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

Dynamically Regularized MVP

by Mads Hebsgaard

Python
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0.7296 Sharpe Ratio
7.30% Annual Return
-38.72% Max Drawdown

Performance Ranking

Better than 21.6% of the 51 other models

Performance Metrics

Primary Rankings

Sharpe Ratio Sharpe Ratio Risk-adjusted return: annualized return divided by annualized volatility. 0.7296
Rank Rank Position on the leaderboard, ordered by Sharpe ratio. #41

Returns

Annualized Return Annualized Return Average yearly return, after scaling the portfolio to the 10% volatility target. This model: Better than 22% of the 51 other models. 7.30%
Best 12M Return Best 12M Return The best compounded return over any 12 consecutive months of the test period. This model: Better than 4% of the 51 other models. 41.28% Aug 1996 – Jul 1997
Worst 12M Return Worst 12M Return The worst compounded return over any 12 consecutive months of the test period. This model: Better than 16% of the 51 other models. -26.79% Mar 1999 – Feb 2000

Risk

Max Drawdown Max Drawdown Largest peak-to-trough decline during the test period. This model: Better than 2% of the 51 other models. -38.72%
Downside Deviation Downside Deviation Standard deviation of negative monthly returns only. This model: Better than 4% of the 51 other models. 2.387%
Annualized Downside Dev Annualized Downside Deviation Downside deviation in annual terms. This model: Better than 4% of the 51 other models. 8.27%

Risk-Adjusted Performance

Sortino Ratio Sortino Ratio Return per unit of downside risk: like Sharpe, but only negative volatility counts. This model: Better than 18% of the 51 other models. 0.8825
Calmar Ratio Calmar Ratio Annualized return divided by the maximum drawdown. This model: Better than 14% of the 51 other models. 0.1884
Win Rate Win Rate Share of months with a positive return. This model: Better than 28% of the 51 other models. 63.7%

Distribution

Skewness Skewness Asymmetry of returns. Positive skew means more large gains than large losses. -0.894 (Left skew (risky))
Kurtosis Kurtosis Tail thickness. Higher values mean more extreme months, in either direction. 2.978 (Near-normal)

Volatility Targeting

Raw Volatility Raw Volatility Volatility of the portfolio before it was scaled to the 10% target. 1.75%
Scaling Factor Scaling Factor Multiplier applied to the portfolio to reach the 10% volatility target. 5.718
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. 0.07
Alpha (ann.) Alpha (ann.) Average yearly return not explained by market exposure (the monthly regression alpha × 12). This model: Better than 20% of the 51 other models. 4.01%
Information Ratio Information Ratio Annualized alpha per unit of residual risk: alpha ÷ std(residuals) × √12. This model: Better than 20% of the 51 other models. 0.5027
R² R² Share of the return variance explained by market movements. 36.6%
Alpha t-stat Alpha t-stat Statistical significance of the alpha estimate. A |t| above about 2 suggests it is not chance. 2.90

Benchmark Comparison

vs. Benchmarks

Dynamically Regularized MVP
0.7296
Benchmark: Instrumented PCA factor portfolio
1.9481
Benchmark: Kozak-Nagel-Santosh rank-weighted factors portfolio
1.4195
Benchmark: Factor-ML
0.7442
Benchmark: Equal-weight portfolio
0.4912
Benchmark: Minimum Variance
0.4731

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. 0.7296 1.7916 4.1105 -59.3% (worse)
Sortino Ratio Sortino Ratio Return per unit of downside risk: like Sharpe, but only negative volatility counts. 0.8825 3.5173 9.7118 -74.9% (worse)
Calmar Ratio Calmar Ratio Annualized return divided by the maximum drawdown. 0.1884 1.3100 4.3808 -85.6% (worse)
Information Ratio Information Ratio Annualized alpha per unit of residual risk: alpha ÷ std(residuals) × √12. 0.5027 1.7392 4.0976 -71.1% (worse)
Win Rate Win Rate Share of months with a positive return. 63.7% 71.6% 90.7% -11.0% (worse)
Annualized Return Annualized Return Average yearly return, after scaling the portfolio to the 10% volatility target. 7.30% 17.92% 41.10% -59.3% (worse)
Max Drawdown Max Drawdown Largest peak-to-trough decline during the test period. -38.72% -21.75% -6.94% -78.0% (worse)

Model Details

Language Python
Competition Year 2024
First Submitted Jan 31, 2026
Scored Jan 31, 2026
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