Anomaly Predictability with the Mean-Variance Portfolio
“Using several prominent factor models and a large cross-section of anomalies, we find that past cumulative risk-adjusted returns predict future anomaly returns.”
AssetEquitiessingle stocks
StrategyPortfolio optimization
TechniqueLinear factor model
ClaimAlphaexcess return
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 neighbourhoodPortfolio optimization 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 | |
|---|---|---|---|---|---|
| An Automated Black-Litterman Framework for Global Fixed Income by Carver et al. | 2026 | ||||
| On the Limits of Low-Frequency OHLCV Signals in Machine Learning-Driven Portfolio Optimization A Cost-Aware, Leak-Free Benchmark on Large-Cap US Equities by Pardo | 2026 | ||||
| Earnings Forecasting and Mean-Variance Efficient Portfolios in the United States by Guerard et al. | 2024 | ||||
| The Development and Evolution of Mean-Variance Efficient Portfolios in Japan: 30 Years After by Guerard and Beheshti | 2023 | ||||
| Black-Litterman, Bayesian Shrinkage, and Factor Models in Portfolio Selection: You Can Have It All by Chong | 2023 |