Title: Commodities vs. S&P 500: causal interaction, temporal analysis and predictive modelling using econometric approach, machine learning, and deep learning
Authors: Ramasamy Murugesan; B. Azhaganathan; Sarit Maitra
Addresses: Department of HSS, National Institute of Technology, Tiruchirappalli, India ' Department of HSS, National Institute of Technology, Tiruchirappalli, India ' Alliance School of Applied Mathematics, Alliance University, India
Abstract: Due to the presence of inherent complex behaviour with nonlinear dynamics, irregular temporal behaviour, high volatility, both in commodities and stock prices, this research aims to quantify, understand, model, and predict such irregular fluctuations of crude oil, gold and silver prices in comparison to S&P 500 index. This is done by employing powerful data modelling techniques followed by econometric approach using Granger causality, impulse response, forecast error variance decomposition and instantaneous phase synchrony prior to predictive modelling. S&P 500 index and commodities exhibiting non-stationary behaviour during the period 1 April 2000-27 July 2019 are considered for the entire analysis. The best model has been chosen comparing the error rate on the results of six powerful algorithms, KNN, DT, SVM, GBM, EF and ANN. The numerical analysis on the modelling and prediction of irregular fluctuations in commodities and stock indexes would practically support delineating the nexus between these two.
Keywords: commodities; S&P 500; econometric approach; machine learning; deep learning.
International Journal of Business Information Systems, 2023 Vol.42 No.3/4, pp.429 - 457
Received: 13 Oct 2021
Accepted: 25 Dec 2021
Published online: 21 Mar 2023 *