Principal Component Analysis

Principal Component Analysis compresses a return matrix into orthogonal drivers. It is useful for diagnosing concentration, hidden common exposures, and whether a multi-asset set is less diversified than it appears.

Access: Public

Open Principal Component Analysis →

What It Answers

How concentrated is this basket? A dominant first component means many assets are moving together.
How many distinct drivers exist? Effective component count and the cumulative variance curve show how quickly the matrix collapses into a few dimensions.
Which assets load on each theme? The loading table shows which assets define each principal component.

Covariance vs Correlation

Covariance PCA preserves absolute volatility, so high-volatility assets dominate more easily.
Correlation PCA standardizes each asset first, making the result about co-movement structure rather than scale.

Use correlation PCA when the question is “how many independent themes are here?” Use covariance PCA when absolute risk concentration matters.

Inputs And Workflow

You can start from explicit tickers or from a Library portfolio. Portfolio mode is useful when you want to diagnose overlap inside a reusable workspace portfolio without retyping its holdings.
Daily mode is better for short-horizon structure. Monthly mode is better when you want a cleaner read on slower strategic relationships and less day-to-day noise.
After the run, you can save, share, and reopen the analysis from Workspace.

Interpretation

PCA components are statistical constructs, not economic labels. Treat them as clues. If the first component is broad and positive across equities, that suggests a common market driver, not a complete causal story by itself.

How To Read The Output

Start with Top Component Share. If one component explains almost half the variance, the basket is less diversified than the ticker count suggests.
Then check Effective Components and the 80% threshold. These summarize how many dimensions are really carrying the portfolio’s behavior.
Finally, inspect the loading table. Assets with similar signs and magnitudes on the same component are moving together; assets with opposite signs are offsetting that theme.

PCA Workflow

Open Principal Component Analysis.
Enter tickers or load a Library portfolio. Portfolio mode diagnoses overlap without retyping holdings.
Choose correlation PCA to measure co-movement structure, or covariance PCA when absolute risk concentration matters.
Run the analysis and check Top Component Share. If one component explains most of the variance, the basket is less diversified than the ticker count suggests.
Inspect the loading table to see which assets define each component. Assets with similar loadings on the same component move together.