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

Journal of Emerging Technologies in Accounting, Vol. 3, pp. 1-19, 2006

19 Pages Posted: 23 Jan 2007

See all articles by Elaine Henry

Elaine Henry

Stevens Institute of Technology - School of Business

Abstract

Similar to a classic event study, this study examines market reaction to firms' 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.

Keywords: CART, content analysis, event study, earnings announcements

JEL Classification: M41, M45, G12

Suggested Citation

Henry, Elaine, Market Reaction to Verbal Components of Earnings Press Releases: Event Study Using a Predictive Algorithm. Journal of Emerging Technologies in Accounting, Vol. 3, pp. 1-19, 2006. Available at SSRN: https://ssrn.com/abstract=958749

Elaine Henry (Contact Author)

Stevens Institute of Technology - School of Business ( email )

Hoboken, NJ 07030
United States

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