Application of Random Forest in Multi-Factor Stock Selection
“In this report, we use the constituents of the CSI300 Index as the stock universe, select 43 commonly used factors to construct the multi-factor stock selection model, and train the model using the Random Forest algorithm.”
AssetEquitiesindices
StrategyFactor investing
TechniqueTree ensemblelinear factor model
GeographyChina
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 neighbourhoodFactor investing by year
FamilyFactor investing240Momentum and trend following289Machine learning return prediction153News sentiment and text signals14818 more · 854
AssetEquities196
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 | |
|---|---|---|---|---|---|
| A Common Rough Latent State in Conditional Factor Pricing: Rethinking the Fama-French Five-Factor Model by Kamdem | 2026 | ||||
| Welcome to the Factor Zoo: Where Mutual Fund Alpha Hides by Malakhov | 2026 | ||||
| The Efficient Cross-Section is Sparse: Factor Timing and the Dimension of the SDF by Mehrabinia | 2026 | ||||
| From Causal Factor Investing to Causal Factor Discovery: Evolving a Neuro-symbolic World Model of the Market by Yuksel | 2026 | ||||
| Taming the Chinese ETF Option Factor Zoo: A Sparse Five-Factor Approach by Ding et al. | 2026 |