Do ANNs Successfully Predict Stock Returns? Testing its Application in Indian Stock Market

The IUP Journal of Computational Mathematics, Vol. III, No. 3, pp. 51-63, September 2010

Posted: 29 Sep 2010

See all articles by Abhijeet Chandra

Abhijeet Chandra

Vinod Gupta School of Management, IIT Kharagpur

Renu Vashisth

Banarsidas Chandiwala Institute of Professional Studies

Date Written: September 28, 2010

Abstract

Artificial Neural Network (ANN) models have been proved to be powerful predictive tools, where a variable is explained by a set of explanatory variables without assuming any structural or linear relationship among the variables. In the field of finance, a large number of models, especially those derived from the field of econometrics, are used for forecasting stock returns. This paper intends to test the forecasting ability of ANN models using Nifty index data from Indian stock market. Daily time series data of the index of National Stock Exchange is analyzed using a three-layer architecture of the ANN. The results of the study reveal that ANN models could efficiently predict daily returns of Nifty index for a given period under investigation. The results of this study are significant value addition to the trading decisions in the stock index futures with special reference to Indian stock market.

Keywords: Artificial Neural Network (ANN), Return forecasting, Multilayer perceptron models

Suggested Citation

Chandra, Abhijeet and Vashisth, Renu, Do ANNs Successfully Predict Stock Returns? Testing its Application in Indian Stock Market (September 28, 2010). The IUP Journal of Computational Mathematics, Vol. III, No. 3, pp. 51-63, September 2010. Available at SSRN: https://ssrn.com/abstract=1683996

Abhijeet Chandra (Contact Author)

Vinod Gupta School of Management, IIT Kharagpur ( email )

Kharagpur, 721302
India

Renu Vashisth

Banarsidas Chandiwala Institute of Professional Studies ( email )

Sector-11, Dwarka
New Delhi, 110033
India

HOME PAGE: http://www.bcips.ac.in/index.php?option=com_content&view=article&id=83&Itemid=145

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