@inproceedings{shah-etal-2024-numerical,
    title = "Numerical Claim Detection in Finance: A New Financial Dataset, Weak-Supervision Model, and Market Analysis",
    author = "Shah, Agam  and
      Hiray, Arnav  and
      Shah, Pratvi  and
      Banerjee, Arkaprabha  and
      Singh, Anushka  and
      Eidnani, Dheeraj Deepak  and
      Chava, Sahasra  and
      Chaudhury, Bhaskar  and
      Chava, Sudheer",
    editor = "Schlichtkrull, Michael  and
      Chen, Yulong  and
      Whitehouse, Chenxi  and
      Deng, Zhenyun  and
      Akhtar, Mubashara  and
      Aly, Rami  and
      Guo, Zhijiang  and
      Christodoulopoulos, Christos  and
      Cocarascu, Oana  and
      Mittal, Arpit  and
      Thorne, James  and
      Vlachos, Andreas",
    booktitle = "Proceedings of the Seventh Fact Extraction and VERification Workshop (FEVER)",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2024.fever-1.21/",
    doi = "10.18653/v1/2024.fever-1.21",
    pages = "170--185",
    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."
}