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

Author

Listed:
  • Andreas Barth

    (Saarland University Saarbruecken, 66041 Saarbrücken, Germany; Goethe University Frankfurt, House of Finance, 60629 Frankfurt, Germany)

  • Sasan Mansouri

    (Goethe University Frankfurt, House of Finance, 60629 Frankfurt, Germany)

  • Fabian Wöbbeking

    (Financial Markets Department, Halle Institute for Economic Research (IWH), 06017 Halle, Germany; Martin-Luther-University Halle-Wittenberg, 06018 Halle, Germany)

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.

Suggested Citation

  • Andreas Barth & Sasan Mansouri & Fabian Wöbbeking, 2023. "“Let Me Get Back to You”—A Machine Learning Approach to Measuring NonAnswers," Management Science, INFORMS, vol. 69(10), pages 6333-6348, October.
    • Handle: RePEc:inm:ormnsc:v:69:y:2023:i:10:p:6333-6348
    DOI: 10.1287/mnsc.2022.4597
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