# Positive Correlation

## What it is:

**Positive correlation** describes a relationship in which changes in one variable are associated with the same kind of changes in another variable.

## How it works (Example):

For example, many economists have discovered that people tend to buy more cars and appliances during economic booms. Thus, one could expect a positive correlation between, say, the employment rate and auto stocks. In other words, when employment rates are up, auto stocks will probably rise across the board.

Statistically speaking, the correlation between any two variables ranges from -1.0 (perfectly negatively correlated) to 1.0 (perfectly correlated). Analysts can also determine whether two things are positively correlated by running a regression analysis on the two items and then calculating their R2. The higher the R2, the more positively correlated two things are. Beta is also a common tool for measuring how correlated a particular security or group of securities is to a broader market index or group of stocks. A beta of 1.0 indicates perfect correlation (meaning that when one goes up, the other does too).

## Why it Matters:

If an investor can find an investment that consistently goes in the same direction as another investment, then holding both investments can dramatically increase returns. This approach can also dramatically increase losses, which is why some investors try to find assets that are negatively correlated. That is, when one asset decreases in value, the other rises in value (this is the idea behind hedging).

Accordingly, positive correlation can be one way to increase the risk in a portfolio (and negative correlation is a way to reduce risk).

It is very important to remember, however, that correlation does not mean causation. In other words, just because two things are positively correlated does not mean that one is causing the other to go in the same direction. Additionally, negative or positive correlation between two variables does not exist under every circumstance.