In 2006, the TradingMarkets/Playboy 2006 Stock Picking Contest was won by Playboy’s Miss May of 1998, Deanna Brooks (shown right). Her portfolio, which bet heavily on oil and gold stocks, gained 46.43 percent on the year and every stock in it provided double-digit returns. She liked Yamana Gold because “What girl doesn’t like a little bling? I’m hot for gold this year.…” It wasn’t her only nugget of sterling analysis. She also liked Petrobras because “oil is making money” and IBM because computers “aren’t going away.” She wasn’t the only Playmate to find a rich vein of success. A higher percentage of participating Playmates bested the S&P 500’s 2006 returns than active money managers. Think about that for a moment. Over the course of a full year, a bunch of Playmates outperformed a whopping majority of highly trained and experienced professionals with vast resources who spend all day every day trying to beat the market.
It’s easy to say that the Playmates got lucky, and they did. But we’d never expect a guy swimming laps at the YMCA to beat Michael Phelps across the pool, a girl off the street to beat a Grandmaster in chess, or an unschooled janitor to solve an insanely complex math problem amidst a spot of cleaning in the afternoon that the best and the brightest need years to figure out. Not even once.
If something like that actually were to happen, we’d treat is as a marvel (as the movie, Good Will Hunting, excerpted above, does), not just as a whimsical curiosity to be used for the purposes of garnering a bit of publicity and ogling attractive women.
It’s tempting simply to say that the contest is too small a sample size to be meaningful and move on. Had she stuck with investing, Miss May’s performance would miss and miss by a lot, probably sooner rather than later, as all investment performance tends to be mean reverting. But we also know that sample size doesn’t mean much when little luck is involved. It doesn’t matter how many times I race Michael Phelps. The chances of my winning will always be vanishingly small — effectively zero.
It’s also important to emphasize (as Michael Mauboussin did in his excellent book, The Success Equation and at The Big Picture Conference recently) the paradox of skill when it comes to investing. As overall skill improves, aggregate performance improves and luck becomes more important to individual outcomes. On account of the growth and development of the investment industry, John Bogle could quite consistently write his senior thesis at Princeton on the successes of active fund management and then go on some years later to found Vanguard and become the primary developer and intellectual forefather of indexing. In other words, the ever-increasing aggregate skill (supplemented by massive computing power) of the investment world has come largely to cancel itself out.
These explanations are good as far as they go, but they hardly tell the entire story. Lady Luck is crucial to investment outcomes. There is no getting around it. Managing one’s portfolio so as to benefit the most from good luck and (even more importantly) to get hurt the least by bad luck are the keys to investment management. Doing so well is a remarkable skill, but not the sort of skill that’s commonly assumed, even (especially!) by professionals.
More to the point, if investment returns depend that heavily on luck and real investment skill is that elusive and rare, what should we do with our (or our clients’) money? For some answers, we turn to the world of…poker? That’s right — poker. Whether poker is deemed a game of chance or skill has significant legal implications for gamblers and those who earn a living from them. Moreover, it is clear that there is a good deal of skill involved in playing poker well. However, for our purposes, since poker is an excellent means to evaluate probabilities under uncertainty and provides a great deal of data, it is an excellent means for investors to learn about the markets, which are also governed by a great deal of uncertainty, meaning that the best investors must deal with probabilities amidst uncertainty extraordinarily well.
Poker involves tremendous luck and tremendous skill. By way of comparison, consider chess, which involves little luck (white plays first) and tremendous skill, Tic-Tac-Toe, which involves little luck (who goes first) but little skill (such that a precocious first grader can quickly get entirely up to speed), and Chutes and Ladders, which involves tremendous luck and little skill. More specifically, the luck component is so high that the volatility of poker “returns” can make it maddeningly difficult for a better player to outperform even over substantial time periods (say many months of daily play, especially if the stakes are limited). Looked at another way (as Nate Silver does in his fine book, The Signal and the Noise), a weaker player might be ahead of a stronger player after tens of thousands of hands. Silver estimates the likely outcomes for a very good (limit) Texas Hold ‘Em player over the course of 60,000 hands to range from up $275,000 to down $35,000 to 95 percent certainty. He also estimates that a player who has won $30,000 over the his first 10,000 hands is still more likely than not to be a long-term loser (and short-term losers have mostly given up). That’s a lot of luck!
