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Alpha Theory Blog - News and Insights

October 07, 2016

LUCK VS. SKILL IN INVESTING (Alpha Theory Book Club with Michael Mauboussin)

On October 3rd, Alpha Theory hosted the “Success Equation” book club with the author, Michael Mauboussin, and 35 PMs, analysts, and allocators. Mr. Mauboussin led the discussion on an array of investing topics centered around the central theme of luck and skill in our profession.

Major takeaways:

    1. Investing is dominated by luck because investor skill level has risen to the point where the market is largely efficient

    2. Managers acknowledge the role of luck, but underestimate it

    3. Process improvements are the easiest way for investors to improve performance

The discussion began by exploring how to determine the influence of skill and luck on an endeavor. The measurements are far from precise, but there are some heuristics that give us strong clues.

In the continuum below, games that are dominated by luck, like blackjack and roulette, are on the left side, and games like chess, that are dominated by skill, are on the right side.




Investing: More Skill or Luck?

We asked the attendees where investing fell on the continuum above. The average answer fell marginally closer to the skill end of the spectrum (near hockey). According to Mauboussin, investing is largely dominated by luck and is only slightly more skill-inclusive than gambling. Skill influences success, but it does not dominate. A monkey throwing darts can beat a sophisticated investor in any given year due to luck because the large number of skilled investors (high intellect, high work ethic, extensive training and experience) has resulted in markets that are largely efficient.

Skill vs Process Improvement

In the case of investing, skill has to be looked at in two dimensions, absolute and relative. Relative skill is key in the investment world, where there has been a dramatic narrowing in skill differences between investors. Because investing is dominated by luck, skill improvements make only small marginal differences in the probability of winning.  The saving grace for investors is that the average investor’s process is far from optimized and small improvements can have meaningful impacts on the probability of winning.

It is important to understand what makes something procedural and another skillful. In blackjack, no skill improvement will increase your chance of winning (assuming one considers card-counting “cheating” or not part of the “legal” rules of the game). On the other hand, process improvements (when to hit/stay/double down) can minimize your losses. You might ask, “why isn’t knowing when to hit/stay/double down a skill?” The answer is because it is formulaic (procedural): when the dealer is showing X and you are showing Y, you always do Z.

Said another way, no matter how good you get, you’re only going to win about 50% of the time. Compare this to chess on the skill side of the spectrum. A player with a 2600 ELO rating will beat a player with a 1600 rating 99.7% of the time. Improvements in skill (like deliberate practice memorizing optimal responses to your opponent’s opening) that improve a player’s ELO rating will increase his probability of winning.

In investing, building a model, making price forecasts, assessing business outlooks, grading the quality of management teams, and evaluating prospects of new products are all skills. Process in investing includes activities such as following a checklist of criteria that should be met for every investment, creating systems for measuring idea quality, tying idea quality to position size, adhering to portfolio rules (liquidity constraints, maximum sector exposures, max drawdown limits, etc.), and analyzing the efficacy of the process to refine it over time. The low-hanging fruit for investors comprise evolutions in process and, according to Mauboussin, are where they should be focusing their improvement efforts, given the heavy luck component at play.

Process enhancements should focus on those that are (1) analytical, (2) behavioral, and (3) organizational.  Alpha Theory speaks to the analytical improvement, where betting one’s edge intelligently is critical.  In terms of managing one’s organization, optimal collaboration is key.  This works best when (1) the size of team is larger, (2) cognitive diversity of the team is greater, and (3) management of the team offers [a.] dependability and [b.] “psychological safety” (fostering an environment where participants have no reason to fear sharing candid views).  Furthermore, the best leaders keep to an agenda, suppress their own points of view, and indeed successfully elicit the team-members’ perspectives – even those of the introverts.  (Alpha Theory can help here as well!)

IQ vs. RQ

Speaking of cognitive diversity and decision processes in investing, it is important to be aware of differences between IQ (intelligence quotient) and RQ (rationality quotient). Most people make the association between smart investors and high-IQ intellectual competency.  But in fact the best type of mental model that leads to appropriate investment decisions is RQ-oriented (really, the ability to make reasoned, judicious decisions efficiently and without equivocation in a fluid environment like the stock market).  Furthermore, one applied psychology study (see Bibliography below) found a surprisingly low correlation coefficient between IQ and RQ.  The investment industry may err on the side of hiring high-IQ analysts when it should be seeking higher RQ as a starting point – although there is not a ready test for RQ as of yet.

Ecology of Decision Rules

The stock market is a classic adaptive complex system – one where there can be ‘diversification breakdowns’ that result in the wisdom of crowds working until it does not work.  Diversity equates to different menus of decision rules each participant has, but when an asset price rises, many participants drop their own rules and conform to a single one, which breaks down diversity.  This tends to be a non-linear function with a ‘snap!’ phase transition, where reflexivity is defined.  But then diversity is restored when overcrowding corrects itself.

