CR-COPEC: Causal Rationale of Corporate Performance Changes to Learn from Financial Reports - Chun, Ye Eun; Kwon, Sunjae; Sohn, Kyunghwan; Sung, Nakwon; Lee, Junyoup; Seo, Byungki; Compher, Kevin; Hwang, Seung Won; Choi, Jaesik (Findings of the Association for Computational Linguistics: EMNLP 2023, 2023)
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.
Keywords: Annual reports; Causal analysis; Corporate performance; Corporates; Domain adaptation; Financial performance; Financial reports; Large-scales; Learn+; Scale domains