  This paper introduces MarketSenseAI, an innovative framework leveraging
GPT-4's advanced reasoning for selecting stocks in financial markets. By
integrating Chain of Thought and In-Context Learning, MarketSenseAI analyzes
diverse data sources, including market trends, news, fundamentals, and
macroeconomic factors, to emulate expert investment decision-making. The
development, implementation, and validation of the framework are elaborately
discussed, underscoring its capability to generate actionable and interpretable
investment signals. A notable feature of this work is employing GPT-4 both as a
predictive mechanism and signal evaluator, revealing the significant impact of
the AI-generated explanations on signal accuracy, reliability and acceptance.
Through empirical testing on the competitive S&P 100 stocks over a 15-month
period, MarketSenseAI demonstrated exceptional performance, delivering excess
alpha of 10% to 30% and achieving a cumulative return of up to 72% over the
period, while maintaining a risk profile comparable to the broader market. Our
findings highlight the transformative potential of Large Language Models in
financial decision-making, marking a significant leap in integrating generative
AI into financial analytics and investment strategies.
