清洁能源股票能否利用混合和高级机器学习模型预测原油市场?

IF 2.5 Q2 ECONOMICS Asia-Pacific Financial Markets Pub Date : 2023-10-11 DOI:10.1007/s10690-023-09432-9
Anis Jarboui, Emna Mnif
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引用次数: 0

摘要

原油市场的波动性和对可持续能源解决方案的迫切需求,激发了人们对能够更好地捕捉市场动态并纳入环保指标的预测方法的极大兴趣。在本研究中,我们针对文献中的空白,提出了基于小波分解与机器学习技术(ANN-Wavelet 和 SVR-Wavelet)以及先进机器学习技术(XGBoost 和 GBM)与先进清洁能源指标相结合的新型混合方法来预测原油价格。这些混合模型减少了噪音,提高了结果的准确性,从而大大推动了该领域的发展。此外,这些方法还用于确定预测原油市场价格的最佳模型。此外,我们还采用了 SHapely Additive exPlanations(SHAP)算法来分析和解释模型,从而提高了透明度和可解释性。随后,我们应用 SHAP 研究了各种资产类别(包括波动率指数 (VIX)、贵金属市场(黄金和白银)、燃料市场(汽油和天然气)以及绿色和可再生能源指数)对原油价格的预测价值。研究结果表明,小波-SVR 模型具有稳定、稳健的预测性能,RMSE 和 MAPE 值较低。此外,GBM 模型准确度高,预测误差小。相反,Wavelet-ANN 和 XGBoost 模型的表现则好坏参半,在全样本中表现出有效性,但在俄乌冲突期间却降低了准确性。值得注意的是,绿色和可再生能源市场(如 CGA 和 NextEra energy (NEE))成为预测原油价格的重要预测因素。这项研究强调了将对环境负责的指标纳入投资组合和政策选择的重要性,从而为在俄乌冲突期间预测石油价格提供了重要指导。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Can Clean Energy Stocks Predict Crude Oil Markets Using Hybrid and Advanced Machine Learning Models?

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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来源期刊
CiteScore
3.00
自引率
0.00%
发文量
34
期刊介绍: The current remarkable growth in the Asia-Pacific financial markets is certain to continue. These markets are expected to play a further important role in the world capital markets for investment and risk management. In accordance with this development, Asia-Pacific Financial Markets (formerly Financial Engineering and the Japanese Markets), the official journal of the Japanese Association of Financial Econometrics and Engineering (JAFEE), is expected to provide an international forum for researchers and practitioners in academia, industry, and government, who engage in empirical and/or theoretical research into the financial markets. We invite submission of quality papers on all aspects of finance and financial engineering. Here we interpret the term ''financial engineering'' broadly enough to cover such topics as financial time series, portfolio analysis, global asset allocation, trading strategy for investment, optimization methods, macro monetary economic analysis and pricing models for various financial assets including derivatives We stress that purely theoretical papers, as well as empirical studies that use Asia-Pacific market data, are welcome. Officially cited as: Asia-Pac Financ Markets
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