Practical analysis for investment professionals
25 July 2012

Optimizing the Investment Process: Michael Mauboussin’s Strategies for Making Decisions under Uncertainty

Decision making under conditions of uncertainty cuts to the heart of the investment process. At the recent CFA Society of the UK Annual Conference in London, Michael Mauboussin, chief investment officer at Legg Mason Capital Management, reviewed some best practices, including paying attention to process versus outcome, having the odds in one’s favor, and understanding the role of time.

Building on what he teaches at Columbia Business School, Mauboussin laid out his “T” theory, which postulates that the best decision makers have more in common with each other than they do with average participants, whether the context is investing or playing poker. “Average” players may lack a disciplined and economic process and fail to recognize that even excellent processes sometimes yield bad results. Mauboussin also contended that the investment community today is driven by financial incentives and is focused on outcomes to the detriment of process, a sentiment that was echoed by the audience of investment professionals in a post-lecture poll.

Best Practices for Investment Decision Making

Mauboussin believes the main difference between good and great investors comes down to temperament and focus. Good processes and good outcomes deliver deserved success, just as bad processes and bad outcomes are a form of poetic justice. Conversely, bad processes that yield good outcomes are just dumb luck. Investors often confuse the two. Successful poker players and renowned economists agree that better decision making comes from evaluating decisions on how well they were made rather than on outcomes.

Asset prices clearly reflect expectations, which successful investors must understand (this is analogous to a gambler having odds in his favor). Mauboussin argued that the greatest failure in the investment business is not distinguishing between fundamentals and expectations as implied by price. As an insightful track bookie might put it, there is no such thing as “liking” a horse, only “an attractive discrepancy between the horse’s chances and his price,” a concept echoed by successful asset managers.

Mauboussin added that time plays a critical role here: The short term cannot distinguish between good and bad processes — a quality process has a long-term focus (a lot longer than that of most investors). He cited Michael Lewis’s observation in Moneyball that, over a season, luck among baseball teams evens out and skill shines through. In the short term, however, skill can be overwhelmed by chance. Famous card gambler Amarillo Slim didn’t care about the results of one game. He focused on decisions, not results: “Do the right thing enough times and the results will take care of themselves.” A quantitative example of “time arbitrage” shows that in 20 coin tosses, 30% come up heads, whereas in 100 tosses, the result is 50% heads.

Expected Value: Probabilities and Outcomes

Applying this to the practice of investing, Mauboussin focuses on setting probabilities and considering outcomes. As Warren Buffett knows well, expected value is the weighted average value for a distribution of possible outcomes. The investor’s goal of achieving net gain from probabilities of gain and loss is an imperfect process.

So, how should we set probabilities? There is the Bayesian approach (subjective, satisfying probability laws), propensity (reflecting system properties — e.g., rolling a die), and frequencies (using a large sample of an appropriate reference class). The latter is favored by the finance community, Mauboussin said. The problem with this approach was articulated by Bradford Cornell: If the data are nonstationary (which in all likelihood they are), the results of the sample (e.g., P/E) are typically not meaningful.

One way to think about outcomes is to look at a frequency distribution (for example, daily returns of the S&P 500 Index from 1978 to 2011). These data show at least five six-sigma events in the course of a single recent year, where theory would predict one six-sigma event every 4 million years!

Another simple example suffices to show that a good probability could result in a bad expected value: 70% probability of a 1% gain could well result in a negative return if there is a 30% probability of a 10% loss.

Why Are Most of Us Suboptimal?

Mauboussin stated that investors, like most of the general population, inevitably encounter pitfalls based on human behavior. As an example, overconfidence results in too narrow an outcome range, and the confirmation trap compels us to seek confirming information and dismiss or discount that which disconfirms. One interesting experiment he cited utilized brain-damaged patients, whose emotions had been suppressed as a result. The experiment found that in contrast to normal participants, these patients did best wagering a $20 stake over 20 rounds and played more rounds, especially after suffering a loss. Solomon Asch’s study of social conformity showed that, under pressure from peers, one-third of individuals conformed to the majority in giving a patently wrong answer. As also shown by researcher Greg Berns, this is a distortion of perception rather than of judgment or action.

Legg Mason’s CIO believes investing is probabilistic and that expected value is the right way to think about security prices. Investors encounter many pitfalls in objectively assessing probabilities and outcomes. He concluded that unless we practice mental discipline, we will lose in the long term to those who can. Indicative of this behavior are the excesses that markets periodically experience. Mauboussin’s  forthcoming book, The Success Equation, strives to untangle skill and luck in business, sports, and investing.

Watch Michael Mauboussin discuss the role of technical competence, “counterintuition,” and the application of multiple mental models to the discipline of financial analysis.

For more insights on behavioral pitfalls, read Bankable Insights: Eight Lessons from Neuroeconomics for Money Managers.

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.

About the Author(s)
Ed Bace, CFA

Ed Bace, CFA, was previously head of education for the Europe, Middle East, and Africa (EMEA) region at CFA Institute. He has also served as a professor of finance at BPP Business School. Bace has more than 20 years of experience in international finance, including roles with the European Bank for Reconstruction and Development, Lehman Brothers, and Standard & Poor’s. He holds an MBA in finance and international business from New York University.

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