Explanation:
KEY TAKEAWAYS
Correlation coefficients are used to measure the strength of the relationship between two variables.
Correlation coefficient greater than zero indicates a positive relationship while a value less than zero signifies a negative relationship and a value of zero indicates no relationship between the two variables being compared.
Negative correlation, or inverse correlation, is a key concept in the creation of diversified portfolios that can better withstand portfolio volatility.
Understanding Correlation
The correlation coefficient (ρ) is a measure that determines the degree to which the movement of two different variables is associated. The most common correlation coefficient, generated by the Pearson product-moment correlation, may be used to measure the linear relationship between two variables. However, in a non-linear relationship, this correlation coefficient may not always be a suitable measure of dependence.
The possible range of values for the correlation coefficient is -1.0 to 1.0. In other words, the values cannot exceed 1.0 or be less than -1.0, and a correlation of -1.0 indicates a perfect negative correlation, and a correlation of 1.0 indicates a perfect positive correlation. Anytime the correlation coefficient is greater than zero, it's a positive relationship. Conversely, anytime the value is less than zero, it's a negative relationship. A value of zero indicates that there is no relationship between the two variables.
Correlation among variables does not (necessarily) imply causation.
In the financial markets, the correlation coefficient is used to measure the correlation between two securities.
When two stocks, for example, move in the same direction, the correlation coefficient is positive. Conversely, when two stocks move in opposite directions, the correlation coefficient is negative.
If the correlation coefficient of two variables is zero, it signifies that there is no linear relationship between the variables. However, this is only for a linear relationship. It is possible that the variables have a strong curvilinear relationship. When the value of ρ is close to zero, generally between -0.1 and +0.1, the variables are said to have no linear relationship (or a very weak linear relationship).
For example, suppose that the prices of coffee and of computers are observed and found to have a correlation of +.0008. This means that there is no correlation, or relationship, between the two variables.
Graphs showing positive, negative, and no correlation
Investopedia / Hugo Lin
Calculating ρ
To calculate correlation, one must first determine the covariance of the two variables in question. Next, one must calculate each variable's standard deviation. The correlation coefficient is determined by dividing the covariance by the product of the two variables' standard deviations.
Standard deviation is a measure of the dispersion of data from its average. Covariance is a measure of how two variables change together. However, its magnitude is unbounded, so it is difficult to interpret. By dividing covariance by the product of the two standard deviations, one can calculate the normalized version of the statistic. This is the correlation coefficient.
\text{Correlation}=\rho=\frac{\text{cov}(X,Y)}{\sigma_X\sigma_Y}Correlation=ρ=
σ
X
σ
Y
cov(X,Y)
Positive Correlation
A positive correlation–when the correlation coefficient is greater than 0–signifies that both variables move in the same direction. When ρ is +1, it signifies that the two variables being compared have a perfect positive relationship; when one variable moves higher or lower, the other variable moves in the same direction with the same magnitude.
The closer the value of ρ is to +1, the stronger the linear relationship. For example, suppose the value of oil prices is directly related to the prices of airplane tickets, with a correlation coefficient of +0.95. The relationship between oil prices and airfares has a very strong positive correlation since the value is close to +1. So if the price of oil decreases, airfares also decrease. And if the price of oil increases, so do the prices of airplane tickets.
In the chart below, we compare one of the largest U.S. banks, JPMorgan Chase & Co. (JPM), with the Financial Select SPDR ETF (XLF).1 2 As you can imagine, JPMorgan Chase & Co. should have a positive correlation to the banking industry as a whole. We can see the correlation coefficient (bottom of the chart) is currently at 0.97 which is signaling a very strong positive correlation. A reading above 0.50 typically signals a positive correlation.
Correlation Coefficient - JPM, XLF
Source: TradingView.
Understanding the correlation between two stocks (or a single stock) and its industry can help investors gauge how the stock is trading relative to its peers. All types of securities, including bonds, sectors, and exchange-traded funds (ETFs) can be compared with the correlation coefficient.