文章基本信息

标题：Testing market response to auditor change filings: A comparison of machine learning classifiers
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作者：Richard Holowczak ; David Louton ; Hakan Saraoglu 等
期刊名称：The Journal of Finance and Data Science
印刷版ISSN：2405-9188
出版年度：2019
卷号：5
期号：1
页码：48-59
DOI：10.1016/j.jfds.2018.08.001
语种：English
出版社：Elsevier
摘要：AbstractThe use of textual information contained in company filings with the Securities Exchange Commission (SEC), including annual reports on Form 10-K, quarterly reports on Form 10-Q, and current reports on Form 8-K, has gained the increased attention of finance and accounting researchers. In this paper we use a set of machine learning methods to predict the market response to changes in a firm's auditor as reported in public filings. We vectorize the text of 8-K filings to test whether the resulting feature matrix can explain the sign of the market response to the filing. Specifically, using classification algorithms and a sample consisting of the Item 4.01 text of 8-K documents, which provides information on changes in auditors of companies that are registered with the SEC, we predict the sign of the cumulative abnormal return (CAR) around 8-K filing dates. We report the correct classification performance and time efficiency of the classification algorithms. Our results show some improvement over the naïve classification method.