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

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  • Annadurai Karmuhil
  • Ramasamy Murugesan

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.

Suggested Citation

  • Annadurai Karmuhil & Ramasamy Murugesan, 2024. "Examining the nexus of GST and selected stock indices: a multivariate time series and vector auto-regressive machine learning model," International Journal of Enterprise Network Management, Inderscience Enterprises Ltd, vol. 15(2), pages 133-158.
    • Handle: RePEc:ids:ijenma:v:15:y:2024:i:2:p:133-158
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