There was a recent CFA event in indianapolis, with a fantastic line-up, including Howard Marks. My colleague, Dustin Hunter of SunRift Capital Partners SunRift Capital Partners, was kind enough to share some notes. Below is Part I from Michael Mauboussin.

From Dustin:

 (These notes are to the best of my recollection and trusty ink pen. Discrepancies are due to my error in understanding & transcribing.)


Part 1 – Michael Mauboussin – Untangling Skill & Risk (Outcomes – past, present, future)

  • In general, outcomes can be attributed to skill (use/application of knowledge readily) and luck(circumstances that either work for or against)
    • Easily applicable to sports, business, and investing
    • Example of luck: Spanish lottery winner who insisted on a ticket with the last number ’48’ because he had two important factors with the number 7… and 7×7=48….
    • Example of skill: Marion Tinsley was a checkers champion over a period of six decades…
    • A test of whether or not skill is involved in a particular activity is “Can you lose on purpose?”
    • He gave the following luck vs. Skill continuum for various activities:


  • ‘Fooled by Randomness’ – He mentioned this book several times during the presentation
    • 9/10 businesses on the ‘success list’ were there by chance,  i.e. luck
    • Luck vs. Skill probability example:
  • Paradox of Skill
    • Why are there no .400 hitters in baseball today? He cites  S.J. Gould study and the general level of improved play… which has led to less variance
    • Sees similar phenomenon in mutual funds…
    • This is explained graphically in the distribution below:
  • Reversion to the Mean
    • Back to Luck vs. Skill probability example
    • The ‘luck’ factor’s role in extreme positive or negative results cannot be counted upon and over time will return to a long term average.
    • The more that luck is a factor, the faster the results will revert to the mean. (‘in-play’ hits in baseball)
    • The more skill is a factor, the slower the results will revert to the mean. (strikeout rate in baseball)
  • Arc of Skill
    • He discussed an ‘arc of skill’ for various activities such as basketball, baseball, golf, and tennis. All peak at relatively young ages, with later peaks for less physically dependant performance. (typically lower 20’s with some around 30)
  • A Bat & Ball…
    • You have five seconds to answer: If a bat and ball together cost $1.10, and the bat costs $1.00 more than the ball, how much is the ball? (See answer below…)
    • The difficulty with this problem is the result of your brain’s attempt to prioritize it’s computing power and put familiar, understood tasks on ‘auto-pilot’. The answer seems obvious, so, boom – done – wrong!
  • Useful Statistics – what to look for
    • Persistent – Mostly Skill, shows up in the future (notes that mutual fund results are not persistent)
    • Predictive – Ability to know what will happen
  • A couple of statistical measures that show promise in predicting performance
    • Active Share (measure of the percentage of stock holdings in a manager’s portfolio that differ from the benchmark index)
    • Tracking Error (divergence between the price behavior of a portfolio and the price behavior of a benchmark)
    • Notes that there is some correlation between the two.

Michael will have a Michael Mauboussinnew book out around November 2012 that explores these ideas and more.

(Answer from Bat & Ball Question: $1.05 +$0.05 = $1.10)

Q & A

Q.   Albert Pujols 10yr, $240m contract, good deal?

A.    Most long term contracts for players over 30 are not a good deal.

Q.   Active Share vs. inappropriate benchmark and how to determine the right one.

A.    Not a good/right answer… It’s being studied.

Q.   How many funds display hi Active Share + low Tracking Error?

A.    The study he cited looked at 400 mutual funds & ranked both factors… few had this. He noted that the ‘Stock Pickers’ and ‘Concentrated Bets’ (16% & 4% of the group respectively) were the ones that did.

His final comment was almost said in passing, but for me, was the most important one of the entire presentation. He suggested finding races where not everyone is fast or the landscape is not saturated with skilled competitors. (Similar to the baseball batting average example, it’s difficult to stand out when everyone is talented and constantly battling for even the smallest advantage.)

(In Part 2, we will round up notes from Howard Marks’ presentation)