Effectiveness of Artificial Neural Networks in Forecasting BSE Sensex Index Values

Abstract

Since stock markets are volatile, dynamic and complicated, forecasting stock market return is considered as a challenging task. Nevertheless, researchers have developed various linear and non linear methods for effective forecasting. Among these neural networks are most suitable for forecasting non linear and chaotic relationships among variables. The current study attempts to forecast the future returns of B.S.E, highly volatile index, with the help of conventional method, i.e. ARIMA (Auto Regression Integrated Moving Average) and Artificial Neural Network M.L.P (Multilayer Perceptron). To examine the efficiency of the models, MAD (Mean Absolute Deviation) and MSE (Mean Square Error) of the two models are compared. The study results revealed that neural network is better for forecasting in comparison to ARIMA.

Keywords: ARIMA, Auto Regression Integrated Moving Average, M.L.P, Multilayer Perceptron Model, NIKKIE 225. Japanese Stock Index , NASDAQ, American Stock Index, HANGSENG, Hong Kong Stock Exchange Index, BSE, Bombay Stock Exchange Sensitive Index

JEL Classification: B23,C53