We used a dataset of daily Bloomberg Financial Market Summaries from 2010 to 2023, reposted on large financial media, to determine how global news headlines may affect stock market movements using ChatGPT and a two-stage prompt approach. We document a statistically significant positive correlation between the sentiment score and future equity market returns over short to medium term, which reverts to a negative correlation over longer horizons. Validation of this correlation pattern across multiple equity markets indicates its robustness across equity regions and resilience to non-linearity, evidenced by comparison of Pearson and Spearman correlations. Finally, we provide an estimate of the optimal horizon that strikes a balance between reactivity to new information and correlation.

利用ChatGPT和两阶段提示方法，我们使用2010年至2023年的Bloomberg金融市场总结数据集，通过大型金融媒体的转载，确定全球新闻头条如何影响股市走势。我们发现情感评分与未来短中期的股市回报间存在显著正相关，而随着时间推移，这种相关性转为负相关。通过对多个股票市场中的相关模式进行验证，证明了其对不同股票地区的鲁棒性和非线性的弹性，通过皮尔逊和斯皮尔曼相关性的比较也得到了证实。最后，我们提供了一个在新信息反应和相关性之间取得平衡的最佳时间跨度的估计。

ChatGPT能够从彭博市场简报中计算可信的情感评分吗?