A cura di Larry Swedroe, Active and Passive Investing, Low Volatility Investing, Research Insights
One of the big problems for the first formal asset pricing model developed by financial economists, the Capital Asset Pricing Model (CAPM), was that it predicts a positive relationship between risk and return. However, the historical evidence demonstrates that, while the slope of the security market line is generally positive (higher-beta stocks provide higher returns than low-beta stocks), it is flatter than the CAPM suggests.
Importantly, the quintile of stocks with the highest beta meaningfully underperform stocks in the lowest-beta quintile in both U.S. and international markets — the highest-beta stocks provide the lowest returns while experiencing much higher volatility. Over the last five decades, defensive stocks have delivered higher returns than the most aggressive stocks, and defensive strategies, at least those based on volatility, have delivered significant Fama-French three-factor (market beta, size, and value) alphas. This runs counter to economic theory, which predicts that higher expected risk is compensated with a higher expected return.
The low-volatility anomaly has been demonstrated to exist in equity markets around the globe. What’s interesting is that this finding is true not only for stocks, but for bonds as well. The academic research, combined with the 2008 bear market, has led to low-volatility strategies becoming the darling of investors.
For example, as of October 2017, there were seven ETFs with at least $1 billion in AUM:
- iShares MSCI USA Minimum Volatility Index Fund (USMV): $14.2 billion
- iShares MSCI EAFE Minimum Volatility Index Fund (EFAV): $7.6 billion
- PowerShares S&P 500 Low Volatility Portfolio (SPLV): $7.2 billion
- iShares MSCI Emerging Markets Minimum Volatility Index Fund (EEMV): $4.2 billion
- iShares MSCI All Country World Minimum Volatility Index Fund (ACWV): $3.5 billion
- PowerShares S&P MidCap Low Volatility Portfolio (XMLV): $1.2 billion
- PowerShares S&P Small Cap Low Volatility Portfolio (XSLV): $1.8 billion
There were 15 more with at least $100 million of AUM (source).
There are three main explanations offered for the low-volatility anomaly:
- Many investors are either constrained against the use of leverage or have an aversion to its use. Such investors who seek higher returns do so by investing in high-beta stocks, despite the fact that the evidence shows that they have delivered poor risk-adjusted returns. Limits to arbitrage and aversion to shorting, as well as the high cost of shorting such stocks, prevent arbitrageurs from correcting the pricing mistake.
- There are individual investors who have a “taste” for lottery-like investments. This leads them to “irrationally” invest in high-volatility stocks (which have lottery-like distributions) despite their poor returns. They pay a premium to gamble.
- Mutual fund managers who are judged against benchmarks have an incentive to own higher-beta stocks. In addition, managers’ bonuses are options on the performance of invested stocks, and thus more valuable for high-volatility stocks.
Explaining the Low-Volatility Factor
Some recent papers, including 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,” argue 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). For example, Fama and French write in their paper that when using their five-factor model, the “returns of low volatility stocks behave like those of firms that are profitable but conservative in terms of investment, whereas the returns of high volatility stocks behave like those of firms that are relatively unprofitable but nevertheless invest aggressively.” They add that positive exposure to RMW (the profitability factor, or robust minus weak) and CMA (the investment factor, or conservative minus aggressive) also go a long way toward capturing the average returns of low-volatility stocks, whether volatility is measured by total returns or residuals from the Fama-French three-factor model.
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