Application of Algorithmic Trading Strategies for Retail Investors
“The empirical analysis performed in this thesis suggests smart beta algorithmic trading or AI based strategies outperform on risk adjusted basis.”
AssetEquitiesETFs
StrategyFactor investingvalue investing
TechniqueTree ensemblelinear factor model
ClaimExcess 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 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 | |
|---|---|---|---|---|---|
| Timing 'Smart Beta' Strategies? Of Course! Buy Low, Sell High! by Arnott et al. | 2017 | ||||
| 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 |