# What Is Positive Correlation?

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For example, assume you have a \$100,000 balanced portfolio that is invested 60% in stocks and 40% in bonds. In a year of strong economic performance, the stock component of your portfolio might generate a return of 12% while the bond component may return -2% because interest rates are rising . Thus, the overall return on your portfolio would be 6.4% ((12% x 0.6) + (-2% x 0.4).

Due to its negative sign, an inverse correlation is also referred to as a negative correlation. A weak positive correlation would indicate that while both variables tend to go up in response to one another, the relationship is not very strong. A strong negative correlation, on the other hand, would indicate a strong connection between the two variables, but that one goes up whenever the other one goes down.

## What Is The Relationship Between Beta And Positive Correlation?

Finally, a value of zero indicates no relationship between the two variables x and y. This article explains the significance of linear correlation coefficient for investors, how to calculate covariance for stocks, and how investors can use correlation to predict the market. We can graph the data used in computing a correlation coefficient.

In general, the correlations of a composite score with the scores from which it is derived tend to be relatively large because of the shared variance of the scores with the composite score. The correlation coefficient is defined as the mean product of the paired standardized scores as expressed in equation (3.3). This interpretation of the correlation coefficient is perhaps best what is a positive correlation illustrated with an example involving numbers. The raw score values of the X and Y variables are presented in the first two columns of the following table. The second two columns are the X and Y columns transformed using the z-score transformation. The correlation coefficient is the slope of the regression line when both the X and Y variables have been converted to z-scores.

## Correlation And Causal Relation

Because it is so time-consuming, correlation is best calculated using software like Excel. Correlation combines statistical concepts, namely, variance andstandard deviation. Variance is the dispersion of a variable around the mean, and standard deviation is the square root of variance. Negative correlation is a relationship between two variables in which one variable increases as the other decreases, and vice versa.

So tracking correlations between variables will help us to understand the movement of one variable with respect to another. 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. Both the Pearson coefficient calculation and basic linear regression are ways to determine how statistical variables are linearly related.

## Correlations

While advertising might bear some influence on the customer’s decision to make a purchase, it won’t be the only factor involved. 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 is currently at 0.98, which is signaling a strong positive correlation. If the correlation coefficient of two variables is zero, there is no linear relationship between the variables.

However, this positive association isn’t causation — a rise in the price S&P 500 likely doesn’t cause the increase in the price of the Facebook stock. Research has shown that people tend to assume FXCM that certain groups and traits occur together and frequently overestimate the strength of the association between the two variables. When the correlation is weak , the line is hard to distinguish.

## Examples Of Negative Correlation

One example of an inverse correlation in the world of investments is the relationship between stocks and bonds. As stock prices rise, the bond market tends to decline, just as the bond market does well when stocks are under performing. A perfectly positive correlation means that 100% of the time, the variables in question move together by the exact same percentage and direction. A positive correlation can be seen between the demand for a product and the product’s associated price. In situations where the available supply stays the same, the price will rise if demand increases. It tells us, in numerical terms, how close the points mapped in the scatterplot come to a linear relationship.

### What is considered a strong negative correlation?

In general, -1.0 to -0.70 suggests a strong negative correlation, -0.50 a moderate negative relationship, and -0.30 a weak correlation. Remember that even though two variables may have a very strong negative correlation, this observation by itself does not demonstrate a cause and effect relationship between the two.

Another difference is the sign of the Pearson correlation coefficient. While a positive correlation coefficient is greater than zero, an inverse correlation what is a positive correlation coefficient is less than zero. Therefore, an inverse correlation coefficient has a negative sign in front of the statistic (e.g., -0.20 or -0.85).