Numerical Claim Detection in Finance: A New Financial Dataset, Weak-Supervision Model, and Market Analysis
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914965013659648 |
|---|---|
| author | Shah, Agam Hiray, Arnav Shah, Pratvi Banerjee, Arkaprabha Singh, Anushka Eidnani, Dheeraj Chava, Sahasra Chaudhury, Bhaskar Chava, Sudheer |
| author_facet | Shah, Agam Hiray, Arnav Shah, Pratvi Banerjee, Arkaprabha Singh, Anushka Eidnani, Dheeraj Chava, Sahasra Chaudhury, Bhaskar Chava, Sudheer |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_11728 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Numerical Claim Detection in Finance: A New Financial Dataset, Weak-Supervision Model, and Market Analysis Shah, Agam Hiray, Arnav Shah, Pratvi Banerjee, Arkaprabha Singh, Anushka Eidnani, Dheeraj Chava, Sahasra Chaudhury, Bhaskar Chava, Sudheer Computation and Language Machine Learning Computational Finance 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. |
| title | Numerical Claim Detection in Finance: A New Financial Dataset, Weak-Supervision Model, and Market Analysis |
| topic | Computation and Language Machine Learning Computational Finance |
| url | https://arxiv.org/abs/2402.11728 |