The 19 Most Important Features for Equity Return Forecasting
Most ML performance in finance doesn't come from the model — it comes from the features.
This guide covers the essential features that actually predict returns, why some features work only in certain regimes, which features are overused and unreliable, and how these features map onto the selectable feature sets in volarixs experiments.
1. Price-Based Features (Core Predictors)
1. Returns (log, pct, normalized)
- Rolling 1D, 5D, 21D
- z-scored returns
- vol-adjusted returns
These capture momentum and short-term reversal patterns.
2. Moving Averages
Windows: 5, 10, 21, 50, 100, 200.
Especially useful:
- Slope of moving average
- Distance to long-term trend
- Golden/death cross flags
3. Trend Strength (ADX, R² of rolling regression)
Trend strength often predicts short-term follow-through only in calm regimes.
4. Volatility (realised, Parkinson, Garman-Klass)
Key for normalizing other features.
5. Skew and Kurtosis (rolling)
Useful for detecting impending breakouts or regime changes.
2. Volume & Liquidity Features
6. Volume z-score
Detects abnormal participation → strong predictive edge around earnings and news.
7. Volume/Volatility ratio
Liquidity-adjusted momentum.
8. Turnover
Stable cross-sectional predictor.
3. Microstructure Features
9. Bid–ask spread (if intraday available)
Predicts near-term returns via order flow imbalance.
10. Order book imbalance
Directional but regime-dependent.
11. Signed volume
Best intraday momentum predictor.
4. Cross-Asset Features
12. Market beta (rolling)
Beta drift contains information about crowding + factor exposures.
13. Sector/industry momentum
Very strong short-term cross-sectional factor.
14. Index return lead-lag
Useful for single-stock prediction.
5. Volatility & Risk Metrics
15. Realised volatility ratio (short vs long window)
Detects transitions between calm → elevated volatility states.
16. Volatility-of-volatility
Useful around macro events and earnings.
17. Correlation regimes
Cross-asset correlation shifts often precede structural changes.
6. Event-Driven Features
18. Earnings date proximity
Pre-earnings drift is well-documented.
19. Overnight returns
One of the strongest predictors in equities (microstructure-driven).
The 19 Essential Features
Click on each category to explore the features. Features marked as "Regime-dependent" may behave differently across market regimes.
Feature Importance Calculator
Feature importance varies by market regime and forecast horizon. Adjust parameters to see how different conditions affect which features matter most.
How volarixs Uses These Features
In volarixs these features live in selectable feature sets you choose when you build an experiment. Because every experiment is split on time and run walk-forward, the rolling transformations behind features like these are computed on past data only:
- Selectable feature sets, chosen per experiment
- Rolling-window transformations on a walk-forward, point-in-time basis
- Regime context — inflation, policy and liquidity state — carried with every run
- Factor exposures alongside the run: betas, R², alpha and residual volatility
You then pair a feature set with a model. volarixs experiments let you pick from:
- Regression (e.g. Ridge, ElasticNet)
- Tree & Boosted (e.g. Random Forest, gradient boosting)
- Neural Networks (e.g. LSTM)
- Time Series
- Volatility
Conclusion
In financial ML, 80% of performance comes from the right features.
This list covers the features that consistently survive out-of-sample tests and provide real predictive edge across market regimes.