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

Li Xian Liu (), Zhiyue Sun, Kunpeng Xu and Chao Chen
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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

IJFS, 2024, vol. 12, issue 3, 1-35

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.

Keywords: financial analysis and reasoning; financial modeling; evaluation metrics; ChatGPT-4o; Human–AI collaboration; AI practical applications in finance (search for similar items in EconPapers)
JEL-codes: F2 F3 F41 F42 G1 G2 G3 (search for similar items in EconPapers)
Date: 2024
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