Deep Learning-Based Methods for Forecasting Brent Crude Oil Return Considering COVID-19 Pandemic Effect

Seyed Mehrzad Asaad Sajadi, Pouya Khodaee, Ehsan Hajizadeh (), Sabri Farhadi, Sohaib Dastgoshade and Bo Du ()
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Seyed Mehrzad Asaad Sajadi: Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran 15914, Iran
Pouya Khodaee: Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran 15914, Iran
Ehsan Hajizadeh: Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran 15914, Iran
Sabri Farhadi: Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran 15914, Iran
Sohaib Dastgoshade: Department of Industrial Engineering, Yazd University, Yazd 89195, Iran
Bo Du: SMART Infrastructure Facility, University of Wollongong, Wollongong, NSW 2522, Australia

Energies, 2022, vol. 15, issue 21, 1-23

Abstract: Forecasting return and profit is a primary challenge for financial practitioners and an even more critical issue when it comes to forecasting energy market returns. This research attempts to propose an effective method to predict the Brent Crude Oil return, which results in remarkable performance compared with the well-known models in the return prediction. The proposed hybrid model is based on long short-term memory (LSTM) and convolutional neural network (CNN) networks where the autoregressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroscedasticity (GARCH) outputs are used as features, along with return lags, price, and macroeconomic variables to train the models, resulting in significant improvement in the model’s performance. According to the obtained results, our proposed model performs better than other models, including artificial neural network (ANN), principal component analysis (PCA)-ANN, LSTM, and CNN. We show the efficiency of our proposed model by testing it with a simple trading strategy, indicating that the cumulative profit obtained from trading with the prediction results of the proposed 2D CNN-LSTM model is higher than those of the other models presented in this research. In the second part of this study, we consider the spread of COVID-19 and its impact on the financial markets to present a precise LSTM model that can reflect the impact of this disease on the Brent Crude Oil return. This paper uses the significance test and correlation measures to show the similarity between the series of Brent Crude Oil during the SARS and the COVID-19 pandemics, after which the data during the SARS period are used along with the data during COVID-19 to train the LSTM. The results demonstrate that the proposed LSTM model, tuned by the SARS data, can better predict the Brent Crude Oil return during the COVID-19 pandemic.

Keywords: CNN; COVID-19; deep learning; energy market; LSTM; return prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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