Stock Index Modeling using EDA based Local Linear Wavelet Neural Network

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 local linear wavelet neural network (LLWNN) technique. To this end, we considered the Nasdaq-100 index of Nasdaq Stock Market^SM and the S&P CNX NIFTY stock index. We analyzed 7-year Nasdaq-100 main index values and 4-year NIFTY index values. This paper investigates the development of novel reliable and efficient techniques to model the seemingly chaotic behavior of stock markets. The LLWNN are optimized by using estimation of distribution algorithm (EDA). This paper investigates whether the proposed method can provide the required level of performance, which is sufficiently good and robust so as to provide a reliable forecast model for stock market indices. Experiment results shown that the model considered could represent the stock indices behavior very accurately