@inproceedings{wang-etal-2025-surf,
title = "{SURF}: A System to Unveil Explainable Risk Relations between Firms",
author = "Wang, Yu-Hsiang and
Chiu, Wei-Ning and
Hsiao, Yi-Tai and
Huang, Yu-Shiang and
Chiang, Yi-Shyuan and
Wu, Shuo-En and
Wang, Chuan-Ju",
editor = "Dziri, Nouha and
Ren, Sean (Xiang) and
Diao, Shizhe",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (System Demonstrations)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-demo.22/",
doi = "10.18653/v1/2025.naacl-demo.22",
pages = "260--267",
ISBN = "979-8-89176-191-9",
abstract = "Firm risk relations are crucial in financial applications, including hedging and portfolio construction. However, the complexity of extracting relevant information from financial reports poses significant challenges in quantifying these relations. To this end, we introduce SURF, a System to Unveil Explainable Risk Relations between Firms. SURF employs a domain-specific encoder and an innovative scoring mechanism to uncover latent risk connections from financial reports. It constructs a network graph to visualize these firm-level risk interactions and incorporates a rationale explainer to elucidate the underlying links. Our evaluation using stock data shows that SURF outperforms baseline methods in effectively capturing firm risk relations. The demo video of the system is publicly available."
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<abstract>Firm risk relations are crucial in financial applications, including hedging and portfolio construction. However, the complexity of extracting relevant information from financial reports poses significant challenges in quantifying these relations. To this end, we introduce SURF, a System to Unveil Explainable Risk Relations between Firms. SURF employs a domain-specific encoder and an innovative scoring mechanism to uncover latent risk connections from financial reports. It constructs a network graph to visualize these firm-level risk interactions and incorporates a rationale explainer to elucidate the underlying links. Our evaluation using stock data shows that SURF outperforms baseline methods in effectively capturing firm risk relations. The demo video of the system is publicly available.</abstract>
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%0 Conference Proceedings
%T SURF: A System to Unveil Explainable Risk Relations between Firms
%A Wang, Yu-Hsiang
%A Chiu, Wei-Ning
%A Hsiao, Yi-Tai
%A Huang, Yu-Shiang
%A Chiang, Yi-Shyuan
%A Wu, Shuo-En
%A Wang, Chuan-Ju
%Y Dziri, Nouha
%Y Ren, Sean (Xiang)
%Y Diao, Shizhe
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (System Demonstrations)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-191-9
%F wang-etal-2025-surf
%X Firm risk relations are crucial in financial applications, including hedging and portfolio construction. However, the complexity of extracting relevant information from financial reports poses significant challenges in quantifying these relations. To this end, we introduce SURF, a System to Unveil Explainable Risk Relations between Firms. SURF employs a domain-specific encoder and an innovative scoring mechanism to uncover latent risk connections from financial reports. It constructs a network graph to visualize these firm-level risk interactions and incorporates a rationale explainer to elucidate the underlying links. Our evaluation using stock data shows that SURF outperforms baseline methods in effectively capturing firm risk relations. The demo video of the system is publicly available.
%R 10.18653/v1/2025.naacl-demo.22
%U https://aclanthology.org/2025.naacl-demo.22/
%U https://doi.org/10.18653/v1/2025.naacl-demo.22
%P 260-267
Markdown (Informal)
[SURF: A System to Unveil Explainable Risk Relations between Firms](https://aclanthology.org/2025.naacl-demo.22/) (Wang et al., NAACL 2025)
ACL
- Yu-Hsiang Wang, Wei-Ning Chiu, Yi-Tai Hsiao, Yu-Shiang Huang, Yi-Shyuan Chiang, Shuo-En Wu, and Chuan-Ju Wang. 2025. SURF: A System to Unveil Explainable Risk Relations between Firms. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (System Demonstrations), pages 260–267, Albuquerque, New Mexico. Association for Computational Linguistics.