Multi-Model Composite Rating System
Our star ratings are powered by an ensemble of proprietary quantitative models, validated across 15 years of market data (2010–2025). Each model captures a different dimension of stock quality, and the composite signal is significantly more robust than any single factor alone.
+4.54%
12M Long/Short Spread
Top vs. Bottom Decile
0.988
Decile Monotonicity
Spearman ρ on Deciles
p < 10⁻¹²
Statistical Significance
Spearman p-value
80K+
Observations Tested
Across All Horizons
15 Years
Backtesting Period
2010 – 2025
How Our Ratings Work
A three-stage pipeline from raw data to actionable star ratings.
Multi-Factor Signal Generation
Multiple proprietary models independently evaluate each stock across fundamental quality, momentum, valuation, earnings stability, and macro sensitivity. Each model contributes a distinct, uncorrelated perspective.
Ensemble Aggregation
Individual model outputs are combined into a unified signal using an optimized weighting scheme. The ensemble approach dramatically reduces noise and model-specific biases, producing a more reliable assessment than any single model.
Star Rating Assignment
The aggregated signal is translated into intuitive 1-5 star ratings using calibrated thresholds. Stars make it easy to instantly identify high-conviction opportunities without interpreting complex data.
Decile Performance Analysis
Higher-rated stocks consistently deliver higher excess returns across all time horizons.
Why Our Approach Works
The composite methodology offers significant advantages over single-factor or discretionary approaches.
Near-Perfect Monotonicity
Decile monotonicity of 0.988 means higher-rated stocks almost always outperform lower-rated ones. There are no "gaps" or inversions — the signal is remarkably clean across the entire spectrum.
Strengthens Over Time
The long/short spread grows from +0.41% at 1 month to +4.54% at 12 months. This suggests the composite captures durable stock quality rather than short-term noise — ideal for buy-and-hold investors.
Statistically Robust
All correlation metrics are highly significant (p < 10⁻¹²) across 80,000+ observations. This isn't curve-fitting — the signal holds up across diverse market conditions and time periods.
Ensemble Diversification
By combining multiple independent models, the ensemble signal is far more stable than any individual factor. Model-specific weaknesses cancel out, resulting in smoother, more reliable ratings over time.
Correlation Statistics
Measuring the relationship between our ratings and future excess returns.
Key Findings
Higher Ratings = Higher Excess Returns
The decile analysis shows a clear, nearly monotonic relationship. Stocks in the top decile (D10) outperform the bottom decile (D1) by +0.41 pp/month, +1.29 pp/quarter, +2.50 pp/half-year, and +4.54 pp/year.
The Effect Strengthens Over Longer Horizons
The long/short spread grows from +0.41% at 1M to +4.54% at 12M, suggesting our ratings are more predictive at longer time frames — making them ideal for medium to long-term investors.
Statistically Significant but Economically Measured
Spearman correlations (0.02-0.06) are highly significant (p < 1e-12) due to the large sample size (~80K+ observations), but the effect sizes are modest. Decile monotonicity is strong (ρ = 0.94-0.99), confirming systematic predictive power.
Consistent Across Market Conditions
Our ratings are tested across 15 years of market data (2010–2025), spanning bull markets, corrections, and bear cycles. The winsorized analysis controls for outliers, ensuring the results reflect persistent stock selection alpha rather than extreme return events.
See the Ratings in Action
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