ResNLS: An Improved Model for Stock Price Forecasting
Jia, Anaissi and Suleiman (2026) study machine learning return prediction in equities using a recurrent neural network. The abstract mentions profits but not a benchmark, an out-of-sample test, transaction costs, multiple testing or the size of the universe.
“Furthermore, ResNLS-5 demonstrates at least a 20% improvement over current state-of-the-art baselines.”
AssetEquities
StrategyMachine learning
TechniqueRecurrent neural network
ClaimProfit
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 | |
|---|---|---|---|---|---|
| Spatiotemporal Signals in Residuals: A Two-Stage Boosting Graph Neural Network for Stock Return Prediction by Liu et al. | 2026 | ||||
| Machine Learning for Cross-Sectional Return Prediction: A Comparative Study of US and Indian Equities by Khatu | 2026 | ||||
| Empirical Asset Pricing via Machine Learning: Evidence from Indian Equity Markets by Sharma | 2026 | ||||
| Stock Return Prediction Guided by a Revised Random Walk Hypothesis: Adaptive Information Smoothing and Signed Correlation-Aware TimeMixing by Seo and Yang | 2026 | ||||
| Visualizing Income Statements by Garfinkel et al. | 2026 |