How does post-earnings announcements sentiment affect firms' dynamics? New evidence from causal machine learning
Journal
Journal of Financial Econometrics
ISSN
1479-8409
Type
forthcoming
Date Issued
2022-07-12
Author(s)
;
;
Huang, Chen
;
Knaus, Michael
;
;
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.
Language
English
HSG Classification
contribution to scientific community
HSG Profile Area
SEPS - Quantitative Economic Methods
Refereed
Yes
Publisher
Oxford University Press
Official URL
Subject(s)
Contact Email Address
francesco.audrino@unisg.ch
Eprints ID
267473