AI-Driven Finance: A Comparative Study of GPT-4 Omni and Gemini Advanced for Risk Assessment and Algorithmic Trading

Abstract: The financial sector is increasingly adopting Artificial Intelligence (AI) technologies to navigate the complex and volatile landscape of modern markets. This study presents a comparative analysis of two advanced Large Language Models (LLMs), GPT-4 Omni and Gemini Advanced, to evaluate their performance in automated financial risk assessment and algorithmic trading strategies. The study employed a simulation-based approach using historical market data, to assess both tools in a range of trading scenarios. The results indicate that while both LLMs have potential to enhance financial operations, there were notable differences in their capabilities. GPT-4 Omni demonstrated a superior accuracy in risk assessment, whereas Gemini Advanced exhibited a more efficient performance in algorithmic trading, which produced higher Sharpe ratios when compared to GPT-4 Omni. These findings have important implications for financial institutions and regulatory bodies, highlighting both the opportunities and challenges of implementing LLMs in financial markets, with a particular focus on ethical and responsible innovation. This study underscores the need for robust testing and evaluation of all AI tools, prior to implementation, and for ongoing review and oversight. Keywords: AI, Research, GPT-4, Gemini Advanced