We examine the potential of ChatGPT, and other large language models, in predicting stock market returns using sentiment analysis of news headlines. We use ChatGPT to indicate whether a given headline is good, bad, or irrelevant news for firms' stock prices. We then compute a numerical score and document a positive correlation between these ``ChatGPT scores'' and subsequent daily stock market returns. Further, ChatGPT outperforms traditional sentiment analysis methods. We find that more basic models such as GPT-1, GPT-2, and BERT cannot accurately forecast returns, indicating return predictability is an emerging capacity of complex models. ChatGPT-4's implied Sharpe ratios are larger than ChatGPT-3's; however, the latter model has larger total returns. Our results suggest that incorporating advanced language models into the investment decision-making process can yield more accurate predictions and enhance the performance of quantitative trading strategies. Predictability is concentrated on smaller stocks and more prominent on firms with bad news, consistent with limits-to-arbitrage arguments rather than market inefficiencies.

翻译：我们探究了ChatGPT及其他大语言模型通过新闻标题情感分析预测股票市场收益的潜力。我们利用ChatGP判断特定标题对相关公司股价而言是利好、利空还是无关信息，进而计算数值化评分，发现这些“ChatGPT评分”与后续个股日收益之间存在正相关性。此外，ChatGPT的表现优于传统情感分析方法。更基础的模型（如GPT-1、GPT-2和BERT）无法准确预测收益，表明收益可预测性是复杂模型涌现出的能力。虽然ChatGPT-4隐含的夏普比率高于ChatGPT-3，但后者的总收益更大。研究结果表明，将先进语言模型融入投资决策过程可提高预测准确性，并增强量化交易策略的表现。预测能力主要集中在小型股票上，且在利空消息的企业中更为显著，这符合套利限制理论而非市场无效假说。