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.
| Original language | English |
|---|---|
| Article number | 9 |
| Journal | Journal of Risk and Financial Management |
| Volume | 12 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Mar 2019 |
| Externally published | Yes |
Keywords
- convolutional neural networks
- crude oil futures prices forecasting
- short-term forecasting
ASJC Scopus subject areas
- Accounting
- Business, Management and Accounting (miscellaneous)
- Finance
- Economics and Econometrics
Fingerprint
Dive into the research topics of 'Can We Forecast Daily Oil Futures Prices? Experimental Evidence from Convolutional Neural Networks'. Together they form a unique fingerprint.Cite this
- APA
- Standard
- Harvard
- Vancouver
- Author
- BIBTEX
- RIS