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

Author

Listed:
  • Agam Shah
  • Arnav Hiray
  • Pratvi Shah
  • Arkaprabha Banerjee
  • Anushka Singh
  • Dheeraj Eidnani
  • Sahasra Chava
  • Bhaskar Chaudhury
  • Sudheer Chava

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.

Suggested Citation

  • Agam Shah & Arnav Hiray & Pratvi Shah & Arkaprabha Banerjee & Anushka Singh & Dheeraj Eidnani & Sahasra Chava & Bhaskar Chaudhury & Sudheer Chava, 2024. "Numerical Claim Detection in Finance: A New Financial Dataset, Weak-Supervision Model, and Market Analysis," Papers 2402.11728, arXiv.org, revised Oct 2024.
    • Handle: RePEc:arx:papers:2402.11728
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    References listed on IDEAS

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