Download this Paper Open PDF in Browser

Strategic Disclosure Misclassification

53 Pages Posted: 12 May 2016 Last revised: 25 May 2017

Andrew Bird

Carnegie Mellon University - Tepper School of Business

Stephen A. Karolyi

Carnegie Mellon University - Tepper School of Business

Paul Ma

University of Minnesota - Carlson School of Management

Date Written: May 23, 2017

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 successfully reduces 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 (May 23, 2017). Available at SSRN: https://ssrn.com/abstract=2778805

Andrew Bird

Carnegie Mellon University - Tepper School of Business ( email )

Pittsburgh, PA 15213-3890
United States

Stephen A. Karolyi

Carnegie Mellon University - Tepper School of Business ( email )

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

Paul Ma (Contact Author)

University of Minnesota - Carlson School of Management ( email )

19th Avenue South
Minneapolis, MN 55455
United States

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

Paper statistics

Downloads
378
Rank
64,155
Abstract Views
1,432