@inproceedings{ahbali-etal-2022-identifying,
title = "{I}dentifying {C}orporate {C}redit {R}isk {S}entiments from {F}inancial {N}ews",
author = "Ahbali, Noujoud and
Liu, Xinyuan and
Nanda, Albert and
Stark, Jamie and
Talukder, Ashit and
Khandpur, Rupinder Paul",
editor = "Loukina, Anastassia and
Gangadharaiah, Rashmi and
Min, Bonan",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track",
month = jul,
year = "2022",
address = "Hybrid: Seattle, Washington + Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-industry.40",
doi = "10.18653/v1/2022.naacl-industry.40",
pages = "362--370",
abstract = "Credit risk management is one central practice for financial institutions, and such practice helps them measure and understand the inherent risk within their portfolios. Historically, firms relied on the assessment of default probabilities and used the press as one tool to gather insights on the latest credit event developments of an entity. However, due to the deluge of the current news coverage for companies, analyzing news manually by financial experts is considered a highly laborious task. To this end, we propose a novel deep learning-powered approach to automate news analysis and credit adverse events detection to score the credit sentiment associated with a company. This paper showcases a complete system that leverages news extraction and data enrichment with targeted sentiment entity recognition to detect companies and text classification to identify credit events. We developed a custom scoring mechanism to provide the company{'}s credit sentiment score ($CSS^{TM}$) based on these detected events. Additionally, using case studies, we illustrate how this score helps understand the company{'}s credit profile and discriminates between defaulters and non-defaulters.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="ahbali-etal-2022-identifying">
<titleInfo>
<title>Identifying Corporate Credit Risk Sentiments from Financial News</title>
</titleInfo>
<name type="personal">
<namePart type="given">Noujoud</namePart>
<namePart type="family">Ahbali</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xinyuan</namePart>
<namePart type="family">Liu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Albert</namePart>
<namePart type="family">Nanda</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jamie</namePart>
<namePart type="family">Stark</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ashit</namePart>
<namePart type="family">Talukder</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Rupinder</namePart>
<namePart type="given">Paul</namePart>
<namePart type="family">Khandpur</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2022-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track</title>
</titleInfo>
<name type="personal">
<namePart type="given">Anastassia</namePart>
<namePart type="family">Loukina</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Rashmi</namePart>
<namePart type="family">Gangadharaiah</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Bonan</namePart>
<namePart type="family">Min</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Hybrid: Seattle, Washington + Online</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Credit risk management is one central practice for financial institutions, and such practice helps them measure and understand the inherent risk within their portfolios. Historically, firms relied on the assessment of default probabilities and used the press as one tool to gather insights on the latest credit event developments of an entity. However, due to the deluge of the current news coverage for companies, analyzing news manually by financial experts is considered a highly laborious task. To this end, we propose a novel deep learning-powered approach to automate news analysis and credit adverse events detection to score the credit sentiment associated with a company. This paper showcases a complete system that leverages news extraction and data enrichment with targeted sentiment entity recognition to detect companies and text classification to identify credit events. We developed a custom scoring mechanism to provide the company’s credit sentiment score (CSS™) based on these detected events. Additionally, using case studies, we illustrate how this score helps understand the company’s credit profile and discriminates between defaulters and non-defaulters.</abstract>
<identifier type="citekey">ahbali-etal-2022-identifying</identifier>
<identifier type="doi">10.18653/v1/2022.naacl-industry.40</identifier>
<location>
<url>https://aclanthology.org/2022.naacl-industry.40</url>
</location>
<part>
<date>2022-07</date>
<extent unit="page">
<start>362</start>
<end>370</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Identifying Corporate Credit Risk Sentiments from Financial News
%A Ahbali, Noujoud
%A Liu, Xinyuan
%A Nanda, Albert
%A Stark, Jamie
%A Talukder, Ashit
%A Khandpur, Rupinder Paul
%Y Loukina, Anastassia
%Y Gangadharaiah, Rashmi
%Y Min, Bonan
%S Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track
%D 2022
%8 July
%I Association for Computational Linguistics
%C Hybrid: Seattle, Washington + Online
%F ahbali-etal-2022-identifying
%X Credit risk management is one central practice for financial institutions, and such practice helps them measure and understand the inherent risk within their portfolios. Historically, firms relied on the assessment of default probabilities and used the press as one tool to gather insights on the latest credit event developments of an entity. However, due to the deluge of the current news coverage for companies, analyzing news manually by financial experts is considered a highly laborious task. To this end, we propose a novel deep learning-powered approach to automate news analysis and credit adverse events detection to score the credit sentiment associated with a company. This paper showcases a complete system that leverages news extraction and data enrichment with targeted sentiment entity recognition to detect companies and text classification to identify credit events. We developed a custom scoring mechanism to provide the company’s credit sentiment score (CSS™) based on these detected events. Additionally, using case studies, we illustrate how this score helps understand the company’s credit profile and discriminates between defaulters and non-defaulters.
%R 10.18653/v1/2022.naacl-industry.40
%U https://aclanthology.org/2022.naacl-industry.40
%U https://doi.org/10.18653/v1/2022.naacl-industry.40
%P 362-370
Markdown (Informal)
[Identifying Corporate Credit Risk Sentiments from Financial News](https://aclanthology.org/2022.naacl-industry.40) (Ahbali et al., NAACL 2022)
ACL
- Noujoud Ahbali, Xinyuan Liu, Albert Nanda, Jamie Stark, Ashit Talukder, and Rupinder Paul Khandpur. 2022. Identifying Corporate Credit Risk Sentiments from Financial News. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track, pages 362–370, Hybrid: Seattle, Washington + Online. Association for Computational Linguistics.