Alshater Muneer M, Kampouris Ilias, Marashdeh Hazem, Atayah Osama F, Banna Hasanul
Faculty of Business, Emirates College of Technology, Abu Dhabi, United Arab Emirates.
College of Business, Abu Dhabi University, P.O. Box 59911, Abu Dhabi, United Arab Emirates.
Ann Oper Res. 2022 Aug 26:1-37. doi: 10.1007/s10479-022-04908-9.
The COVID-19 pandemic has inflicted the global economy and caused substantial financial losses. The energy sector was heavily affected and resulted in energy prices massively tumbling. The Russian invasion of Ukraine has fueled the energy maker more volatile. In such uncertain contexts, an Early Warning System (EWS) would efficiently contribute to stabilizing market swings. It will leverage the ability to control operating costs and pave the way for smooth economic recovery. Within this framework, we deploy Machine Learning (ML) models to forecast energy equity prices by employing uncertainty indices as a proxy for predicting energy market volatility. We empirically examine the comparative effectiveness of prevalent ML models and conventional approaches (regression) to forecast the energy equity prices by utilizing the daily data from 1/6/2011 to 18/1/2022 for four US uncertainty and eight energy equity indices. Results show that the Nonlinear Autoregressive with External (Exogenous) parameters (NARX) of Neural Networks (NN) scored significantly better accuracy than all other (25) ML models and conventional approaches. The study outcomes are beneficial for policymakers, governments, market regulators, investors, hedge and mutual funds, and corporations. They improve stakeholders' resilience to exogenous shocks, blaze the recovery path, and provide evidence-based for assets allocation strategies.
新冠疫情给全球经济带来重创，造成了巨大的经济损失。能源行业受到严重影响，导致能源价格大幅下跌。俄罗斯对乌克兰的入侵加剧了能源市场的波动。在这种不确定的背景下，一个早期预警系统（EWS）将有效地有助于稳定市场波动。它将利用控制运营成本的能力，为经济的平稳复苏铺平道路。在此框架内，我们部署机器学习（ML）模型，通过使用不确定性指数作为预测能源市场波动的代理指标来预测能源股票价格。我们利用2011年1月6日至2022年1月18日期间四个美国不确定性指数和八个能源股票指数的每日数据，实证检验了流行的ML模型和传统方法（回归）预测能源股票价格的相对有效性。结果表明，神经网络（NN）的带外部（外生）参数的非线性自回归（NARX）模型在准确性上显著优于所有其他（25个）ML模型和传统方法。该研究结果对政策制定者、政府、市场监管机构、投资者、对冲基金和共同基金以及企业都有益。它们提高了利益相关者对外生冲击的抵御能力，为复苏之路指明方向，并为资产配置策略提供了循证依据。
