Modern portfolio theory (MPT) and its precepts have widespread influence among contemporary portfolio managers. The theory states that risk among simultaneously held assets can be diversified away by reducing positive correlation among them. Managers use statistical analysis of the historical performance of various assets in an attempt to construct portfolios that have the highest potential for positive returns, as long as those returns aren't correlated too closely.

Correlation and Diversification

Correlation is a metric used in statistics to measure the relationship between two variables. A strong positive correlation shows that two variables tend to rise and fall together at the same time. Strong negative correlations show that the two variables tend to move in opposite directions.

This simple concept is the key tool behind portfolio diversification. A diversified portfolio combines different investments to reduce overall risk by finding returns that don't correlate closely. The most common example of assets with low correlation are stocks and bonds. Even though stocks and bonds have gone through periods when they rise and fall at the same time, bonds have typically performed much better in down markets and much worse during times of high growth.

Correlation and Investment Portfolios

Why do fund managers try to reduce positive correlation among assets in their portfolios? The assumption is that a high positive correlation coefficient indicates that both asset prices are driven by the same market forces. It would be risky to invest entirely in assets that move together at the same time; ostensibly, one single economic event could cause all of the assets to lose a lot of value simultaneously.

There are several techniques that fund managers use to estimate potential future correlation. Some use correlation matrices, which presents the correlation among several assets in tabular form. Others rely on the tools of MPT, such as beta and R-squared. Some use more complex econometric regression analysis to see both covariance and probably causation among broad indicators. The end goal is always the same: reducing correlation to reduce downside risk.

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