Inverse Correlation

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Definition of 'Inverse Correlation'

Inverse correlation is a statistical relationship between two variables in which one variable increases as the other decreases. This is the opposite of positive correlation, in which both variables move in the same direction.

Inverse correlation can be used to identify potential trading opportunities. For example, if two stocks are inversely correlated, then a trader could buy one stock and sell the other, expecting to make a profit as the prices of the two stocks move in opposite directions.

However, it is important to note that inverse correlation is not always perfect. In fact, the two variables may not always move in exactly opposite directions. This is why it is important to use caution when trading based on inverse correlation.

There are a few different ways to measure inverse correlation. One common method is to use the Pearson correlation coefficient. This coefficient ranges from -1 to 1, where -1 indicates perfect inverse correlation and 1 indicates perfect positive correlation. A value of 0 indicates no correlation.

Another method of measuring inverse correlation is to use the Spearman rank correlation coefficient. This coefficient is similar to the Pearson correlation coefficient, but it is not affected by outliers.

Inverse correlation can be a useful tool for traders, but it is important to use caution when using it. It is important to understand the limitations of inverse correlation and to use it in conjunction with other technical analysis tools.

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