Can We Forecast Daily Oil Futures Prices? Experimental Evidence from Convolutional Neural Networks

Zhaojie Luo, Xiaojing Cai, Katsuyuki Tanaka, Tetsuya Takiguchi, Takuji Kinkyo and Shigeyuki Hamori
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Zhaojie Luo: Graduate School of System Informatics, Kobe University, 2-1 Rokkodai, Nada-Ku, Kobe 657-8501, Japan
Xiaojing Cai: Graduate School of Economics, Kobe University, 2-1 Rokkodai, Nada-Ku, Kobe 657-8501, Japan
Tetsuya Takiguchi: Graduate School of System Informatics, Kobe University, 2-1 Rokkodai, Nada-Ku, Kobe 657-8501, Japan
Takuji Kinkyo: Graduate School of Economics, Kobe University, 2-1 Rokkodai, Nada-Ku, Kobe 657-8501, Japan

JRFM, 2019, vol. 12, issue 1, 1-13

Abstract: This paper proposes a novel approach, based on convolutional neural network (CNN) models, that forecasts the short-term crude oil futures prices with good performance. In our study, we confirm that artificial intelligence (AI)-based deep-learning approaches can provide more accurate forecasts of short-term oil prices than those of the benchmark Naive Forecast (NF) model. We also provide strong evidence that CNN models with matrix inputs are better at short-term prediction than neural network (NN) models with single-vector input, which indicates that strengthening the dependence of inputs and providing more useful information can improve short-term forecasting performance.

Keywords: crude oil futures prices forecasting; convolutional neural networks; short-term forecasting (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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