Market Reaction to Verbal Components of Earnings Press Releases: Event Study Using a Predictive Algorithm

Similar to a classic‐event study, this study examines market reaction to firmsa' earnings announcements. This study extends the examination to include a broad range of concurrent disclosure contained in earnings press releases: financial disclosure captured as accounting ratios; and verbal components of disclosure, both content and style, which are captured using elementary computer‐based content analysis. Extending the analysis to such a broad range of concurrent disclosures requires a methodology designed to utilize a large number of predictor variables, and predictive data mining algorithms are specifically designed to do so. Therefore, this study employs a widely used data‐mining algorithm—classification and regression trees (CART). Results of the study show that inclusion of predictor variables capturing verbal content and writing style of earnings‐press releases results in more accurate predictions of market response.