Title: Examining the nexus of GST and selected stock indices: a multivariate time series and vector auto-regressive machine learning model

Authors: Annadurai Karmuhil; Ramasamy Murugesan

Addresses: Department of Humanities and Social Sciences, National Institute of Technology, Tiruchirappalli – 620015, India ' Department of Humanities and Social Sciences, National Institute of Technology, Tiruchirappalli – 620015, India

Abstract: Research reveals few analyses of contemporary relationships and dynamic interactions between goods and services tax (GST) revenues and sectoral stock indices. An in-depth analysis of these economic variables was not seen in literature. This study investigates the relationship between GST revenues and seven sectoral stock indices using a multivariate time series and vector autoregressive machine learning (ML) model for 2017-2021. Performing VAR analysis, impulse response, and forecast error variance decomposition (FEVD) the study showed no significance in the relationship between GST revenue and selected stock indices except fast moving consumer goods (FMCG). A strong correlation was found between FMCG, pharmaceuticals automobiles, energy, and information technology (IT) of the stock indices. Forecasting evaluation was performed with error matrices of MAPE and RMSE.

Keywords: GST-revenue; stock indices; machine learning; ML; multivariate time series; and VAR.

DOI: 10.1504/IJENM.2024.139489

International Journal of Enterprise Network Management, 2024 Vol.15 No.2, pp.133 - 158

Received: 09 Mar 2022
Accepted: 13 Feb 2023
Published online: 02 Jul 2024 *

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