Ryan Litfin & Dale Creed Francis often use the word “non-correlated” to identify an important characteristic of investments. Some readers and Financial Fortitude radio show listeners might be unfamiliar with its meaning, or maybe they could use a quick refresher on correlation.
Correlation is important to investing because, along with risk, it explains diversification. Correlation is a statistic that measures how much two things co-vary – how much they change in the same direction at the same time or vice versa.
Portfolio managers generally favor low-correlated assets because their low covariance [i] mollifies volatility and creates diversification. These next three graphs [ii] provide hypothetical illustrations of what correlation can look like.
The “High Correlation” graph illustrates performance of two assets whose correlations are high. To make an apples-to-apples comparison, the assets’ returns were designed to impose the same level of risk and yield nearly identical returns; these are not necessary conditions for high correlation. These assets replicate each others’ performance, so portfolio managers would not normally hold both of these assets in the same portfolio. Although this is an extreme example of high correlation, it resembles the kind of correlation many stock portfolios produce. This is especially true during bear markets and severe sell-offs when correlations increase. [iii]
The “Zero Correlation” graph illustrates two zero-correlated assets. In the interest of consistency, both assets impose the same level of risk, and returns are nearly identical. However, when one asset’s price increases, the other’s price changes unpredictably.
The graph titled “Zero-Correlated vs High-Correlated Portfolios” treats the assets represented in each of the first two graphs as a portfolio. The graph illustrates changes in the value of the two portfolios. The only difference between the two portfolios lies in the correlation of assets inside the portfolios.
Notice differences between blue and red portfolio lines. The blue line represents the value of a portfolio containing high-correlated assets. Compared to the red line, the blue line’s highs are higher and its lows are lower. The portfolio represented by the red line gives a smoother ride. This smoother ride is the diversification effect, the mollification of volatility, the benefit of combining low- or zero-correlated assets.
These hypothetical examples are based on the prices of investments in stocks. Unfortunately, even when their correlations usually equal zero, financial assets – securities backed by paper and issuers’ promises, not physical assets –can still co-vary on the bigger trends. This outcome is evident in the declines and increases of both lines in all three graphs between about day 14 and day 26. A jarring real life example was the performance of stocks and real estate investment trusts during the 2007 – 2009 Great Recession sell-off.
This is where “non-correlated” performance parts with “zero-correlated” performance.
The distinction is subtle but meaningful. Non-correlated performance implies true independence from the performance of other kinds of assets. Based on historical performance, the bigger trends in non-correlated assets do not typically correlate with the bigger trends witnessed in financial assets. [iv] In our opinion, non-correlated assets deliver a high level of portfolio diversification.
In the 1952 article [v] that spawned “Modern Portfolio Theory,” Harry Markowitz wrote: We should diversify across industries because firms in different industries, especially industries with different economic characteristics, have lower covariances than firms within an industry.
With the benefit of hindsight, we know Dr. Markowitz was on to something powerful. For the next 67 years, investing methods and strategies evolved. Today, opportunities to diversify are not limited to investment across industries; they include new tactics and strategies for managing risk.
[i] “Covariance” and “correlation” are often used interchangeably. Technically speaking, correlation is a statistically standardized form of covariance.
[ii] Data used to construct these graphs are based on actual stock prices and returns. However, to help render apples-to-apples comparisons of risk and returns, the data were modified. These are realistic but hypothetical illustrations.
[iii] Longin, F. Solnik, B. (2001). Extreme correlation of international equity markets. The Journal of Finance, 56(2), 649-676.
[iv] Nobody guarantees this relationship, and historical performance does not guarantee future performance.
[v] Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77-91.
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