Beta Neutral Cross-Sectional MLP

Performance Ranking

0.15 59.5th percentile (Sharpe Ratio) 4.39

Better than 59.5% of the 79 other models

Performance Metrics

Primary Rankings

Sharpe Ratio Sharpe Ratio Risk-adjusted return: annualized return divided by annualized volatility. 2.6039

Rank Rank Position on the leaderboard, ordered by Sharpe ratio. #33

Returns

Annualized Return Annualized Return Average yearly return, after scaling the portfolio to the 10% volatility target. This model: Better than 60% of the 79 other models. 26.04%

Best 12M Return Best 12M Return The best compounded return over any 12 consecutive months of the test period. This model: Better than 60% of the 79 other models. 114.89% Sep 2000 – Aug 2001

Worst 12M Return Worst 12M Return The worst compounded return over any 12 consecutive months of the test period. This model: Better than 65% of the 79 other models. -9.11% Oct 2019 – Sep 2020

Risk

Max Drawdown Max Drawdown Largest peak-to-trough decline during the test period. This model: Better than 52% of the 79 other models. -15.91%

Downside Deviation Downside Deviation Standard deviation of negative monthly returns only. This model: Better than 30% of the 79 other models. 1.945%

Annualized Downside Dev Annualized Downside Deviation Downside deviation in annual terms. This model: Better than 30% of the 79 other models. 6.74%

Risk-Adjusted Performance

Sortino Ratio Sortino Ratio Return per unit of downside risk: like Sharpe, but only negative volatility counts. This model: Better than 46% of the 79 other models. 3.8654

Calmar Ratio Calmar Ratio Annualized return divided by the maximum drawdown. This model: Better than 54% of the 79 other models. 1.6370

Win Rate Win Rate Share of months with a positive return. This model: Better than 58% of the 79 other models. 79.9%

Distribution

Skewness Skewness Asymmetry of returns. Positive skew means more large gains than large losses. -0.037 (Near-symmetric)

Kurtosis Kurtosis Tail thickness. Higher values mean more extreme months, in either direction. 2.584 (Near-normal)

Volatility Targeting

Raw Volatility Raw Volatility Volatility of the portfolio before it was scaled to the 10% target. 2.67%

Scaling Factor Scaling Factor Multiplier applied to the portfolio to reach the 10% volatility target. 3.738

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. 0.05

Alpha (ann.) Alpha (ann.) Average yearly return not explained by market exposure (the monthly regression alpha × 12). This model: Better than 54% of the 79 other models. 24.48%

Information Ratio Information Ratio Annualized alpha per unit of residual risk: alpha ÷ std(residuals) × √12. This model: Better than 60% of the 79 other models. 2.5521

R² R² Share of the return variance explained by market movements. 8.2%

Alpha t-stat Alpha t-stat Statistical significance of the alpha estimate. A |t| above about 2 suggests it is not chance. 14.70

Benchmark Comparison

vs. Benchmarks

Beta Neutral Cross-Sectional MLP

2.6039

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.	2.6039	2.2380	4.3949	+16.3% (better)
Sortino Ratio Sortino Ratio Return per unit of downside risk: like Sharpe, but only negative volatility counts.	3.8654	4.7888	17.3078	-19.3% (worse)
Calmar Ratio Calmar Ratio Annualized return divided by the maximum drawdown.	1.6370	2.0343	11.7867	-19.5% (worse)
Information Ratio Information Ratio Annualized alpha per unit of residual risk: alpha ÷ std(residuals) × √12.	2.5521	2.2041	4.3562	+15.8% (better)
Win Rate Win Rate Share of months with a positive return.	79.9%	75.4%	91.2%	+5.9% (better)
Annualized Return Annualized Return Average yearly return, after scaling the portfolio to the 10% volatility target.	26.04%	22.38%	43.95%	+16.3% (better)
Max Drawdown Max Drawdown Largest peak-to-trough decline during the test period.	-15.91%	-18.80%	-3.51%	+15.4% (better)

Model Details

Language Python

Competition Year 2024

First Submitted Jul 26, 2026

Scored Jul 30, 2026

Downloads

Download Code (Python) 40 downloads

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