In his 2012 paper, “Enhancing a Low-Volatility Strategy is Particularly Helpful When Generic Low Volatility is Expensive,” Pim van Vliet found that, while on average, low-volatility strategies tend to have exposure to the value factor, that exposure is time-varying.
The low-volatility factor spends about 62% of the time in a value regime and 38% of the time in a growth regime. The regime-shifting behavior affects the performance of low-volatility strategies. When low-volatility stocks have value exposure, on average, they outperformed the market by 2.0%. However, when low-volatility stocks have growth exposure, they have underperformed by 1.4%, on average.
Luis Garcia-Feijoo, Lawrence Kochard, Rodney Sullivan and Peng Wang, authors of the 2015 study “Low-Volatility Cycles: The Influence of Valuation and Momentum on Low-Volatility Portfolios,” found that there was no alpha in a four-factor model except in extremely cheap, low-volatility environments.
This finding is important because, as you will see in the following table, the “curse of popularity” has caused low-beta stocks to move from the value regime to the growth regime. In large-cap, midcap and small-cap categories, low-volatility stocks are more “growthy” than their asset class, and by wide margins. Data is from Morningstar.
More Recent Research
The 2016 study by Bradford Jordan and Timothy Riley, “The Long and Short of the Vol Anomaly,” which covered the period July 1991 through December 2012, was motivated by prior research showing that both high-volatility stocks and stocks with high short interest exhibit poor risk-adjusted future performance.
The authors found that, among high-volatility stocks, those with low short interest actually experience extraordinary positive returns. On the other hand, those with high short interest experience equally extraordinary negative returns. The bottom line is that high volatility on its own is not an indicator of poor future returns; in other words, it’s not an independent factor.
The latest contribution to the research on the low-beta anomaly is from Adam Zaremba, author of the August 2018 study “Small-Minus-Big Predicts Betting-Against-Beta: Implications for International Equity Allocation and Market Timing.” Zaremba examined returns on betting-against-beta (BAB) and small-minus-big (SMB) factor portfolios in 24 developed markets for the years 1989 through June 2018.
An equal-weighted portfolio going long (short) in BAB factors in the quintile of countries with the highest (lowest) three-month SMB return produces a mean return of 1.5% per month. This return was highly significant (t-stat of 7.8). The effect is robust to formation periods and to controlling for major risk factors in equity markets, alternative portfolio construction methods and subperiod analysis. The predictability of BAB performance using SMB returns is also present in time-series of individual country returns. BAB performance is particularly strong following months with high small-firm premiums within and across countries.
This is similar to a finding from the aforementioned study “Time-Varying Leverage Demand and Predictability of Betting-Against-Beta,” which determined that BAB performance is strongest following periods of high market returns.
The research shows not only that returns to the low-volatility anomaly are explained by exposure to other equity factors, but that they are explained by exposure to the term premium.
The fact that low-volatility strategies have exposure to term risk (the duration factor) should not be a surprise. Generally speaking, low-volatility/low-beta stocks are more “bondlike.” They are typically large stocks, the stocks of profitable and dividend-paying firms, and the stocks of firms with mediocre growth opportunities. In other words, they are stocks with the characteristics of safety as opposed to risk and opportunity. Thus, they show higher correlations with long-term bond returns.
The findings from the following papers are all consistent in showing low volatility’s exposure to the term factor: The 2011 study “Understanding Low Volatility Strategies: Minimum Variance” by Ronnie Shah; the 2014 study “A Study of Low-Volatility Portfolio Construction Methods” by Tzee-man Chow, Jason Hsu, Li-lan Kuo and Feifei Li; and the 2014 study “Interest Rate Risk in Low-Volatility Strategies” by David Blitz, Bart van der Grient and Pim van Vliet.