Low-volatility strategies have quickly become the darling of many investors, thanks largely to trauma caused by the bear market that arose from the 2008-2009 financial crisis combined with academic research showing that the low-volatility anomaly exists in equity markets around the globe.
Earlier this week, we took a detailed look at a 2016 study from David Blitz, “The Value of Low Volatility,” which explored whether low volatility was a unique investment factor or if its performance could be explained by other well-known factors (specifically, value). Today we’ll review some additional research on this issue.
A Deeper Dive Into Low Volatility
Ronnie Shah, author of the 2011 study “Understanding Low Volatility Strategies: Minimum Variance,” found that for the period 1963 through June 2010, the low-beta strategy had exposure to term risk. Its “loading factor” (degree of exposure) on term risk was a statistically significant 0.09 (with a t-stat of 2.6). As further evidence, Tzee-man Chow, Jason Hsu, Li-lan Kuo and Feifei Li, authors of “A Study of Low-Volatility Portfolio Construction Methods,” which appears in the Summer 2014 issue of The Journal of Portfolio Management, found a correlation of 0.2 between the betting-against-beta factor and the duration factor.
Given their positive exposure to term risk, low-volatility stocks have benefited from the cyclical bull market in bonds we have experienced since 1982. That rally can’t be repeated now, with interest rates at historic lows. In addition, the low-volatility factor may not be as unique as Blitz found.
Robert Novy-Marx has also examined the low-volatility factor. His 2016 study, “Understanding Defensive Equity,” covered the period 1968 through 2015. Novy-Marx found that when ranking stocks by quintiles of either volatility or beta, the highest-quintile stocks dramatically underperform, while the performance of the other four quintiles are very similar and marketlike.
In fact, the second-highest-volatility quintile (the fourth quintile) posted the highest returns, followed by the third, second and, finally, the first quintile. This nonlinear relationship is quite different from what we typically find with other common factors, where the returns across deciles, quintiles or quartiles tend to be linear.
Novy-Marx also found that high-volatility and high-beta stocks tilt strongly to small, unprofitable and growth firms. These tilts can explain the poor absolute performance of the most aggressive stocks; stocks that are often referred to as “jackpots” or “lottery tickets.” These stocks make up a very small percentage of total market capitalization. But it is the underperformance of these high-risk (small, unprofitable and growth) stocks that drives the abnormal performance of defensive equity. Novy-Marx also found that a stock’s profitability is a significant negative predictor of its volatility, and it is the single most significant predictor of low volatility.
