Principal Component Analysis

Principal Component Analysis for Exposure and Concentration Diagnostics

Use PCA to understand common drivers, exposure concentration, and dimensionality before assuming a portfolio is diversified.

PCA is useful when correlation tables are too blunt and you need a compact view of the forces driving a set of assets.

This page covers what PCA measures, when to use it, and how to interpret component loadings and explained variance.

Open PCARead PCA Docs

What PCA helps answer

  • Whether a portfolio is dominated by a small number of common factors.
  • How much diversification is present in the return matrix.
  • Which assets contribute most to each dominant component.

When to use it

Use PCA when you already have a return matrix and the next question is structural: how many independent drivers are really present, and where is concentration hiding?

What it does not do

  • It does not replace factor interpretation by itself.
  • It does not prove forward-looking diversification.
  • It does not eliminate the need for historical backtests.

Best companion workflow

Use PCA alongside Factor Regression, Correlation analysis, and Portfolio Backtest when you want both structural and historical views of the same asset set.

FAQ

Is a low number of dominant components always bad?

Not necessarily. It is a concentration signal, not a judgment by itself. The important question is whether that concentration matches the exposure you intended to take.

Related Pages

Factor Regression Docs

Complement PCA with explicit factor exposures.

Correlation

Pair PCA with simpler overlap diagnostics.

PCA Docs

Detailed workflow and interpretation notes.