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Can GPT models be Financial Analysts? An Evaluation of ChatGPT and GPT-4 on mock CFA Exams

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Listed:
  • Ethan Callanan
  • Amarachi Mbakwe
  • Antony Papadimitriou
  • Yulong Pei
  • Mathieu Sibue
  • Xiaodan Zhu
  • Zhiqiang Ma
  • Xiaomo Liu
  • Sameena Shah

Abstract

Large Language Models (LLMs) have demonstrated remarkable performance on a wide range of Natural Language Processing (NLP) tasks, often matching or even beating state-of-the-art task-specific models. This study aims at assessing the financial reasoning capabilities of LLMs. We leverage mock exam questions of the Chartered Financial Analyst (CFA) Program to conduct a comprehensive evaluation of ChatGPT and GPT-4 in financial analysis, considering Zero-Shot (ZS), Chain-of-Thought (CoT), and Few-Shot (FS) scenarios. We present an in-depth analysis of the models' performance and limitations, and estimate whether they would have a chance at passing the CFA exams. Finally, we outline insights into potential strategies and improvements to enhance the applicability of LLMs in finance. In this perspective, we hope this work paves the way for future studies to continue enhancing LLMs for financial reasoning through rigorous evaluation.

Suggested Citation

  • Ethan Callanan & Amarachi Mbakwe & Antony Papadimitriou & Yulong Pei & Mathieu Sibue & Xiaodan Zhu & Zhiqiang Ma & Xiaomo Liu & Sameena Shah, 2023. "Can GPT models be Financial Analysts? An Evaluation of ChatGPT and GPT-4 on mock CFA Exams," Papers 2310.08678, arXiv.org.
    • Handle: RePEc:arx:papers:2310.08678
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    Cited by:

    1. Georgios Fatouros & Konstantinos Metaxas & John Soldatos & Dimosthenis Kyriazis, 2024. "Can Large Language Models Beat Wall Street? Unveiling the Potential of AI in Stock Selection," Papers 2401.03737, arXiv.org, revised Apr 2024.

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