Global Oil Export Destination Prediction: A Machine Learning Approach

Haiying Jia, Roar Adland and Yuchen Wang

The Energy Journal, 2021, vol. 42, issue 4, 111-130

Abstract: We use classification methods from machine learning to predict the destination of global crude oil exports by utilising micro-level crude oil shipment data that incorporates attributes related to the contract, cargo specifications, vessel specifications and macroeconomic conditions. The results show that micro-level information about the oil shipment such as quality and cargo size dominates in the destination prediction. We contribute to the academic literature by providing the first machine learning application to oil shipment data, and by providing new knowledge on the determinants of global crude oil flows. The machine-learning models used to predict the importing country can reach an accuracy of above 71% for the major oil exporting countries based on out-of-sample tests and outperform both naive models and discrete regression models.

Keywords: Random forests; Gradient boosted trees; Machine learning; Crude oil; Choice models (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:sae:enejou:v:42:y:2021:i:4:p:111-130

DOI: 10.5547/01956574.42.4.hjia

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