It’s not breaking news that growth stocks have far outpaced those labeled or placed in the ‘value’ bucket. By definition the former should be growing, its share price increasing, at a faster pace than the latter. Value investors know they are going to need patience and time for others to discover and learn the true worth of the businesses they believe aren’t getting their due. Today’s investing advice explores the wisdom of this approach.
As it happens, the premium being paid for growth over value is now at the highest level since the late 2000 dot.com bubble.
And while we know one of the basic tenants of investing is that to achieve higher returns, one usually has to accept greater risk, ValueWalk has done a nice analysis showing how the equation also ties into respective volatility levels across various asset classes.
Here they present Deconstructing the Low Volatility/Low Beta Anomaly. It’s bit wonky, but worth the effort:
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.
Ciliberti Stefano, Yves Lemperiere, Alexios Beveratos, Guillaume Simon, Laurent Laloux, Marc Potters and Jean-Philippe Bouchaud provide the latest contribution to the literature on the low-volatility/low-beta anomaly with the study “Deconstructing the Low Anomaly,” which appears in the Fall 2017 issue of The Journal of Portfolio Management (online copy available here). Depending on the market, their study covered the period from (market in parentheses) 1970 (U.S.), 2002 (Europe), 2001 (U.K.), 1993 (Japan), 2002 (Australia), 2001 (Canada), 2002 (Hong Kong) and 2001(Brazil) through July 16, 2015.
The following is a summary of their findings:
- They confirmed the strength and persistence of the low-volatility and low-beta effect on a pool of nine different countries, and found that the two anomalies are very strongly correlated (about 0.9). This suggests “that these two anomalies are in fact one and the same.”
- The skewness of low-volatility portfolios is small but systematically positive, suggesting that the low-volatility excess returns cannot be identified with a hidden risk-premium.
- The profitability of the low-volatility/low-beta strategy is negatively correlated with the size factor (-0.6/-0.3) and positively correlated with the three value factors studied, book-to-price (0.2/0.3), dividend-to-price (0.6/0.6) and earnings-to-price (0.5/0.4). There is virtually no correlation with the momentum factor. Once the common factors of value and profitability are controlled for, the performance of low volatility/low beta becomes insignificant. (A similar finding is identifiedin the U.S. market.)
- S. mutual funds are systematically overexposed to high-volatility/small-cap stocks and underexposed to low-volatility/large-cap stocks, in agreement with the aforementioned leverage constraint and/or bonus incentives explanations.
Another important insight was that, due to the borrowing required to short stocks, the profitability of the strategies is sensitive to the financing rate — they performed poorly in the high-rate environment of the 1970s and they have benefited from the low-rate environment of the last decade. All of these findings are consistent with those of the prior literature, including the sensitivity to interest rates. Ronnie Shah, author of the 2011 paper “Understanding Low Volatility Strategies: Minimum Variance,” found that for the period from 1973 through June 2010, the low-beta strategy has statistically significant exposure to term risk.
The authors also noted that the dividend factor (D/P) has significant explanatory power for the performance of the low-volatility/low-beta strategies — a point that has not been discussed in the literature despite its importance for taxable investors, as high-dividend strategies are tax inefficient. Thus, the excess return of the strategy (or a significant portion of it) could be eaten up by taxes on dividends. The reason is that for the low-volatility stocks where the strategy is long, receive on average higher dividends than high-volatility stocks where the strategy is short.
