We revisit the role played by sentiment extracted from news articles related to earnings announcements as a driver of firms’ return, volatility, and trade volume dynamics. To this end, we apply causal machine learning on the earnings announcements of a wide cross-section of U.S. companies. This approach allows us to investigate firms’ price and volume reactions to different types of post-earnings announcement sentiment (positive, negative, and mixed sentiments) under various underlying macroeconomic, financial, and aggregated investors’ moods in a properly defined causal framework. Our empirical results support the presence of (i) economically sizable differences in the effects among sentiment types that are mostly of a non-linear nature depending on the underlying economic and financial conditions; (ii) a leverage effect in sentiment where reactions are (on average) larger for negative sentiment; and (iii) investors’ underreaction to news. In particular, we show that the difference in the average causal effects of the sentiment’s types is larger and more relevant when the general macroeconomic conditions are worse, the investors are pessimist about the behavior of the market and/or its uncertainty is higher, and in market regimes characterized by high stocks’ liquidity.

中文翻译：

---

财报发布后的情绪如何影响公司的动态？因果机器学习的新证据

我们重新审视了从与收益公告相关的新闻文章中提取的情绪作为公司回报、波动性和交易量动态的驱动因素所起的作用。为此，我们将因果机器学习应用于广泛的美国公司的收益公告。这种方法使我们能够在适当定义的因果框架下，在各种潜在的宏观经济、金融和综合投资者情绪下，调查公司对不同类型的盈利公告后情绪（正面、负面和混合情绪）的价格和数量反应。我们的实证结果支持（i）情绪类型之间的影响在经济上存在显着差异，这些差异主要是非线性的，取决于潜在的经济和金融状况；(ii) 情绪中的杠杆效应，负面情绪的反应（平均而言）更大；(iii) 投资者对消息反应不足。特别是，我们表明，当总体宏观经济状况较差、投资者对市场行为持悲观态度和/或其不确定性较高时，情绪类型的平均因果效应的差异更大且更相关。以高股票流动性为特征的市场制度。