A recent study by the University of Chicago shows that AI models are surpassing human financial analysts in predicting future earnings, highlighting the growing integration of AI in finance for improved accuracy and efficiency.
Banks, hedge funds, and wealth managers are increasingly integrating AI in their operations, primarily for routine tasks. A recent draft paper by Alex Kim, Maximilian Muhn, and Valeri Nikolaev of the University of Chicago reveals that generative AI has surpassed financial analysts in predicting future earnings, outperforming them by 8 percentage points. The AI model achieved this by using anonymized balance sheets and income statements, without considering contextual factors like sector issues or personnel changes.
Unlike human analysts who rely on various information sources such as earnings calls and management discussions, ChatGPT employed chain of thought reasoning, focusing on operating efficiency and leverage ratios to forecast earnings. The AI’s basic input handling suggests potential for even greater accuracy with more contextual data.
Currently, AI in finance mainly handles tasks such as transcribing earnings calls, summarizing data, and analyzing text, thereby reducing human error and time. Despite the AI’s predictive success, a hybrid model combining AI and human insights is considered the most effective, ensuring that financial analysts continue to play a crucial role.
