Numerical Claim Detection in Finance: A New Financial Dataset, Weak-Supervision Model, and Market Analysis
This work addresses the need for better claim detection in finance to analyze market influences, though it is incremental as it builds on existing weak-supervision methods with domain-specific enhancements.
The paper tackles the problem of detecting claims in financial texts to analyze their impact on market returns, constructing a new dataset and a weak-supervision model that outperforms existing approaches, with the model achieving improved performance metrics.
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.