Global Factor Data
Global Factor Data
Global Factor Data
Global Factor Data

Boosted Stock Rankings in a Two-Book Mean-Variance Portfolio

by Marco Chen, Cecilia Xin, Simon Zhang, Magie Ma

R
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3.8609 Sharpe Ratio
38.61% Annual Return
-6.92% Max Drawdown

Performance Ranking

Better than 86.3% of the 80 other models

Performance Metrics

Primary Rankings

Sharpe Ratio Sharpe Ratio Risk-adjusted return: annualized return divided by annualized volatility. 3.8609
Rank Rank Position on the leaderboard, ordered by Sharpe ratio. #12

Returns

Annualized Return Annualized Return Average yearly return, after scaling the portfolio to the 10% volatility target. This model: Better than 86% of the 80 other models. 38.61%
Best 12M Return Best 12M Return The best compounded return over any 12 consecutive months of the test period. This model: Better than 90% of the 80 other models. 145.34% Oct 1999 – Sep 2000
Worst 12M Return Worst 12M Return The worst compounded return over any 12 consecutive months of the test period. This model: Better than 79% of the 80 other models. -4.48% Feb 2020 – Jan 2021

Risk

Max Drawdown Max Drawdown Largest peak-to-trough decline during the test period. This model: Better than 94% of the 80 other models. -6.92%
Downside Deviation Downside Deviation Standard deviation of negative monthly returns only. This model: Better than 69% of the 80 other models. 1.299%
Annualized Downside Dev Annualized Downside Deviation Downside deviation in annual terms. This model: Better than 69% of the 80 other models. 4.50%

Risk-Adjusted Performance

Sortino Ratio Sortino Ratio Return per unit of downside risk: like Sharpe, but only negative volatility counts. This model: Better than 85% of the 80 other models. 8.5770
Calmar Ratio Calmar Ratio Annualized return divided by the maximum drawdown. This model: Better than 94% of the 80 other models. 5.5819
Win Rate Win Rate Share of months with a positive return. This model: Better than 85% of the 80 other models. 88.0%

Distribution

Skewness Skewness Asymmetry of returns. Positive skew means more large gains than large losses. 0.273 (Near-symmetric)
Kurtosis Kurtosis Tail thickness. Higher values mean more extreme months, in either direction. 0.483 (Thin tails)

Volatility Targeting

Raw Volatility Raw Volatility Volatility of the portfolio before it was scaled to the 10% target. 25.38%
Scaling Factor Scaling Factor Multiplier applied to the portfolio to reach the 10% volatility target. 0.394
Observations Observations Number of monthly returns used for the evaluation. 408 months

Market Exposure (CAPM)

Beta Beta Sensitivity to market movements. 1.0 moves one-for-one with the market. 0.24
Alpha (ann.) Alpha (ann.) Average yearly return not explained by market exposure (the monthly regression alpha × 12). This model: Better than 86% of the 80 other models. 37.81%
Information Ratio Information Ratio Annualized alpha per unit of residual risk: alpha ÷ std(residuals) × √12. This model: Better than 86% of the 80 other models. 3.8178
R² R² Share of the return variance explained by market movements. 2.2%
Alpha t-stat Alpha t-stat Statistical significance of the alpha estimate. A |t| above about 2 suggests it is not chance. 21.99

Benchmark Comparison

vs. Benchmarks

Boosted Stock Rankings in a Two-Book Mean-Variance Portfolio
3.8609
Benchmark: Markowitz-ML
2.5212
Benchmark: Instrumented PCA factor portfolio
1.9481
Benchmark: Kozak-Nagel-Santosh rank-weighted factors portfolio
1.4195
Benchmark: Minimum Variance
0.9470
Benchmark: Factor-ML
0.8901
Benchmark: Equal-weight portfolio
0.4912

Performance Radar

Each axis shows the model's percentile rank within the leaderboard (outer edge = best). Customize the metrics using the dropdowns below.

vs. Leaderboard

Metric This Model Average Top Model vs. Avg
Sharpe Ratio Sharpe Ratio Risk-adjusted return: annualized return divided by annualized volatility. 3.8609 2.2472 4.3949 +71.8% (better)
Sortino Ratio Sortino Ratio Return per unit of downside risk: like Sharpe, but only negative volatility counts. 8.5770 4.8069 17.3078 +78.4% (better)
Calmar Ratio Calmar Ratio Annualized return divided by the maximum drawdown. 5.5819 2.0485 11.7867 +172.5% (better)
Information Ratio Information Ratio Annualized alpha per unit of residual risk: alpha ÷ std(residuals) × √12. 3.8178 2.2146 4.3562 +72.4% (better)
Win Rate Win Rate Share of months with a positive return. 88.0% 75.5% 91.2% +16.5% (better)
Annualized Return Annualized Return Average yearly return, after scaling the portfolio to the 10% volatility target. 38.61% 22.47% 43.95% +71.8% (better)
Max Drawdown Max Drawdown Largest peak-to-trough decline during the test period. -6.92% -18.68% -3.51% +63.0% (better)

Model Details

Language R
Competition Year 2024
First Submitted Oct 8, 2026
Scored Oct 9, 2026
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