Low-volatility indexes can make a lot of sense, but the devil’s really in the details.
As investors continue to seek ways to help manage volatility within their portfolios, indexes focused on low-volatility stocks have gained increasing interest in recent years. It makes sense to the extent that investing in funds that use such indexes make for a smoother ride.
Since the first exchange-traded fund focused on low-volatility stocks was launched in 2011, ETFs based on these indexes have seen total assets grow to about $11 billion as of Jan. 31, 2014, according to ETF.com.
These strategies allow investors to maintain equity market exposure while seeking to help manage some of the volatility within the equity portion of their portfolio.
As with any index strategy, however, it’s critical that investors understand the construction methodology of the underlying index, as the approach can vary significantly from one low-volatility index to another. Those differences, in turn, can result in significant differences in exposures and performance.
Can Lower Risk Generate Higher Return?
Part of the growing interest in low-volatility strategies is increasing awareness of the wide body of academic research focused on the so-called low-volatility anomaly. This research extends back to the 1970s with a paper by Robert Haugen and James Heins titled, “Risk and the Rate of Return on Financial Assets: Some Old Wine in New Bottles,” Journal of Financial and Quantitative Analysis, 1975.
More recent research includes papers by Malcolm Baker, Brendan Bradley and Jeffrey Wurgler, “Benchmarks as Limits to Arbitrage: Understanding the Low-Volatility Anomaly,” Financial Analysts Journal, 2011; and Jason Hsu, Hideaki Kudoh and Toru Yamada, “When Sell-Side Analysts Meet High-Volatility Stocks: An Alternative Explanation for the Low-Volatility Puzzle,” Journal of Investment Management, 2013.
This research has consistently found that low-volatility portfolios have outperformed high-volatility portfolios over the long term across various time periods and geographies. This is considered an anomaly, of course, because it runs counter to traditional financial theory, in which higher expected return goes hand in hand with higher risk.
Explanations for why this anomaly exists are less conclusive and remain the subject of considerable debate and ongoing research. Among the explanations are a behavioral tendency by many investors for so-called lottery stocks, or stocks with high volatility and seemingly high potential payoffs.
Other explanations include constraints on leverage that push some investors to prefer higher-beta stocks to meet expected return objectives; and tracking error constraints that cause some investors to view low-volatility portfolios as “risky” because they tend to exhibit moderate-to-high tracking error relative to a market-capitalization-weighted benchmark.