Swedroe Spotlight: Explaining The Low Risk Effect
Image source: Pixabay

Swedroe Spotlight: Explaining The Low Risk Effect
Image source: Pixabay

Swedroe Spotlight: Explaining The Low Risk Effect
Image source: Pixabay

Swedroe Spotlight: Explaining The Low Risk Effect
Image source: Pixabay

Swedroe Spotlight: Explaining The Low Risk Effect
Image source: Pixabay

Swedroe Spotlight: Explaining The Low Risk Effect
Image source: Pixabay

Swedroe Spotlight: Explaining The Low Risk Effect
Image source: Pixabay

Swedroe Spotlight: Explaining The Low Risk Effect
Image source: Pixabay

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.

Low Risk
Photo by geralt (Pixabay)

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.


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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.


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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).”


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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.


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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

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