How does post-earnings announcement sentiment affect firms' dynamics? New evidence from causal machine learning
Type
working paper
Date Issued
2020-12-22
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 US 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 and aggregated investors' moods in a properly defined causal framework. Our empirical results support the presence of (i) investors' overconfidence and mispricing due to biased expectations; (ii) a leverage effect in sentiment where reactions are (on average) larger for negative sentiment; and (iii) investors' underreaction to news. Finally, we show that the difference in the average causal effects of the sentiment's types is larger when the general macroeconomic conditions are worse or the uncertainty in the global financial market is higher.
Language
English
HSG Classification
contribution to scientific community
HSG Profile Area
SEPS - Quantitative Economic Methods
Pages
30
Contact Email Address
francesco.audrino@unisg.ch
Eprints ID
261799
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open.access
Name
paper_v1.pdf
Size
986.88 KB
Format
Adobe PDF
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