CR-COPEC: Causal Rationale of Corporate Performance Changes to Learn
  from Financial Reports
Ye Eun Chun Sunjae Kwon Kyunghwan Sohn Nakwon Sung Junyoup Lee Byungki Seo Kevin Compher Seung-won Hwang Jaesik Choi
In this paper, we introduce CR-COPEC called Causal Rationale of Corporate
Performance Changes from financial reports. This is a comprehensive large-scale
domain-adaptation causal sentence dataset to detect financial performance
changes of corporate. CR-COPEC contributes to two major achievements. First, it
detects causal rationale from 10-K annual reports of the U.S. companies, which
contain experts' causal analysis following accounting standards in a formal
manner. This dataset can be widely used by both individual investors and
analysts as material information resources for investing and decision making
without tremendous effort to read through all the documents. Second, it
carefully considers different characteristics which affect the financial
performance of companies in twelve industries. As a result, CR-COPEC can
distinguish causal sentences in various industries by taking unique narratives
in each industry into consideration. We also provide an extensive analysis of
how well CR-COPEC dataset is constructed and suited for classifying target
sentences as causal ones with respect to industry characteristics. Our dataset
and experimental codes are publicly available.