How Does Post-Earnings Announcement Sentiment Affect Firms’ Dynamics? New Evidence from Causal Machine Learning

Francesco Audrino, Jonathan Chassot, Chen Huang, Michael Knaus, Michael Lechner and Juan-Pablo Ortega

Journal of Financial Econometrics, 2024, vol. 22, issue 3, 575-604

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.

Keywords: causal machine learning; heterogeneity analysis; modified causal forest; post-earnings announcement drift; volatility; and volume; sentiment (search for similar items in EconPapers)
JEL-codes: C31 C58 G14 G41 (search for similar items in EconPapers)
Date: 2024
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