Numerical Claim Detection in Finance: A New Financial Dataset,
  Weak-Supervision Model, and Market Analysis
Agam Shah Arnav Hiray Pratvi Shah Arkaprabha Banerjee Anushka Singh Dheeraj Eidnani Bhaskar Chaudhury Sudheer Chava
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.