How Many of Our Successes and Failures Should Be Attributed to Luck?
In the junk-bond fueled M&A boom in the late ’80s, I owned some stocks that were taken over at generous premiums. My returns were impressive over a very short interval, and for a bit longer than that, I believed myself to be a skilled stock picker. But when results are due to luck, the passage of time brings mean reversion, and two decades later I’ve a pretty good notion of where I sit on the investment luck/skill continuum. (Hint: I do not manage portfolios for a living.)
According to Michael J. Mauboussin who spoke at the 15th Annual Equity Research and Valuation Conference on December 6, in Philadelphia, investing is one of many activities that involve both luck and skill, and it is not always easy to untangle the two. How many hot hitting baseball players in April are still hitting in August? How many stock pickers who outperform this year will beat a dart board next year? The answer depends upon whether luck or skill had more to do with their results, and this is the subject of Mauboussin’s recent book, The Success Equation: Untangling Skill and Luck in Business, Sports, and Investing.
What makes an outcome “lucky”? Mauboussin says there are three conditions of luck: it works the same for an individual or a group; it can be good or bad; and another outcome could have occurred. Coin flipping is a game of luck, and so is rolling fair dice. If you could lose either game intentionally, that would indicate the presence of skill. Instead, over multiple games observed results will revert to the mean, which is what you’d expect in a game of pure luck.
But most games, including investing, reflect combinations of skill and luck, and according to Mauboussin, we learn three lessons from observing them.
- First, both skill and luck are involved in outlier results (e.g., the ball hit over the fence or the year of outperformance).
- Second, results attributable to skill should not show mean reversion, so whether you are a Major League hitter or a professional investor, where you lie on the continuum will determine the rate of mean-reversion your results experience over time.
- Third, there exists a paradox of skill, which says that in activities involving luck and skill, luck becomes more important in determining an outcome as the level of skill improves. In other words, as skill levels increase, whether you finish ahead or behind your skilled competitor may very well come down to luck.
As humans, our own biases will lead us to underestimate the role luck plays in our results. Measuring skill remains a challenge, and Mauboussin notes that a useful measurement statistic will be persistent and correlate with itself over time, and will be predictive and have a high correlation with outcomes. By their very definition, such measurements cannot be applied to short-term results, implying that in the realm of investing, performance results are not a particularly effective measure of manager skill.
Because randomness and luck impact performance results to varying degrees, Mauboussin suggests that true long-term investment skill might be measured by the extent to which an analyst or manager is able to anticipate the changes in the market expectations that drive changes in value. While not always completely efficient, the stock market does a pretty good job of processing existing information about the economy, interest rates, and businesses, and current prices are based on the collective expectations of the market. The stock of a company reporting earnings growth may underperform the stock of a company reporting a loss, depending upon how their results adhered to or deviated from the market expectations reflected in their stock prices before the announcement. An investment process that can anticipate key changes in expectations over the long-term, may not only be a successful strategy but can, over time, provide evidence that skill makes a significant contribution to a manager’s results.
Mauboussin explained in simple terms how an expectations approach to investing might work (the topic of his book Expectations Investing, coauthored with Alfred Rappaport). The first step is estimating the market expectations implied by the price of a stock, assuming the market is discounting expectations of future cash-flow. After establishing a base-case set of market expectations, analysis can then focus on identifying “expectation opportunities” — that is, which expectations might be more likely to face revision, and which expectations revisions matter most. Step three in the process would be making buy or sell decisions based on a distribution of possible outcomes that with some probability would lead to a significant revision in market expectations, and Mauboussin noted that frequency of correctness is not the key, but the magnitude of correctness is what ultimately delivers value.
“It’s quite possible for a strategy with a high probability of making money to have an expected return that is actually negative,” Mauboussin said, illustrating with a simple example (see table below). “It’s not how often you are right that matters, but how much money you make when you are right versus how much you lose when wrong.”
If Mauboussin is correct, than measuring manager skill should actually be possible over a long enough time period, but don’t forget that luck never completely leaves the equation. As often as not, good luck will surely be followed by bad luck and vice versa, and sometimes the dart board wins.
Please note that the content of this site should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute.
Photo credit: ©iStockphoto.com/MHJ