@inproceedings{shah-etal-2024-numerical,
title = "Numerical Claim Detection in Finance: A New Financial Dataset, Weak-Supervision Model, and Market Analysis",
author = "Shah, Agam and
Hiray, Arnav and
Shah, Pratvi and
Banerjee, Arkaprabha and
Singh, Anushka and
Eidnani, Dheeraj Deepak and
Chava, Sahasra and
Chaudhury, Bhaskar and
Chava, Sudheer",
editor = "Schlichtkrull, Michael and
Chen, Yulong and
Whitehouse, Chenxi and
Deng, Zhenyun and
Akhtar, Mubashara and
Aly, Rami and
Guo, Zhijiang and
Christodoulopoulos, Christos and
Cocarascu, Oana and
Mittal, Arpit and
Thorne, James and
Vlachos, Andreas",
booktitle = "Proceedings of the Seventh Fact Extraction and VERification Workshop (FEVER)",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.fever-1.21",
pages = "170--185",
abstract = "In this paper, we investigate the influence of claims in analyst reports and earnings calls on financial market returns, considering them as significant quarterly events for publicly traded companies. To facilitate a comprehensive analysis, we construct a new financial dataset for the claim detection task in the financial domain. We benchmark various language models on this dataset and propose a novel weak-supervision model that incorporates the knowledge of subject matter experts (SMEs) in the aggregation function, outperforming existing approaches. We also demonstrate the practical utility of our proposed model by constructing a novel measure of *optimism*. Here, we observe the dependence of earnings surprise and return on our optimism measure. Our dataset, models, and code are publicly (under CC BY 4.0 license) available on GitHub.",
}
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<abstract>In this paper, we investigate the influence of claims in analyst reports and earnings calls on financial market returns, considering them as significant quarterly events for publicly traded companies. To facilitate a comprehensive analysis, we construct a new financial dataset for the claim detection task in the financial domain. We benchmark various language models on this dataset and propose a novel weak-supervision model that incorporates the knowledge of subject matter experts (SMEs) in the aggregation function, outperforming existing approaches. We also demonstrate the practical utility of our proposed model by constructing a novel measure of *optimism*. Here, we observe the dependence of earnings surprise and return on our optimism measure. Our dataset, models, and code are publicly (under CC BY 4.0 license) available on GitHub.</abstract>
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%0 Conference Proceedings
%T Numerical Claim Detection in Finance: A New Financial Dataset, Weak-Supervision Model, and Market Analysis
%A Shah, Agam
%A Hiray, Arnav
%A Shah, Pratvi
%A Banerjee, Arkaprabha
%A Singh, Anushka
%A Eidnani, Dheeraj Deepak
%A Chava, Sahasra
%A Chaudhury, Bhaskar
%A Chava, Sudheer
%Y Schlichtkrull, Michael
%Y Chen, Yulong
%Y Whitehouse, Chenxi
%Y Deng, Zhenyun
%Y Akhtar, Mubashara
%Y Aly, Rami
%Y Guo, Zhijiang
%Y Christodoulopoulos, Christos
%Y Cocarascu, Oana
%Y Mittal, Arpit
%Y Thorne, James
%Y Vlachos, Andreas
%S Proceedings of the Seventh Fact Extraction and VERification Workshop (FEVER)
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F shah-etal-2024-numerical
%X In this paper, we investigate the influence of claims in analyst reports and earnings calls on financial market returns, considering them as significant quarterly events for publicly traded companies. To facilitate a comprehensive analysis, we construct a new financial dataset for the claim detection task in the financial domain. We benchmark various language models on this dataset and propose a novel weak-supervision model that incorporates the knowledge of subject matter experts (SMEs) in the aggregation function, outperforming existing approaches. We also demonstrate the practical utility of our proposed model by constructing a novel measure of *optimism*. Here, we observe the dependence of earnings surprise and return on our optimism measure. Our dataset, models, and code are publicly (under CC BY 4.0 license) available on GitHub.
%U https://aclanthology.org/2024.fever-1.21
%P 170-185
Markdown (Informal)
[Numerical Claim Detection in Finance: A New Financial Dataset, Weak-Supervision Model, and Market Analysis](https://aclanthology.org/2024.fever-1.21) (Shah et al., FEVER 2024)
ACL
- Agam Shah, Arnav Hiray, Pratvi Shah, Arkaprabha Banerjee, Anushka Singh, Dheeraj Deepak Eidnani, Sahasra Chava, Bhaskar Chaudhury, and Sudheer Chava. 2024. Numerical Claim Detection in Finance: A New Financial Dataset, Weak-Supervision Model, and Market Analysis. In Proceedings of the Seventh Fact Extraction and VERification Workshop (FEVER), pages 170–185, Miami, Florida, USA. Association for Computational Linguistics.