Earnings Calls

Numerical Claim Detection in Finance: A New Financial Dataset, Weak-Supervision Model, and Market Analysis ArXiv ID: 2402.11728 “View on arXiv” Authors: Unknown 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. ...

Corporate Climate Risk: Measurements and Responses ArXiv ID: ssrn-3508497 “View on arXiv” Authors: Unknown Abstract This paper conducts a textual analysis of earnings call transcripts to quantify climate risk exposure at the firm level. We construct dictionaries that measure Keywords: Climate Risk, Textual Analysis, Earnings Calls, Environmental Exposure, Corporate Equities Complexity vs Empirical Score Math Complexity: 4.0/10 Empirical Rigor: 6.0/10 Quadrant: Street Traders Why: The research focuses on textual analysis and dictionary construction with relatively basic statistical measures, placing it in low-to-moderate math complexity. However, the use of earnings call transcripts, firm-level quantification, and likely implementation of text mining tools suggests a data-heavy, backtest-ready approach suited for practical trading or risk management. flowchart TD A["Research Goal Quantify firm-level climate risk"] --> B["Data Source Earnings Call Transcripts"] B --> C["Methodology Textual Analysis & Dictionary Construction"] C --> D["Computational Process Measure Risk Exposure Scores"] D --> E{"Key Outcomes"} E --> F["Climate Risk Quantified at Firm Level"] E --> G["Discriminates between Physical & Transition Risks"]