Modeling Financial Analysts’ Decision Making via the Pragmatics and Semantics of Earnings Calls

Katherine Keith, Amanda Stent


Abstract
Every fiscal quarter, companies hold earnings calls in which company executives respond to questions from analysts. After these calls, analysts often change their price target recommendations, which are used in equity re- search reports to help investors make deci- sions. In this paper, we examine analysts’ decision making behavior as it pertains to the language content of earnings calls. We identify a set of 20 pragmatic features of analysts’ questions which we correlate with analysts’ pre-call investor recommendations. We also analyze the degree to which semantic and pragmatic features from an earnings call complement market data in predicting analysts’ post-call changes in price targets. Our results show that earnings calls are moderately predictive of analysts’ decisions even though these decisions are influenced by a number of other factors including private communication with company executives and market conditions. A breakdown of model errors indicates disparate performance on calls from different market sectors.
Anthology ID:
P19-1047
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
493–503
Language:
URL:
https://www.aclweb.org/anthology/P19-1047
DOI:
10.18653/v1/P19-1047
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PDF:
http://aclanthology.lst.uni-saarland.de/P19-1047.pdf
Supplementary:
 P19-1047.Supplementary.pdf