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Article
Publication date: 31 July 2018

Abhishek Mishra

415

Abstract

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Journal of Indian Business Research, vol. 10 no. 3
Type: Research Article
ISSN: 1755-4195

Open Access
Article
Publication date: 28 April 2023

Himanshu Goel and Bhupender Kumar Som

This study aims to predict the Indian stock market (Nifty 50) by employing macroeconomic variables as input variables identified from the literature for two sub periods, i.e. the…

1061

Abstract

Purpose

This study aims to predict the Indian stock market (Nifty 50) by employing macroeconomic variables as input variables identified from the literature for two sub periods, i.e. the pre-coronavirus disease 2019 (COVID-19) (June 2011–February 2020) and during the COVID-19 (March 2020–June 2021).

Design/methodology/approach

Secondary data on macroeconomic variables and Nifty 50 index spanning a period of last ten years starting from 2011 to 2021 have been from various government and regulatory websites. Also, an artificial neural network (ANN) model was trained with the scaled conjugate gradient algorithm for predicting the National Stock exchange's (NSE) flagship index Nifty 50.

Findings

The findings of the study reveal that Scaled Conjugate Gradient (SCG) algorithm achieved 96.99% accuracy in predicting the Indian stock market in the pre-COVID-19 scenario. On the contrary, the proposed ANN model achieved 99.85% accuracy in during the COVID-19 period. The findings of this study have implications for investors, portfolio managers, domestic and foreign institution investors, etc.

Originality/value

The novelty of this study lies in the fact that are hardly any studies that forecasts the Indian stock market using artificial neural networks in the pre and during COVID-19 periods.

Details

EconomiA, vol. 24 no. 1
Type: Research Article
ISSN: 1517-7580

Keywords

Abstract

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Journal of Global Operations and Strategic Sourcing, vol. 15 no. 3
Type: Research Article
ISSN: 2398-5364

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