Swedroe Spotlight: Explaining the Low Risk Effect

As my co-author, Andrew Berkin, and I⁽¹⁾ explain in our new book, “Your Complete Guide to Factor-Based Investing,”⁽²⁾ one of the big problems for the first formal asset pricing model developed by financial economists, the CAPM, was that it predicts a positive relation between risk and return. But empirical studies have found the actual relation to be flat, or even negative. Over the last 50 years, the most “defensive” (low-volatility, low-beta) stocks have delivered both higher returns and higher risk-adjusted returns than the most “aggressive” (high-volatility, high-beta) stocks. In addition, defensive strategies, at least those based on either volatility or beta, have delivered significant Fama-French three-factor and four-factor alphas.

What is the Low Risk Effect?

Before proceeding, it’s important to note that beta and volatility are related, though not the same. Beta depends on volatility and correlation to the market, whereas volatility is related to idiosyncratic risk (see here for an explanation of how to calculate the different measures).

The superior performance of low-volatility and low-beta 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.” And 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 as well. In other words, it has been pervasive.

Why Does the Low Risk Effect Exist?

There have been two categories of competing theories that attempt to explain the low risk effect. The first is that it’s related to one of the assumptions of the CAPM, which is that there are no constraints on either leverage or short-selling. However, in the real world, many investors are either 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 hard-to-borrow stocks can be quite high. Such limits can prevent arbitrageurs from correcting mispricings. Another assumption of the CAPM is that markets have no frictions, meaning there are neither transaction costs nor taxes. And, of course, that isn’t true in the real world. And the historical evidence shows that the most mispriced stocks are the ones with the highest costs of shorting.

The explanation for the low-volatility/low-beta 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 garner more of the equity risk premium. This extra demand for high-volatility/high-beta securities, and reduced demand for low-volatility/low-beta securities, may explain the flat (or even inverted) relationship between risk and expected return relative to the predictions of the CAPM model.

Another explanation long posited in the literature is that constraints on short-selling can cause stocks to be overpriced — in a market with little or no short selling, the demand for a particular security comes from the minority who hold the most optimistic expectations about it. This phenomenon is also referred to as the winner’s curse. Divergence of opinion is likely to increase with risk — high-risk stocks are more likely to be overpriced than low-risk stocks because their owners have the greatest bias.

Another explanation for the low-risk premium comes from the fact that while one of the assumptions under the CAPM is that investors are risk-averse, we know that in the real world there are investors with a “taste,” or preference, for lottery-like investments — investments that exhibit positive skewness and excess kurtosis (example of this research is here). This leads them to “irrationally” (from an economist’s perspective) invest in high-volatility stocks (which have lottery-like distributions) despite their poor returns. In other words, they pay a premium to gamble. Among the stocks that fall into the “lottery ticket” category are IPOs, small-cap growth stocks that are not profitable, penny stocks and stocks in bankruptcy. Again, limits to arbitrage and the costs and fear of shorting prevent rational investors from correcting the mispricings.

In our book, my co-author and I provide further explanations from the academic research, including investor overconfidence, regulatory constraints and even fund manager incentives. Summarizing, we can split the explanations into two major groups: leveraged constraints or behavioral effects.

The Latest Research on the Low Risk Effect

The latest contribution to the literature on the low-risk phenomenon is from Cliff Asness, Andrea Frazzini, Niels Joachim Gormsen and Lasse Heje Pedersen, authors of the January 2017 study “Betting Against Correlation: Testing Theories of the Low-Risk Effect.” They suggest that if the low-risk effect is driven by leverage constraints, risk should be measured using systematic risk (beta). On the other hand, if the low-risk effect is driven by behavioral effects, then risk should be measured using idiosyncratic risk (volatility) — stocks with low idiosyncratic risk outperform stocks with high idiosyncratic risk.

The authors noted that “the challenge with the existing literature is that it seeks to run a horse race between factors that are, by construction, highly correlated since risky stocks are usually risky in many ways… Hence, the most powerful way to credibly distinguish these theories is to construct a new factor that captures one theory while at the same being relatively unrelated to factors capturing the alternative theory. To accomplish this, we decompose BAB into two factors: betting against correlation (BAC) and betting against volatility (BAV). BAC goes long stocks that have low correlation to the market and shorts those with high correlation, while seeking to match the volatility of the stocks that are bought and sold.” Note that stocks with low correlation to the market are likely to have low betas. They write: “Likewise, BAV goes long and short based on volatility, while seeking to match correlation. This decomposition of BAB creates a component that is relatively unrelated to the behavioral factors (BAC) and a closely related component (BAV).”

Their study tested these theories using broad global data, controlling for more existing factors, using measures of the economic drivers and using new factors that they call betting against correlation (BAC) and scaled MAX (SMAX), with MAX being the average of the five highest daily returns over the last month. Note that a stock can have a high MAX because of high volatility or high positive skewness. The study covered the period from 1926 through 2015 for U.S. stocks and additional data for 23 other developed markets (with data going back to July 1990). They found that BAC is about as profitable as the BAB factor and BAC has a highly significant CAPM alpha as predicted by the theory of leverage constraints.

They then turned to the behavioral theory, considering the factors that go long stocks with low MAX return (LMAX) or low idiosyncratic volatility (IVOL). For LMAX they created a new factor that helps differentiate alternative hypotheses by removing the common component (namely, volatility). Just as they created BAC to remove the effect of volatility from beta (which left just correlation), they removed the effect of volatility from MAX, using their SMAX factor that goes long stocks with low MAX return divided by ex-ante volatility and shorts stocks with the opposite characteristic. This factor captures lottery demand in a way that is not as mechanically related to volatility as it is more purely about the shape of the return distribution. Behavioral theories imply that these idiosyncratic risk factors should have positive alphas. And the data confirmed the hypothesis.

