A genetic fuzzy expert system for stock price forecasting

Forecasting stock price time series is very important and challenging in the real world because they are affected by many highly interrelated economic, social, political and even psychological factors, and these factors interact with each other in a very complicated manner. This article presents an approach based on Genetic Fuzzy Systems (GFS) for constructing a stock price forecasting expert system. We use a GFS model with the ability of rule base extraction and data base tuning for next day stock price prediction to extract useful patterns of information with a descriptive rule induction approach. We evaluate capability of the proposed approach by applying it on stock price forecasting case study of International Business Machines Corporation (IBM), and compare the outcomes with previous stock price forecasting methods using mean absolute percentage error (MAPE). Results show that the proposed approach is able to cope with the fluctuation of stock price values and it also yields good prediction accuracy in short term stock price forecasting.