Different versions of the correlation coefficient
There are several different variations of the correlation coefficient, but the Pearson coefficient is the most commonly used. The Pearson coefficient measures the strength of linear correlations between two variables, but it is possible for two variables to have a nonlinear correlation as well.
We’ll spare you the mathematics behind how correlation coefficients are calculated, but the general takeaway is the closer to zero a correlation coefficient is, the weaker the correlation between the two variables. And the closer to 1 or -1 it is, the stronger the correlation.
Why does it matter to investors?
There are several reasons correlation coefficients can be meaningful to investors. Portfolio diversification is one reason. Financial advisors might check to see if an investor’s portfolio is filled with investments whose performance is closely correlated, and, if there is too much correlation, they might take steps to diversify.
There’s also a popular investing metric known as beta that is a close cousin of the correlation coefficient since it expresses how volatile a stock’s performance is compared with the S&P 500 benchmark index. For example, a beta of 1 means that if the S&P 500 rises by 1%, the stock can be expected to rise by the same amount. On the other hand, a beta of 2 implies that a stock is twice as reactive to market movements.