Despite the old adage to buy low, investors have a proven tendency to invest in markets late in the game, often using fund managers at the top of their performance cycle.
This has been confirmed by research by the Investment Company Institute, linking fund flows to equity market performance,¹ as well as by ratings agency Morningstar, tracking flows into 4- and 5-star funds. Given the mean-reverting nature of markets and managers, this would seem a surefire path to underperforming benchmarks. So why do we do it?
In the third of a series of articles on how human behavior can affect investment decisions, we look at the reasons why we’re predisposed to chase performance, and we highlight a few techniques we can employ to avoid it.
“[The star-rating system] is not meant to be used in isolation or as a predictive measure. Reversion to the mean is a powerful force that can affect any investment vehicle.”—Morningstar
The higher the Morningstar rating, the higher the flows
While much of the research on our tendency to chase performance comes from the field of cognitive psychology, recent contributions from the field of neuroscience are filling in some vital pieces of the behavioral puzzle. Researchers believe that the human brain has evolved very little over the past 50,000 years and is designed, first and foremost, to ensure our physical survival. One of the ways it does this is by recognizing and anticipating patterns, which once helped us identify the habits of prey animals and the seasonal trends affecting the gathering of edible plants, all critical to survival in an evolving world.
Using MRI technology, studies have confirmed that specific regions of the brain activate when shown as few as two or three numbers or shapes, with reward chemicals released in the brain when patterns persist. Further, this neural function operates on a preconscious level and can’t be turned off. The most significant aspect of the research, from an investment perspective, is that the brain automatically assumes cause-and-effect relationships and rejects randomness—an essential feature of mean reversion.²
Most are familiar with the term, but let’s examine it in greater detail. The phenomenon was first chronicled by Sir Francis Galton in a study on the height of offspring. His central finding was that parents who were above or below average height tended to have children closer to the average. And the more extreme the deviation from the average, the more likely the tendency to revert to the mean.
Psychologist and economist Daniel Kahneman went further when describing the presence of mean reversion, stating: “Any system that combines luck and skill will revert to the mean over time. Causal explanations will be evoked when regression is detected, but they will be wrong because the truth is that regression to the mean has an explanation but does not have a cause.”³
Dr. Kahneman went on to conclude that the greater the role of luck in outcomes, the more likely the presence of mean reversion. Where this gets especially tricky is manager selection, where there’s a subset of managers who are truly skillful and can navigate the top decile, but they’re the rare exception. Most who find the top decile don’t tend to stay there for long. This is a difficult admission for many in the investment business, because we don’t like to admit the role of luck in outcomes or that the mean-reverting tendencies of markets are a more powerful force.
Application of base rates to avoid performance chasing
One proven method to detect the potential for mean reversion is the application of base rates. Base rates use distributional information such as historical averages and ranges to estimate probable outcomes versus plausible-sounding ideal outcomes.
By understanding how high and low markets have been, their average level, and where they are now, investors can make more informed decisions regarding their entry points when committing capital and whether they should expect returns that are below or above average from a particular allocation. A common timeframe to use is rolling three-year returns, which coincide with the typical holding period for retail investors and the hire/fire timeframe for many investment consultants (although other time periods can be used). Is it foolproof? No. But, applied consistently, using base rates offers a repeatable process for making decisions and has the potential to tilt the odds of success in investors’ favor. It also provides a useful framework to gauge market forecasts and reality test planning assumptions.
Because markets are mean reverting, managers tend to be mean reverting too. As a result, base rating can be applied to manager selection, albeit with some modifications. The first cut is deciding whether to go active or passive based on the overall success rate of managers within a particular category versus benchmark averages (see our “Building better portfolios” white paper for more information). In certain categories, investors are already tilting the odds of success in their favor by understanding whether more than 50% of active managers historically beat the index. In categories where the percentage of managers outperforming the benchmark is low, a passive approach may make more sense. The second cut is understanding where managers are in their performance cycle versus their peers (it’s assumed the candidate pool is already vetted by a due diligence team).
Similar to markets, rolling three years is a common time period to evaluate performance, but percentile rankings are substituted for returns. Given the choice between two high-performing managers in the same category, selecting a manager who’s currently out of favor may tilt the odds of success in an investor’s favor versus selecting the managers at the peak of their performance cycle.
The portfolio consulting group at John Hancock is here to help. They offer a range of services, from formal model reviews to manager selection to investment decision process. For more information on building your own signals dashboard, contact us today.