Cross-section shrinkage lab (ETF panel)

This project studies how much dimensionality is needed to explain cross-sectional ETF variation without overfitting. It combines principal-component structure, ridge shrinkage, and out-of-sample style checks to evaluate signal stability.

Build an ETF cross-section panel and standardize features to a consistent scale.

Estimate principal components and inspect explained-variance concentration to identify effective rank.

Measure cross-sectional fit quality as factor count K increases (R² vs K curves).

Run pseudo out-of-sample folds and ridge regularization sweeps to test robustness under shrinkage.

If R² saturates quickly as K rises, the signal is low-dimensional and easier to regularize.

If ridge penalties stabilize fold-level performance, estimates are likely less sensitive to sampling noise.

Use the interactive charts on this page to compare in-sample fit against pseudo-OOS behavior before selecting production settings.

The page renders from generated JSON concept charts. Re-run the data pipeline to refresh diagnostics and keep conclusions synchronized with current market history.