The article considers, for example, which sports have relatively high components of skill (e.g. running, professional basketball), and which are heavy on luck (e.g. baseball), as well as which individual measures of player performance have a high skill component (e.g. strike out percentage), and which do not.
Investing, it turns out, have a very high luck component. For example, about 75% of mutual funds fail to generate excess returns from their active management that significantly exceed their costs. In any given year, about 60% of mutual funds do worse than benchmark indexes. While not actually a pure luck endeavor as many kinds of gambling are, it involves far more luck than professional baseball or football, for example.
Return on investment in publicly held non-financial corporations, likewise, are highly random, as shown by their tendency to revert to the mean from results in any given year. The more strongly results revert to the mean, the more likely it is that the results are simply a product of luck, rather than anything fundamental.
The ten year return on investment for the bottom 60% of Russell 3000 firms in the first year of a study on the subject showed them to be virtually indistinguishable in returns ten years later. Firms in quintiles that did poorly in the first year of the study (1999) and firms the middle quintile, all had remarkable similar returns ten years late (reverting to the mean). A roughly 45 percentage point spread between quintiles in first year performance collapsed to just a few percentage points ten years later.
Firms that were in the 60th to 80th percentile of first year returns (the fourth quintile) did slightly but distinguishably better, ten years later, than firms in the bottom three quintiles based on first year returns returns, but only by a couple of percentage points. Only firms that were in the top 20% of first year returns did significantly better (collectively) ten years later, with a not quite ten percentage points edge over other firms, but their edge in the first year was muted dramatically in the long run. "[T]he spread between the highest and lowest quintiles shrinks from 70 percentage points in 1999 to about 10 percentage points in 2009."
Thus, while some public held non-financial companies in the Russell 3000 (roughly the 3000 largest capitalization publicly held companies) did provide consistently higher long term returns on investment, only about one in five of these big publicly held non-financial companies fit that profile.
Very high long term returns are almost never sustainable; businesses that survive a long period of time tend to be stable, low rate of return businesses.
Investment returns of mutual funds and a wide variety of other investments revert to the mean (and hence have returns that are likely to involve a greater component of luck rather than skill) to an even greater extent than the companies they own.
Jack Bogle, a luminary of the investment industry, illustrates this by ranking mutual funds in quartiles based on results in the 1990s and seeing how those quartiles performed in the 2000s. The top quartile, which had handily outpaced the average fund in the 1990s, saw a 7.8 percentage point drop in relative performance. Symmetrically, the bottom quartile in the 1990s witnessed a sharp 7.8 percentage point gain in results in the 2000s. . . . By 2009, the excess returns for the best performing funds in 2000 was effectively zero, while the lowest quintile delivered strong excess returns. Because investing results have a large dose of randomness, reversion to the mean is mighty. . . . reversion to the mean in the investment business extends well beyond the results for mutual funds. It applies to classifications within the market (small capitalization versus large capitalization, or value versus growth), across asset classes (bonds versus stocks) and spans geographic boundaries (U.S. versus non-U.S.). There are few corners of the investment business where reversion to the mean does not hold sway. . . .
To illustrate, the S&P 500 Index generated returns of 8.2 percent in the twenty years ended 2009. The average mutual fund saw returns of about 7 percent, reflecting the performance drag of fees. But the average investor earned a return of less than 6 percent, about two-thirds of the market’s return. The reason investors did worse than the average fund is bad timing: they put money in when markets (or funds) were doing well and pulled money out when markets (or funds) were doing poorly. This is the opposite of the behavior you would expect from investors who understand reversion to the mean.
Of course, we can't simply blame investors for being short sighted in their market timing; their below index investment returns are also a product of necessity. Investors invest when they have money to invest, which tends to be when business is doing well and the stock market is generating good returns. They tend to take money out when they need it, often when the economy is weak. The whole point of investing is to invest when you don't need money as badly and to take money out when you need it.
This would seem like a firm case for index investing, at least, and it is suggestive of that notion, but even the benefits of active investing vary considerably at different phases of the market:
The 1990s were one of the worst decades for active management, with an average of only 35 percent of funds generating returns in excess of the S&P 500 annually. The 2000s were one of the best decades for active management, with an average of half of all funds beating the index in each year.
There is also another interpretation of the reversion to the mean phenomena, which is popular in part because it doesn't make large numbers of financial professionals look like idiots that are no smarter than monkeys throwing darts. This is the efficient market hypothesis. The efficient market hypothesis argues that the capital markets are actually extremely smart and incorporate almost all available information to generate prices that predict the future to the greatest extent that skill makes possible.
A strong version of the efficient market hypothesis is not true. Price bubbles (i.e. prices that do not accurately measure long term value given all available information) in a wide variety of markets can be apparent for sustained periods of time. But, the evidence in favor of a weak version of the efficient market hypothesis that inaccurate prices do not persist in the markets over the medium to long term (i.e. periods of several years to decades) and are less likely to persist the longer the time period involved, is quite solid. Price bubbles almost almost collapse sooner or later, usually less than a decade, often much sooner.
