A hybrid system by integrating case based reasoning and fuzzy decision tree for financial time series data

Stock price predictions suffer from two well known difficulties, i.e., complicated and non-stationary variations within the large historic data. This paper establishes a novel financial time series-forecasting model by a case based fuzzy decision tree induction for stock price movement predictions in Taiwan Stock Exchange Corporation (TSEC). This forecasting model integrates a case based reasoning technique, a fuzzy decision tree (FDT) and genetic algorithms (GA) to construct a decision-making system based on historical data and technical indexes. The model is major based on the idea that the historic price data base can be transformed into a smaller case-base together with a group of fuzzy decision rules. As a result, the model can be more accurately react to the current tendency of the stock price movement from these smaller case based fuzzy decision tree inductions. Hit rate is applied as a performance measure and the effectiveness of our proposed CBFDT model is demonstrated by experimentally compared with other approaches on various stocks from TSEC. The average hit rate of CBFDT model is 91% the highest among others.