Research Affiliates: Black Ice: Low Volatility Investing In Theory And Practice

October 29, 2015

Key Points

  1. Implementing a low-volatility strategy entails mitigating out-of-sample estimation errors; over-concentration in sectors, regions, and names; and high transaction costs.
  2. Representative constraints succeed in making simulated minimum-variance portfolios more investable but push them in the direction of the cap-weighted benchmark.
  3. Constraints that are similarly designed to improve the investability of heuristically constructed low-volatility portfolios tend to preserve the intended portfolio characteristics.

Equity investors have endured two extreme market downturns since the turn of the century. The broad U.S. market, represented by the S&P 500 Index, fell by 44% in the aftermath of the dot-com bubble and 51% in the great recession. These devastating experiences reawakened institutional and individual investors to the downside of market volatility and, for a while, prompted great interest in low-volatility investing. Over the last six years, however, the market has been climbing; at the end of July 2015, the price level of the S&P 500 was over 200% higher than its trough in March 2009.1 Low-volatility strategies have languished, and many investors appear to be sleepwalking again—possibly toward a cliff.

While human nature conditions us to chase whatever has been working best—a strategy that we know will backfire badly for the long-term investor—we also know that inertia generally doesn't pay off. Given the immense gains of this bull market, it may be timely to take some profits off the table, and to dampen our overall portfolio risk through exposure to the well-documented low-volatility effect.2 But, like most things that sound inviting, not all low-volatility portfolio strategies are equally attractive. It pays to understand the differences. Let's focus first on issues surrounding the implementation of minimum-variance strategies. The same challenges arise for heuristic low-volatility portfolio construction; we consider their impact below.

The Need For Constraints

There are essentially two approaches to low-volatility investing. One of them, called minimum-variance investing, is based on quantitative optimization techniques,3 while the other employs heuristic portfolio construction rules. Some products use combinations of the two approaches, but for this purpose, we will focus on the two primary approaches.

  • The minimum-variance portfolio approach uses a numerical optimizer to select a set of non-negative stock weights such that the resulting predicted portfolio volatility is minimized.
  • A heuristic approach to low-volatility investing typically uses a common risk measure (e.g., beta or volatility) to screen out volatile companies, and assigns weights to the remaining securities by their market capitalizations or the inverse of the company-specific risk measure.

Solidly grounded in finance theory, the minimum-variance method is clearly a sound approach to constructing a low-volatility portfolio. Nonetheless, implementing this method may be more problematic than many investors realize, and the chosen solutions unavoidably affect investment results.4 The challenges relate to "implementation shortfall," including disappointing out-of-sample performance due to estimation errors,5 extreme and unstable portfolio characteristics, and high transaction costs.6

In addition to applying advanced statistical techniques,7 asset managers and index providers often mitigate estimation errors—and address other minimum-variance implementation issues—by imposing constraints on the optimization process. They typically apply minimum and maximum weight constraints to avoid over-concentration in individual stocks; sector and regional weight constraints to forestall excessive allocations to any one industry group or geographical area; and turnover constraints to control trading costs.

These restrictions are successful in fixing the identified problems, and as a result, they make minimum-variance portfolios more investable. But the improvements come at a price. The constraints progressively nudge the portfolio closer to the market-cap-weighted index and, more importantly, introduce a link between the price of a stock and its weight in our portfolio. As we (and others) have demonstrated, the link between stock price and the portfolio weight has a cost; indeed, severing that link is the main source of alpha for fundamentally weighted and other non-cap-weighted strategies. As a practical matter, it appears that optimization-based minimum-variance strategies cannot be implemented without meaningful slippage.

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