Swedroe: Explaining The Low Vol Anomaly

May 31, 2017

A major problem for the first formal asset pricing model developed by financial economists, the CAPM, is that while it predicts a positive relation between risk and return, empirical studies have found the actual relation to be flat, or even negative.

Over the last 50 years, the most “defensive” (low-volatility, low-risk) stocks have delivered both higher returns and higher risk-adjusted returns than the most “aggressive” (high-volatility, high-risk) 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.” Interestingly, the low-volatility anomaly has also been found to exist in equity markets around the globe, and not only for stocks but for bonds as well. In other words, it is pervasive.

While finance students are taught that expected return is a linear function of risk within most asset classes, highly risky assets have generated consistently lower returns than those with average risk, and after transaction costs are included, risky asset classes such as options have been poor investments. And the riskier the investment, the worse the result. Yet risky assets attract excessive interest from individual as well as institutional investors.

Publication of the findings on the low-volatility anomaly combined with the bear market caused by the financial crisis of 2008-2009 led to a dramatic increase in the popularity of low-volatility strategies.

As one example, as of April 2017, the iShares Edge MSCI Minimum Volatility USA ETF (USMV) had assets exceeding $12 billion, and the PowerShares S&P 500 Low Volatility ETF (SPLV) had assets exceeding $6 billion.

Explaining The Anomaly

Pim Van Vliet and Jan de Koning’s new book, “High Returns from Low Risk: A Remarkable Stock Market Paradox,” provides the evidence and theory behind the anomaly. The book is a very simple and easy read, without complex, technical jargon or math that can be difficult for nonprofessionals to comprehend. Van Vliet presents his journey from novice investor to academic to practitioner, employing the lessons learned from his own experiences and the published research.

While academics explain the anomaly through such complex mechanisms as limits to arbitrage (which prevent sophisticated investors from correcting mispricings), the authors provide easy-to-understand behavioral explanations (also in the literature).

As an example, they show how relative (instead of absolute) risk can be a career-killer for fund managers. “Relative risk means that you deviate from your peers and your benchmark. Suppose you find a perfect stock with a guaranteed 10% return per year and the market goes up by 30% in one year and down 7% the next. After two years both investments would have grown by an average compounded rate of 10%.”


“However, in the first year you lag the market by 20% and in the second year outperform by 17%. In the first year you won’t receive any bonus and could even be fired. In the second year, if you still have your job, you will outperform by 17% but only make up your previous underperformance. So from a relative perspective, this no-risk stock is very risky and unattractive. So from the relative risk perspective of an investment professional managing his career, it makes perfect sense to pass up the opportunity of investing in a low-risk strategy.”

The authors go on to explain that relative risk and career risk keep the pros away from low-risk stocks, and the unloved strategies become underowned, providing opportunities for individuals to exploit—as long as they don’t care about relative risk (how they are doing relative to some market benchmark).

In other words, individuals are less constrained then professionals. As they note: “If you don’t have a benchmark, lucky you!”

They provide another explanation for the anomaly. The research shows that, in general, individual investors have a preference for high-risk stocks, otherwise known as the “lottery effect.” Investors have a “taste” for exciting, high-risk investments and they overinvest in these “lottery tickets.”

The authors add that young equity analysts hoping to climb the career ladder can also have a preference for stocks with high potential to deliver outperformance. In other words, they place career risk ahead of decades of evidence.

Low-Vol Valuations
As I noted above, the publication of the academic research and the bear market of 2008 has led to a heightened interest in low-volatility investing. This creates the risk of “overgrazing,” which could cause the superior performance to disappear or even reverse.

We can see evidence of the impact of cash flows into low-volatility stocks by comparing the valuation metrics of USMV to those of the iShares Russell 1000 ETF (IWB), which is a market-oriented fund, and the iShares Russell 1000 Value ETF (IWD).

Note that, historically, low-volatility stocks are value stocks—stocks with low prices to metrics such as book-to-market and earnings. Data is from Morningstar as of April 6, 2017.



It’s clear that cash inflows have raised the valuations of low-volatility stocks, dramatically reducing their once-significant exposure to the value premium to zero or negative, lowering expected returns.

Specifically, as low-volatility stocks have been bid up in price, low-volatility portfolios have lost their value characteristics, in turn reducing the forward-looking returns. In other words, while low volatility still may predict low volatility, it may no longer result in higher returns than high-volatility stocks.


Van Vliet and de Koning address this issue by noting that it’s not sufficient to look at low volatility. They explain that in managing Robeco’s Conservative Equity funds, they focus on buying low-volatility stocks that also have high-dividend yields and share buybacks (as both are sources of income to shareholders).

They also screen for momentum, which the research has shown helps to avoid what are called “value traps”—stocks that are cheap for a very good reason. Thus, they look for low-volatility stocks with high total dividend yield and positive momentum (price trend).

Specifically, they rank the 500 low-volatility stocks by both total yield and momentum and then buy the 100 stocks with the highest combined score. It’s that simple.

Beyond Just Low Vol

I’d add that these two additional screens are important, as some of the latest academic research shows that much of the low-volatility phenomenon can be explained by the exposure of low-volatility stocks to the new factors of investment and profitability (which are now included in the new Fama-French five-factor model.

Thus, without these additional screens, while low-volatility stocks will likely continue to be low volatility (the research shows that past volatility predicts future volatility), given the huge increase in cash flows and valuations, they might otherwise no longer produce superior risk-adjusted returns.

Van Vliet and de Koning’s simple algorithm-based methodology is similar to the simple algorithm approach, or “magic formula,” presented by Joel Greenblatt in his book “The Little Book That Beats the Market.”

His two screens are for value and profitability. Both demonstrate that simple algorithms (which provide systematic approaches to gaining exposure to well-documented factors) have been able to outperform the vast majority of professional investors using their discretion. It’s the “machine is superior to man” story, the explanation being that man is subject to many biases that are difficult to overcome.

Summarizing the book, Van Vliet and de Koning conclude that the fact that low-risk stocks beat high-risk stocks is an inconvenient truth—inconvenient because it demonstrates that standard asset pricing models are wrong. For those interested in low-risk investing, reading “High Returns from Low Risk: A Remarkable Stock Market Paradox” is well worth the time.

And if you’re interested in the academic research on the low-volatility anomaly, you’ll find it summarized in Appendix D of my new book, co-authored with Andrew Berkin, “Your Complete Guide to Factor-Based Investing: The Way Smart Money Invests Today.”

Larry Swedroe is the director of research for The BAM Alliance, a community of more than 140 independent registered investment advisors throughout the country.


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