Abstract
The volatility of crude oil markets and the pressing need for sustainable energy solutions have sparked significant interest in forecasting methodologies that can better capture market dynamics and incorporate environmentally responsible indicators. In this study, we address the gaps in the literature by proposing novel hybrid approaches based on combining wavelet decomposition with machine learning techniques (ANN-Wavelet and SVR-Wavelet) and advanced machine learning techniques (XGBoost and GBM) with advanced clean energy indicators to predict crude oil prices. These hybrid models significantly advance the field by reducing noise and improving result accuracy. Besides, these approaches were used to determine the best model for predicting crude oil market prices. Additionally, we employed the SHapely Additive exPlanations (SHAP) algorithm to analyze and interpret the models, enhancing transparency and explainability. Subsequently, we applied SHAP to investigate the predictive value of various asset classes, including the volatility index (VIX), precious metal markets (gold and silver), fuel markets (gasoline and natural gas), as well as green and renewable energy indices, about crude oil prices. The results reveal that the wavelet-SVR model demonstrates consistent and robust forecasting performance with low RMSE and MAPE values. Additionally, the GBM model emerges as highly accurate, yielding shallow forecasting errors. Conversely, the wavelet-ANN and XGBoost models exhibit mixed performance, showing effectiveness in the Full Sample but reduced accuracy during the Russia–Ukraine conflict. Notably, green and renewable energy markets, such as CGA and NextEra energy (NEE), emerge as significant predictors in forecasting crude oil prices. This research provides critical guidance amidst the Russia–Ukraine conflict in predicting oil prices by emphasizing the importance of incorporating environmentally responsible indicators into investment portfolios and policy choices.
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References
Aromi, D., & Clements, A. (2019). Spillovers between the oil sector and the S&P500: The impact of information flow about crude oil. Energy Economics, 81, 187–196.
Aussem, A., Campbell, J., & Murtagh, F. (1998). Wavelet-based feature extraction and decomposition strategies for financial forecasting. Journal of Computational Intelligence in Finance, 6(2), 5–12.
Behera, J., Pasayat, A. K., & Behera, H. (2022). COVID-19 Vaccination Effect on Stock Market and Death Rate in India. Asia-Pacific Financial Markets, 29(4), 651–673. https://doi.org/10.1007/s10690-022-09364-w
Ben Jabeur, S., Khalfaoui, R., & Ben Arfi, W. (2021). The effect of green energy, global environmental indexes, and stock markets in predicting oil price crashes: Evidence from explainable machine learning. Journal of Environmental Management, 298, 113511. https://doi.org/10.1016/J.JENVMAN.2021.113511
Białek, J., Bujalski, W., Wojdan, K., Guzek, M., & Kurek, T. (2022). Dataset level explanation of heat demand forecasting ANN with SHAP. Energy, 261, 125075. https://doi.org/10.1016/J.ENERGY.2022.125075
Boungou, W., & Yatié, A. (2022). The impact of the Ukraine-Russia war on world stock market returns. Economics Letters, 215, 110516. https://doi.org/10.1016/J.ECONLET.2022.110516
Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 785–794. https://doi.org/10.1145/2939672.2939785
Cen, Z., & Wang, J. (2019). Crude oil price prediction model with long short term memory deep learning based on prior knowledge data transfer. Energy, 169, 160–171
Dai, H., Huang, G., Zeng, H., & Zhou, F. (2022). PM2.5 volatility prediction by XGBoost-MLP based on GARCH models. Journal of Cleaner Production, 356, 131. https://doi.org/10.1016/J.JCLEPRO.2022.131898
de Freire, P. K. M. M., Santos, C. A. G., & da Silva, G. B. L. (2019). Analysis of the use of discrete wavelet transforms coupled with ANN for short-term streamflow forecasting. Applied Soft Computing, 80, 494–505. https://doi.org/10.1016/J.ASOC.2019.04.024
Deng, S., Zhu, Y., Duan, S., Yu, Y., Fu, Z., Liu, J., Yang, X., & Liu, Z. (2023). High-frequency forecasting of the crude oil futures price with multiple timeframe predictions fusion. Expert Systems with Applications, 217, 119580. https://doi.org/10.1016/J.ESWA.2023.119580
Dichtl, H., Drobetz, W., & Otto, T. (2022). Forecasting Stock Market Crashes via Machine Learning. Journal of Financial Stability. https://doi.org/10.1016/J.JFS.2022.101099
Efimova, O., & Serletis, A. (2014). Energy markets volatility modelling using GARCH. Energy Economics, 43, 264–273. https://doi.org/10.1016/J.ENECO.2014.02.018
Gu, R., & Zhang, B. (2016). Is efficiency of crude oil market affected by multifractality? Evidence from the WTI crude oil market. Energy Economics, 53, 151–158. https://doi.org/10.1016/J.ENECO.2014.10.014
Haar, A. (1909). Zur theorie der orthogonalen funktionensysteme. Georg-August-Universitat.
