MGPraxis6,400 papers identified by name

Do earnings call transcripts predict post-announcement returns?

Mat eacute o Molinaro·2026

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

Institutional-grade audit of this strategy.
£5 — Request the audit
  • Audit of the paper claims accounting for costs, luck, crises, originality, geography, etc.
  • Reproduction code integratable into Bloomberg, IBKR, etc.
  • Bias-free datasets for backtesting included
  • Delivered in 2 working days
The paper's neighbourhoodEvent-driven strategies by year
■ equities■ volatility and options■ real estate■ other assets20003200520103320154320208This paper2026■ equities■ volatility and options■ real estate■ other assets20003200520103320154320208This paper2026
The family is counted across the whole universe. Asset, method and authors are counted inside the family.
Similar paperscitationsame methodsame assetsame authors
Firm Characteristics and the Cross-Sectional Variation in Post-Earnings Announcement Drift by DiPasquale2026
Listening to the Tape Does Earnings Press-Release Language Predict Post-Announcement Returns? by Nag2026
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 Liu2025
Capturing dynamics of post-earnings-announcement drift using genetic algorithm-optimised supervised learning by Ye and Schuller2020
Not yet audited by MarketGraphs Praxis. Tags from the abstract. Counts from the universe.
RSS · Contacts