Summary of Numerical Claim Detection in Finance: a New Financial Dataset, Weak-supervision Model, and Market Analysis, by Agam Shah et al.
Numerical Claim Detection in Finance: A New Financial Dataset, Weak-Supervision Model, and Market Analysis
by Agam Shah, Arnav Hiray, Pratvi Shah, Arkaprabha Banerjee, Anushka Singh, Dheeraj Eidnani, Sahasra Chava, Bhaskar Chaudhury, Sudheer Chava
First submitted to arxiv on: 18 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG); Computational Finance (q-fin.CP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates how claims in analyst reports and earnings calls affect financial market returns for publicly traded companies. A new financial dataset is constructed to analyze the impact of these claims. Various language models are benchmarked, and a novel weak-supervision model is proposed that incorporates subject matter expert knowledge. The model outperforms existing approaches and demonstrates practical utility by measuring optimism. The study finds dependence between earnings surprise and return on this measure. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how reports from financial analysts affect the stock market. It makes a new dataset to help analyze these reports. The paper compares different computer models that can understand language, and it proposes a new model that works well because it uses expert knowledge. This helps us understand why companies’ stocks go up or down. |
