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

Andreas Barth, Sasan Mansouri, Fabian Wöbbeking

Management Science, forthcoming

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.

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23.05.2023 • 14/2023

Analysis of earnings calls: Blathering managers harm their company

If a senior executive refuses to give information to professional investors, the company's stock market value drops afterwards. This is shown in a study by the Halle Institute for Economic Research (IWH) after evaluating 1.2 million answers from tele-phone conferences.

Fabian Wöbbeking

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Mental Health

Mental Health Mental health encompasses emotional, psychological, and social well-being, influencing cognition, perception, and behavior. According to World Health...

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