Strategic Disclosure Misclassification

56 Pages Posted: 12 May 2016 Last revised: 19 Jan 2018

Andrew Bird

Carnegie Mellon University

Stephen A. Karolyi

Carnegie Mellon University - David A. Tepper School of Business

Paul Ma

University of Minnesota

Date Written: January 19th, 2018

Abstract

We apply modern machine learning techniques to characterize disclosure misclassification by public companies. We find that 12-26% of disclosures are misclassified; those concerning material definitive agreements, executive or director turnover, and delistings are most commonly misclassified. Using EDGAR search traffic data, we provide evidence that misclassification is associated with less investor attention. Through this attention channel, misclassification leads to a significant and persistent impact on absolute market returns. For misclassified filings, search traffic is 4-12% lower and absolute market reactions are 46-79 bps smaller. Consistent with strategic motives, misclassification is more likely for negative news and when market attention is high.

Keywords: Information Overload; Materality Threshold; Disclosure Misclassification; Voluntary Disclosure; 8-K; Latent Dirichlet Allocation

JEL Classification: D83, G14, M48

Suggested Citation

Bird, Andrew and Karolyi, Stephen A. and Ma, Paul, Strategic Disclosure Misclassification (January 19th, 2018). Available at SSRN: https://ssrn.com/abstract=2778805 or http://dx.doi.org/10.2139/ssrn.2778805

Andrew Bird

Carnegie Mellon University ( email )

Pittsburgh, PA 15213-3890
United States

Stephen A. Karolyi

Carnegie Mellon University - David A. Tepper School of Business ( email )

5000 Forbes Avenue
Pittsburgh, PA 15213-3890
United States
4122682909 (Phone)

Paul Ma (Contact Author)

University of Minnesota ( email )

19th Avenue South
Minneapolis, MN 55455
United States

HOME PAGE: http://www.paulma.org

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