Do earnings call transcripts predict post-announcement returns?
Molinaro (2026) studies event-driven strategies in equities using a transformer. The abstract mentions an out-of-sample test but not a benchmark, transaction costs, multiple testing or the size of the universe.
“We construct a systematic machine learning pipeline that transforms raw transcript text into quantitative signals and evaluates their out-of-sample forecasting performance on the Russell 1000 universe.”
AssetEquities
StrategyEvent-driven strategiesnews sentiment
TechniqueTransformertree ensemble
GeographyUS
Also stated: Quarterly, Long-short, Fundamentals, Text and news, out-of-sample test
Missing from abstract: benchmark, costs, multiple testing, universe
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The paper's neighbourhoodEvent-driven strategies by year
FamilyEvent-driven strategies52Momentum and trend following289Factor investing240Machine learning return prediction15318 more · 950
AssetEquities40
The family is counted across the whole universe. Asset, method and authors are counted inside the family.
| Similar papers | citation | same method | same asset | same authors | |
|---|---|---|---|---|---|
| Firm Characteristics and the Cross-Sectional Variation in Post-Earnings Announcement Drift by DiPasquale | 2026 | ||||
| Listening to the Tape Does Earnings Press-Release Language Predict Post-Announcement Returns? by Nag | 2026 | ||||
| Which Voices Move Markets? Speaker Identity and the Cross-Section of Post-Earnings Returns by Sidhu et al. | 2026 | ||||
| What Do Investors Hear When Firms Talk? Evidence from Analyst Reactions to Corporate Earnings Calls by Fan and Liu | 2025 | ||||
| Capturing dynamics of post-earnings-announcement drift using genetic algorithm-optimised supervised learning by Ye and Schuller | 2020 |