We investigate whether large language models (LLMs) can successfully perform financial statement analysis in a way similar to a professional human analyst. We provide standardized and anonymous financial statements to GPT4 and instruct the model to analyze them to determine the direction of firms' future earnings. Even without narrative or industry-specific information, the LLM outperforms financial analysts in its ability to predict earnings changes directionally. The LLM exhibits a relative advantage over human analysts in situations when the analysts tend to struggle. Furthermore, we find that the prediction accuracy of the LLM is on par with a narrowly trained state-of-the-art ML model. LLM prediction does not stem from its training memory. Instead, we find that the LLM generates useful narrative insights about a company's future performance. Lastly, our trading strategies based on GPT's predictions yield a higher Sharpe ratio and alphas than strategies based on other models. Our results suggest that LLMs may take a central role in analysis and decision-making.

翻译：本研究探讨大型语言模型（LLMs）能否以类似专业人类分析师的方式成功执行财务报表分析。我们向GPT4提供标准化且匿名的财务报表，并指令模型通过分析这些报表来判断公司未来盈利的变动方向。即使在没有叙述性或行业特定信息的情况下，该LLM在预测盈利方向性变化的能力上仍超越了金融分析师。在人类分析师通常面临困难的场景中，LLM表现出相对优势。此外，我们发现LLM的预测准确性与经过专门训练的最先进机器学习模型相当。LLM的预测并非源于其训练记忆，相反，我们发现LLM能够生成关于公司未来表现的有用叙述性见解。最后，基于GPT预测构建的交易策略相比基于其他模型的策略，获得了更高的夏普比率和阿尔法收益。我们的研究结果表明，LLMs可能在分析与决策中扮演核心角色。