Interesting Facts to Know About Correlation Coefficient

Correlation coefficient is a statistical measure, with its purpose being the determination of strength relationship between two variables. By comparing two variables in this way, it becomes easy to know about the relative movement of those variables. The correlation coefficient has a fixed range of values. It falls between -1.0 and 1.0. If calculated values fall beyond this range, it means that the measurement of values has some inaccuracy.

And if we talk about a specified range of values, here both aspects show perfect values. For example, the negative value of -1.0 shows that the relationship is perfectly negative. And a positive value in the form of 1.0 shows that the correlation is perfectly positive. Also, it is common to have 0 value in the measurement. This is because zero value shows that when two variables move, they don’t go for linear movement. So for zero value, it is considered as a nonlinear relationship between the two variables.

The use of correlation coefficient is commonly observed within the field of finance and especially when writing a dissertation. Also, all investing sectors use this parameter for many measurements. Now let’s discuss how it plays a role in both of these departments.

Correlation Coefficient and the Investors

For investors, correlation coefficient works in the best way. With the help of this relation, investors get to know about market trends. These trends can be related to finance, or the economy. In the same way, stock market prices can also be evaluated by using this strong relationship. In finance market, the relation of two variables is the most important thing. Let’s understand it with the help of an example. By having a correlation coefficient, it becomes easy to understand the concept of mutual fund performance. This mutual fund performance is evaluated as per the fixed benchmark index. Or you can also check this mutual fund performance as per another asset class.

Here the presence of negative correlation saves you from the market risks. It also helps in understanding that the investors have diverse benefits. The negative value of correlation coefficient works as a barrier for the portfolios of investors. It provides a safe side to the investors from price variations of the market. Now the main source of investors’ incomes comes from stock. And the rate of loss reduces up to a great extent as well. The rate of variation between two variables shows the rate of change in statistics in general.

Correlation Coefficient in the Banks

Suppose the case of a bank and its financial dealings. In the case of bank stock, you will see a highly positive correlation. This highly positive correlation is related to interest. And the calculation of loan is always based on market interest rate. The relation in stock price and interest rate are inversely proportional to each other. The fall in one causes the rise in other. Like, the falling rate of stock price indicates a rise in the interest rate. In the same way, a rise in stock price causes fall in the interest rate.

Reasons for Falling Stock Prices

There could be two reasons for the falling rate of stock price. There might be an overall fall in all the banks that is caused by interest rates. But if a particular bank is facing this fall, it means that there is a lack of good performance. There can be internal, or external factors that are causing poor performance of the team as well. If you sort out such fundamental issues it will result in better performance. And ultimately, there will be a rise in the bank’s stock price too. Same is the case is with all other organisations.

Correlation Coefficient in the Oil Companies

The oil prices are mostly compared with the gas prices. Correlation is taken positively to some extent. And this positive correlation is totally logical as gas is the by-product of oil. Same goes for the relation of two products varies based on their specifications. The statistical analysis shows that the positive value of correlation coefficient is limited, but it does exist. The correlation coefficient does statistical measurement in the form of degree. And show a shift of price in terms of degree.

The positive value in the form of 1.0 shows that both prices are moving in the same direction. And shift in the form of degree is also similar for both the products. Suppose the price of oil is increasing. A 1.0 value shows that the price of gas is also increasing. And if the value of the correlation coefficient is -1.0, it shows that both prices are moving opposite to each other. And that the proportion of both prices is same all the time. The third scenario is also evident within this context. It should be that both of the prices don’t have any relationship. And this way, you get the value of 0. It means that the rise, or fall of one price does not affect the other.

Record of Correlation Coefficient

The record of correlation coefficient shows that the value was 0.45 in 2004. And this value is moderately positive. In 2010 it fell down to -0.006. In 2014 it maintained a positive correlation up to 0.075. The correlation of -0.006 and 0.075 is taken as little correlations until now. In 2015 it went up to 0.195, which is a moderately positive correlation. From 2009 to 2020, the correlation again remains insignificant. The highly positive correlation coefficient was observed in 2005, and the lowest in 2010. Its highest value went up to 0.699. And the lowest value went up to -0.21. If we wind up this correlation coefficient overall, we can say that the correlation is falling.

Pros, And Cons Of The Correlation Coefficient

The advantage of correlation coefficient is that it is the easiest way of interpreting something. And you can easily work on this. You don’t need to spend so much timing for learning it either. This is because you can easily get the working system. Use the equation, and put the values of parameters. This way you will end up with a relationship in front of you.

But there are some disadvantages of the correlation coefficient as well. If you want a linear relationship, then the correlation coefficient works best. But if you want to have a nonlinear relationship, then the correlation coefficient would not work. And if you try nonlinear relationships here, there will be many errors in it. In short, it will cause wastage of your time, and effort. Similarly, correlation coefficients do not work for a categorical data type either. I hope these indications will help you in making better use of the correlation coefficient in your research.