Choices Matter When Training Machine Learning Models For Return Prediction
A 2023 paper by Howard on machine learning return prediction in equities: the abstract does not mention a benchmark, an out-of-sample test, transaction costs, multiple testing or the size of the universe.
“Common approaches that fail to account for heterogeneity or imbalanced stock representation in training data can lead to suboptimal performance.”
AssetEquitiessingle stocks
StrategyMachine learning
GeographyUS
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 | |
|---|---|---|---|---|---|
| The Anatomy of Machine Learning-Based Portfolio Performance by Coulombe et al. | 2023 | ||||
| Contextual Fundamentals, Models, and Active Management by Sorensen et al. | 2005 | ||||
| Spatiotemporal Signals in Residuals: A Two-Stage Boosting Graph Neural Network for Stock Return Prediction by Liu et al. | 2026 | ||||
| Causal Network Representations in Factor Investing by Howard et al. | 2024 | ||||
| Machine Learning for Cross-Sectional Return Prediction: A Comparative Study of US and Indian Equities by Khatu | 2026 |