Comparing 2 models side-by-side
Each axis shows a model's percentile rank within the leaderboard (outer edge = best). Customize the metrics using the dropdowns below.
| Metric |
Factor Forecasting and Regularized Portfolio Allocation
#16
|
Benchmark: Markowitz-ML
#34
|
|---|---|---|
| Primary Rankings | ||
| Sharpe Ratio Sharpe Ratio Risk-adjusted return: annualized return divided by annualized volatility. | 3.5862 (best) | 2.5212 |
| Rank Rank Position on the leaderboard, ordered by Sharpe ratio. | #16 | #34 |
| Returns | ||
| Annualized Return Annualized Return Average yearly return, after scaling the portfolio to the 10% volatility target. | 35.86% (best) | 25.21% |
| Risk | ||
| Max Drawdown Max Drawdown Largest peak-to-trough decline during the test period. | -18.24% | -16.63% (best) |
| Downside Deviation Downside Deviation Standard deviation of negative monthly returns only. | 1.369% (best) | 1.450% |
| Annualized Downside Dev Annualized Downside Deviation Downside deviation in annual terms. | 4.74% (best) | 5.02% |
| Risk-Adjusted Performance | ||
| Sortino Ratio Sortino Ratio Return per unit of downside risk: like Sharpe, but only negative volatility counts. | 7.5644 (best) | 5.0195 |
| Calmar Ratio Calmar Ratio Annualized return divided by the maximum drawdown. | 1.9666 (best) | 1.5157 |
| Win Rate Win Rate Share of months with a positive return. | 84.6% (best) | 77.0% |
| Distribution | ||
| Skewness Skewness Asymmetry of returns. Positive skew means more large gains than large losses. | -0.013 | 0.136 |
| Kurtosis Kurtosis Tail thickness. Higher values mean more extreme months, in either direction. | 0.214 | 0.530 |
| Volatility Targeting | ||
| Raw Volatility Raw Volatility Volatility of the portfolio before it was scaled to the 10% target. | 11.17% | 14.65% |
| Scaling Factor Scaling Factor Multiplier applied to the portfolio to reach the 10% volatility target. | 0.895 | 0.683 |
| Observations Observations Number of monthly returns used for the evaluation. | 408 mo | 408 mo |
| Market Exposure (CAPM) | ||
| Beta Beta Sensitivity to market movements. 1.0 moves one-for-one with the market. | 0.11 | 0.03 |
| Alpha (ann.) Alpha (ann.) Average yearly return not explained by market exposure (the monthly regression alpha × 12). | 35.00% (best) | 25.03% |
| Information Ratio Information Ratio Annualized alpha per unit of residual risk: alpha ÷ std(residuals) × √12. | 3.5405 (best) | 2.5016 |
| R² R² Share of the return variance explained by market movements. | 2.5% | 0.1% |
| Alpha t-stat Alpha t-stat Statistical significance of the alpha estimate. A |t| above about 2 suggests it is not chance. | 20.39 | 14.41 |
| Metadata | ||
| Language | Python | R |
| Competition Year | 2024 | 2024 |
| Entry Type | User | Benchmark |