Understanding risk and having proper expectations around how it manifests in a securities portfolio is an essential part of making sound investment decisions. All too often, investors and their advisors allow their focus to tilt excessively toward the more positive and exciting side of the coin—forecasting expected returns—while giving short shrift to the more uncomfortable side—evaluating the potential risks. Particularly in today’s environment of quiet volatility,1 investors may be tempted to ignore risk altogether. This is a mistake.
In the first article of our new series, John West and Amie Ko highlighted the pitfalls associated with using historical returns to set future long-term return expectations. In this second article of the series, we examine a concept joined at the hip with returns—that is, the risks investors face in the financial markets.
Growing up in Alaska, I spent many summer evenings hiking the trails of Chugach State Park, a 500,000-acre sprawl of wilderness buttressing the city limits of Anchorage. A late summer trek up a nearby 5,000-foot peak would often reveal stunning vistas of the city and alpenglow settling on more-distant mountains. I quickly became accustomed to the risks of hiking in the Alaskan wilds: sunny skies at the trailhead didn’t rule out mid-August snow flurries blowing in at higher elevations, exploring off-trail (a brazen habit I have yet to be cured of) resulted in several sketchy scrambles down sheer cliffs, and more than one bear and I met face to face. I found that these risks were largely unavoidable, but embarking from the trailhead with full knowledge of what dangers lurked in the wilderness, and traveling well prepared to meet them, allowed me to respond effectively when they inevitably arose, rather than react in a panicked frenzy.
The same attitude has served me well in navigating the risks that are present in the financial markets. My guideposts are to enter an investment opportunity with a full assessment of the potential risks involved and their outcomes, and have a prepared response for when those bad outcomes inevitably occur. In this article, we focus on two types of investment risk that lie at the center of a critical advisor–client discussion: 1) volatility of absolute returns and 2) tracking error of relative returns, which is a variety of maverick risk, so-named because it reflects the discomfort of receiving outcomes different from one’s benchmark or peers.
Both of these risks measure variability around expected returns, so it’s best to think about expected returns and risk as two sides of the same coin. An expected return is simply the mid-point for a range of outcomes. In this manner, we can think of the expected return as our likeliest long-term “destination.” Risk, by contrast, helps us understand the uncertainty in the “journey” to the destination. An advisor whose focus is on better investor outcomes ensures clients have a full appreciation of the many paths that journey can take, and like my early days in the Chugach, have a plan of action (or inaction!) for when shortfalls occur.
Tempting as it is to try to quantify the risks that investors face, we cannot escape the fact that the future is, by definition, uncertain. Regardless of how finely we sharpen our risk measurement tools, so-called black swan events can wreak havoc with even the best-laid investment plans. In Against the Gods: The Remarkable Story of Risk, Peter Bernstein explains the difference between risk and uncertainty. Uncertainty describes how our practice of quantifying risk inevitably misses the mark at times, leading to truly unexpected and often material consequences. Although we focus here on two common definitions of risk, many other variants warrant consideration in an extended conversation around risk, especially those types that cannot be easily measured.
Volatility: Translating Standard Deviation
The statistical practice of measuring risk as volatility sits at the core of every investments class and the curriculums of our industry’s well-respected investment credentialing programs. While an imperfect conceptualization, volatility nevertheless presents a tractable, measurable summary of the distribution of possible outcomes available in the markets, and lends itself well to quantitative modeling. Volatility also gives us a very sensible counterpoint to expected return: for a given level of return, investors should logically prefer a less-volatile asset, and at a given level of volatility, investors should seek out the highest-returning strategies.2This simple precept underlays grand financial paradigms such as modern portfolio theory (Markowitz, 1952) and the capital asset pricing model (Sharpe, 1964; Lintner, 1965).
The Asset Allocation Interactive tool on the Research Affiliates website visually displays this spectrum of risk and return for a wide variety of asset classes.
Expected return is shown on the vertical axis, and expected volatility (standard deviation of returns) is displayed on the horizontal axis. Assets lying to the left and higher on the graph offer a superior tradeoff between expected return and expected volatility. The efficient portfolios (those combinations of assets that provide the highest return for exposure to a given level of risk) are shown as grey dots, with the dashed line sketching out the efficient frontier, illustrating the typical return premium; that is, buying portfolios with higher volatility provides higher expected returns. Clicking on the portfolios on the efficient frontier brings up a summary card with the portfolio’s return and risk characteristics. For example, based on data as of August 31, 2017,3 the 10.0% volatility portfolio shows a 5.5% expected return with a 10.2% standard deviation.4
Of course, very few clients understand the concept of standard deviation, so it’s useful to transform volatility, or standard deviation of returns, into a discussion tool to convey how widely shorter-term returns can vary around long-term expectations. Under normal distributions, investors can expect to receive a return within one standard deviation of the mean approximately 2 years out of 3, and to receive a return more than two standard deviations beyond the mean about once every 20 years.5 Because market returns have “fat tails,” crashes occur more frequently than expected and in more dramatic fashion than would be predicted by a normal distribution.6