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

Systematic Orthogonal - PLS - MVO

by Haoyu Wang, Leo Xiao, Dulani Dulani Jayasuriya, Xing Han

Python
Compare
3.1319 Sharpe Ratio
31.32% Annual Return
-12.86% Max Drawdown

Performance Ranking

Better than 82.4% of the 51 other models

Performance Metrics

Primary Rankings

Sharpe Ratio Sharpe Ratio Risk-adjusted return: annualized return divided by annualized volatility. 3.1319
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 82% of the 51 other models. 31.32%
Best 12M Return Best 12M Return The best compounded return over any 12 consecutive months of the test period. This model: Better than 80% of the 51 other models. 122.35% Apr 2000 – Mar 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 51 other models. -10.15% Feb 2019 – Jan 2020

Risk

Max Drawdown Max Drawdown Largest peak-to-trough decline during the test period. This model: Better than 75% of the 51 other models. -12.86%
Downside Deviation Downside Deviation Standard deviation of negative monthly returns only. This model: Better than all 51 other models. 0.931%
Annualized Downside Dev Annualized Downside Deviation Downside deviation in annual terms. This model: Better than all 51 other models. 3.22%

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 51 other models. 9.7118
Calmar Ratio Calmar Ratio Annualized return divided by the maximum drawdown. This model: Better than 80% of the 51 other models. 2.4348
Win Rate Win Rate Share of months with a positive return. This model: Better than 80% of the 51 other models. 82.8%

Distribution

Skewness Skewness Asymmetry of returns. Positive skew means more large gains than large losses. 0.565 (Right skew (good))
Kurtosis Kurtosis Tail thickness. Higher values mean more extreme months, in either direction. 0.455 (Thin tails)

Volatility Targeting

Raw Volatility Raw Volatility Volatility of the portfolio before it was scaled to the 10% target. 1.94%
Scaling Factor Scaling Factor Multiplier applied to the portfolio to reach the 10% volatility target. 5.151
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. 0.00
Alpha (ann.) Alpha (ann.) Average yearly return not explained by market exposure (the monthly regression alpha × 12). This model: Better than 82% of the 51 other models. 31.19%
Information Ratio Information Ratio Annualized alpha per unit of residual risk: alpha ÷ std(residuals) × √12. This model: Better than 82% of the 51 other models. 3.1164
R² R² Share of the return variance explained by market movements. 0.1%
Alpha t-stat Alpha t-stat Statistical significance of the alpha estimate. A |t| above about 2 suggests it is not chance. 17.95

Benchmark Comparison

vs. Benchmarks

Systematic Orthogonal - PLS - MVO
3.1319
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. 3.1319 1.7916 4.1105 +74.8%
Sortino Ratio Sortino Ratio Return per unit of downside risk: like Sharpe, but only negative volatility counts. 9.7118 3.5173 9.7118 +176.1%
Calmar Ratio Calmar Ratio Annualized return divided by the maximum drawdown. 2.4348 1.3100 4.3808 +85.9%
Information Ratio Information Ratio Annualized alpha per unit of residual risk: alpha ÷ std(residuals) × √12. 3.1164 1.7392 4.0976 +79.2%
Win Rate Win Rate Share of months with a positive return. 82.8% 71.6% 90.7% +15.6%
Annualized Return Annualized Return Average yearly return, after scaling the portfolio to the 10% volatility target. 31.32% 17.92% 41.10% +74.8%
Max Drawdown Max Drawdown Largest peak-to-trough decline during the test period. -12.86% -21.75% -6.94% +40.9%

Model Details

Language Python
Competition Year 2024
First Submitted Sep 20, 2026
Scored Sep 21, 2026

Downloads

Download Code (Python) 16 downloads Download Documentation 14 downloads

Related Models

Similar Profile

Models with a similar overall risk/return profile — comparable risk-adjusted return, market exposure, worst drawdown, monthly win rate, and return asymmetry. Sorted closest profile first.

Δ is the profile distance (lower is closer); the other figures are each model's difference from this one: β is market beta, MaxDD and Win are in percentage points.

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