Leveraging cryptocurrency and AI indices for crude oil market forecasting with automated machine learning

Purpose Set against a rapidly evolving technologically driven investment landscape, this research aims to explore the complex interrelations among artificial intelligence, alternative energy stocks, eco-friendly investments, geopolitical risks (GPRs) and Ethereum’s energy consumption. Design/methodology/approach This work encompasses deploying the H2O Automated Machine Learning approach, explicitly focusing on analyzing market indicators. Additionally, the research emphasizes the evaluation of feature significance, identifying crucial variables that significantly influence the predictive outcomes. Besides, this study employs Shapley Additive Explanations to interpret the model’s output, offering a detailed analysis of feature contributions and enhancing the model’s transparency. Findings Key variables such as GPR, clean market (PBW) and the natural gas index (NG) significantly influence oil price predictions. The model demonstrates reliability, with areas for improvement in capturing unexplained variance. Practical implications This study offers valuable insights for energy sector market analysts, traders and policymakers, aiding in strategic decision-making and understanding market trends. Social implications This research emphasizes fostering clean and sustainable energy markets. It emphasizes the crucial role of advancements in artificial intelligence and renewable energy investments in accelerating the transition to environmentally responsible energy markets, highlighting their significance in fostering sustainability and mitigating climate change impacts. Originality/value This study pioneers integrating cutting-edge machine learning methodologies with crude oil market analysis, shedding light on critical influencing factors and forecasting aspects.