Integrating Ensemble of Intelligent Systems for Modeling Stock Indices

The use of intelligent systems for stock market predictions has been widely established. In this paper, we investigate how the seemingly chaotic behavior of stock markets could be well-represented using ensemble of intelligent paradigms. To demonstrate the proposed technique, we considered Nasdaq-100 index of Nasdaq Stock Market and the S&P CNX NIFTY stock index. The intelligent paradigms considered were an artificial neural network trained using LevenbergMarquardt algorithm, support vector machine, Takagi-Sugeno neurofuzzy model and a difference boosting neural network. The different paradigms were combined using two different ensemble approaches so as to optimize the performance by reducing different error measures. The first approach is based on a direct error measure and the second method is based on an evolutionary algorithm to search the optimal linear combination of the different. Experimental results reveal that the ensemble techniques performed better than the individual methods and the direct ensemble approach seems to work well for the problem considered.