CoachA basis expansion replaces each feature by functions (splines, indicators of bins, polynomials) and fits a linear model on them; the model is additive in the features and nonlinear in each.
| model (parameter) | IC | (HAC) | Sharpe gross | Sharpe net | turnover | |
|---|---|---|---|---|---|---|
| ordinary least squares | 0.25% | 0.060 | 5.6 | 1.85 | 1.61 | 0.83 |
| ridge () | 0.30% | 0.060 | 5.6 | 1.83 | 1.59 | 0.83 |
| lasso () | 0.32% | 0.066 | 6.7 | 2.17 | 1.91 | 0.80 |
| elastic net | 0.32% | 0.066 | 6.7 | 2.17 | 1.91 | 0.80 |
| PCR (10 components) | 0.13% | 0.043 | 4.1 | 1.23 | 0.99 | 0.77 |
| PLS (1 component) | 0.25% | 0.061 | 5.6 | 1.87 | 1.62 | 0.82 |
| logistic regression | 0.22% | 0.060 | 5.7 | 1.84 | 1.59 | 0.84 |
| ridge on splines | 0.35% | 0.064 | 6.2 | 1.98 | 1.69 | 0.95 |
| boosted trees | 0.47% | 0.076 | 7.8 | 2.20 | 1.90 | 0.97 |
| ceiling | 0.71% |
ml_baselines.table.