arash Ghanbari

  • Affiliations:

  • Department of Industrial Engineering, Sharif University of Technology, P.O. Box 11365-9466, Tehran, Iran;Department of Industrial Engineering, Sharif University of Technology, P.O. Box 11365-9466, Tehran, Iran;Department of Industrial Engineering, College of Engineering, University of Tehran, P.O. Box 11155-4563, Tehran, Iran

  • Venue:
  • Knowledge-Based Systems
  • Year:
  • 2010

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Abstract

Stock market prediction is regarded as a challenging task in financial time-series forecasting. The central idea to successful stock market prediction is achieving best results using minimum required input data and the least complex stock market model. To achieve these purposes this article presents an integrated approach based on genetic fuzzy systems (GFS) and artificial neural networks (ANN) for constructing a stock price forecasting expert system. At first, we use stepwise regression analysis (SRA) to determine factors which have most influence on stock prices. At the next stage we divide our raw data into k clusters by means of self-organizing map (SOM) neural networks. Finally, all clusters will be fed into independent GFS models with the ability of rule base extraction and data base tuning. We evaluate capability of the proposed approach by applying it on stock price data gathered from IT and Airlines sectors, and compare the outcomes with previous stock price forecasting methods using mean absolute percentage error (MAPE). Results show that the proposed approach outperforms all previous methods, so it can be considered as a suitable tool for stock price forecasting problems.