Now, it is a fair criticism of this weaker version of the efficient market hypothesis that it is correct almost by definition. A price that does not correct itself in the medium to long term is presumably a correct price based on the fundamentals. But, a studies of skill relative to luck for professional athletes or professional investors, as Mouboussin notes, obscure the fact that some of the randomness is due to the fact that nobody participating is horribly unskilled.
Baseball looks much more skill based when you look at exhibition games between professional baseball players and amateurs, or major league and minor league players. The fact that publicly held companies tend to have similar long term returns on investment does not necessarily mean that businesses promising enough and proven enough to lead to public offerings of their stock and enter the ranks of the largest 3000 companies in the United States aren't a select group that as a whole produce better returns on investments than unproven small companies that can't secure significant outside investment.
Similarly, while mutual fund managers really do struggle to look more skilled than monkeys throwing darts, this doesn't mean that they aren't doing their clients a favor. Being in the market, in any kind of equity investment, produces better long term returns for most investors most of the time, than the alternative. Even in my generation, which has been cursed with a "lost decade" where equity investments have done no better than savings accounts, investing in equities hasn't actually been a significantly worse decision than not investing in equities in the long term.
The most important investment decision for most people is the decision to invest at all, not the particular investments actually made by those people. Due to the wonders of reversion to the mean, or the weak efficient market hypothesis, whichever you prefer, even people who ignored their securities law and finance professors and invested in an undiversified portfolio in a single industry or asset class have probably come out ahead, so long as they haven't kept switching their investment from one previously hot kind of investment to another; which is demonstrably the worst possible strategy (and the one implicitly encouraged by the only well reported data that ordinary investors consistently look at and think that they understand, charts of past performance).
Even what looks like periods when active management really matters suggest that the big thing that investment professionals bring to the table is getting people into the investment world in a way that they otherwise wouldn't have, rather than picking stocks:
The reason active managers did so much better in the recent decade has little to do with skill and a lot to do with style. Most funds that use the S&P 500 as a benchmark construct portfolios with stocks that have an average market capitalization that is much smaller than that of the broad index. This suggests a simple relationship: when large cap stocks outperform small cap stocks, active managers will struggle. Conversely, when small cap outperforms large cap, active managers will shine. This was the case with the 1990s versus the 2000s. In the 1990s, large cap stocks beat small caps by an average of 6.6 percentage points a year. By contrast, small caps generated returns 4.5 percentage points greater than large caps, on average, in the 2000s. As transitivity suggests, different strategies win from one environment to the next.
As the article notes, however, styles of investing, like the kinds preferred by active investors over large capitalization index fund investors, rarely work all the time. Sometimes small stocks do better than average, other times they don't. Even trends that have held for long periods of time won't necessarily continue to hold true. One can expect some fundamentals to hold, approximately, in the long term. Higher risk should produce greater returns, on average, in the long term, for example. But the weasel words in that assumption hides a multitude of sins.
Certainly, it is possible, in theory, to identify moments at which the market has incorrectly priced something, make a bet on that fact, and profit. A suitably skilled person ought to be able to do so repeatedly. But, the evidence seems to suggest that the luck to skill ratio in investing is high even for the very best of the best, and that luck utterly swamps skill for the vast majority of investors, professional and amateur alike.
Of course, there is a certain amount of skill and strategy that goes into even pure gambling, and one of the best expositions of the general principles that apply in gambling and surely also in investing because it has such a high component of luck, is that of Lester E. Dubbins and Leonard J. Savage who wrote the classic "How to Gamble if You Must."
They looked at the question of what sort of strategy you should use when you need a certain sum of dollars and are trying to turn your current pool of money into that larger amount by gambling. For example, what strategy should you use when you have $1000, you need a $1,0000 to pay off a loan shark, and the consequences from the mob of not being able to pay the full $10,000 are far more important than the consequences of losing your $1000 stake?
One of their basic insight was that the best strategy to employ changes dramatically depending upon whether the odds are for you or against you. When the odds are in your favor, you want to make as many small bets as you can, because that makes a result close to an average one most likely and when the odds are in your favor you want an average result.
In contrast, when the odds are against you, you want to bet as infrequently as possible. The less often you play, the more likely it is that you will get an atypical result in favor. If you make many small bets, in contrast, the law of averages is sure to catch up with you.
Another insight was that the best strategy for securing a certain amount of money that you need when the odds are against you is to stop playing when you have attained it. Any strategy based on continuing to play indefinitely when the odds are against you is doomed to lose. Strategy makes it possible to win some of the time when the odds are against you and to maximize that possibility. But, it remains the case that when the odds are against you that you will usually lose.
The insights, it turns out, a deep insights on strategy in life in any situation that has a component of luck. The strategies that make sense in favorable environments where the odds are in your favor, are very different than the strategies that make sense in unfavorable environments where you will usually lose.
When you need to perform far better than you are likely to in order to avoid big downside risks, you need to take big risks that can produce big payoffs. When par for the course is good enough, you are better taking small chances with small potential returns and not putting all of your eggs in one basket.
The most important rule of gambling, where the odds are designed to be against you, is to not play if you don't have to, while the most important rule of investing, where the odds in the long run are in your favor, are to play if you can.