Michael J. Mauboussinis Chief Investment Strategist at Legg Mason Capital Management in the United States. He is also the author of bestselling books on investing like Think Twice: Harnessing the Power of Counterintuition, andMore Than You Know: Finding Financial Wisdom in Unconventional Places. His latest book, The Success Equation: Untangling Skill and Luck in Business, Sports, and Investing(Harvard Business Review Press, Rs 995), has just come out.
In this freewheeling interview withVivek Kaul, he talks about how short-term outcomes have a good dose of luck attached to them and how as people get more skillful at doing a particular thing, luck becomes the deciding factor. In this part of the interview, the second part, he explains why a star in one context is unable to perform elsewhere, and why even Playboy Playmates do better than professional fund managers in terms of short-term returns. (Read the first part here)
You write that "organisations tend to overestimate the degree to which the star's skills are transferrable".
This argument is an extension of the work done by Boris Groysberg, a professor at Harvard Business School. Groysberg has studied many cases in which stars switch organisations and found that in most cases their performance deteriorates. So skill is not as portable as we tend to think.One example he provides is that of executives from General Electric. GE is well known to have among the best management training programmes in the world. Further, rising to the ranks of GE management undoubtedly requires skill.
Groysberg studied 20 executives who left GE over a two decade span and who took leadership positions at other companies. What he found was that those who went to firms organisationally very similar to GE tended to do quite well, while those that went to firms that were different fared poorly. So it's not just skills that matter, but the match between skills and the environment.
You talk about the Playboy Playmates selecting stocks that generated greater returns than the broader market....
As a promotional stunt, a trading company asked former Playboy Playmates to pick 10 stocks at the beginning of 2006. The best of the Playmates did much better than the market, and a higher percentage of Playmates outperformed the market than did professional money managers. Right away, this should cause you to ponder an important question. How can a handful of presumably untrained individuals outperform diligent and dedicated professionals? In how many fields can amateurs beat the professionals?
What is the broader point you were trying to make?
The broader point I was making is that in fields where there is a good dose of luck, short-term outcomes do little to reveal differential skills. Over a longer period, you would most certainly expect the pros to do better than the amateurs. But it is common in business and investing to use evaluation periods that are simply too short to allow for any kind of concrete conclusions about differences in skill.
What is the paradox of skill?
The paradox of skill says that as skill improves in an activity that includes both skill and luck, then luck becomes more important in determining outcomes. So more skill leads to more luck - the paradox.The paradox of skill says that as competitors in a field become more skillful, luck becomes more important in determining results. The key to this idea is what happens when skill improves in a field. There are two effects. First, the absolute level of ability rises. And second, the variance of ability declines.
Could you explain through an example?
Let me give one example from athletics and then turn to investing. The paradox of skill makes a specific, testable prediction in sports that are measured against a clock. You should see absolute times improve, bumping into the limits of human physiology, and you see relative times cluster, which means that the finishers are all bunched. This is precisely what we see in swimming and marathon.Men today run the race about 26 minutes faster than they did 80 years ago. But in 1932, the time difference between the man who won and the man who came in 20thwas close to 40 minutes. Today that difference is well under 10 minutes.
And the investing example?
The idea applies well to investing and has been the subject of discussion for decades. For example, Charles Ellis wrote a famous essay in 1975 called "The Loser's Game," which makes the same essential point. Ellis argued that in the 1950s and 1960s, institutional investors could outwit individuals because there was a wide range of skill. But as the markets became dominated by institutions, the difference in skill narrowed, making the game harder to win.
In investing, the idea is that skilled investors are very efficient at reflecting information into asset prices. So only new information, which is by definition random, should affect stock prices. Hence, stocks follow a "random walk." This is a statement of the efficient market hypothesis.Now, the efficient market hypothesis is not accurate. Stock price movements do not follow random walks, and there is differential skill. But the basic point remains true. Because prices capture the skill of investors, luck is very important in determining results-especially in the short term.
You write "people who work in businesses where social influence operates are often paid for good luck, although they generally don't suffer symmetrically from bad luck." Can you explain this statement in the context of the financial crisis.
I think this idea is why some many people were so upset by the financial crisis. The basic idea is that gains are privatised when times are good and (losses are) socialised when times are bad. In other words, executives make lots of money in good times and taxpayers have to bail out companies in bad times. That feels deeply unfair.
