Low-volatility strategies have quickly become the darling of many investors, thanks largely to trauma caused by the bear market that arose from the 2008-2009 financial crisis combined with academic research showing that the low-volatility anomaly exists in equity markets around the globe.
Earlier this week, we took a detailed look at a 2016 study from David Blitz, “The Value of Low Volatility,” which explored whether low volatility was a unique investment factor or if its performance could be explained by other well-known factors (specifically, value). Today we’ll review some additional research on this issue.
A Deeper Dive Into Low Volatility
Ronnie Shah, author of the 2011 study “Understanding Low Volatility Strategies: Minimum Variance,” found that for the period 1963 through June 2010, the low-beta strategy had exposure to term risk. Its “loading factor” (degree of exposure) on term risk was a statistically significant 0.09 (with a t-stat of 2.6). As further evidence, Tzee-man Chow, Jason Hsu, Li-lan Kuo and Feifei Li, authors of “A Study of Low-Volatility Portfolio Construction Methods,” which appears in the Summer 2014 issue of The Journal of Portfolio Management, found a correlation of 0.2 between the betting-against-beta factor and the duration factor.
Given their positive exposure to term risk, low-volatility stocks have benefited from the cyclical bull market in bonds we have experienced since 1982. That rally can’t be repeated now, with interest rates at historic lows. In addition, the low-volatility factor may not be as unique as Blitz found.
Robert Novy-Marx has also examined the low-volatility factor. His 2016 study, “Understanding Defensive Equity,” covered the period 1968 through 2015. Novy-Marx found that when ranking stocks by quintiles of either volatility or beta, the highest-quintile stocks dramatically underperform, while the performance of the other four quintiles are very similar and marketlike.
In fact, the second-highest-volatility quintile (the fourth quintile) posted the highest returns, followed by the third, second and, finally, the first quintile. This nonlinear relationship is quite different from what we typically find with other common factors, where the returns across deciles, quintiles or quartiles tend to be linear.
Novy-Marx also found that high-volatility and high-beta stocks tilt strongly to small, unprofitable and growth firms. These tilts can explain the poor absolute performance of the most aggressive stocks; stocks that are often referred to as “jackpots” or “lottery tickets.” These stocks make up a very small percentage of total market capitalization. But it is the underperformance of these high-risk (small, unprofitable and growth) stocks that drives the abnormal performance of defensive equity. Novy-Marx also found that a stock’s profitability is a significant negative predictor of its volatility, and it is the single most significant predictor of low volatility.
Adding In Profitability
Novy-Marx found that by including profitability as a factor, the performance of the defensive (low-volatility) strategy is well explained by controlling for the common factors of size, profitability and relative valuations. Novy-Marx also found that defensive strategies tilt strongly toward large stocks (they are 30 times as large at the end of his sample, and the long-short portfolio has a size factor loading of -1.12), value stocks (the long-short portfolio has a value loading of 0.42) and profitable stocks.
The profitability tilt obscures the extent to which defensive strategies tilt toward value, because value and profitability tend to be strongly negatively correlated. Unless you control for profitability, the value loadings of defensive strategies will be lowered (an important insight).
Novy-Marx also found that five-sixths of the Fama-French three-factor alpha (57 out of 68 basis points per month) was delivered through the aggressive stocks on the short side of the strategy with only one-sixth, or 12 basis points per month, coming from the actual defensive stocks. And he found similar results when looking at a low-beta strategy.
A second important consideration is that, while the low-volatility factor may well be somewhat unique, and in the past it has provided a premium, the dramatic inflows into the strategy have altered the very nature of the strategy’s valuation characteristics.
Have Low-Volatility Strategies Become Overgrazed?
As is the case with so many well-known anomalies and factors, the problem of potential overgrazing does exist. Findings regarding the premium, combined with the bear market caused by the financial crisis of 2008, led to the aforementioned dramatic increase in the popularity of low-volatility strategies.
The cash inflows have raised the valuations of defensive (low-volatility/low-beta) stocks, reducing their exposure to the value premium and thus lowering expected returns. Specifically, as low-volatility stocks are bid up in price, low-volatility portfolios become more “growthy” (which reduces their forward-looking returns).
Specifically, we’ll take a look at the valuation metrics of the two largest low-volatility ETFs, the iShares Edge MSCI USA Minimum Volatility ETF (USMV), with $15.1 billion in AUM; and the PowerShares S&P 500 Low Volatility Portfolio (SPLV), with $7.9 billion in AUM. We will then compare their value metrics to those of the iShares Russell 1000 ETF (IWB), which is a market-oriented fund, and the iShares Russell 1000 Value ETF (IWD).
The table below is based on Morningstar data as of July 7, 2016.
What is clear from the data is that the demand for these strategies has altered their nature. The valuation metrics of USMV and SPLV certainly don’t look like a value-oriented fund. Their price-to-earnings, book-to-market, price-to-sales and price-to-cash flow ratios are all quite a bit higher than those of IWD. In fact, their metrics indicate that both are now more “growthy” than the marketlike IWB. What’s more, the price-to-earnings ratios of both USMV and SPLV were even higher than the iShares Russell 1000 Growth ETF’s (IWF) ratio of 20.7.
Another Look At Exposure To Value Factor
We can also see how the increased popularity of low-volatility strategies has changed their very nature by looking at how the loading factors have shifted over time. Using the tool provided by Portfolio Visualizer, we’ll take a look at the results of regression analysis on the largest ETF, USMV.
The first full month since the inception date of this ETF was November 2011. Data is available for the fund through April 2016. We’ll split the period into two equal parts, November 2011 through January 2014; and February 2014 through April 2016. The regressions include the Fama-French factors of beta, size, value and momentum, and the two bond factors of term and default. In the first half of the period, USMV had a loading on the value factor of 0.21. For the second half of the period, the value loading was -0.04. In other words, USMV moved from loading positively on the value factor to now having a slight loading on growth.
Results from the regression analysis confirm what a simple look at the valuation metrics told us. In addition, the regressions show that the fund has statistically significant exposures to the term premium. The loading on the term premium in the first half of the period was 0.29 and in the second half it was 0.25. At the very least, with interest rates at historical lows, investors should be aware of this exposure to term risk.
There’s a cliché in finance that success can sow the seeds of its own destruction. The flow of cash into the low-volatility strategy has changed the very nature of the funds. While they may still be low volatility, they no longer look like value funds. The lower exposure to the value premium means that they now have lower expected returns.
In other words, since 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. In addition, it doesn’t seem likely that low-volatility strategies will benefit as much in the future as they have in the past from their exposure to term risk.
The bottom line is that the evidence suggests you would be better served by investing in vehicles that screen out the high-volatility (or high-beta), high-risk stocks. In other words, invest directly in size, value and profitability rather than doing so in the indirect way characteristic of defensive strategies.
Larry Swedroe is the director of research for The BAM Alliance, a community of more than 140 independent registered investment advisors throughout the country.