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

  • Authors:

  • Robert K. Lai;Chin-Yuan Fan;Wei-Hsiu Huang;Pei-Chann Chang

  • Affiliations:

  • Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 32026, Taiwan, ROC;Department of Industries Management, Yuan Ze University, Taoyuan 32026, Taiwan, ROC;Department of Industries Management, Yuan Ze University, Taoyuan 32026, Taiwan, ROC;Department of Information Management,Yuan Ze University, Taoyuan 32026, Taiwan, ROC

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2009

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Abstract

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.