Residual Books (LightGBM, industry-neutral)

Performance Ranking

0.15 98.8th percentile (Sharpe Ratio) 4.59

Better than 98.8% of the 86 other models

Performance Metrics

Primary Rankings

Sharpe Ratio Sharpe Ratio Risk-adjusted return: annualized return divided by annualized volatility. 4.4680

Rank Rank Position on the leaderboard, ordered by Sharpe ratio. #2

Returns

Annualized Return Annualized Return Average yearly return, after scaling the portfolio to the 10% volatility target. This model: Better than 99% of the 86 other models. 44.68%

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 86 other models. 147.11% Sep 1998 – Aug 1999

Worst 12M Return Worst 12M Return The worst compounded return over any 12 consecutive months of the test period. This model: Better than 94% of the 86 other models. 1.26% Jun 2022 – May 2023

Risk

Max Drawdown Max Drawdown Largest peak-to-trough decline during the test period. This model: Better than 98% of the 86 other models. -5.93%

Downside Deviation Downside Deviation Standard deviation of negative monthly returns only. This model: Better than 97% of the 86 other models. 0.858%

Annualized Downside Dev Annualized Downside Deviation Downside deviation in annual terms. This model: Better than 97% of the 86 other models. 2.97%

Risk-Adjusted Performance

Sortino Ratio Sortino Ratio Return per unit of downside risk: like Sharpe, but only negative volatility counts. This model: Better than 98% of the 86 other models. 15.0355

Calmar Ratio Calmar Ratio Annualized return divided by the maximum drawdown. This model: Better than 98% of the 86 other models. 7.5375

Win Rate Win Rate Share of months with a positive return. This model: Better than 99% of the 86 other models. 92.4%

Distribution

Skewness Skewness Asymmetry of returns. Positive skew means more large gains than large losses. 0.446 (Near-symmetric)

Kurtosis Kurtosis Tail thickness. Higher values mean more extreme months, in either direction. 0.266 (Thin tails)

Volatility Targeting

Raw Volatility Raw Volatility Volatility of the portfolio before it was scaled to the 10% target. 823.62%

Scaling Factor Scaling Factor Multiplier applied to the portfolio to reach the 10% volatility target. 0.012

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. 14.16

Alpha (ann.) Alpha (ann.) Average yearly return not explained by market exposure (the monthly regression alpha × 12). This model: Better than 98% of the 86 other models. 43.23%

Information Ratio Information Ratio Annualized alpha per unit of residual risk: alpha ÷ std(residuals) × √12. This model: Better than 99% of the 86 other models. 4.4797

R² R² Share of the return variance explained by market movements. 7.1%

Alpha t-stat Alpha t-stat Statistical significance of the alpha estimate. A |t| above about 2 suggests it is not chance. 25.80

Benchmark Comparison

vs. Benchmarks

Residual Books (LightGBM, industry-neutral)

4.4680

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.	4.4680	2.3622	4.5917	+89.1% (better)
Sortino Ratio Sortino Ratio Return per unit of downside risk: like Sharpe, but only negative volatility counts.	15.0355	5.2084	17.3078	+188.7% (better)
Calmar Ratio Calmar Ratio Annualized return divided by the maximum drawdown.	7.5375	2.2417	11.7867	+236.2% (better)
Information Ratio Information Ratio Annualized alpha per unit of residual risk: alpha ÷ std(residuals) × √12.	4.4797	2.3312	4.5541	+92.2% (better)
Win Rate Win Rate Share of months with a positive return.	92.4%	76.3%	92.6%	+21.0% (better)
Annualized Return Annualized Return Average yearly return, after scaling the portfolio to the 10% volatility target.	44.68%	23.62%	45.92%	+89.2% (better)
Max Drawdown Max Drawdown Largest peak-to-trough decline during the test period.	-5.93%	-18.04%	-3.51%	+67.1% (better)

Model Details

Language Python

Competition Year 2024

First Submitted Oct 16, 2026

Scored Oct 18, 2026

Downloads

Download Code (Python) 29 downloads Download Documentation 51 downloads

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