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。

金融文本数据的多标签主题模型

Multi-Label Topic Model for Financial Textual Data

Neural machine translation (NMT) has set new quality standards in automatic
translation, yet its effect on post-editing productivity is still pending
thorough investigation. We empirically test how the inclusion of NMT, in
addition to domain-specific translation memories and termbases, impacts speed
and quality in professional translation of financial texts. We find that even
with language pairs that have received little attention in research settings
and small amounts of in-domain data for system adaptation, NMT post-editing
allows for substantial time savings and leads to equal or slightly better
quality.

通过实证研究发现，即使是对于研究相对较少的语言对和系统适应所需的小量领域数据，采用神经机器翻译（NMT）后编辑可显著节省时间，并且能够实现等同或略有更好的翻译质量。