Publications / The Gate Stays Closed

Under journal review2026

When the Gate Stays Closed: Empirical Evidence of Near-Zero Cross-Sectional Predictability in Large-Cap NASDAQ Equities Using an IC-Gated Machine Learning Framework

Rajveer Singh Pall

A deployment gate for financial ML — and the discipline to report that it stayed closed.

SSRN preprint ↗Code ↗

The discovery in one figure

CAN THE MODEL PROVE PREDICTIVE SKILL BEFORE IT TRADES?Stage 1 · HAC t-test✗ t = −0.09Stage 2 · Permutation✗ ≈ randomGATE CLOSEDDO NOT DEPLOYTHE TWISTECE < 0.025 across all 12 folds — perfectly calibrated, and yetthe gate opened 0 / 12. Calibration is not deployment readiness.
A perfectly calibrated model with zero skill. The correct scientific decision is not to deploy.

The paper in five minutes

Most trading-model papers report wins. This one builds the exam a model must pass before it is allowed to trade real money — and then reports that its own model failed that exam twelve times out of twelve. That is the contribution: a statistical gate that separates 'looks profitable in a backtest' from 'provable skill'. A naive statistical test would have green-lit a skill-less model 11.8% of the time; the full gate never did. One more twist: the model's probability estimates were nearly perfect even though it had zero predictive skill — proof that a well-calibrated model is not the same as a deployable one.

The research question

Can a financial ML model prove cross-sectional predictive skill before deployment — and what should happen when it cannot?

How it works

IC-Gated Deployment Framework: a two-stage statistical gate (Newey–West HAC t-test on daily information coefficients plus permutation confirmation) over 12 expanding walk-forward folds, 1,512 out-of-sample days, with isotonic calibration and a momentum positive control.

  1. 01
    Build honestly49 strictly causal features, 30 NASDAQ stocks, no lookahead
  2. 02
    Walk forward12 expanding-window folds, 1,512 out-of-sample days, 2-day embargo
  3. 03
    Measure skilldaily information coefficient → Newey–West HAC t-test
  4. 04
    Confirm by permutationboth stages must pass — either fails, no deployment
  5. 05
    Report the nullgate closed 0/12; calibration excellent anyway (ECE < 0.025)

Experimental results

Mean IC −0.0005; the gate opens in 0 of 12 folds. Calibration stays excellent (ECE < 0.025) despite zero discrimination — calibration quality is not deployment readiness. The naive t-test alternative false-positives 11.8% of the time; the full gate, 0.0%.

False-positive rate on skill-less models (%)
Naive t-test
11.8%
Full ICGDF gate
0.0%

Simulated null (no real signal): the naive test deploys anyway; the gate never does.

  • 0 / 12folds passing the deployment gate
  • < 0.025ECE while IC ≈ 0 — calibration ≠ readiness
  • 11.8% → 0%false-positive rate, naive test → ICGDF

Figures from the paper

Fold-level information coefficients across all 12 walk-forward folds: the gate never opens.
Fig. 1Fold-level information coefficients across all 12 walk-forward folds: the gate never opens.
The permutation test: the observed IC sits squarely inside the no-skill distribution.
Fig. 2The permutation test: the observed IC sits squarely inside the no-skill distribution.
Power analysis: the study could have detected a much smaller real signal than practitioners claim.
Fig. 3Power analysis: the study could have detected a much smaller real signal than practitioners claim.

Figures generated by the paper's own pipeline — reproducible from the repository.

What this changes

An honest null result engineered as methodology: the same audit-first stance the program applies to healthcare and lending, applied to the temptation-rich domain of trading.

Resources

SSRN preprint ↗Code ↗
  • Financial ML
  • Deployment Shift
  • Trustworthy ML

Citation

Manuscript under review — citation will be posted on acceptance. Reach me at rajveerpall04@gmail.com.

Where this leads next