CR-COPEC: Causal Rationale of Corporate Performance Changes to Learn from Financial Reports
This provides a resource for investors and analysts to efficiently extract causal insights from financial documents, though it is incremental as it adapts existing methods to a new domain-specific dataset.
The authors introduced CR-COPEC, a dataset for detecting causal sentences in financial reports to identify corporate performance changes, achieving industry-specific classification by analyzing 10-K reports from U.S. companies across twelve industries.
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.