firms.25 The primary responsibility of these analysts is to determine whether or not a given stock is attractive within the industry that they follow. (I used to be one of these analysts.) Institutional Investor magazine ranks the analysts annually, which provides a measure of quality.
Groysberg examined all of the moves by ranked analysts over a twenty-year period and found 366 instances of a star analyst moving to another firm. If the skill were associated solely with the analyst, you would expect the star's performance to remain stable when he or she changed jobs. That is not what the data showed. Groysberg writes, “Star analysts who switched employers paid a high price for jumping ship relative to comparable stars who stayed put: overall, their job performance plunged sharply and continued to suffer for at least five years after moving to a new firm.”26 He considered a number of explanations for the deterioration in performance and concluded that the main factor was that they left behind a good fit between their skills and the resources of their employer.
General Electric is a well-known source of managerial talent, and its alumni are disproportionately represented among CEOs in the S&P 500. Groysberg and his colleagues tracked the performance of twenty managers from GE that other organizations hired as chairman, CEO, or CEO-designate between 1989 and 2001. They found a stark dichotomy. Ten of the hiring companies resembled GE, so the skills of the executives were neatly transferable and the companies flourished. The other ten companies were in lines of business different from GE. For example, one GE executive went to a company selling groceries, whereas his experience had been in selling appliances. Even with a GE-trained executive at the helm, those companies delivered poor returns to shareholders. Again, developing skill is a genuine achievement. And skill, once developed, has a real influence on what we can do and how successful we are. But skill is only one factor that contributes to the end result of our efforts. The organization or environment in which a CEO works also has an influence. The evidence shows that employers systematically overestimate the power of an individual's skill and underestimate the influence of the organization in which he or she operates.
Along with some fellow researchers, Groysberg showed this point neatly by analyzing the performance of players who switched teams in the National Football League. They compared wide receivers with punters in the period between 1993 and 2002. Since each team has eleven players on the field at a time, wide receivers rely heavily on the strategy of the team and on interaction with their teammates, factors that can vary widely from team to team. Punters pretty much do the same thing no matter which team they play for, and have more limited interaction with teammates. The contrast in interaction allowed the scientists to separate an individual's skill from the influence of the organization on performance. They found that star wide receivers who switched teams suffered a decline in performance for the subsequent season compared with those who stayed with the team. Their performance then improved as they adjusted to their new team. Whether a punter changed teams or stayed put had no influence on his performance. Punters are more portable than wide receivers.27
As with testing too much or too little, the difficulty in determining the portability of a skill lies in the relationship between cause and effect. Groysberg's work dwells on stars and finds that the organizations that support them contribute meaningfully to their success. Yet we see people consistently overestimate skill in fields as diverse as catching touchdown passes and selling motorcycles.
Stories Can Obscure Skills
We re-create events in the world by creating a narrative that is based on our own beliefs and goals. As a consequence, we often struggle to understand cause and effect, and especially the relative contributions of skill and luck in shaping the events we observe.28 As we've seen, we may make the mistake of drawing conclusions from samples that are too small. We may fail to consider all of the causes that might lead to particular events. We might test too much—so much, in fact, that we wind up finding causes where we're simply seeing the results of chance. Or we may look at high performance and believe we are seeing a star with exceptional skill, when in reality we are seeing the combined effects of skill and the powerful influence that an organization can exert on someone. All of these mistakes are manageable, but it is critical to learn about them and to see where they apply if we are going to overcome them. The effort of untangling skill from luck, even with its practical difficulties, still yields great value when we are trying to improve the way we make decisions.
CHAPTER 3
THE LUCK-SKILL CONTINUUM
IN 2006, TRADINGMARKETS, a company that helps people trade stocks, asked ten Playboy Playmates to select five stocks each. The idea was to see if they could beat the market. The winner was Deanna Brooks, Playmate of the Month in May 1998. The stocks she picked rose 43.4 percent, trouncing the S&P 500, which gained 13.6 percent, and beating more than 90 percent of the money managers who actively try to outperform a given index. Brooks wasn't the only one who fared well. Four of the other ten Playmates had better returns than the S&P 500 while less than a third of the active money managers did.1
Although the exercise was presumably a lighthearted effort at attracting attention, the results raise a serious question: How can a group of amateurs do a better job of picking stocks than the majority of dedicated professionals? You would never expect amateurs to outperform professional dentists, accountants, or athletes over the course of a year. In this case, the answer lies in the fact that investing is an activity that depends to a great deal on luck, especially over a short period of time. In this chapter, I'll develop a simple model that will allow us to take a more in-depth look at the relative contributions of luck and skill. I'll also provide a framework for thinking about extreme outcomes and show how to anticipate the rate of reversion to the mean. A deeper discussion of the continuum between luck and skill can help us to avoid some of the mistakes described in chapters 1 and 2 and to make better decisions.
Sample Size, Not Time
Visualizing the continuum between luck and skill can help us to see where an activity lies between the two extremes, with pure luck on one side and pure skill on the other. In most cases, characterizing what's going on at the extremes is not too hard. As an example, you can't predict the outcome of a specific fair coin toss or payoff from a slot machine. They are entirely dependent on chance. On the other hand, the fastest swimmer will almost always win the race. The outcome is determined by skill, with luck playing only a vanishingly small role (for example, the fastest swimmer could contract food poisoning in the middle of a match and lose). But the extremes on the continuum capture only a small percentage of what really goes on in the world. Most of the action is in the middle, and having a sense of where an activity lies will provide you with an important context for making decisions.
As you move from right to left on the continuum, luck exerts a larger influence. It doesn't mean that skill doesn't exist in those activities. It does. It means that we need a large number of observations to make sure that skill can overcome the influence of luck. So Deanna Brooks would have to pick a lot more stocks and outperform the pros for a lot longer before we'd be ready to say that she is skillful at picking stocks. (The more likely outcome is that her performance would revert to the mean and look a lot more like the average of all investments.) In some endeavors, such as selling books and movies, luck plays a large role, and yet best-selling books and blockbuster movies don't revert to the mean over time. We'll return to that subject later to discuss why that happens. But for now we'll stick to areas where luck does even out the results over time.
When skill dominates, a small sample is sufficient to understand what's going on. When Roger Federer was in his prime, you could watch him play a few games of tennis and know that he was going to win against all but one or two of the top players. You didn't have to watch a thousand games. In activities that are strongly influenced by luck, a small sample is useless and even dangerous. You'll need a large sample to draw any reasonable conclusion about what's going to happen next. This link between luck and the size of the sample makes complete sense, and there is a simple model that demonstrates this important lesson.
