Several studies have predicted the stock market price in the past decades. Min Qi (1999) examined the forecasting ability of the United States (US) stock market returns by using Linear Regression and Nonlinear Neural Network model. The study found that the Nonlinear NN model fits data better than the linear model and provides relatively accurate forecast than the linear model. Phua, et al. (2000) used NN with Genetic Algorithm to predict the Singapore stock market. The study found that the model predicts 81 percent accuracy of the direction of the stock market. Yochanan and Dorota (2000) examined the dynamic interrelations among Canada, France, Germany, Japan, United Kingdom (UK), US and World stock markets by using Ordinary Least Squares, General Linear Regression, Multi-layer Perceptron models of ANN. The study reported that NN consist of Multilayer perceptron model with logistic activation function predicts the daily stock market returns better than traditional Ordinary Least Squares and General Linear Regression model. The Multilayer Perceptron, with five units in the hidden layer, better predicts the stock indices of US, France, Germany, UK and World stock markets.