What is it?
Correlation and diversification: Correlation summarizes linear co-movement over a chosen sample. Diversification depends on the structure of relationships among exposures, not merely on the number of names. Correlations can change through time and can behave differently in stressed conditions.
What question does it help answer?
Which series tend to move together, and how stable is that dependence across the conditions that matter?
What can it tell you?
- Where exposures cluster under the chosen dependence measure and sample.
- Which individual series have many strong relationships with others.
- Why a collection of many positions can still behave like a small number of common risk groups.
What can it not tell you?
- Historical correlation is not a fixed law and can change across regimes.
- Zero linear correlation does not imply independence.
- A correlation matrix alone does not capture nonlinear dependence, tail dependence, liquidity, or all portfolio risks.
See the shape before reading the formula.
The Research Lab arranges 18 synthetic series into three labeled families and draws only strong illustrative relationships. Node distance supports readability; the line strength, not geometric distance, carries the relationship encoding.
Open this concept in Research Lab →Use the idea without turning it into a shortcut.
Keep the context: use this concept to interpret evidence and assumptions. For TraderCockpit controls and workflow details, open Docs. The app is in development; these examples are educational.