Evolving and clustering fuzzy decision tree for financial time series data forecasting

• Published in: Expert systems with applications Vol. 36; no. 2; pp. 3761 - 3773
• Main Authors: Lai, Robert K., Fan, Chin-Yuan, Huang, Wei-Hsiu, Chang, Pei-Chann
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.03.2009
• Subjects: Decision tree; Fuzzy theory; Genetic algorithm; Step-wise regression; Stock price forecasting; Turning points; Turning points; Step-wise regression; Genetic algorithm; Fuzzy theory; Decision tree; Stock price forecasting
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2008.02.025

Stock price predictions have always been a subject of interest for investors and professional analysts. Nevertheless, determining the best time to buy or sell a stock remains very difficult because there are many factors that may influence the stock prices. This paper establishes a novel financial time series-forecasting model by evolving and clustering fuzzy decision tree for stocks in Taiwan Stock Exchange Corporation (TSEC). This forecasting model integrates a data clustering technique, a fuzzy decision tree (FDT), and genetic algorithms (GA) to construct a decision-making system based on historical data and technical indexes. The set of historical data is divided into k sub-clusters by adopting K-means algorithm. GA is then applied to evolve the number of fuzzy terms for each input index in FDT so the forecasting accuracy of the model can be further improved. A different forecasting model will be generated for each sub-cluster. In other words, the number of fuzzy terms in each sub-cluster will be different. Hit rate is applied as a performance measure and the proposed GAFDT model has the best performance of 82% average hit rate when compared with other approaches on various stocks in TSEC.