Hassan, M. K., Kamran, M., Djajadikerta, H. G., & Choudhury, T. (2022). Search for safe havens and resilience to global financial volatility: Response of GCC equity indexes to GFC and Covid-19. Pacific-Basin Finance Journal, 73, 101768. https://doi.org/10.1016/J.PACFIN.2022.101768
Jarboui, A., Dammak Ben Hlima, N., & Bouaziz, D. (2023). Do sustainability committee characteristics affect CSR performance? Evidence from India. Benchmarking: an International Journal, 30(2), 628–652. https://doi.org/10.1108/BIJ-04-2021-0225
Javanmard, M. E., & Ghaderi, S. F. (2022). A hybrid model with applying machine learning algorithms and optimization model to forecast greenhouse gas emissions with energy market data. Sustainable Cities and Society, 82, 103886.
Kumar, G. S., Sampathila, N., & Tanmay, T. (2022). Wavelet based machine learning models for classification of human emotions using EEG signal. Measurement: Sensors, 24, 100554. https://doi.org/10.1016/J.MEASEN.2022.100554
Leng, N., & Li, J. C. (2020). Forecasting the crude oil prices based on Econophysics and Bayesian approach. Physica a: Statistical Mechanics and Its Applications, 554, 124663. https://doi.org/10.1016/J.PHYSA.2020.124663
Lin, K., & Gao, Y. (2022). Model interpretability of financial fraud detection by group SHAP. Expert Systems with Applications, 210, 118354. https://doi.org/10.1016/J.ESWA.2022.118354
Lin, L., Jiang, Y., Xiao, H., & Zhou, Z. (2020). Crude oil price forecasting based on a novel hybrid long memory GARCH-M and wavelet analysis model. Physica a: Statistical Mechanics and Its Applications, 543, 123532. https://doi.org/10.1016/J.PHYSA.2019.123532
Lu, X., Ma, F., Xu, J., & Zhang, Z. (2022). Oil futures volatility predictability: New evidence based on machine learning models. International Review of Financial Analysis, 83, 102299. https://doi.org/10.1016/J.IRFA.2022.102299
Lundberg, S. M., Erion, G. G., & Lee, S.-I. (2018). Consistent individualized feature attribution for tree ensembles. https://arxiv.org/abs/1802.03888
Lyócsa, Š, Todorova, N., & Výrost, T. (2021). Predicting risk in energy markets: Low-frequency data still matter. Applied Energy, 282, 116146. https://doi.org/10.1016/J.APENERGY.2020.116146
Mallat, S. G. (1989) A theory for multiresolution signal decomposition: The wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7), 674–693. https://doi.org/10.1109/34.192463
Mensi, W., Hammoudeh, S., Nguyen, D. K., & Yoon, S. M. (2014). Dynamic spillovers among major energy and cereal commodity prices. Energy Economics. https://doi.org/10.1016/j.eneco.2014.03.004
Mnif, E., Mouakhar, K., & Jarboui, A. (2023). Energy-conserving cryptocurrency response during the COVID-19 pandemic and amid the Russia–Ukraine conflict. The Journal of Risk Finance, 24(2), 169–185. https://doi.org/10.1108/JRF-06-2022-0161
Mnif, E., Salhi, B., & Jarboui, A. (2020). Herding behaviour and Islamic market efficiency assessment: Case of Dow Jones and Sukuk market. International Journal of Islamic and Middle Eastern Finance and Management, 13(1), 24–41
