Positive Correlation Definition
Positive Correlation is the positive relationship between two variables wherein the movements of variables are positively linked and therefore, if one variable goes up and the other variable also goes up, and vice-versa.
Explanation
- It is the degree by which two variables act similarly. Suppose there is a positive correlation of say 1 between two variables. Then it means that both the variables act exactly the same way. If one goes up by 10%, then the other will also go up by 10% and vice versa.
- A correlation of +0.5 means that if one variable goes up by 10%, the other variable will go up by 5%. So it gives us the degree of dependency of one variable with another. It is very important in predicting the financial crisis and to determine stock prices. It comes from covarianceCovarianceCovariance is a statistical measure used to find the relationship between two assets and is calculated as the standard deviation of the return of the two assets multiplied by its correlation. If it gives a positive number then the assets are said to have positive covariance i.e. when the returns of one asset goes up, the return of second assets also goes up and vice versa for negative covariance.read more.
- Covariance gives the direction of Linear Relationship between two variables. Covariance can take any positive and Negative values.
- Say Covariance between variables X and Y are 1000, and Covariance between variables M and K is 2000. By seeing 1000 and 2000, you can say that both X-Y and M-K are positively related. Means, if one goes up, then others will also go up, but you can’t say that the relation between M-K is doubly strong than the relation between X-Y. So covariance only gives the direction. Correlation is the standardized form of Covariance, which is bounded between +1 to -1. It gives both direction and strength.
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For eg:
Source: Positive Correlation (wallstreetmojo.com)
COV(X,Y) = Covariance between X and Y
- SDX = Standard Deviation of X
- SDY = Standard Deviation of Y
Types
There are mainly three types of positive correlations –
#1 – Strong Correlation (+1.0)
When one variable move in one direction, then other variables also moves in the exact same direction in the same degree, then that is strong. It ranges from Greater than “+0.8” to “+1.0”. A correlation of +1 indicates that the variables are perfectly positively correlated. Means if one variable moves by 10%, then other variables will also move by 10% in the same direction. So it gives both the strength and direction.
#2 – Medium Correlation (+0.5)
When one variable moves in one direction, then other variables also moves in the same direction, but its degree is not the same. Say one stock increased by 10%, and another stock increases by 5%, then both the stocks are moving in the same direction, but the magnitude is not the same.
#3- Low Correlation (+0.2)
Here both variables move in the same direction, but the degree differs immensely. If one variable gives a return of 10%, then another may give a return of 2%. So seeing this, one may just predict that they will move in the same direction, but the movement is really small to gain from it.
Examples of Positive Correlation
Below are the examples to understand the concept in a better way –
Example #1
When the price of petrol increases, the demand for Electric care increases. So every time with an increase in petrol price, it has been found that demand for Electric car has increased, say the correlation between both the products is +0.8
Example #2
Correlation between stocks and markets are measured by Beta in Finance. If a stock has a beta of 1, then it means that if the market on an average gives a 10% return, then the stock will also give a 10% return. So it moves exactly like the market.
If a stock with Beta 1 is added to portfolio replicating Stock Index, then the risk of the portfolio will remain unchanged. If a stock with Beta 0.5 is added, then it will decrease the overall risk of the portfolio as the stock is less risky than the market. Similarly, a Stock with a Beta more than 1 will increase the overall risk of the portfolio.
Example #3
It has been empirically found that when the GDP of a country increases, then the demand for luxury goods also increases. So both the demand for Luxury goods and GDP has a positive Correlation.
Example #4
The price of the Bond is positively Correlated to the Coupon Rate. If the Coupon Rate of a BondCoupon Rate Of A BondThe coupon rate is the ROI (rate of interest) paid on the bond's face value by the bond's issuers. It determines the repayment amount made by GIS (guaranteed income security). Coupon Rate = Annualized Interest Payment / Par Value of Bond * 100%read more is high, then its price will also be high as the bond is giving higher coupons, so the bond will be more attractive in the market, and its price will also start to rise to ignore the risk of the bond.
Example #5
As the Export of a particular country increases, so the demand for the home currency in the international currency market increases because people will need your home currency to make payments for the goods purchased from your country. So the Home currency starts appreciating. This is a Positive Correlation between currency and Exports.
Positive Correlation vs Negative Correlation
Positive correlation shows the positive linear movement of variables in the same direction. If one stock increases and another stock also increases with it, then that it is a positive correlation. A negative correlation is where both variables act in the opposite direction. If one stock increases and other stock decreases, then they are showing Negative Correlation. Positive and Negative correlations are found in many commodities, stocks, and other financial instruments
Conclusion
Positive Correlation is a very important measure that helps us to estimate the degree of the positive linear relationship between two variables. It is the most important measure that is being used by investors and fund managers to increase or decrease risk in a portfolio. It helps us to predict many financial downturns beforehand. If a particular market is positively related to GDP, and if GDP falls, then it can be predicted that the market will also fall. So tracking correlations between variables will help us to understand the movement of one variable with respect to another.
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This has been a guide to Positive Correlation and its definition. Here we discuss examples of positive correlation along with its types and differences from negative correction. You can learn more about from the following articles –
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