Dynamic prediction of Indian stock market: an artificial neural network approach

Himanshu Goel and Narinder Pal Singh

International Journal of Ethics and Systems, 2021, vol. 38, issue 1, 35-46

Abstract: Purpose - Artificial neural network (ANN) is a powerful technique to forecast the time series data such as the stock market. Therefore, this study aims to predict the Indian stock market closing price using ANNs. Design/methodology/approach - The input variables identified from the literature are some macroeconomic variables and a global stock market factor. The study uses an ANN with Scaled Conjugate Gradient Algorithm (SCG) to forecast the Bombay Stock Exchange (BSE) Sensex. Findings - The empirical findings reveal that the ANN model is able to achieve 93% accuracy in predicting the BSE Sensex closing prices. Moreover, the results indicate that the Morgan Stanley Capital International world index is the most important variable and the index of industrial production is the least important in predicting Sensex. Research limitations/implications - The findings of the study have implications for the investors of all categories such as foreign institutional investors, domestic institutional investors and investment houses. Originality/value - The novelty of this study lies in the fact that there are hardly any studies that use ANN to forecast the Indian stock market using macroeconomic indicators.

Keywords: Artificial neural networks; Stock market prediction; BSE Sensex; Macroeconomic variables; India (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:eme:ijoesp:ijoes-11-2020-0184

DOI: 10.1108/IJOES-11-2020-0184

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