If you like to believe that we get what we pay for, the semi-annual SPIVA scorecard is not reassuring, especially for investors who like to bet on the talent of stock-picking fund managers.
SPIVA—for S&P Indices vs. Active Funds—measures the performance of actively managed funds against passively managed index funds, represented by relevant benchmarks. Over the 10 years that S&P has published them, SPIVA results show an annualized average of 56 percent of all actively managed domestic equity funds, 59 percent of active U.S. large-cap equity funds, and 64 percent of active mid-cap equity funds underperformed broad-market indexes.
Which raises a question: You might be paying your active fund manager for the exercise of skill, but how much of your portfolio's returns are due to skill and how much to luck?
It's a perennial question for which there is no clear answer, but it's an important one to investors because of the way we size up funds and fund managers. Most of us lean toward funds and managers with strong records, even though we're always being told not to chase past performance. That bias toward previous success looks even less rational if it turns out that what we're really chasing is not past performance but past randomness. How to know the difference?
Perhaps the first thing to understand is that active fund managers, like the rest of humanity, toil under the tyranny of the bell curve—that totemic, mound-shaped line on a graph whose peak represents average and whose tails represent outliers, whether losers (on the left) or winners (on the right). Over any time period, the vast majority of funds turn in performances that are somewhere in the fat part of the curve. The few who end up on the tails for some period almost inevitably get pulled back toward the median in subsequent periods, thanks to the phenomenon of "mean regression." So most long-term investors, who buy and hold funds for years (if not decades), will simply be pulled along as well, often paying hefty fees to their managers along the way.
It's not just mean regression that resists sustained outperformance; it's the market, too. Efficient Market Theory, a concept developed by University of Chicago economist Eugene Fama in the 1960s, holds that in a market where all relevant information is available to everyone, prices always represent fair value and no one investor should get the chance to do better than investors overall.
True, the market is not always perfectly transparent. Even if it were, human biases often trump rational analysis, so there are moments when the market misprices some, if not all, stocks. The people who spot these "arbitrage opportunities" first and most often are the people who beat the market. But are they, too, fate's randomly selected outliers, like the first guy who happens to see a dollar someone dropped on the sidewalk?
A 2009 paper by Fama and Dartmouth finance professor Kenneth French argues that there's no way to distinguish the impact of skill from the impact of luck. What's more, argue the professors—longtime advocates of index investing—actively managed funds in the aggregate pretty much match the market before expenses, which means most fall short of the market after expenses.
That's not to say there is no skill in investing, just that it's relatively rare and hard to distinguish in the aggregate from the effects of chance. That's one reason people get "fooled by randomness," to reference a popular book, by Nassim Nicholas Taleb, about the hazards of underestimating the power of chance.
Happily, Legg Mason's Michael Mauboussin, in an entertaining 2010 paper called "Untangling Skill and Luck," provides a bit of clarity. "There's a simple and elegant test of whether there is skill in an activity," he writes. "Ask whether you can lose on purpose. If you can't lose on purpose, or if it's really hard, luck likely dominates that activity."