"Let me get back to you" - A machine learning approach to measuring non-answers

Abstract

Using a supervised machine learning framework on a large training set of questions and answers, we identify 1,364 trigrams that signal non-answers in earnings call 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.

Keywords: Econlinguistics, textual analysis, natural language processing, multinomial inverse regression, MNIR, non-answers

JEL Classification: D80, D82, G10, G14, G30