Causal Momentum: Information Channel Alignment in Network-Based Cross-Sectional Stock Prediction
A 2026 paper by Shah on machine learning return prediction in equities, using a feedforward neural network on a 2016–2026 sample: the abstract mentions an information ratio and the size of the universe and leaves out a benchmark, an out-of-sample test, transaction costs and multiple testing.
“Applied to an S&P 500 universe of 530 stocks over 2016-2026, CM features achieve a test information coefficient (IC) of 0.203 and an IC information ratio (IR) of 1.42, representing a 14.5% relative improvement over a strong 34-feature fundamental-plus-volatility baseline (IC = 0.177).”
AssetEquitiessingle stocks
StrategyMachine learningmomentum
TechniqueFeedforward networkgraph neural network
Sample window2016–2026
Also stated: Information ratio, Long-short, Fundamentals, universe of 530 stocks
Missing from abstract: benchmark, out-of-sample test, costs, multiple testing
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 |