Mohamad, A. (2022). Safe flight to which haven when Russia invades Ukraine? A 48-hour story. Economics Letters, 216, 110558. https://doi.org/10.1016/J.ECONLET.2022.110558
Nobre, J., & Neves, R. F. (2019). Combining Principal Component Analysis, Discrete Wavelet Transform and XGBoost to trade in the financial markets. Expert Systems with Applications, 125, 181–194. https://doi.org/10.1016/J.ESWA.2019.01.083
Raimundo, M. S., & Okamoto, J. (2018). SVR-wavelet adaptive model for forecasting financial time series. 2018 International Conference on Information and Computer Technologies, ICICT 2018, 111–114. https://doi.org/10.1109/INFOCT.2018.8356851
Risse, M. (2019). Combining wavelet decomposition with machine learning to forecast gold returns. International Journal of Forecasting, 35(2), 601–615. https://doi.org/10.1016/J.IJFORECAST.2018.11.008
Si, D. K., Li, X. L., Xu, X. C., & Fang, Y. (2021). The risk spillover effect of the COVID-19 pandemic on energy sector: Evidence from China. Energy Economics, 102, 105498. https://doi.org/10.1016/J.ENECO.2021.105498
Theiri, S., Nekhili, R., & Sultan, J. (2022). Cryptocurrency liquidity during the Russia–Ukraine war: the case of Bitcoin and Ethereum. The Journal of Risk Finance. https://doi.org/10.1108/JRF-05-2022-0103
Ullah, I., Liu, K., Yamamoto, T., Zahid, M., & Jamal, A. (2023). Modeling of machine learning with SHAP approach for electric vehicle charging station choice behavior prediction. Travel Behaviour and Society, 31, 78–92. https://doi.org/10.1016/J.TBS.2022.11.006
Wan, D., Xue, R., Linnenluecke, M., Tian, J., & Shan, Y. (2021). The impact of investor attention during COVID-19 on investment in clean energy versus fossil fuel firms. Finance Research Letters, 43, 101955. https://doi.org/10.1016/J.FRL.2021.101955
Wang, L., Zhao, C., Liang, C., & Jiu, S. (2022). Predicting the volatility of China’s new energy stock market: Deep insight from the realized EGARCH-MIDAS model. Finance Research Letters, 48, 102981. https://doi.org/10.1016/J.FRL.2022.102981
Wu, D., Wang, X., & Wu, S. (2022). A hybrid framework based on extreme learning machine, discrete wavelet transform, and autoencoder with feature penalty for stock prediction. Expert Systems with Applications, 207, 118006. https://doi.org/10.1016/J.ESWA.2022.118006
Yarovaya, L., & Mirza, N. (2022). The price reaction and investment exposure of equity funds: Evidence from the Russia–Ukraine military conflict. The Journal of Risk Finance, 23(5), 669–676. https://doi.org/10.1108/JRF-07-2022-0174
Zhang, C., Lan, Q., Mi, X., Zhou, Z., Ma, C., & Mi, X. (2023). A denoising method based on the nonlinear relationship between the target variable and input features. Expert Systems with Applications, 218, 119585. https://doi.org/10.1016/j.eswa.2023.119585
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Jarboui, A., Mnif, E. Can Clean Energy Stocks Predict Crude Oil Markets Using Hybrid and Advanced Machine Learning Models?. Asia-Pac Financ Markets (2023). https://doi.org/10.1007/s10690-023-09432-9
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DOI: https://doi.org/10.1007/s10690-023-09432-9