What Is Cross-Correlation? Definition, How It's Used, and Example

What Is Cross-Correlation?

Cross-correlation is a measurement that tracks the movements of two or more sets of time series data relative to one another. It is used to compare multiple time series and objectively determine how well they match up with each other and, in particular, at what point the best match occurs. Cross-correlation may also reveal any periodicities in the data.

Stock investors measure cross-correlation to identify stocks that move in similar patterns and determine how closely they move in tandem.

Understanding Cross-Correlation

Cross-correlation is generally used when measuring information between two different time series. The possible range for the correlation coefficient of the time series data is from -1.0 to +1.0. The closer the cross-correlation value is to 1, the more closely the sets are identical.

Investors and analysts employ cross-correlation to understand how the prices of two or more stocks—or other assets—perform against one another. This is particularly important for correlation trades such as dispersion strategies and pairs trading.

Above all, cross-correlation is used in portfolio management to measure the degree of diversification among the assets contained in a portfolio. Investors increase the diversification of their assets to reduce the risk of big losses.

For example, the prices of two technology stocks might move in the same direction most of the time, but a technology stock and an oil stock might move in opposite directions. Cross-correlation helps the investor pin down their patterns of movement more precisely.

A purchase of shares of stocks that move in tandem might be hedged with a purchase of a stock that moves in the opposite direction. That protects the overall value of the portfolio.

Cross-Correlation Relationships

In its simplest version, the formula for cross-correlation can be described as an independent variable, X, and two dependent variables, Y and Z. If independent variable X influences variable Y and the two are positively correlated, then as the value of X rises so will the value of Y.

If the same is true of the relationship between X and Z, then as the value of X rises, so will the value of Z. Variables Y and Z can be said to be cross-correlated because their behavior is positively correlated as a result of each of their relationships to variable X.

How Cross-Correlation Is Used

Cross-correlation can be used to gain perspective on the overall nature of the larger market. For example, after the financial crisis in 2011, various sectors within the S&P 500 exhibited a strong degree of correlation. That means that all of those sectors moved virtually in lockstep with each other.

It was difficult to pick stocks that outperformed the broader market during that period. It was also hard to select stocks in different sectors to increase the diversification of a portfolio. Investors had to look at other types of assets to help manage their portfolio risk.

On the other hand, the high market correlation meant that investors could buy shares in index funds to gain exposure to the market, rather than attempting to pick individual stocks.

Limitations of Cross-Correlation

Cross-correlation is used in portfolio management to measure the degree of diversification among the assets contained in a portfolio. Modern portfolio theory (MPT) uses a measure of the correlation of all the assets in a portfolio to help determine the most efficient frontier. This concept helps to optimize expected returns against a certain level of risk.

Including assets that have a low correlation to each other helps to reduce the overall risk in a portfolio.

Cross-correlation can only be measured historically. Moreover, it can change over time. Two assets that have had a high degree of correlation in the past can become uncorrelated and begin to move separately.

This is, in fact, one shortcoming of MPT. It assumes stable correlations among assets.

The Bottom Line

Measuring the cross-correlation of stocks can be a useful exercise for an investor looking to predict future performance based on the past performance of similar stocks. On the other hand, looking for stocks that show no signs of correlation at all can be useful as well. It can help an investor select a stock that will serve as a hedge against occasional losses elsewhere in the portfolio.