Stock index prediction using regression and neural network models under non normal conditions

Nonparametric Linear Regression and Artificial Neural Network models have been developed based on different perspectives and assumptions. In this paper a survey is made to compare the predictive performances of the nonparametric models of closing prices of Stock Index data, where the data is non normal. Comparative studies with the existing statistical prediction models indicate that the proposed neural network model is very promising and can be implemented into real time trading system for stock price prediction.