In the markets, the average investor underperforms due to costs. Poker is similar on account of the house’s rake. Yet most investors — like most poker players and most people generally, due to optimism bias — think they are better (and often much better) than the norm. With poker players, the truth can be beaten into them as their losses mount. Since the markets are biased upward (most underperformers have positive returns overall), investors tend to remain delusional for a much longer time. Some never recover.
In a new ‘quasi-experimental’ study, researchers set out to examine these questions in poker. They got together a group of both expert and novice poker players to play fixed games, meaning that the players received hands that the researchers had set up – without the knowledge of the players – to test how things would go under various scenarios. The results revealed that while the cards dealt (luck) largely predicted the winner, skill was crucial to reducing losses when players were dealt a bad hand. That’s a true if unsurprising result as far as it goes. But the conclusion of the study (“that poker should be regarded as a game of chance”) is clearly overstated.
The study has been rightly criticized for not looking at enough data. It’s surely true that over the short-term, luck dominates skill in poker. However, over longer and longer periods of time – a much larger data base of hands – a slight skill advantage will result in a positive win rate because no player will have better cards in the aggregate. In other words, given enough time, luck cancels itself out. As Silver argues in The Signal and the Noise, especially when the skill differential is not great, the interesting question is how long it will take for skill to win out. Another interesting question is why skill wins out. I suspect that – consistent with the study – the primary reason is that the expert player makes fewer mistakes. Science seeks the truth by uncovering what is false. What’s left is likely to be true. So, if you are a beginner, you’d better have beginner’s luck or you might be broke by the end of the game.
Inverting also combines skill and luck, but is more complicated because of the personal aspect (our psychology makes it hard for us to invest optimally), the difficulty in parsing out the market’s “psychology” the way a good poker player reads tells, and the much larger numbers of variables involved in ultimate outcomes. There is decidedly less market skill on display in the markets than poker skill at major tournaments. As Charley Ellis has pointed out, “in a random 12-month period, about 60% of mutual fund managers underperform; lengthen the period to 10 years and the proportion of managers who underperform rises to about 70%. Although the data are not robust for 20-year periods, the proportion of managers who fall behind the market for this longer period is about 80%. At least as concerning, equity managers who underperform do so by roughly twice as much as the ‘outperforming’ funds beat their chosen benchmarks.” Moreover, “[N]ew research on the performance of institutional portfolios shows that after risk adjustment, 24% of funds fall significantly short of their chosen market benchmark and have negative alpha, 75% of funds roughly match the market and have zero alpha, and well under 1% achieve superior results after costs — a number not statistically significantly different from zero.”
Despite those poor results and thus the difficulty in making a case for investment skill, our tendency is to give money to managers who have recently done well and pull money from managers who have recently done poorly. Consistent with many studies over a wide variety of time periods, this tendency applies to individuals, advisors, and “expert” consultants alike. Sadly, however, this past performance does not indicate future results.
As Kahneman and Klein have established, true expertise can be developed such that expert intuition and guidance are routinely reliable, but only in areas that involve little luck. However, as Kahneman emphasizes, when intuition cannot work (as with investing), we tend to substitute a simpler question for the real question at issue and answer it. We can thus oversimplify a difficult problem or even miss the point entirely. That’s why investment professionals all acknowledge (as their marketing materials must) that past performance is not indicative of future results and then begin their presentations by talking about their past performance (at least when that performance is reasonably good). Even so, being human, underperforming managers attribute their prior success to skill (which led to their hiring) and the more current problems to bad luck — classic self-serving bias.
