ML for alpha is easy to overfit and hard to productionize. This list links to primary sources that stress validation, leakage, and deployment constraints. Read the foundations first, then the large-scale empirical papers, then the deep learning work.
Foundations and methodology
Start here before tuning hyperparameters. Finance-specific cross-validation and multiple-testing bias are where most ML alpha projects die.
Advances in Financial Machine Learning
Labeling, sample weights, purged k-fold CV, and backtest overfitting. The standard practitioner reference.
Multiple Testing and the Cross-Section of Expected Returns
Multiple testing in factor research. Adjusted t-stats and why published significance thresholds are too low.
The 7 Reasons Most Machine Learning Funds Fail
Short, blunt checklist on data leakage, false discovery, and production gaps in ML-driven funds. SlideShare mirror
Large-scale return prediction
Canonical empirical work comparing ML methods on equity cross-sections. Useful for understanding what actually works at scale versus in toy backtests.
Empirical Asset Pricing via Machine Learning
Neural nets, trees, and elastic net on 30,000+ stocks. Strong baseline for ML in cross-sectional equity prediction. Journal page
Deep Neural Networks, Gradient-Boosted Trees, Random Forests: Statistical Arbitrage on the S&P 500
Early systematic horse race of ML models on US equities. Useful for understanding model turnover and decay. Journal page
Taming the Factor Zoo: A Test of New Factors
Model selection framework for new signals. Relevant when your ML features overlap with known factors. Journal page
Deep learning and nonlinear signals
Deep Learning in Asset Pricing
Autoencoder + factor model hybrid. Shows where deep learning adds structure rather than black-box fitting. Journal page
101 Formulaic Alphas
Industry-side alpha construction at scale. Good reality check on signal correlation and turnover.
Before you ship
Walk-forward validation, purged k-fold, and explicit transaction cost models are non-optional. If Sharpe only exists in one in-sample window, fix the pipeline before adding features.
Also follow ongoing preprints at arXiv q-fin and journal pages via DOI.