“Let Me Get Back to You”—A Machine Learning Approach to Measuring NonAnswers

Andreas Barth, Sasan Mansouri, Fabian Wöbbeking

Research output: Contribution to journalArticleAcademicpeer-review

3 Citations (Scopus)

Abstract

Using a supervised machine learning framework on a large training set of questions and answers, we identify 1,364 trigrams that signal nonanswers in earnings call questions and answers (Q&A). We show that this glossary has economic relevance by applying it to contemporaneous stock market reactions after earnings calls. Our findings suggest that obstructing the flow of information leads to significantly lower cumulative abnormal stock returns and higher implied volatility. As both our method and glossary are free of financial context, we believe that the measure is applicable to other fields with a Q&A setup outside the contextual domain of financial earnings conference calls.
Original languageEnglish
Pages (from-to)6333-6348
Number of pages16
JournalManagement Science
Volume69
Issue number10
DOIs
Publication statusPublished - Oct-2023
Externally publishedYes

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