What does an inverse correlation mean?
Inverted correlation is when the value of one variable is high, likely, the value of the other variable is also high. It is also called a negative correlation.
If variable A has a high value, then variable B has a low value; if variable A has a low value, then variable B has a high value. In statistics, the correlation coefficient “r” usually shows an inverse correlation between -1 and 0. Several -1 means that there is no inverse correlation at all.
Making a graph of inverse correlation
To see if two data sets are related, you can put them on a graph with an x-axis and a y-axis. There is a visual way to see if there is a positive or negative connection. It is called a scatter diagram. The graph below shows that there is a solid inverse connection between the two sets of data points.
What does the opposite of correlation tell you?
It means that when one variable is high, the other will likely be low. You can learn valuable things about the connection between two variables through correlation analysis. For example, it can show why the stock and bond markets often move in different directions.
The correlation coefficient is often used to guess essential numbers, like how much risk reduction comes from diversifying your assets. If two assets’ returns are negatively tied to each other, they can cancel each other out if they are in the same portfolio.
The relationship between the U.S. dollar and gold is likely the most well-known case of an inverse correlation in the financial world. The dollar price of gold tends to go up when the U.S. dollar falls against major currencies and down when the U.S. dollar rises against significant currencies.1
What Not to Do When Using Inverse Correlation
Regarding a negative connection, there are two things to keep in mind. First, a negative association, or any correlation, does not always mean that one thing leads to another. This finding does not prove that one variable causes the other, even though the two have a strong inverse correlation.
Second, the connection between two variables is not always the same when you work with time series data, like most financial data. It can change over time. In other words, the factors may have a negative correlation sometimes and a positive correlation other times. Since this is the case, drawing the same statement from the results of correlation analysis to future data is risky.
Conclusion
- When two factors in a set are linked to make one high and the other low, this is called an inverse (or unfavorable) correlation.
- There is no guarantee that the behavior of one variable will cause the other to change just because they have a high negative correlation.
- Over time, the link between two variables can change, and there may even be times when they are positively correlated.
