收益
杠杆(统计)
经济
收益惊喜
波动性(金融)
市场流动性
情绪分析
市场情绪
货币经济学
金融经济学
计量经济学
收益反应系数
盈利后公告漂移
财务
计算机科学
机器学习
作者
Francesco Audrino,Jonathan Chassot,Chen Huang,Michael C. Knaus,Michael Lechner,Juan-Pablo Ortega
出处
期刊:Journal of Financial Econometrics
[Oxford University Press]
日期:2022-07-12
标识
DOI:10.1093/jjfinec/nbac018
摘要
Abstract 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.
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