53 Pages Posted: 12 May 2016 Last revised: 25 May 2017
Date Written: May 23, 2017
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: 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