Navigating Complexity: GPT-4's Performance in Predicting Earnings and Stock Returns in China's A-Share Market
Year: 2024
Type: article
Abstract: This study investigates the application of GPT-4, a large language model, in predicting earnings changes and stock returns within China's A-share market from 2000 to 2023. We evaluate the model's performance using various metrics, including prediction accuracy, F1 score, stock returns, Sharpe ratio, and alpha. Our findings reveal significant fluctuations in the model's predictive accuracy, ranging from 10.62% to 48.67%, with an average F1 score of 0.30. Despite inconsistent accuracy, the model maintained high prediction confidence levels between 75% and 90%. Stock returns associated with the model's predictions varied widely, from -4.86% to 13.59%, showing no consistent correlation with prediction accuracy. The study highlights the challenges of applying AI models to financial analysis in emerging markets, particularly given the unique characteristics of China's A-share market, such as frequent policy interventions and a high proportion of retail investors. We discuss the implications of these findings for the future of AI-driven financial analysis, emphasizing the need for improved model calibration, ethical considerations, and regulatory frameworks.
DOI: 10.54097/4rwdat95
Authors: Yaoda Dai, Mingzhang Liao, Zewei Li
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4404550655