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AI-Driven Financial Analysis: Exploring ChatGPT’s Capabilities and Challenges

Author

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  • Li Xian Liu

    (College of Business, Law & Governance, James Cook University, 1 James Cook Drive, Douglas, QLD 4811, Australia)

  • Zhiyue Sun

    (School of Accounting, Economics & Finance, Curtin University, Kent Street, Perth, WA 6102, Australia)

  • Kunpeng Xu

    (School of Statistics and Information, Shanghai University of International Business and Economics, 1900 Wenxiang Rd, Songjiang District, Shanghai 201613, China)

  • Chao Chen

    (Accounting, Information System and Supply Chain, RMIT University, 124 La Trobe St, Melbourne, VIC 3000, Australia)

Abstract

The transformative impact of AI technologies on the financial sector has been a topic of increasing interest. This study investigates ChatGPT’s applications in financial reasoning and analysis and evaluates ChatGPT-4o’s effectiveness and limitations in conducting both basic and complex financial analysis tasks. By designing a series of multi-step, advanced reasoning tasks and establishing task-specific evaluation metrics, we assessed ChatGPT-4o’s performance compared to human analysts. Results indicate that while ChatGPT-4o demonstrates proficiency in basic and some complex financial tasks, it struggles with deep analytical and critical thinking tasks, especially in specialized finance areas. This study underscores the need for meticulous task formulation and robust evaluation in AI financial applications. While ChatGPT enhances efficiency, integrating it with human expertise is crucial for effective decision-making. Our findings highlight both the potential and limitations of ChatGPT-4o in financial analysis, providing valuable insights for future AI integration in the finance sector.

Suggested Citation

  • Li Xian Liu & Zhiyue Sun & Kunpeng Xu & Chao Chen, 2024. "AI-Driven Financial Analysis: Exploring ChatGPT’s Capabilities and Challenges," IJFS, MDPI, vol. 12(3), pages 1-35, June.
    • Handle: RePEc:gam:jijfss:v:12:y:2024:i:3:p:60-:d:1423687
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    References listed on IDEAS

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    1. Martin Jullum & Anders Løland & Ragnar Bang Huseby & Geir Ånonsen & Johannes Lorentzen, 2020. "Detecting money laundering transactions with machine learning," Journal of Money Laundering Control, Emerald Group Publishing Limited, vol. 23(1), pages 173-186, January.
    2. Luiz Félix & Roman Kräussl & Philip Stork, 2020. "Implied volatility sentiment: a tale of two tails," Quantitative Finance, Taylor & Francis Journals, vol. 20(5), pages 823-849, May.
    3. Leippold, Markus, 2023. "Sentiment spin: Attacking financial sentiment with GPT-3," Finance Research Letters, Elsevier, vol. 55(PB).
    4. Dowling, Michael & Lucey, Brian, 2023. "ChatGPT for (Finance) research: The Bananarama Conjecture," Finance Research Letters, Elsevier, vol. 53(C).
    5. Markus Leippold, 2023. "Sentiment Spin: Attacking Financial Sentiment with GPT-3," Swiss Finance Institute Research Paper Series 23-11, Swiss Finance Institute.
    6. Hartmann, Jochen & Heitmann, Mark & Siebert, Christian & Schamp, Christina, 2023. "More than a Feeling: Accuracy and Application of Sentiment Analysis," International Journal of Research in Marketing, Elsevier, vol. 40(1), pages 75-87.
    7. Li Xian Liu & Shuangzhe Liu & Milind Sathye, 2021. "Predicting Bank Failures: A Synthesis of Literature and Directions for Future Research," JRFM, MDPI, vol. 14(10), pages 1-24, October.
    8. Paul C. Tetlock & Maytal Saar‐Tsechansky & Sofus Macskassy, 2008. "More Than Words: Quantifying Language to Measure Firms' Fundamentals," Journal of Finance, American Finance Association, vol. 63(3), pages 1437-1467, June.
    Full references (including those not matched with items on IDEAS)

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