Some things to consider before jumping on the low-volatility bandwagon.
A number of academic papers have demonstrated within the last few years that low-volatility stocks have provided greater returns than higher-volatility stocks. As I’ve mentioned before, these findings run counter to economic theory, which predicts that higher expected risk is compensated with a higher expected return. The result is what’s known as the low-volatility anomaly.
A pair of papers recently examined two different aspects of this anomaly. Xi Li, Rodney Sullivan and Luis Garcia-Feijóo, the authors of the 2013 study, “The Limits to Arbitrage and the Low-Volatility Anomaly,” explored whether the abnormal returns associated with the low-volatility anomaly can be effectively captured, or whether the returns are actually subsumed by some limits to investors effectively arbitraging them away.
To accomplish their objective, the authors examined the role of portfolio rebalancing and transaction costs in an investor’s attempt to extract profits from the low-risk anomaly. The study, which appeared in the January/February 2014 issue of Financial Analysts Journal, covered the period from July 1963 through December 2010. The following is a summary of the authors’ findings:
- The excess returns associated with forming long low-volatility and short high-volatility portfolios are basically present only in the first month after formation, and they are largely subsumed by the high transaction costs associated with stocks with low liquidity.
- The returns within value-weighted portfolios are largely eliminated when omitting low-priced (less than $5) stocks, and are not at all present within equal-weighted portfolios. In fact, the average price of stocks in the highest-volatility quintile was just more than $7.
- The low-risk effect has been noticeably weaker since 1990.
The authors concluded: “Our finding cast some doubt on the practical profitability of a low-risk trading strategy.”
In the second paper, Wes Crill at Dimensional Fund Advisors (DFA) looked at whether the low-volatility anomaly can be explained by other drivers of returns. The table below shows the results of a four-factor regression—market return or beta (MKT), size (SMB), value (HML) and momentum (UMD)—for a low-beta and high-beta portfolio:
|1928 – 2013||0.09%||1.60||0.65||-0.10||0.07||0.03|
|1928 – 1969||0.04%||0.53||0.67||0.00||-0.08||0.03|
|1970 – 2013||0.06%||0.79||0.72||-0.13||0.28||0.02|
|1928 – 2013||-0.11%||-1.58||1.35||0.28||-0.04||-0.10|
|1928 – 1969||-0.12%||-1.45||1.31||0.13||0.23||-0.05|
|1970 – 2013||-0.02%||-0.17||1.28||0.33||-0.33||-0.12|
The t-stats on the regression intercepts (alphas) are less than 2, or -2, for both the low- and high-market-beta portfolios across the entire sample. This is especially true when we look at the period beginning in 1970.
The results of the regression analysis show that the returns from low- or high-beta portfolios are thoroughly explained by the market, size, value and momentum factors. These results, combined with the findings from the Xi Li, Rodney Sullivan and Luis Garcia-Feijóo paper in Financial Analysts Journal, suggest that investors should look before they leap into a low-volatility strategy.
Given the high transaction costs found in a long-short strategy, funds might instead consider running long-only portfolios that screen out high-beta stocks from their eligible buy list.
Larry Swedroe is the director of research for the BAM Alliance, a community of more than 140 independent registered investment advisors throughout the country.