Evidence of Financial Market Synergies. A Combined Statistical and Machine Learning Research Approach

The research investigates how geopolitical conflicts influence global commodities and financial markets, aiming to identify effects on oil companies' economic and financial indicators within the context of financial market synergies. An integrated approach is implemented in the study, combining traditional statistical methods with advanced machine learning techniques. The results of the investigation were analyzed at two distinct levels: (1) that of the evolution of global commodity prices during the conflicts in Ukraine and the Gaza Strip, and (2) that of the financial performance of 250 companies in the oil industry, summarized in seven key indicators. Eloquent findings indicate that both conflicts have triggered significant changes in commodity prices, with strong investor reactions to safe haven assets such as gold and silver. In addition, there are clear manifestations of behavior reflecting supply-side concerns, as energy prices have risen considerably. Correlation analysis has shown a significant link between upward oil price movements and changes in some financial indicators (such as EBITDA) as well as asset increases. By integrating traditional statistical methods and advanced machine learning techniques, the ability of GARCH-ML hybrid models to accurately predict stock index volatility in the context of geopolitical tensions is highlighted. The research results highlight the benefits of the holistic approach in analyzing the complex interconnections between commodity markets, financial markets and company performance.