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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
  • 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. 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.

Suggested Citation

  • Agam Shah & Arnav Hiray & Pratvi Shah & Arkaprabha Banerjee & Anushka Singh & Dheeraj Eidnani & 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.
    • Handle: RePEc:arx:papers:2402.11728
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    References listed on IDEAS

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    1. Narasimhan Jegadeesh & Woojin Kim, 2010. "Do Analysts Herd? An Analysis of Recommendations and Market Reactions," The Review of Financial Studies, Society for Financial Studies, vol. 23(2), pages 901-937, February.
    2. Kai Li & Feng Mai & Rui Shen & Xinyan Yan, 2021. "Measuring Corporate Culture Using Machine Learning," NBER Chapters, in: Big Data: Long-Term Implications for Financial Markets and Firms, pages 3265-3315, National Bureau of Economic Research, Inc.
    3. Allen Hu & Song Ma, 2021. "Persuading Investors: A Video-Based Study," NBER Working Papers 29048, National Bureau of Economic Research, Inc.
    4. Francis, J & Philbrick, D, 1993. "Analysts Decisions As Products Of A Multitask Environment," Journal of Accounting Research, Wiley Blackwell, vol. 31(2), pages 216-230.
    5. Michaely, Roni & Womack, Kent L, 1999. "Conflict of Interest and the Credibility of Underwriter Analyst Recommendations," The Review of Financial Studies, Society for Financial Studies, vol. 12(4), pages 653-686.
    6. Brown, Anna Bergman & Lin, Guoyu & Zhou, Aner, 2022. "Analysts’ forecast optimism: The effects of managers’ incentives on analysts’ forecasts," Journal of Behavioral and Experimental Finance, Elsevier, vol. 35(C).
    7. Kai Li & Feng Mai & Rui Shen & Xinyan Yan, 2021. "Measuring Corporate Culture Using Machine Learning [Machine learning methods that economists should know about]," The Review of Financial Studies, Society for Financial Studies, vol. 34(7), pages 3265-3315.
    8. Barber, Brad M. & Lehavy, Reuven & Trueman, Brett, 2007. "Comparing the stock recommendation performance of investment banks and independent research firms," Journal of Financial Economics, Elsevier, vol. 85(2), pages 490-517, August.
    9. Sean Cao & Wei Jiang & Baozhong Yang & Alan L. Zhang, 2020. "How to Talk When a Machine is Listening?: Corporate Disclosure in the Age of AI," NBER Working Papers 27950, National Bureau of Economic Research, Inc.
    10. Pekka Malo & Ankur Sinha & Pekka Korhonen & Jyrki Wallenius & Pyry Takala, 2014. "Good debt or bad debt: Detecting semantic orientations in economic texts," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(4), pages 782-796, April.
    11. Corwin, Shane A. & Larocque, Stephannie A. & Stegemoller, Mike A., 2017. "Investment banking relationships and analyst affiliation bias: The impact of the global settlement on sanctioned and non-sanctioned banks," Journal of Financial Economics, Elsevier, vol. 124(3), pages 614-631.
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