Throwing Curveballs: A Language-Based Model of Curveball Questions in Quarterly Earnings Calls Uncovers their Consequences and Antecedents

In evaluative contexts, evaluatees typically seek to present themselves in a favorable light, while evaluators ask penetrating questions to assess these claims. Here we develop a framework to identify curveball questions: ones that are on-topic yet perplexing (i.e., difficult to predict) relative to prior statements. We develop a language-based measure of curveball questions and apply it to a corpus of quarterly earnings calls. After validating this question-level measure, we next demonstrate that a call-level curveball measure predicts absolute returns, absolute abnormal returns, and changes in a firm’s average analyst rating. Finally, we identify the types of analysts who are most likely to pose curveballquestions, the types of firms that are most likely to receive them, and the conditions under which they tend to arise.