Misstatement verifiability and managers’ earnings warning decisions
Bae, J. ORCID: 0000-0003-1580-8718 & Yu, J. (2023). Misstatement verifiability and managers’ earnings warning decisions. Journal of Accounting and Public Policy, 42(6), article number 107152. doi: 10.1016/j.jaccpubpol.2023.107152
Abstract
We examine whether the verifiability of misstatements in prior forward-looking earnings disclosures contributes to managers’ decisions to issue earnings warnings. Using securities class action lawsuits from 1996 to 2019 pertaining to forward-looking earnings disclosures, we find that earnings warnings are positively associated with the verifiability of misstatements in such disclosures. The results survive entropy balancing and firm-fixed effects to mitigate endogeneity concerns. The positive relation between earnings warnings and misstatement verifiability is more pronounced for firms 1) with a general counsel in the top management team and 2) that face higher ex-ante litigation risk, and less pronounced for firms whose managers engaged in insider selling during the class action lawsuit period. We also show that earnings warnings help to increase the likelihood of a lawsuit dismissal (i.e., lowering litigation costs) when the lawsuit involves misstatements that are more (rather than less) verifiable. Taken together, our findings suggest that managers issue earnings warnings when it helps to reduce litigation costs, consistent with the notion that managers can achieve a greater reduction in litigation costs by issuing earnings warnings.
Publication Type: | Article |
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Additional Information: | This is an open access article distributed under the terms of the Creative Commons CC-BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
Publisher Keywords: | Earnings Warnings, ,SEC Rule 10b-5, Litigation Costs, Private information, Misstatement verifiability |
Subjects: | H Social Sciences > HG Finance |
Departments: | Bayes Business School Bayes Business School > Finance |
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Available under License Creative Commons Attribution.
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