The low-beta anomaly may be persistent and pervasive across the globe, but, research shows its returns are explained well by other now-common factors and its premium is dependent on certain circumstances.

As director of research for Buckingham Strategic Wealth and The BAM Alliance, I’ve been getting lots of questions lately regarding the advisability of investing in the low-beta/low-volatility anomaly. These concerns have been heightened by the financial media’s focus both on the fact that we are now in the longest bull market in history and that current valuations are at historically high levels.

With that in mind, I thought I would review the literature on the low-beta/low-volatility anomaly and the related issue of the “curse of popularity” (that is, what happens when a trade gets “crowded”).

One of the big problems for the first formal asset pricing model developed by financial economists, the CAPM, was that it predicts a positive relationship between risk and return. But empirical studies have found the actual relationship to be basically flat, or even negative. “Defensive” stocks have produced high returns on average in comparison to more “aggressive” stocks. In addition, defensive strategies, at least those based on volatility, have delivered significant Fama-French three-factor and four-factor alphas.

The superior performance of low-volatility stocks was first documented in the literature in the 1970s—by Fischer Black in 1972, among others—even before the size and value premiums were “discovered.” The low-volatility anomaly has been shown to exist in equity markets around the world. Interestingly, this finding is true not only for stocks, but for bonds. In other words, it has been pervasive.

My book, co-authored with Andrew Berkin, “Your Complete Guide to Factor-Based Investing,” included an in-depth discussion of the explanations for the existence and persistence of the anomaly. Among the explanations are:

  • Many investors are constrained against the use of leverage (by their charters) or have an aversion to its use. The same is true of short-selling.
  • Borrowing costs for some hard-to-borrow stocks can be quite high. Such limits can prevent arbitrageurs from correcting the pricing mistake.
  • While an assumption of the CAPM is that markets have no frictions, meaning there are neither transaction costs nor taxes, in the real world, there are costs. The evidence shows that the most mispriced stocks are the ones with the highest costs of shorting.
  • Regulatory constraints, which often don’t differentiate between the risks of low-beta and high-beta stocks, lead some investors to prefer high-beta stocks.
  • There is a preference for “lottery tickets”—high-beta stocks with a low average return but a small chance of a large return.

The academic research, combined with the 2008 bear market, led low-volatility strategies to become the darling of investors. But is it worthy of such admiration as an independent factor? Let’s examine the research.

Other Factor Exposures Explain Returns To Low Beta

Both Robert Novy-Marx’s 2016 study, “Understanding Defensive Equity,” and Eugene Fama and Kenneth French’s 2015 study, “Dissecting Anomalies with a Five-Factor Model,” argued that the low-volatility and low-beta anomalies are well-explained by asset pricing models that include the newer factors of profitability and investment (in addition to market beta, size and value).

Stefano Ciliberti, Yves Lemperiere, Alexios Beveratos, Guillaume Simon, Laurent Laloux, Marc Potters and Jean-Philippe Bouchaud, authors of the 2017 paper “Deconstructing the Low-Vol Anomaly,” studied the factor on a global basis and found that once the common factors of value and profitability are controlled for, the performance of low volatility/low beta becomes insignificant.

Esben Hedegaard, author of the June 2018 study, “Time-Varying Leverage Demand and Predictability of Betting-Against-Beta,” found that high (low) past returns on the market forecast high (low) future returns on the BAB factor—realized BAB returns are higher (lower) following high (low) past market returns. Because expected returns move opposite to prices, high (low) market returns lead to contemporaneously low (high) returns on the BAB factor.

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.

Term Exposure

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.

Performance Analysis

Before summarizing, using the regression tool at Portfolio Visualizer, I’ll analyze the performance of three low-volatility ETFs through a factor model lens to see if the results are consistent with the academic research I have reviewed. The loadings on each factor are in parentheses.

I’ll begin with the iShares Edge MSCI Min Vol U.S.A. ETF (USMV). Data is available for the period November 2011 through June 2018. The table presents the annualized alphas.


First, the loadings should not be surprising. For example, low-volatility stocks tend to be large (hence the negative loading on size) and high-quality, defensive stocks. The negative loading on credit also reflects the high quality of low-volatility stocks.

Second, in terms of alpha, the results are entirely consistent with the findings we have been discussing. While low-volatility strategies have high alphas in a single-factor CAPM and three-factor world, the alphas turn negative once the newer factors of quality and low beta, as well as the term factor, are considered. (Note that this finding is a bit surprising, as I would expect that the inclusion of the low-beta factor would drive alpha toward zero.) In other words, investors are better served by directly targeting exposure to the factors.

We see similar results looking at the performance of the Invesco S&P MidCap Low Volatility ETF (XMLV). The period covers the time for which data is available, from March 2013 through June 2018.


The only major difference from the results we saw with USMV is that, because XMLV is a midcap fund, it has positive exposure to the size factor. Again, once we account for all common exposures, the alphas turn negative.

Again, we see similar results when looking at the performance of the Invesco S&P SmallCap Low Volatility ETF (XSLV). The period also is from March 2013 through June 2018. The one main difference is that, because most of low volatility’s benefit comes from excluding high-volatility, small-cap stocks from the portfolio, the alpha is greater in small stocks for models that don’t account for fixed-income factors and the low-beta factor itself.


Among the three low-volatility funds we examined, there is almost $19 billion in assets generating large negative risk-adjusted alphas. It’s hard to imagine that investors are aware of this.


The low-beta anomaly was documented almost 50 years ago. It has been persistent and pervasive around the globe and across asset classes. However, research demonstrates not only that returns to the anomaly are well-explained by exposure to what are now considered other common factors (mainly value, quality and term), but that the premium is dependent on whether low volatility is in the value or growth regime, whether past recent returns were high or low, and the performance of the size premium.

The returns to the premium have only justified investing when low-beta stocks are in the value regime, after periods of strong market and small-cap stock performance, and when they exclude high-beta stocks that have low short interest. This may be why live funds have been generating large negative alphas once we account for common factor exposures.

In addition, taxable investors should consider the negative tax impact of dividends, as low-beta/low-volatility strategies tend to pay higher dividends. Finally, today’s investors should be concerned about the curse of popularity and the resulting rise in valuations, which historically have predicted negative returns to the low-volatility anomaly.

It’s also important to note that long-only funds that don’t focus on this anomaly can benefit from screening out lottery stocks that drive the poor performance of securities in the highest quintile of beta. For example, firms such as Bridgeway Capital Management and Dimensional Fund Advisors have long screened out such stocks. They also have suspended purchases when stocks are “on special” (that is, they have high security lending fees). Thus, they are able to benefit from the anomaly without shorting. (Full disclosure: My firm, Buckingham Strategic Wealth, recommends Bridgeway and Dimensional funds in constructing client portfolios.)

This commentary originally appeared September 14 on

By clicking on any of the links above, you acknowledge that they are solely for your convenience, and do not necessarily imply any affiliations, sponsorships, endorsements or representations whatsoever by us regarding third-party Web sites. We are not responsible for the content, availability or privacy policies of these sites, and shall not be responsible or liable for any information, opinions, advice, products or services available on or through them.

The opinions expressed by featured authors are their own and may not accurately reflect those of the BAM ALLIANCE®. This article is for general information only and is not intended to serve as specific financial, accounting or tax advice.

© 2018, The BAM ALLIANCE®

Similar Posts