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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
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Publication Year
2023-01-01
Journal
Findings of the Association for Computational Linguistics: EMNLP 2023
Publisher
Association for Computational Linguistics (ACL)
Citation
Findings of the Association for Computational Linguistics: EMNLP 2023, pp.339-355
Mesh Keyword
Annual reportsCausal analysisCorporate performanceCorporatesDomain adaptationFinancial performanceFinancial reportsLarge-scalesLearn+Scale domains
All Science Classification Codes (ASJC)
Computational Theory and MathematicsComputer Science ApplicationsInformation SystemsLanguage and LinguisticsLinguistics and Language
Abstract
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.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/37010
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85183293380&origin=inward
DOI
https://doi.org/2-s2.0-85183293380
Type
Conference
Funding
Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No.2022-0-00984, Development of Artificial Intelligence Technology for Personalized Plug-and-Play Explanation and Verification of Explanation, No.2019-0-00075, Artificial Intelligence Graduate School Program (KAIST), NO.2022-0-00184, Development and Study of AI Technologies to Inexpensively Conform to Evolving Policy on Ethics).
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Lee, Junyoup 이준엽
Department of Business Administration
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