Hybrid forecasting model research on stock data mining

The synergy effect´s benefit is widely accepted. The object of this paper is to investigate whether a hybrid approach combining different stock prediction approaches together can dramatically outperform the single approach and compare the performance of different hybrid approaches. The hybrid model includes three well-researched algorithms: back propagation neural network (BPNN), adaptive network-based fuzzy neural inference system (ANFIS) and support vector machine (SVM). First, we utilize them independently to single-step forecast the stock price, and then integrate the three forecasts into a final result by a combining strategy. Two different combining methods are investigated. The first method is a linear combination of the three forecasts. The second method combines them by a neural network. We have all of the algorithms experiment on the S&P500 Index. The experiment verifies that by combining the single algorithm appropriately, better performance can be achieved.