@inproceedings{chun-etal-2023-cr,
title = "{CR}-{COPEC}: Causal Rationale of Corporate Performance Changes to learn from Financial Reports",
author = "Chun, Ye and
Kwon, Sunjae and
Sohn, Kyunghwan and
Sung, Nakwon and
Lee, Junyoup and
Seo, Byoung and
Compher, Kevin and
Hwang, Seung-won and
Choi, Jaesik",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.26",
doi = "10.18653/v1/2023.findings-emnlp.26",
pages = "339--355",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T CR-COPEC: Causal Rationale of Corporate Performance Changes to learn from Financial Reports
%A Chun, Ye
%A Kwon, Sunjae
%A Sohn, Kyunghwan
%A Sung, Nakwon
%A Lee, Junyoup
%A Seo, Byoung
%A Compher, Kevin
%A Hwang, Seung-won
%A Choi, Jaesik
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F chun-etal-2023-cr
%X 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.
%R 10.18653/v1/2023.findings-emnlp.26
%U https://aclanthology.org/2023.findings-emnlp.26
%U https://doi.org/10.18653/v1/2023.findings-emnlp.26
%P 339-355
Markdown (Informal)
[CR-COPEC: Causal Rationale of Corporate Performance Changes to learn from Financial Reports](https://aclanthology.org/2023.findings-emnlp.26) (Chun et al., Findings 2023)
ACL
- Ye Chun, Sunjae Kwon, Kyunghwan Sohn, Nakwon Sung, Junyoup Lee, Byoung Seo, Kevin Compher, Seung-won Hwang, and Jaesik Choi. 2023. CR-COPEC: Causal Rationale of Corporate Performance Changes to learn from Financial Reports. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 339–355, Singapore. Association for Computational Linguistics.