Correlation is a statistical term that refers to the relationship between two or more variables. To be more precise, in financial analytics, it measures if there’s any sort of connection in the movements of securities. Correlation shows the presence or absence of such a connection, but it doesn’t indicate that this connection is accidental or is caused by something.
Correlation is measured and presented in a form of correlation coefficient, which value ranges between -1.0 and +1.0. If the coefficient is -1.0, it means that there’s a perfect negative correlation - the securities move in opposite directions, when one of the securities increase, another decreases respectively. The coefficient of +1.0 indicates a perfect positive correlation — the securities move in the same way, increasing and decreasing in a direct relation to each other. If the coefficient is somewhere around zero, no connection is found between the moves of the securities, or there is a connection which is considered insignificant or unimportant.
Importance of Correlation
This coefficient is frequently used in financial analytics to forecast and set up prices, find out current trends and predict possible future trends, by analyzing the history of correlations it’s possible to make informative conclusions and well-grounded theories.
Calculating and studying correlations between the securities in a portfolio might be used as a good way to protect an investor from losses. It’s considered to be a reasonable and safe policy to have diverse assets in the portfolio and monitor the correlation between them. Zero correlation between different securities is a good sign, since it indicates that if one of the securities suddenly falls in price dramatically, other assets won’t likely be affected by it and will move independently. So, observing the correlation of the assets is a useful tool for managing investment risks.
Examples of Correlation
Correlations might be found among various figures, but in financial sphere the most important ones are the correlations between stock prices, between stock prices and benchmark indices, companies’ stock prices and commodity prices and some others. The S&P 500 Index is often used as a benchmark to find out the correlation between it and the security in question, with large cup mutual funds often showing a very high correlation with it, while smaller funds have a lower but still significant correlation as well.
As calculating correlations is highly important for financial analytics, there are different ways to do it, using various software and applications. The main data necessary for the calculation is usually two sets of values (for example, x and y), and the correlation is found in three steps:
- Firstly, it’s necessary to find the sum of all the values of the first set, the sum of values of the second set, and the sum of their combined corresponding values.
- Then, the sums of the squared values of each set must be found.
- At last, the correlation is calculated using the figures from the previous steps and the following formula, where ‘r’ stands for the correlation coefficient, and ‘n’ stands for the number of observations: