Swedroe: Low Vol Advantage Not What You’d Expect

One of the problems for the first formal asset pricing model developed by financial economists, the Capital Asset Pricing Model (CAPM), was that it predicted a positive relationship between risk and return. However, empirical studies have found the actual relationship to be flat, or even negative. Over the last 50 years, the most “defensive” stocks have delivered higher returns than the most “aggressive” stocks.

Additionally, defensive strategies, at least those based on volatility, have delivered significant Fama-French three-factor (beta, size and value) and Carhart four-factor (adding momentum) alphas.

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

Explaining The Low-Volatility Advantage 

One of the assumptions of the CAPM is that there are no constraints on either leverage or short-selling. But in the real world, 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, and the borrowing costs for some difficult-to-borrow stocks can be quite high. Such limits to arbitrage, combined with an aversion to shorting and the high costs of shorting certain stocks, can prevent arbitrageurs from correcting the pricing mistake.

Another assumption made by the CAPM is that markets have no frictions, meaning there are neither transaction costs nor taxes. Of course, in the real world, there are costs. The evidence shows that the most mispriced stocks are the ones with the highest costs of shorting.

The explanation for the low-volatility anomaly, then, is that, faced with constraints and frictions, investors looking to increase their return choose to tilt their portfolios toward high-beta securities to capture more of the equity risk premium. This extra demand for high-beta securities, and reduced demand for low-beta securities, may explain the anomaly of a flat or even inverted relationship between risk and expected return relative to the predictions of the CAPM.

The academic research, combined with the experience of the bear market in 2008, has led to low-volatility strategies becoming the darling of investors. For example, as of June 2016, there were five ETFs with at least $3 billion in AUM:

• PowerShares S&P 500 Low Volatility Portfolio (SPLV): $7.9 billion
• iShares Edge MSCI Minimum Volatility USA ETF (USMV): $15.1 billion
• iShares Edge MSCI Minimum Volatility Emerging Markets ETF (EEMV): $3.8 billion
• iShares Edge MSCI Minimum Volatility Global ETF (ACWV): $3.2 billion
• iShares Edge MSCI Minimum Volatility EAFE ETF (EFAV): $7.5 billion

Two Schools Of Thought

There are two main explanations offered in the academic literature for the low-volatility advantage:

1. Many investors are either constrained against the use of leverage or have an aversion to employing it. Such investors who seek higher returns tend to do so by investing in high-beta (or high-volatility) stocks, despite the fact that the evidence shows they have delivered poor risk-adjusted returns. Limits to arbitrage and aversion to shorting, as well as the high costs associated with shorting, prevent arbitrageurs from correcting the pricing mistake.
2. There are investors who have a “taste,” or preference, for lotterylike investments. This leads them to “irrationally” invest in high-volatility stocks (which have lotterylike distributions) despite their poor returns. In other words, they pay a premium to gamble.

The research has also found that much of the anomaly can be explained by the poor performance of high-volatility stocks, not the outperformance of low-volatility stocks. In fact, there has been very little difference in returns between low- and medium-volatility stocks. Thus, you don’t need to adopt a low-volatility strategy to benefit from this information. You just have to avoid owning the highest-volatility stocks (such as small growth stocks with low profitability and high investment, IPOs and very-low-priced stocks).

David Blitz contributes to the literature with his paper, “The Value of Low Volatility,” which appeared in the Spring 2016 issue of The Journal of Portfolio Management. His study covered the period 1928 through 2014. Using both equal-weighted and value-weighted portfolios, Blitz examined whether low volatility was a unique factor, or whether its performance could be explained by other well-known factors (specifically value).

Defining value by book-to-market ratios, Blitz found that the Fama-French value factor is unable to explain the alpha of low-volatility strategies (with the exception of the period from 1963 through 1984, and even in this case, it was true only for large value stocks).

Blitz also found that on a value-weighted basis, large-cap low-volatility strategies have a similar (or slightly lower) average return than the market, over the entire sample period and also during each subperiod. But their volatility was about 20% below that of the market. He writes: “As a result, the Sharpe ratios of the large-cap low-volatility strategies are consistently higher than those of the market, which confirms the existence of a low-volatility effect.”

When examining small-cap stocks, Blitz found: “The small-cap low-volatility strategies also exhibit consistently higher Sharpe ratios, in fact even more so than their large-cap counterparts, but mainly because of their high mean return. The volatility of the small-cap low-volatility strategies shows a mixed picture, being higher than the market in some instances, and lower than the market in other instances.”

Low-Volatility Factor Separate From Value
Blitz further found that three-factor alphas for various low-volatility strategies “are all significant at the 1% level over the full-sample period from January 1929 to December 2014.” Thus, their alphas were all significant after controlling for the beta, size and value factors. In addition, he found that the loading on the value factor varied significantly over time. This reflects an important point we’ll discuss later on.

 Finally, Blitz concluded: “The low-volatility effect is a distinct phenomenon which cannot simply be dismissed as another manifestation of the value effect. Comparing the results for the two effects, it seems that the combined evidence for the low-volatility effect is at least as strong as that for the value effect, or perhaps even stronger.”

He added: “Instead of choosing between harvesting either the value premium or the low-volatility premium, investors should simply benefit from both. The two factors are distinct phenomena, and the results in this paper suggest that during prolonged periods of time when one factor fails to deliver, the other factor can provide relief.”

Before you draw any conclusions, however, it’s important to review some of the other research. Later this week, we’ll delve into additional studies on low volatility, explore whether low-volatility strategies have now become overgrazed, and take another look at low-volatility strategies’ exposure to the value factor.

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