This paper presents a multi-label topic model for financial texts like ad-hoc
announcements, 8-K filings, finance related news or annual reports. I train the
model on a new financial multi-label database consisting of 3,044 German ad-hoc
announcements that are labeled manually using 20 predefined, economically
motivated topics. The best model achieves a macro F1 score of more than 85%.
Translating the data results in an English version of the model with similar
performance. As application of the model, I investigate differences in stock
market reactions across topics. I find evidence for strong positive or negative
market reactions for some topics, like announcements of new Large Scale
Projects or Bankruptcy Filings, while I do not observe significant price
effects for some other topics. Furthermore, in contrast to previous studies,
the multi-label structure of the model allows to analyze the effects of
co-occurring topics on stock market reactions. For many cases, the reaction to
a specific topic depends heavily on the co-occurrence with other topics. For
example, if allocated capital from a Seasoned Equity Offering (SEO) is used for
restructuring a company in the course of a Bankruptcy Proceeding, the market
reacts positively on average. However, if that capital is used for covering
unexpected, additional costs from the development of new drugs, the SEO implies
negative reactions on average.

本研究提出了一种适用于金融文本的多标签主题模型，使用新的金融多标签数据库进行训练，并通过分析股市反应来研究不同主题之间的差异以及协同出现主题对股市反应的影响，模型的最佳表现可达到超过 85% 的宏观 F1 得分。