Abstract
This study investigates the Indian crude oil futures market efficiency using cointegration tests with an autoregressive distributed lag (ARDL) model. Additionally, machine learning algorithms, support vector regression, and XGBoost are used with auto-regressive and multi-variate approaches to predict the crude oil futures prices. The experimental results found an efficient market and no significant gains can be made by prediction techniques modeled on the data. Moreover, findings suggest that the auto-regressive model outperforms the multi-variate model which is designed based on different market variables of commodity, exchange rate and equity markets.
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