In real-life poker, a high win percentage is negatively correlated with win rate. In other words, the players that win the most money don’t win the most hands. They win the biggest hands. As one of the world’s great poker players, Tom Dwan, told Nate Silver, “It’s important in most areas of life to come up with a probability instead of a yes or no. It’s a huge flaw that people make in a lot of areas they analyze.” Sports betting has a slightly different emphasis in that (like the markets), there are more variables. The great gambler Billy Walters uses a probabilistic sports betting model that is correct roughly 57 percent of time. He expects and plans for being wrong 43 percent of the time. Since he can’t predict the timing of his successes and failures, he has to be prepared for long losing streaks. Common gambling practice had been (and often still is) to make fewer bets – to bet only on those games one is most sure of. But that approach is not thinking probabilistically. Walters makes as many bets as he can within the confines of his model (when he thinks the point spread is off by at least one-and-one-half points), even though he bets more the more he thinks the line is off. Investors should thus diversify and make more “bets” — like Walters, even as they “bet” more money on investments most likely to outperform over time (like the best poker players).
As Silver writes in The Signal and the Noise, in a highly competitive probabilistic enterprise, it is really hard to make money. It’s only possible to do so “around the margin.” And the more less-than-stellar players are removed (or remove themselves) from the game, the harder it is to profit. It is obvious that as the investment landscape has become increasingly professionalized and computerized, it has become ever harder to outperform.
As Morningstar’s John Rekenthaler argues, in order to be successful, active management requires (a) low costs; (b) patience; and (c) skin in the game. It also demands sectors and approaches that have been shown to work over time. These include the smart-beta flavors of small-company, value, fundamental weighting, equal-weighting, and momentum. Concentration can work too if the manager is especially skilled (like Seth Klarman). Low volatility has worked for a substantial period and seems to persist, but its current popularity makes me nervous.
As Silver emphasizes in The Signal and the Noise, we readily overestimate the degree of predictability in complex systems. The experts we see in the media are much too sure of themselves (I wrote about this problem in our industry from a slightly different angle recently). Much of what we attribute to skill is actually luck. Invest accordingly.
“In real-life poker, a high win percentage isnegatively correlated with win rate. In other words, the players that win the most money don’t win the most hands. They win the biggest hands. As one of the world’s great poker players, Tom Dwan, told Nate Silver, “It’s important in most areas of life to come up with a probability instead of a yes or no. It’s a huge flaw that people make in a lot of areas they analyze.” Sports betting has a slightly different emphasis in that (like the markets), there are more variables. The great gambler Billy Walters uses a probabilistic sports betting model that is correct roughly 57 percent of time. He expects and plans for being wrong 43 percent of the time. Since he can’t predict the timing of his successes and failures, he has to be prepared for long losing streaks. Common gambling practice had been (and often still is) to make fewer bets – to bet only on those games one is most sure of. But that approach is not thinking probabilistically. Walters makes as many bets as he can within the confines of his model (when he thinks the point spread is off by at least one-and-one-half points), even though he bets more the more he thinks the line is off. Investors should thus diversify and make more “bets” — like Walters, even as they “bet” more money on investments most likely to outperform over time (like the best poker players).”
are we just saying that to “realize” the probabilites u need to bet a lot? sort of like the law of large numbers?
seems there are two dimensions on a lots of bets- many bets over time and many bets at once…. what is the difference in those two strategies?
great article as always!
I think you’re making too much of the would-be differences in the “two strategies” and not enough of the similarlities. You should want to “play” whenever the odds are sufficiently in your favor (and not just those instances when you have the best odds). The vast majority of the time, that will be more than one “play” at a time. For example, instead on buying the “best” value stock, you’d buy a basket of all stocks with sufficient value characteristics. Because there is so much luck involved, if you only bought one and your luck was poor (perhaps the CEO dies and a competitor comes up with an unexpected innovation), you’d have a real mess on your hands.
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