Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/238942 
Year of Publication: 
2019
Citation: 
[Journal:] Journal of Risk and Financial Management [ISSN:] 1911-8074 [Volume:] 12 [Issue:] 1 [Publisher:] MDPI [Place:] Basel [Year:] 2019 [Pages:] 1-13
Publisher: 
MDPI, Basel
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.
Subjects: 
crude oil futures prices forecasting
convolutional neural networks
short-term forecasting
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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