Reproduce “Corporate Earnings Calls and Analyst Beliefs” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Giuseppe Matera
Giuseppe Matera's research quantifies how narratives in corporate earnings calls influence financial analysts' beliefs and predict future firm fundamentals, employing large language models and a novel text-morphing methodology to uncover systematic biases in analyst information processing. The study demonstrates that linguistic features provide significant incremental predictive power for analyst forecasts and future earnings, while revealing specific over- and under-reactions by analysts to narrative dimensions like sentiment and uncertainty.
Key insights
- Narrative features embedded in earnings calls hold substantial standalone predictive power for analyst expectations and future firm fundamentals, and offer significant incremental information beyond traditional quantitative data.
- Analysts exhibit systematic biases in their processing of narrative information, overreacting to optimistic sentiment and global framing, while consistently underreacting to cues related to risk, uncertainty, confidence, and specific future guidance.
- The influence of managerial narratives extends beyond altering the level of analysts' forecasts to also significantly impacting forecast disagreement, with uncertainty-related language being a primary driver of increased dispersion.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.