After correcting the historical price data, do false positives still materially damage the Ridge and XGBoost top-40 portfolios?
A false positive is defined here as a stock selected into the sector-capped top 40 whose realized five-session return is zero or negative. A severe false positive loses at least 10%.
The analysis separates:
- the published evaluation with contaminated labels;
- the original frozen scores with returns recomputed from corrected prices;
- fully refitted models using corrected prices, sectors, labels, and predictors.
| Model and scenario | Positive precision | False-positive rate | Severe false-positive rate | Mean selected return | Worst selected return |
|---|---|---|---|---|---|
| Ridge, published contaminated | 53.94% | 46.06% | 3.45% | 0.6375% | -88.65% |
| Ridge, original scores with corrected returns | 54.00% | 46.00% | 3.17% | 0.4897% | -33.58% |
| Ridge, corrected refit | 53.65% | 46.35% | 3.53% | 0.5395% | -46.83% |
| XGBoost, published contaminated | 52.79% | 47.21% | 3.82% | 0.8335% | -89.96% |
| XGBoost, original scores with corrected returns | 52.77% | 47.23% | 3.53% | 0.3828% | -50.62% |
| XGBoost, corrected refit | 51.59% | 48.41% | 3.93% | 0.2216% | -46.83% |
The data error greatly exaggerated return magnitude, especially for XGBoost, but it did not create the false-positive rate. The rate is almost unchanged after corrected relabeling and remains high after complete refitting.
The table below classifies each holding window using the realized five-session SPY return and the realized return of the eligible universe. This is an attribution diagnostic, not information available when the portfolio is selected.
| Model | Realized market state | Positive precision | Severe false-positive rate | Mean selected return | Excess over universe |
|---|---|---|---|---|---|
| Ridge | Broad market negative | 29.12% | 7.96% | -2.58% | -0.08% |
| Ridge | Mixed | 50.37% | 1.69% | -0.11% | 0.00% |
| Ridge | Broad market positive | 74.22% | 0.81% | 3.25% | 0.72% |
| XGBoost | Broad market negative | 26.85% | 8.69% | -2.86% | -0.36% |
| XGBoost | Mixed | 47.72% | 2.21% | -0.24% | -0.13% |
| XGBoost | Broad market positive | 72.56% | 0.90% | 2.82% | 0.30% |
False positives are strongly conditional on what the market subsequently does. This supports the economic intuition behind regime-aware risk control, but future market direction cannot itself be used as the regime input. The ranker also fails to preserve relative outperformance in broad negative markets, especially for XGBoost, so this is not merely an absolute-return labeling issue.
A causal regime uses only the decision-date SPY close and its trailing 200-session average. It enters weak below 98% of the average and returns to strong above 102%.
| Model | Decision-time regime | Positive precision | Severe false-positive rate | Mean selected return |
|---|---|---|---|---|
| Ridge | Strong | 53.53% | 1.98% | 0.53% |
| Ridge | Weak | 53.93% | 7.01% | 0.57% |
| XGBoost | Strong | 51.65% | 2.92% | 0.29% |
| XGBoost | Weak | 51.47% | 6.21% | 0.08% |
The causal regime does not improve ordinary precision. It does, however, identify a much higher severe-loss rate. Moving entirely to cash is not supported because Ridge still earns a positive average return in weak regimes. A narrower tail-risk guardrail is more consistent with the data.
No stable safety gradient appears inside the selected 40.
| Model and selected rank | Positive precision | Severe false-positive rate |
|---|---|---|
| Ridge, 1 to 10 | 55.00% | 4.45% |
| Ridge, 31 to 40 | 54.40% | 2.36% |
| XGBoost, 1 to 10 | 51.48% | 5.16% |
| XGBoost, 31 to 40 | 50.71% | 3.41% |
The highest model scores are not the safest holdings. A rank-margin or score-confidence rule cannot be assumed to remove severe losses.
| Model | Losing weeks | Losses reversed by removing worst stock | Median worst-stock share of weekly loss | 90th percentile share |
|---|---|---|---|---|
| Ridge | 84 | 7 | 18.33% | 80.64% |
| XGBoost | 81 | 6 | 14.91% | 67.98% |
A single stock is decisive in only about 7% to 8% of losing weeks. However, it can dominate the worst loss episodes. Severe false positives account for approximately 29.6% of Ridge's total negative holding-level drag and 29.4% of XGBoost's.
The corrected worst observations are genuine events, including the March 2020 collapse, EPAM in February 2022, Netflix in April 2022, and other event-driven losses. They are no longer split-scale artifacts.
The weakest period is 2022 H1:
| Model | Positive precision | Severe false-positive rate | Mean selected return |
|---|---|---|---|
| Ridge | 44.42% | 5.87% | -0.43% |
| XGBoost | 42.12% | 5.38% | -0.89% |
This supports requiring fold-level and year-level tail performance. Pooled precision alone hides important regime failures.
False positives remain a real model and market-regime problem after the data correction.
- The prior false-positive concern is valid.
- The corrupted data exaggerated losses but did not cause the high error rate.
- XGBoost is worse than Ridge after correction.
- Ordinary precision gating is not supported by the current probability model.
- A causal weak-market signal is useful for identifying tail risk, not for predicting whether every selected stock will be positive.
- The next model should target severe-loss recall under a bounded veto fraction.
- Ridge top 40 remains the benchmark while that tail model is developed.