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

Two-View GBM + Factor MVO

by EK Im, Souveer Aggarwal, JiWhan Moon, Abdul-Kabiru Nashiru

R
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3.9554 Sharpe Ratio
39.55% Annual Return
-4.71% Max Drawdown

Performance Ranking

Better than 88.6% of the 79 other models

Performance Metrics

Primary Rankings

Sharpe Ratio Sharpe Ratio Risk-adjusted return: annualized return divided by annualized volatility. 3.9554
Rank Rank Position on the leaderboard, ordered by Sharpe ratio. #10

Returns

Annualized Return Annualized Return Average yearly return, after scaling the portfolio to the 10% volatility target. This model: Better than 89% of the 79 other models. 39.55%
Best 12M Return Best 12M Return The best compounded return over any 12 consecutive months of the test period. This model: Better than 98% of the 79 other models. 163.74% May 1999 – Apr 2000
Worst 12M Return Worst 12M Return The worst compounded return over any 12 consecutive months of the test period. This model: Better than 95% of the 79 other models. 1.42% Aug 2022 – Jul 2023

Risk

Max Drawdown Max Drawdown Largest peak-to-trough decline during the test period. This model: Better than 99% of the 79 other models. -4.71%
Downside Deviation Downside Deviation Standard deviation of negative monthly returns only. This model: Better than all 79 other models. 0.660%
Annualized Downside Dev Annualized Downside Deviation Downside deviation in annual terms. This model: Better than all 79 other models. 2.29%

Risk-Adjusted Performance

Sortino Ratio Sortino Ratio Return per unit of downside risk: like Sharpe, but only negative volatility counts. This model: Better than all 79 other models. 17.3078
Calmar Ratio Calmar Ratio Annualized return divided by the maximum drawdown. This model: Better than 99% of the 79 other models. 8.3991
Win Rate Win Rate Share of months with a positive return. This model: Better than 95% of the 79 other models. 90.7%

Distribution

Skewness Skewness Asymmetry of returns. Positive skew means more large gains than large losses. 0.850 (Right skew (good))
Kurtosis Kurtosis Tail thickness. Higher values mean more extreme months, in either direction. 1.454 (Thin tails)

Volatility Targeting

Raw Volatility Raw Volatility Volatility of the portfolio before it was scaled to the 10% target. 10.44%
Scaling Factor Scaling Factor Multiplier applied to the portfolio to reach the 10% volatility target. 0.958
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 89% of the 79 other models. 38.97%
Information Ratio Information Ratio Annualized alpha per unit of residual risk: alpha ÷ std(residuals) × √12. This model: Better than 89% of the 79 other models. 3.9148
R² R² Share of the return variance explained by market movements. 1.2%
Alpha t-stat Alpha t-stat Statistical significance of the alpha estimate. A |t| above about 2 suggests it is not chance. 22.55

Benchmark Comparison

vs. Benchmarks

Two-View GBM + Factor MVO
3.9554
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. 3.9554 2.2380 4.3949 +76.7% (better)
Sortino Ratio Sortino Ratio Return per unit of downside risk: like Sharpe, but only negative volatility counts. 17.3078 4.7888 17.3078 +261.4% (better)
Calmar Ratio Calmar Ratio Annualized return divided by the maximum drawdown. 8.3991 2.0343 11.7867 +312.9% (better)
Information Ratio Information Ratio Annualized alpha per unit of residual risk: alpha ÷ std(residuals) × √12. 3.9148 2.2041 4.3562 +77.6% (better)
Win Rate Win Rate Share of months with a positive return. 90.7% 75.4% 91.2% +20.2% (better)
Annualized Return Annualized Return Average yearly return, after scaling the portfolio to the 10% volatility target. 39.55% 22.38% 43.95% +76.7% (better)
Max Drawdown Max Drawdown Largest peak-to-trough decline during the test period. -4.71% -18.80% -3.51% +75.0% (better)

Model Details

Language R
Competition Year 2024
First Submitted Oct 7, 2026
Scored Oct 8, 2026
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