In seeking to meet their clients’ financial goals, advisors face two sizeable headwinds: clients’ investing biases and the difficulties in identifying skilled managers who are able to reliably produce alpha for their investors. Let’s review briefly what the academic literature has found regarding these two challenges.

Biases of Advisors and Their Clients
Retail investors are generally susceptible to a number of biases. Most notably, their trend-chasing behavior leads to poor buy and sell decisions and disappointing investment outcomes. Barber and Odean (2000) found that the average retail brokerage investor underperformed the market by about 1.5% a year. What was even more telling was that investors who made the most buy and sell decisions had the worst performance, underperforming by 6.5%! This hazardous tendency manifests itself meaningfully when it comes to picking mutual funds and other managed products.

Hsu, Myers, and Whitby (2015) showed that investors earned about 2% less than the mutual funds they invest in because of a bias toward chasing performance (i.e., buying high and selling low). Their research also demonstrated that larger performance gaps exist in high-expense-ratio funds (again more likely to be held by retail investors) versus low-expense-ratio funds. Hsu, Myers, and Whitby concluded that less-sophisticated investors, often those who invest in retail funds, underperformed by a greater margin (i.e., suffered a larger return gap) than those who qualified for institutional share-class funds.

Advisors face tremendous challenges in overcoming such client biases. Mullainathan, Noeth, and Schoar (2012) found evidence that suggests advisors have difficulty de-biasing their clients, and as a result engage in “catering” behavior, seeking to please existing or new clients by being supportive of returns-chasing behavior. Linnainmaa, Melzer, and Previtero (2016) also found that the average advisor has difficulty overriding retail investors’ biases, often exacerbating them with recommendations of frequent trading and expensive, actively managed products.

Picking Winning Managers
Human nature induces us to want more of what has provided comfort and profit, and less of what has given us pain and loss; this behavioral bias leads advisors to recommend the managers their clients want—those with the best trailing performance. As a result, many advisors put their clients on the “hamster wheel” of manager selection, continuously replacing poor performers with good performers. The literature tells us, however, that this form of performance chasing likely puts advisors and their clients on the outside track to future excess returns.

Cornell, Hsu, and Nanigian (2017) have documented mean reversion in mutual fund performance, finding that, when measured by trailing three-year performance from 1994 through 2015, top-decile managers underperformed the bottom-decile managers by 2.3% a year. Arnott, Kalesnik, and Wu (2017), controlling for fund expenses, showed a similar monotonic drop-off in the subsequent performance of prior winners. The evidence makes it pretty clear we shouldn’t use historical performance as our primary manager selection criteria. Well, maybe we should—just in the opposite direction!

Advisors who acknowledge the pitfalls of a pure performance selection criterion could choose to spend their due diligence efforts on the so-called soft “Ps”: philosophy, process, and people. Indeed, the institutional investment consulting community has relied heavily on nonperformance factors for decades to make manager selection decisions. Jenkinson, Jones, and Martinez (2016)⁴ found that consultants’ recommendations correlated partly with the past performance of fund managers, but more so with nonperformance factors, suggesting that consultants’ recommendations do not merely represent a returns-chasing strategy.⁵ Obviously, the consultants’ research staff were swayed more strongly by nonperformance criteria.

But the additional insights gained by nonperformance factors has not led to an ability to, on average, select “winners.” On a value-weighted basis, Jenkinson, Jones, and Martinez found no evidence that the managers’ products recommended by investment consultants outperformed the products the consultants did not recommend. On an equally weighted basis, they found that recommended products underperformed other products by approximately 1% a year, leading the authors to conclude that nonrecommended funds performed at least as well as recommended funds.

Beyond the pursuit of benchmark-beating performance, other benefits can be realized through careful and well-resourced manager selection. For example, behavioral finance frequently references individual investors’ willingness to forgo higher wealth accumulation in favor of nonmonetary emotional benefits. We assert as much in our own investment beliefs: investor preferences are broader than risk and return (Brightman, Treussard, and Masturzo, 2014). Additionally, benefits of a well-documented manager research effort can satisfy certain statutory regulations such as ERISA, mitigate regret risk in performance “blow-ups” (particularly with negative press headlines for public entities), and/or provide a layer of—real or perceived—fiduciary insurance (i.e., by performing an extensive due diligence review before recommending a manager, the advisor or consultant best positions themselves to explain a poor-performing manager).

Nonetheless, the literature suggests that financial advisors shouldn’t expect, nor communicate to clients an expectation of, market-beating results via manager selection, at least not with the current (sometimes overwhelming) investor bias of making buy and sell decisions based on performance metrics.