The authors also found that BAB and BAC “are predicted by measures of leverage constraints, while these factors are not predicted by investor sentiment.”⁽³⁾ In contrast, Asness, et al found that their behavioral measures MAX and IVOL are related to sentiment, but not measures of leverage constraints. They concluded: “This evidence is consistent with both of the alternative theories playing a role and that the alternative factors may, to some extent, capture different effects.”

There were other interesting findings:

• The BAC factor loads substantially on the small-minus-big factor as firms with, for the same volatility, low correlation often are small, undiversified firms.
• The BAC factor has a positive loading on the value factor (HML), consistent with the theory of leverage constraints. The theory of leverage constraints predicts that safe stocks, those with low correlation and volatility, become cheap because they are “abandoned” by leverage constrained investors, giving rise to a positive HML loading.
• For the BAC factor, the loadings on the profitability factor RMW (robust minus weak) and the investment factor CMA (conservative minus aggressive) also tend to be positive, especially those of RMW. This should be expected as they are measures of quality and safety.
• While low-correlation stocks, holding volatility constant, tend to be small stocks, low-volatility stocks, holding correlation constant, tend to be big stocks.
• The idiosyncratic (behavioral) factors tend to load on the quality variables RMW and CMA.
• Both BAC and BAB have higher future returns when leverage constraints are high (margin debt is low) and contemporaneous increases in margin debt are associated with positive returns to BAB and BAC. This is consistent with the theory that investors shift their portfolios toward low-risk stocks when leverage constraints decrease.
• IVOL has higher returns when ex-ante investor sentiment is high, consistent with behavioral demand, but is unrelated to margin debt (and thus is unrelated to leverage constraints).

Summary

The bottom line for investors is that in the case of the low-risk phenomenon, the world isn’t black or white.⁽⁴⁾ As the authors concluded: “The low-risk effect can be driven by more than one economic effect and the evidence is not inconsistent with both leverage constraints and lottery demand playing a role.” This is very similar to the findings on the value factor. The source of the value premium is one of the great debates in finance: Is it risk or behavior? With strong evidence for both explanations, it seems likely that while the value premium isn’t a free lunch (there are simple/logical risk-based explanations), it might just be a free stop at the dessert tray (there are good behavioral explanations as well).

The strong evidence demonstrating the superior risk-adjusted performance of low-risk stocks has led to a dramatic increase in investor interest, and flows into funds seeking to capture to returns of low-risk strategies.

Appendix: Have Low-Risk Strategies Become Overgrazed?

As is the case with so many well-known anomalies and factors, the problem of potential overgrazing does exist.⁽⁵⁾ Published research on the premium, combined with the bear market caused by the financial crisis of 2008, led to a dramatic increase in the popularity of low-volatility strategies. For example, as of February 2017, the iShares Edge MSCI Minimum Volatility USA ETF (USMV) had more than $12 billion in assets. Strong cash inflows have raised the valuations of defensive (low-volatility/low-beta) stocks, dramatically reducing their exposure to their value premium from quite high to negative, and thus lowering expected returns. Specifically, as low-volatility stocks are bid up in price, low-volatility portfolios can lose their value characteristics, which reduces their forward-looking returns (see here for a detailed discussion on the issues of buying high valuation low-volatility stocks).

We will take a look at the valuation metrics the iShares Edge MSCI Minimum Volatility USA ETF (USMV). We will then compare its value metrics to those of the iShares Russell 1000 EFT (IWB), which is a market-oriented fund, and the iShares Russell 1000 Value ETF (IWD). Data is from Morningstar as of February 20, 2017.

What is clear from the data is that the demand for these strategies has altered their very nature. In the past, the valuation metrics of USMV were more value-oriented than the Russell 1000. However, now these metrics certainly do not mirror the holdings of a classic value-oriented fund. These funds’ price-to-earnings, book-to-market, and price-to-cash flow ratios are all quite a bit higher than those of IWD. In fact, the metrics indicate that USMV is now more “growthy” than the market-like IWB. In other words, because there is an ex-ante value premium, what low volatility is predicting at this point in time is not higher returns, just low future volatility. While investors should always prefer buying stocks at lower valuations, what we do not know is how big of an impact valuations will have on low-volatility strategies. Research from a 2012 white paper by Pim van Vliet, “Enhancing a Low Volatility Strategy is Particularly Helpful When Generic Low Volatility is Expensive,” sheds some light on this question. Using data from 1929 through 2010, he 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 percent of the time in a value regime and 38 percent of the time in a growth regime.

This regime-shifting behavior impacts the performance of low-volatility strategies. When low-volatility stocks have value exposure, they have outperformed the market, returning an average of 9.5 percent annually versus the market’s 7.5 percent. The low-volatility factor has also exhibited lower volatility, with an annual standard deviation of 13.5 percent versus the market’s 16.5 percent. However, when low-volatility stocks have growth exposure, they have underperformed, returning an average of 10.8 percent annually versus the market’s 12.2 percent. The low-volatility factor did continue to have lower annual volatility, at 15.3 percent versus 20.3 percent for the market. The result has been a higher risk-adjusted return in either regime. The bottom line is that in either regime, low volatility predicts future low volatility. However, when low volatility has negative exposure to the value factor (as it did in mid-2016), it also forecasts below-market returns.

It seems likely that at least some investors have taken notice of the high valuations and become concerned because, despite rising equity prices, UMLV actually has slightly less assets under management than it did in April 2016.

Appendix: Summary

The evidence suggests that you might be better served by investing in vehicles that screen out high-volatility (or high-beta), high-risk stocks. In other words, consider investing directly in size, value and profitability/quality rather than doing so indirectly (like defensive strategies do).