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Predicting Stock Returns

An Experiment of the Artificial Neural Network in Indian Stock Market

Chakradhara Panda (corresponding author) is Assistant Professor, Economics Department, Faculty of Business and Economics, Addis Ababa University, P.O. Box: 1176, Ethiopia. E-mail: chdpanda{at}yahoo.com

V. Narasimhan

V. Narasimhan is Reader, Department of Economics, University of Hyderabad, Central University, Hyderabad, 500 046, India. E-mail: vnss{at}uohyd.ernet.in

In this article, we use the artificial neural network in the forecasting of daily Bombay Stock Exchange (BSE) Sensitive Index (Sensex) returns. We compare the performance of the neural network with performances of random walk and linear autoregressive models by using six performance measures. The major findings are that neural network out-performs linear autoregressive and random walk models by all performance measures in both in-sample and out-of-sample forecasting of daily BSE Sensex returns. The findings suggest that stock markets do not follow a random walk and there exists a possibility of predicting stock returns. The superiority of the neural network model over linear autoregressive and random walk models in forecasting daily BSE Sensex returns indicates that neural network is able to capture non-linearities contained in stock returns.

Key Words: JEL: C45 • Feedforward Neural Network • Training • Generalization • In-sample Prediction • Out-of-Sample Prediction

South Asia Economic Journal, Vol. 7, No. 2, 205-218 (2006)
DOI: 10.1177/139156140600700203


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