Now that volatility has returned to the market, we thought we’d revisit some basics about low volatility equity strategies. These strategies all have a common objective: less volatility, lower drawdowns, and market-like or -better returns. But since the last major equity market drawdown, the category of low volatility equity strategies has matured – both in magnitude and in the variety of investment approaches.
What’s in a Name?
Recall the quote from Romeo and Juliet, “That which we call a rose by any other name would smell as sweet.” Shakespeare touched on a universal truth in this play – one that is still relevant even today – when Juliet laments on the fact that the Montague name is meaningless – the name did not make the man. Similarly, the label of low volatility strategies is arbitrary compared to their intrinsic qualities. Focus on the strategies’ key characteristics and you can start to see where they’re alike and different.
Quantitative vs. Fundamental
Most defensive equity strategies are quantitative, with differing inputs, models, objectives, and limitations. Nevertheless, the residual outcomes of fundamental approaches could improve equity allocations by providing diversification and a defensive posture. Although they may be categorized as dividend-focused or value strategies, they share some characteristics with low volatility strategies, including lower beta, defensive stock and sector exposure, and downside protection. However, for our purposes, the remaining differentiators and analysis will mostly apply to quantitative strategies that explicitly set risk reduction as their central objective.
Rules-based vs. Optimized
The portfolio construction process is likely where the line in the sand for quantitative approaches is most frequently discovered. In particular, whether a strategy a) constructs its portfolio by ranking names in a universe according to some measure of risk, establishing a cut-off, and implementing a weighting scheme, or b) mathematically optimizes holdings for lower portfolio-level volatility, using estimates of volatility and correlation for each stock.
The former rules-based, or rank-based, method is easier to understand and less opaque, but it may be more vulnerable to concentration risk since it implicitly excludes higher volatility equities and relies too much on the companies with the lowest volatility. The latter optimization approach offers the potential for narrower outcomes and higher risk reduction, but it is more complex and only as good as its covariance estimates. Due to the inclusion of companies that aren’t strictly low volatility due to their attractive correlations with others, the portfolio-optimized approach may also be better equipped to avoid valuation or overcrowding risks.
Both methods need well-conceived and carefully applied constraints, especially the portfolio optimization approach. Optimized strategies require a deft touch to exploit stock correlations for risk reduction while avoiding overexposure to particular sectors or countries, liquidity traps, and high turnover.
Return Expectations
The most typical expectation is for long-term returns to match a cap-weighted index. Outperformance in market downturns is always a part of the performance contours for low volatility strategies. Typically, this is exchanged for trailing cap-weighted index returns during bullish periods. There are some low volatility strategies, however, that also attempt to outperform capitalization-weighted indexes in up markets.
Risk Expectations
The assumption that a strategy’s “risk” is lower than a cap-weighted equity benchmark is likely obvious, but the magnitude and terms of their risk-reduction claims are important to uncover. The projected risk characteristics of a low volatility strategy are frequently described as a percentage of benchmark volatility (for example, 60–90%) or, alternatively, as a percentage of reduced volatility compared to the benchmark (for example, 20–30%). A beta expectation, which reflects the percentage of reduction in benchmark-driven volatility, may be stated less frequently (e.g., 0.60-0.75). For some strategies, volatility reduction and beta may naturally occupy a narrow range over an entire market cycle, but others may have a wider range and exhibit more variability depending on the market environment.
Stock- and Portfolio-level Risk
Low volatility investing methods often incorporate some input to gauge stock-level. This input could be statistical (e.g., standard deviation, beta), fundamental (e.g., measures of quality or value), or a combination of both. Stock volatility predictions must strike a balance between being out of date and favoring recent history. You may think of these estimations as the true “secret sauce” of these models because it is difficult to build a successful defensive portfolio without accurate estimates of a stock’s future volatility.
