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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
Katsuyuki Tanaka: 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

Journal of Risk and Financial Management, 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
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