战略披露错误分类

Andrew Bird, S. Karolyi, Paul Ma
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引用次数: 5

摘要

我们应用现代机器学习技术来描述上市公司披露错误分类的特征。我们发现,12-25%的披露被错误分类;那些涉及重大最终协议、高管或董事更替以及退市的文件最常被错误分类。使用EDGAR搜索流量数据,我们提供了错误分类成功降低投资者注意力的证据。通过这一注意通道,错误分类导致对绝对市场收益的显著和持续影响。对于错误分类的文件,搜索流量降低了4-12%,绝对市场反应减少了46-79个基点。与战略动机一致,在负面新闻和市场关注度高的情况下,错误分类的可能性更大。
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Strategic Disclosure Misclassification
We apply modern machine learning techniques to characterize disclosure misclassification by public companies. We find that 12-25% 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.
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