The authors aim to explain three anomalous retail investor traits that are contrary to the norms of standard portfolio theory: (1) concentrated holdings encompassing few stocks, (2) active trading, and (3) preferences for local stocks. Developing a model based on differentiated investor skills, they distinguish between “smart” and “dumb” investors by using standard demographic characteristics. They hypothesize that the former group’s decisions reflect superior information, whereas the latter group’s choices entail behavioral biases. Comparing the several possible portfolio distortions with investor skill, the authors find support for their hypothesis and note that actual investor performance matches the expected result (i.e., smart investors attain relatively superior risk-adjusted returns).

An increasingly required mandate, if not the law, for everyone catering to retail investors it to truly know one’s customers and apply related insights to advance their best interests (via the universal application of fiduciary standards). So, if the authors have indeed identified a reliable and straightforward way to characterize investor capabilities, it can aid in directing appropriate guidance toward those consumer tendencies that could prove quite damaging if overlooked. This finding is particularly useful if the possible patterns can be forecast based on fairly neutral and objective demographic correlates, such as age, education, and income, to avoid the potential sensitivities that result from subjective, self-reported measures of competencies.

Obviously, no one ever welcomes the label of “dumb” investor, no matter how respected the counselor saying it is. If investment sophistication denotes greater familiarity with all the investment theories, and as the authors suggest, adherence to those theories counterintuitively degrades “smart” investor performance to the level of their less talented peers, then it may be a signal for a special form of re-education. Finally, there even may be some new profit-making possibilities. The authors indicate that stocks held by “smart” investors outperform those of “dumb” investors by several percentage points annually. But with all of these findings, methodology may have a significant influence on their robustness.

Relying on cognitive psychology, the authors run multiple regressions to specify intelligence correlates, ascribing a positive correlation of 0.664 to the simple linear combined effect of age, education, social network, and wealth. They try to cross validate with a direct intelligence measure, such as SAT scores, but a potential dataset weakness is that its aggregation lacks any related metrics for each individual. Furthermore, their sample covers monthly trades of approximately 60,000 customers of a major US discount brokerage firm for the period of January 1991–November 1996.

Holding an average of four stocks in their portfolios, whose average size was approximately $35,000, these investors were predominantly self-directed with fairly modest equity holdings. They traded in the first half of what was a substantial market bubble, a potentially distinctive sample characteristic that can introduce some bias. For example, given that traditional portfolio theory advocates buy-and-hold with well-diversified portfolios and this period witnessed increasingly momentum-driven returns, performance may reflect affective receptivity to contemporary popular trends over cognitive intelligence. Attentive to possible alternate explanations, the authors check performance differentials to determine whether the findings are the result of improper portfolio risk adjustment, specific stock categories (e.g., tech stocks), geographic concentration of investors, preferences of older investors (e.g., portfolios amounted to a gambling stake for them), unobserved assets (the portfolios were just an unspecified share of total household wealth), age based exclusions (people under 50 were not in the sample), or access to insider information. They report the model passed all these tests.

Intelligence is an elusive and relative concept with multiple forms (cognitive, emotional, practical, etc.) and changing influences, such as the effect of learning or the synergy of collective thinking. It is quite challenging to proceed from determining unambiguous and reliable intelligence proxies to accurate analysis and ultimately sound decisions. Behavioral economics reveals that even sophisticated experts can be susceptible to self-defeating thought processes. Ascribing effective rationality to the “smart” and harmful biases to the “dumb,” as the authors do with their crucial research distinction, may upon further study be another instance of overfitting a model to given data. Nonetheless, they raise an intriguing research issue worth thorough consideration.