This is also relevant in equity-based compensation. Stock price moves reflect changes in the expectations of a company's prospects plus macro factors such as interest rates, tax rates, regulation, the perceived equity risk premium, and so forth. Ideally, you want to pay executives for superior performance, but the macro factors can swamp the company-specific factors. In bull markets, that means executives are getting paid handsomely for good luck. In bear markets, it means that even those executives who are skillful fare poorly. Neither outcome serves the core purpose.So the ideal is to figure out how to pay executives for good skill. Indexing options or restricted stock units is a solid first step in achieving this goal.
"Poor quality makes a company uncompetitive but so does quality that is too high. The relationship between quality and value is not all clear," you write. Why do you say that?
This boils down to what a company's governing objective should be. What's essential is that a company cannot optimise multiple factors at the same time. Quality is a good example. If the quality of a company's goods or services is too low, customers won't want to do business. On the other hand, if quality is too high, leading to prices that are too high, then customers won't want to do business. So the trick for managers is to find a balance between price and value that makes the customers happyandcreates value for the company.
In the book, I give the example of a company that was determined to win a prestigious quality award and succeeded. The problem was that the expense the company incurred for the award was greater than the price increases it could charge its customers. So while the customers happily enjoyed the higher quality, the company's finances suffered. It eventually filed for bankruptcy.
That sounds very similar to what happened to Kingfisher Airlines in India. Moving on, can you tell us what is the luck-skill continuum?
Imagine a continuum where on one side results are determined solely through luck - think roulette wheels and lotteries - and on the other side solely by skill, such as chess matches and running races. Most activities in life are between these extremes, and knowing where an activity lies can be very helpful. For example, as you move from the skill to the luck side, you need an increasingly large sample size in order to detect skill because luck dilutes the signal. Another way to think about this is cause and effect. On the skill side of the continuum, outcomes correlate exactly with skill. Think of a great performance by a pianist. Just hearing the music indicates the skill of the musician. Cause and effect are tightly linked, and feedback is clear.
By contrast, outcomes correlate with skill only loosely on the luck side of the continuum. Success or failure comes with an attached probability. For example, even if you play your cards right in a card game, luck ensures that you'll lose some of the time. This means that feedback is much more difficult since a good process can lead to a bad outcome or a bad process can lead to a good outcome. The way to deal with that is to focus on the process, not the outcome, when luck is involved. A good process provides the best chance for a good result over time.
You point out that one can reduce the influence of luck by effectively tying cause to effect. Could you explain through an example?
There's an old saying in advertising that half of a company's advertising budget is wasted, it's just that no one knows which half it is. The broader point is that no one has been able to effectively measure the effect of advertising spending.That is changing, and that is what I meant when I discussed better understanding cause and effect. Take online advertising as an example. One large retailer tested their advertising using a control group. In other words, they showed advertising to some people and compared the results to a demographically similar group that didn't see the ads. By comparing results, they could see the effectiveness of the ads.This type of analysis is spreading fast, in large part because technology today allows it. It has also spread to realms including politics. Politicians in the US used to spend money without knowing the payoff, measured in votes. Political scientists now have ways to measure the efficacy of spending much more accurately.
What is the undersampling of failure?
Undersampling failure means that when we review the past to see what may work in the future, we have a tendency to dwell on success and not examine failure. Think of it this way. Say companies could choose one of two strategies, risky or safe. Of the companies that choose the risky strategy, some succeed wildly and others fail. Of the companies that choose the safe strategy, some are mildly successful and others mildly disappointing, but all stay in business. In other words, the outcomes for the risky strategy have high variance and the outcomes for the safe strategy have a low variance.
Now you're a new company coming along and want to be successful. So you study the records of companies and see that the wildly successful ones are those that selected the risky strategy. You don't see the companies that chose the risky strategy and failed because they are dead. That is undersampling failure. The question is not: which strategy did the successful companies pursue? The question is: what are the outcomes ofallof the companies that chose this strategy?A related point is that the success of a strategy only occurs with some probability. Major variables, including the tastes of consumers, the actions of competitors, and technological change, are simply impossible to fully anticipate
Can you give us an example?
Michael Raynor, a consultant at Deloitte, offers the example of Sony's MiniDisc. He argues that Sony's strategy was brilliantly conceived, yet the product totally flopped. There are no sure things.
Vivek Kaul is a writer. He can be reached at email@example.com