Can Large Language Models Beat Wall Street? Unveiling the Potential of AI in Stock Selection

Georgios Fatouros, Konstantinos Metaxas, John Soldatos and Dimosthenis Kyriazis

Papers from arXiv.org

Abstract: 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.

Date: 2024-01, Revised 2024-04
New Economics Papers: this item is included in nep-ain, nep-big, nep-cmp and nep-fmk
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Citations: View citations in EconPapers (3)

Published in Neural Computing and Applications (2024) 1-16

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