A Tweet-based Dataset for Company-Level Stock Return Prediction
Machine learning return prediction in equities, studied by Sowinska and Madhyastha in 2020. The abstract does not mention a benchmark, an out-of-sample test, transaction costs, multiple testing or the size of the universe.
“Our dataset consists of 862, 231 labelled instances from twitter in English, we also release a cleaned subset of 85, 176 labelled instances to the community.”
AssetEquities
StrategyMachine learningnews sentiment
InputsText and news
Missing from abstract: benchmark, out-of-sample test, 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 neighbourhoodMachine learning return prediction by year
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 | |
|---|---|---|---|---|---|
| Spatiotemporal Signals in Residuals: A Two-Stage Boosting Graph Neural Network for Stock Return Prediction by Liu et al. | 2026 | ||||
| Machine Learning for Cross-Sectional Return Prediction: A Comparative Study of US and Indian Equities by Khatu | 2026 | ||||
| Empirical Asset Pricing via Machine Learning: Evidence from Indian Equity Markets by Sharma | 2026 | ||||
| DistillSem: LLM-Distilled Financial Headline Semantics for Cross-Sectional Return Prediction by Guo | 2026 | ||||
| Stock Return Prediction Guided by a Revised Random Walk Hypothesis: Adaptive Information Smoothing and Signed Correlation-Aware TimeMixing by Seo and Yang | 2026 |