In the midst of the first major market correction since the Global Financial Crisis, the appeal of defensive equity strategies seems obvious. But the proliferation within this category over the past decade has resulted in a glut of products that can be hard to parse. Even managers’ own descriptions of their defensive equity strategies reveal a lot of similar terms with different meanings, and vice versa. Their strategy names – e.g., low volatility, managed volatility, defensive equity – aren’t a reliable indicator of their approach or intended outcome. We offer six key attributes to sort where they’re alike, and where they diverge.

1. Quantitative vs. Fundamental

Inputs, models, objectives and constraints may vary, but most explicitly defensive equity strategies are quantitative in nature. That said, some more traditional-looking fundamental strategies may grant diversification and defensive positioning as residual benefits to an overall equity allocation. They may be described as dividend-focused or value strategies, but exhibit similar-enough behavior – lower beta, exposure to typically defensive stocks and sectors, and downside protection – to be attractive to investors seeking to complement growth strategies and de-risk equities, despite not necessarily targeting these outcomes. Derivative strategies are another commonly explored alternative of portfolio insurance, but these tend to be accompanied with added complexity.

2. Rank-based vs. Optimized

Within quantitative approaches, perhaps the most commonly identified line in the sand can be found within portfolio construction methodology. Specifically, whether a strategy a) builds its portfolio by ranking names in a universe by some measure of risk, designating a cut-off, and applying a weighting scheme, or b) mathematically optimizes holdings and weights by targeting an objective (e.g., lowest total risk), including various constraints, and using estimates of volatility and correlation for each stock to solve for the best possible portfolio for a given universe.

“…perhaps the most commonly identified line in the sand can be found within portfolio construction methodology.”

The former rank-based approach is simpler and more transparent, but may be prone to concentration risk due to implicit exclusion of higher-volatility stocks, and overreliance on the lowest-volatility stocks. The latter optimized approach is more complex and in many ways only as good as its covariance estimates, but allows for potentially narrower outcomes and greater risk reduction. It may also be able to better avoid valuation or overcrowding concerns due to the inclusion of stocks that aren’t strictly considered low volatility due to their attractive (i.e., low) correlations with others. Well-designed and prudently implemented constraints are important for both, but especially for the optimization method. A deft touch is necessary, both to maintain the benefits of defensive positioning and to exploit correlation for portfolio risk reduction, while at the same time avoiding overexposure to certain sectors and countries, liquidity issues, or prohibitively high turnover.

3. Return Expectations

Long-term returns in line with a cap-weighted index, or alternately, outperformance in the 1-4% range, are the most common expectation. Contours of performance always include outperformance in down markets. Typically this comes in exchange for a lag, or potentially index-like returns for more flexible strategies, during bullish periods. With the increasing history and acceptance of low and minimum volatility indexes, there are also strategies that attempt to outperform these indexes within a tracking error range – in the same way a typical active core strategy aims to beat the S&P 500.

4. Risk Expectations

It probably goes without saying that “risk” lower than a cap-weighted equity benchmark is an assumed feature, but strategies differ in a) the magnitude and terms of their risk-reduction claims, and b) whether such risk reduction is purported to be static or dynamic in the context of the market environment. Often, a strategy’s expected risk parameters will be expressed in terms of a percentage of benchmark volatility (e.g., 60-90%), or, equivalently, as a percentage of reduction in volatility relative to the benchmark (e.g., 20-30%). Less commonly, a beta expectation may be stated (e.g., 0.6-0.75), which expresses the percentage of reduction in equity-benchmark-driven volatility. For some, volatility reduction and beta may occupy a narrow range over an entire market cycle by design, but others may vary significantly, from very defensive positioning in volatile corrections all the way up to benchmark levels of risk with no volatility reduction in calmer bull markets. This increased variability may come with somewhat decreased maximum downside protection, but the potential for greater upside capture might lessen the possible “buyer’s remorse” that may result from committing to a defensive equity strategy through rising tides.

5. Risk Measures

Models typically include some kind of input to measure risk at the security level, which may be statistical (e.g., standard deviation, beta), fundamental (e.g., measures of quality or value), or some combination thereof. Estimates of stock volatility must strike a balance between being outdated and overweighting recent history. It’s in these estimates that you might consider the real “secret sauce” of these strategies. Constructing an effective defensive portfolio is impossible without reliable assessments of a stock’s volatility moving forward.

“It’s in these estimates (of stock volatility) you might consider the real ‘secret sauce’ of these strategies, as constructing an effective defensive portfolio is impossible without reliable assessments of a stock’s volatility moving forward.”

At the portfolio level, heuristic strategies may assume a reduction in risk as a natural residual based on the low volatility stocks they include, without assigning an explicit objective function to it within their process. Beyond that, many quantitative managers tout their “proprietary” portfolio optimization designed to minimize risk, but it’s critical to dig deeper into what this really means. Are they merely applying weighting limits for diversification following their selection model screen? Are they genuinely accounting for correlation between stocks, or is their optimization no more than a slightly more sophisticated weighting scheme on top of a ranking approach? In other words, is the heavy lifting already done in the screening step, to the point that there’s little room left in a narrow universe to really take advantage of a covariance matrix?

6. Alpha Engine (or lack thereof)

While more naïve, passive-seeming smart beta options are plentiful, many managers aren’t content to rely on the low volatility “anomaly” to match or beat the market over the long term. They may prefer to add an alpha source to the mix – often their proprietary return forecast model, be it based on valuation, momentum, or more exotic combinations of factors.

Go Deeper on Outcomes

It should be clear by now that not all defensive equity strategies are created equal – far from it. Now that you understand some key differences in their approaches, you might be wondering: how do you evaluate and classify their outcomes? Learn more by downloading our eBook, “How to Evaluate Defensive Equity Strategies.”

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