Heuristic strategies may assume a decrease in risk at the portfolio level as a natural residual based on the low volatility stocks they include, despite defining an objective function for it in their models. Optimized strategies advertise the use of proprietary algorithms intended to reduce portfolio-level volatility, but it’s essential to discover what this actually means. Do they only limit stock-level weights for diversification in accordance with their selection model screen? Do they actually take into account stock correlation, or is their optimization just an improved weighting system on top of a ranking approach? Is the screening step already doing the heavy lifting to the extent that there are so few stocks left to truly benefit from a covariance matrix?
Appetite for Alpha
Many managers aren’t satisfied to rely on the low volatility “anomaly” to match or outperform the market over the long run, even when there are many more naive, passive-appearing smart beta solutions available. They might choose to include an alpha source in the mix, which is frequently their own return forecast model that is based on valuation, momentum, or other unusual combinations of variables.
| Why Not Just Use Derivatives?
Derivatives are instruments or contracts that are based on the price of something else. Their value depends on a different asset, and hence is derivative from it. The classic derivatives are puts and calls on stock and futures contracts. While this paper focuses exclusively on long-only low volatility equity strategies, derivative strategies are another commonly explored alternative of portfolio insurance. The most common choices are: purchasing a straight protective-put, a put-spread, or a zero-cost collar. These derivatives come with added complexity, increasing costs explicitly and implicitly.
The protective put choice is analogous to accessing unlimited fire insurance on one’s house. Meanwhile, a put-spread involves the purchase of a put option, which is simultaneously financed by the sale of a put option that is extremely out-of-the-money. Choosing this is akin to purchasing limited fire protection on one’s house; should your house burn down, an excess (i.e., deductible) has to be paid before collecting your insurance. Finally, a zero-cost collar is akin to accessing free unlimited fire insurance. Upfront payment is not required. However, in the case of fire, a full pay-out will take place after you pay an excess, and if the value of your house had increased prior to the fire, the insurer has the right to some portion of your home’s equity. Regardless of which of the volatility-mitigating derivative strategies you might select, each has associated costs which can rise rapidly and proportionately to fluctuating market dynamics, the desired level of timing precision, and the willingness to roll the strategy forward – all of which can pose a drag on performance. While option strategies can be customized, the experience often disappoints, where costs can be higher than anticipated and the level of protection less than what was ideally desired. What’s more, derivatives-related risks may come with unanticipated costs. They potentially invite an increase in your plan’s governance budget in order to provide the necessary education, liability and derivatives-based reporting, hiring of experts, implementing procedures around managing derivatives, and documentation. Depending on the circumstances of a plan sponsor, these fees can vary greatly. The added cost of implementing derivatives may be minimal for large plans with dedicated investment staffs because these organizations frequently have fewer educational requirements and pre-existing derivatives-based policies, procedures, and documentation. Additional governance costs, however, can be very high for smaller plans without a full-time committed staff. |
Need More Than Basics?
Intech has been managing low volatility equity portfolios for over a decade now. We offer a range of resources on the subject, including how to evaluate low volatility equity strategies and their role in portfolio construction. Contact us to learn more.
The information expressed herein is subject to change based on market and other conditions and is issued by Intech. The views presented are for general informational purposes only and are not intended as investment advice, as an offer or solicitation of an offer to sell or buy, or as an endorsement, recommendation, or sponsorship of any company, security, advisory service, or fund nor do they purport to address the financial objectives or specific investment needs of any individual reader, investor, or organization. This information should not be used as the sole basis for investment decisions. All content is presented by the date(s) published or indicated only, and may be superseded by subsequent market events or for other reasons. Past performance is no guarantee of future results. Investing involves risk, including possible loss of principal and fluctuation of value. Indexes are unmanaged and cannot be invested in directly.
Low volatility strategies are likely to underperform the index during periods of strong up markets and may not achieve the desired level of protection in down markets.
Indices are not available for direct investment; therefore, performance does not reflect the expenses associated with the active management of an actual portfolio.