Ways to Improve Forecasting

Several process improvement steps come directly from “Success Equation” and are called suggestions to improve the “art of good guesswork”:

    1. Understand where you are on the luck-skill continuum

    2. Assess sample size, significance, and swans

    3. Always consider a null hypothesis

    4. Think carefully about feedback and rewards

    5. Make use of counterfactuals

    6. Develop aids to guide and improve your skills

    7. Have a plan for strategic interactions

    8. Make reversion to the mean work for you

    9. Develop useful statistics

    10. Know your limitations


SLIDES: Here is a link to a set of slides very similar to the one’s Mr. Mauboussin used and a video of him discussing “Success Equation”.


BASE RATE BOOK: A hot topic was the use of base rates to improve forecasting and decision making. Without a doubt, this is one of the best and easiest ways to improve your process. You can check out Mauboussin’s “The Base Rate Book” here and get a primer on how to implement it.


BIBLIOGRAPHY: One of the amazing things about Mr. Mauboussin is the catalog of referenceable articles, studies, and books in his head. Here is a list of all of those he referenced during the Book Club:

“Even God Would Get Fired As An Active Investor” by Wesley Gray

“On the Impossibility of Informationally Efficient Markets” by Sanford Grossman and Joseph Stiglitz

 “Agent Based Models” by Blake LeBaron

David Swensen quoted in “Asset Allocation or Alpha?” by Mimi Lord

“Vicarious Learning, Undersampling of Failure, and the Myths of Management” by Jerker Denrell

“The Three Rules” by Michael Raynor and Mumtaz Ahmed

“Luck versus Skill in the Cross-Section of Mutual Fund Returns” by Eugene Fama and Kenneth French

“Should Airplanes Be Flying Themselves” by Vanity Fair

“The Base Rate Book ” by Michael Mauboussin

Good Judgement Project  

Solomon Asch Experiments    

Greg Berns – Emory University

“What intelligence tests miss” by Keith Stanovich

 “Comprehensive Assessment of Rational Thinking” by Keith Stanovich

Cognitive Reflection Test (“Poor Man’s Test for RQ”) by Shane Frederick

Freestyle Chess

“What we miss when we judge a decision by the outcome?” by Francesca Gino

“Deep Survival” by Laurence Gonzolez

CFA Institute survey late 2008/09 – Quants vs. Fundamentals

“Use Cognitive Diversity to get the most of the Workplace” by Mark Miller

“Peak” by Anders Ericsson – Theory of 10,000 Hours book

“Robert’s Rules of Order” by Henry M. Robert (No one can speak 2x on a topic until everyone has had a chance to speak at least 1x)

“Forms Follows Functions” by Michael Mauboussin

"IQ vs. RQ" by Michael Mauboussin and Dan Callahan


Co Authored by: Cameron Hight & Dana Lambert

September 12, 2016

The Day the Music Died

For the past few years, I have had a significant edge in my fantasy football leagues because of my pre-draft preparation.  Each year, I would go online to do my preparation before the draft and pull data from multiple sources and look around for novel analytical approaches to player selection which I could combine with the data I was able to source.  Unfortunately, it looks like the years of my fantasy football spreadsheet’s domination has come to an end. I knew this day would come. Each year the data and analysis I was running sourcing seemed to get better and better. This year, I found 95% of my bespoke spreadsheet already prepared by someone else online at: http://apps.fantasyfootballanalytics.net/.  Undoubtedly, several of my competitors found it too.

Key attributes of my fantasy football sheet:

1. Improvement over Average (also called Value over Replacement) – this is by far the most important attribute. It basically states how much better any particular player is to the average player that will be drafted at the same position. Without IOA, the players with the highest potential points will always pop to the top, which is inaccurate because you can’t fill a team with just one position. For example, a QB may be forecast to score 350 points over the year and a WR is forecast to score 325. On that basis, the QB looks like the favorite. But the average drafted QB will score 280 and the average drafted WR will score 180 points. That means that the WR will add 145 points (325-180) against other teams vs the QB which will only add 70 points (350-280).

2. Multiple Sources – I pull projections from multiple sites to create a “wisdom of crowds” improvement.

3. Forecast Risk – measure standard deviation of projections from multiple sites to see how likely a player was to hit the average I was using in my model.

4. Average Draft Position – pulled Average Draft Position from multiple sources to compare my ranking to what was likely to happen.

5. Over/Undervalued to Average Draft Position – this allowed me to see if a player that my sheet really liked, could be selected in a later round because folks on average were drafting him later.

6. Dropoff – used to quickly see how big the difference is between the player and the next best player at the same position.


Now let’s compare to FFAnalytics.net:


The only thing my sheet has is a Strength of Schedule adjustment (#7) and I can just add that in after I download the data.

My reasons for writing this blog are two-fold. One, I want everyone to feel sorry for me that my edge is gone. Two, we all should recognize that with each passing year, data becomes easier to access and manipulate. If there are things in your business you consider your “edge” (competitive advantages), do a careful assessment of the landscape and forecast the probability that it can be undermined by data ubiquity, data analytics tools, or machine learning (artificial intelligence). Old businesses are being disintermediated in a matter of a few years by the likes of Google, Alibaba, Amazon, Uber, AirBNB, etc. Asset managers would be well served to take a hard look at what parts of their spreadsheets/processes and analytics are no longer novel and which are likely to persist and contribute as their true “edge”.    Without an edge in your processes or analytics, there is a limited likelihood that they will see an “edge” in their returns.