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？

Can ChatGPT Compute Trustworthy Sentiment Scores from Bloomberg Market  Wraps?

Current multimodal sentiment analysis frames sentiment score prediction as a
general Machine Learning task. However, what the sentiment score actually
represents has often been overlooked. As a measurement of opinions and
affective states, a sentiment score generally consists of two aspects: polarity
and intensity. We decompose sentiment scores into these two aspects and study
how they are conveyed through individual modalities and combined multimodal
models in a naturalistic monologue setting. In particular, we build unimodal
and multimodal multi-task learning models with sentiment score prediction as
the main task and polarity and/or intensity classification as the auxiliary
tasks. Our experiments show that sentiment analysis benefits from multi-task
learning, and individual modalities differ when conveying the polarity and
intensity aspects of sentiment.

本文研究了多模态情感分析中情感得分的两个方面：情感极性和情感强度，并构建了单模态和多模态的多任务学习模型进行情感得分预测和情感极性和 / 或强度分类，结果表明多任务学习对情感分析有益，并且每种模态在表达情感极性和强度方面存在差异。