英文标题：
《Investor Reaction to Financial Disclosures Across Topics: An Application
  of Latent Dirichlet Allocation》
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作者：
Stefan Feuerriegel, Nicolas Pr\\\"ollochs
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最新提交年份：
2018
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英文摘要：
  This paper provides a holistic study of how stock prices vary in their response to financial disclosures across different topics. Thereby, we specifically shed light into the extensive amount of filings for which no a priori categorization of their content exists. For this purpose, we utilize an approach from data mining - namely, latent Dirichlet allocation - as a means of topic modeling. This technique facilitates our task of automatically categorizing, ex ante, the content of more than 70,000 regulatory 8-K filings from U.S. companies. We then evaluate the subsequent stock market reaction. Our empirical evidence suggests a considerable discrepancy among various types of news stories in terms of their relevance and impact on financial markets. For instance, we find a statistically significant abnormal return in response to earnings results and credit rating, but also for disclosures regarding business strategy, the health sector, as well as mergers and acquisitions. Our results yield findings that benefit managers, investors and policy-makers by indicating how regulatory filings should be structured and the topics most likely to precede changes in stock valuations.
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中文摘要：
本文全面研究了不同主题的股票价格对财务披露的反应。因此，我们特别揭示了大量的文件，这些文件的内容不存在先验分类。为此，我们利用数据挖掘的一种方法，即潜在Dirichlet分配，作为主题建模的一种手段。这项技术有助于我们提前对来自美国公司的70000多份8-K监管文件的内容进行自动分类。然后，我们评估随后的股市反应。我们的经验证据表明，不同类型的新闻故事在其相关性和对金融市场的影响方面存在很大差异。例如，我们发现，对盈利结果和信用评级，以及对商业战略、卫生部门以及并购的披露，都有统计上显著的异常回报。我们的研究结果表明，监管文件的结构应该如何，以及股票估值变化之前最可能出现的主题，从而使管理者、投资者和决策者受益。
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Computation and Language        计算与语言
分类描述：Covers natural language processing. Roughly includes material in ACM Subject Class I.2.7. Note that work on artificial languages (programming languages, logics, formal systems) that does not explicitly address natural-language issues broadly construed (natural-language processing, computational linguistics, speech, text retrieval, etc.) is not appropriate for this area.
涵盖自然语言处理。大致包括ACM科目I.2.7类的材料。请注意，人工语言（编程语言、逻辑学、形式系统）的工作，如果没有明确地解决广义的自然语言问题（自然语言处理、计算语言学、语音、文本检索等），就不适合这个领域。
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一级分类：Quantitative Finance        数量金融学
二级分类：General Finance        一般财务
分类描述：Development of general quantitative methodologies with applications in finance
通用定量方法的发展及其在金融中的应用
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