Can Large Language Models Beat Wall Street? Unveiling the Potential of AI in Stock Selection
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| Format: | Preprint |
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2024
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| _version_ | 1866909471543918592 |
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| author | Fatouros, Georgios Metaxas, Konstantinos Soldatos, John Kyriazis, Dimosthenis |
| author_facet | Fatouros, Georgios Metaxas, Konstantinos Soldatos, John Kyriazis, Dimosthenis |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2401_03737 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Can Large Language Models Beat Wall Street? Unveiling the Potential of AI in Stock Selection Fatouros, Georgios Metaxas, Konstantinos Soldatos, John Kyriazis, Dimosthenis Computational Finance Artificial Intelligence Computational Engineering, Finance, and Science Computation and Language Machine Learning 68T07, 68T50, 91G10, 91G15 I.2.1; I.2.7; J.4 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. |
| title | Can Large Language Models Beat Wall Street? Unveiling the Potential of AI in Stock Selection |
| topic | Computational Finance Artificial Intelligence Computational Engineering, Finance, and Science Computation and Language Machine Learning 68T07, 68T50, 91G10, 91G15 I.2.1; I.2.7; J.4 |
| url | https://arxiv.org/abs/2401.03737 |