Forecasting crude oil futures price using machine learning methods: Evidence from China

Lili Guo, Xinya Huang, Yanjiao Li and Houjian Li

Energy Economics, 2023, vol. 127, issue PA

Abstract: Crude oil is an indispensable energy resource. With the establishment of the local crude oil futures market in China, providing accurate forecasts for crude oil futures price is urgent. To cope with this challenge, this paper introduces artificial intelligence methods to evaluate the optimal forecasting strategy for China's crude oil futures price. We use machine learning to process historical information, volatility and non-linear features. Using daily data from March 26, 2018 to February 28, 2023, we estimate the forecasting effects of RNN, LSTM, GRU, SVR, MLP, CNN and BP models on China crude oil futures, respectively. With a series of evaluation tests, we demonstrate that the GRU model outperforms other models in terms of forecast accuracy and performance for China's crude oil futures price. Taking multiple influencing factors into account, the forecasting accuracy of proposed models is improved by including influential factors. Therefore, these findings effectively explore the forecasting of China's crude oil futures prices, contributing to the improvement of the emerging crude oil futures market and the management of energy price risks.

Keywords: Crude oil futures; Price forecast; Machine learning; INE (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:127:y:2023:i:pa:s014098832300587x

DOI: 10.1016/j.eneco.2023.107089

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