# Negative Correlation

Negative correlation is a term that refers to a type of a correlation in which an increase in one variable is associated with a decrease in another variable.

Correlation shows the measure of the relationship (presence or absence) between two values. The closer it is to one, the more synchronously the analyzed variables move. In this case the negative correlation coefficient is between zero and minus one. Negative correlation means an inverse relationship: growth of one value causes the other value to fall. In trading it is used for building balanced portfolios and risk management.

## Negative Correlation explained

Traders use negative correlation in the following areas:

- diversification of portfolio;
- prediction of market movements;
- losses reduction.

Stocks with negative correlation help traders to create a wise investment portfolio. It is important as market fluctuations in the value of one asset will be fully or partially offset by the opposite change in the price of another.

When the correlation is negative, the rise in price of one asset is accompanied by a fall in value of the other. Such predictability helps to predict the market situation.

If a trader opens many positions, he should make sure that some of them have average or strong negative correlation. This will help reduce losses in case of significant unforeseen market movements. Just as with normal correlation, there are certain limitations for all of these applications.

## Features of Negative Correlation

A trader who calculates negative correlation should keep in mind its features and limitations when using it. The features include:

- ease of calculation;
- clarity.

**Ease of calculation.** When calculating negative correlation the only thing you have to do is to transfer the data to Excel and use the CORREL function (or the analysis package, if there are a lot of data).

**Clarity.** The correlation coefficient, which has a range from 0 to -1, shows the presence of a negative relationship and it is easy to interpret.