Numerical Claim Detection in Finance: A New Financial Dataset, Weak-Supervision Model, and Market Analysis

• URL: http://arxiv.org/abs/2402.11728v1
• Date: Sun, 18 Feb 2024 22:55:26 GMT
• Title: Numerical Claim Detection in Finance: A New Financial Dataset, Weak-Supervision Model, and Market Analysis
• Authors: Agam Shah, Arnav Hiray, Pratvi Shah, Arkaprabha Banerjee, Anushka Singh, Dheeraj Eidnani, Bhaskar Chaudhury, Sudheer Chava
• Abstract summary: We construct a new financial dataset for the claim detection task in the financial domain. We propose a novel weak-supervision model that incorporates the knowledge of subject matter experts (SMEs) in the aggregation function. We demonstrate the practical utility of our proposed model by constructing a novel measure optimism"
• Score: 4.9524454709622585
• License: http://creativecommons.org/licenses/by/4.0/
• 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. Furthermore, we demonstrate the practical utility of our proposed model by constructing a novel measure ``optimism". Furthermore, we observed the dependence of earnings surprise and return on our optimism measure. Our dataset, models, and code will be made publicly (under CC BY 4.0 license) available on GitHub and Hugging Face.

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