Leveraging Big Data, Artificial Intelligence, and Predictive Analytics to Optimize Commodity Trading Strategies in the Oil and Gas Sector - Joyce Efekpogua Fiemotongha; Abbey Ngochindo Igwe; Chikezie Paul-Mikki Ewim; Ekene Cynthia Onukwulu (International Journal of Scientific Research in Humanities and Social Sciences, 2025)
The oil and gas sector operates in a highly volatile and dynamic environment, where effective commodity trading strategies are crucial for maximizing profitability and mitigating risks. This paper explores how advanced technologies, including Big Data, Artificial Intelligence (AI), and Predictive Analytics, are transforming commodity trading by enhancing market forecasting, optimizing decision-making, and improving risk management. By leveraging vast datasets and sophisticated analytical tools such as Power BI, Tableau, and Bloomberg Terminal, traders can extract actionable insights that drive strategic trading decisions. Big Data analytics enables the processing of structured and unstructured data from diverse sources, including market reports, financial statements, geopolitical developments, and real-time price fluctuations. AI techniques, including machine learning (ML) and natural language processing (NLP), facilitate the identification of patterns, anomalies, and correlations within large datasets, enhancing the accuracy of market forecasts. Predictive analytics further refines trading strategies by utilizing historical data and statistical models to anticipate price trends, demand fluctuations, and supply chain disruptions. This study delves into how these technologies are integrated into commodity trading frameworks to enhance price discovery, optimize hedging strategies, and improve operational efficiency. AI-driven models, such as reinforcement learning algorithms, can continuously learn from market dynamics to refine trading strategies in real time. The application of Power BI and Tableau in data visualization aids in presenting complex market trends, while Bloomberg Terminal provides access to financial data, market intelligence, and advanced analytics. Moreover, this research highlights the role of sentiment analysis in extracting valuable insights from news articles, financial reports, and social media trends, offering a comprehensive approach to risk assessment. The fusion of AI, big data, and predictive analytics enables oil and gas traders to make more informed and timely decisions, reducing exposure to market uncertainties. The findings validate the transformative impact of these technologies in revolutionizing commodity trading, underscoring their potential to increase market efficiency, enhance portfolio performance, and drive competitive advantage. The study provides a roadmap for industry professionals seeking to adopt data-driven trading strategies to navigate the complexities of the oil and gas market.