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Alpha Theory Blog - News and Insights

42 posts categorized "External Articles"

May 03, 2018

Positive Skew…Part 2 – Maybe It’s Not So Bad for Active Managers After All

In my last post, I discussed the negative impact of positive skew for active managers. Basically, that more than 50% of all stocks in a given market underperform the average because there are stocks that go up more than 100% but no stocks that go down more than 100%. This means that if you pick a random portfolio of stocks from the market, you have a greater than 50% chance of underperforming the market because most portfolios will not hold those few stocks that went up more than 100%.

 

Because of the popularity of the last post and TV appearance, we spent time digging further into the data to answer questions posed by readers and viewers. We noticed that there was a tendency for the returns between the average stock return and the index return to be different.

 

And that is the problem with using the average stock return as the hurdle for funds. Investors are not measured against the average stock return, they’re measured against the benchmark, typically the S&P 500. Most indexes are market cap weighted, meaning that the index return and the average stock return are generally different.

 

In the example below, we’ve taken the current S&P 500 constituents and calculated their return since the beginning of 2012 and compared that to an average return (Equal Weighted) and the actual return of the S&P 500. The S&P 500 over that period was up 136% vs 175% for the average stock (this isn’t a perfect analysis because the constituents in the portfolio changed over that time but it is an approximation).

 

Positive Skew-part2

 

The graph above shows the distribution of individual stock returns over that period. You can see the outliers that pull the average stock return (red line) up to a point where 63% of individual securities underperform the average of 176%. But the S&P 500 was up 136% (green line) over that period so only 51% of stocks underperformed the benchmark. Pretty much a coin flip.

 

We brought positive skew up with Andrew Wellington at Lyrical Asset Management. They have done some great analysis comparing the top 1000 stocks by market cap in the US to the S&P 500 each year going back to 1998.

 

Chart2

Source: FactSet and Lyrical Asset Management

 

As you can see in the chart above, the average stock beating the S&P 500 index is a coin flip. For the past 20 years, the likelihood of any individual stock beating the S&P 500 in any given year is 50.2%. If I build random portfolios using the Top 1000 stocks in the US, there is a high likelihood that the portfolio return will be close to the S&P 500 return.

 

Some years are clearly better than others. ’98 and ’99 were horrible stock picking years. If you didn’t own the few stocks that had meteoric rises, you had a high likelihood of underperforming the S&P 500. ’01 and ’02 were good stock picking years. Over 60% of stocks beat the index.

 

What this means, is that any given fund’s batting average should be compared to the batting average of the universe of stocks compared to the benchmark. A 54% batting average in ’98 is heroic, in ’03, 54% is just inline. Take a look at 2017. It was the 3rd hardest stock picking environment in the last 20 years using this metric.

 

But what about other indices? Thankfully, our friend Julien Messias from Quantology Capital Management has done the analysis (1999-2014) comparing the S&P 500 and Russell 2000. Below are thoughts from Julien on the topic:

 

The Russell 2000 components returns exhibit a much more leptokurtic distribution (fat-tailed) than S&P 500, meaning that you have a huge part of the index’s components suffering from huge loss (or even bankruptcies), with an average of more than 60% of the components underperforming the index performance and 2% of the components with huge performance (more than 500% per year). The performance of the index is therefore pulled up by those latter 2%.

Assuming a stock-picker operates at random to choose its investment within the index universe, this means that his performance should be closer to the median performance of the components, than to the index performance itself. Therefore, given that the median performance is almost always lower than the index performance (see chart below), an investor in Russell 2000 securities is very likely to underperform and very unlikely to outperform.

The S&P 500 distribution is much more mean-centered, with very shallow/thin tails, meaning that the average stock picker is much more likely to generate a performance close to the index performance (graph from Lyrical AM) and less likely to underperform.

 

Chart3

Source: Quantology CM

 

The Russell 2000 index more apparently displays the impacts of positive skew because it is less impacted by a contribution of a few very large companies. AAPL, MSFT, GOOG, AMZN make up 12.2% of the S&P 500 while the Russell 2000’s top 4 positions make up 1.7% of the index. The result is that the average of all stocks in the Russell 2000 is much closer to the Russell 2000 index return than the average of all stocks in the S&P 500 (recall the large difference from the 2012 to 2018 analysis that showed the S&P 500 return was 136% vs 175% average of all stocks).

 

This means that the index chosen as the benchmark for your fund has a profound impact on your ability to beat it. More specifically, the probability of beating the S&P 500 with a random portfolio is 50%, for the Russell 2000, it’s 42%.

 

There has been quite a bit of press regarding positive skew. It’s a great conversation but, for the average fund that is measured against the S&P 500, the impact is overblown. Almost every investor is compared against a benchmark. I recommend that you dig a layer into your benchmark and measure its positive skew, the likelihood of beating the average stock return, the likelihood of beating the index return, and compare your hit rate against the hit rate each year to know how difficult or easy it was for you on any given year.

 

Quantology Capital Management Russell 2000 and S&P 500 Analysis:

 ­ Screen Shot 2018-05-03 at 10.03.58 AM Screen Shot 2018-05-03 at 10.05.52 AM

Does not include management fees

Data is cleaned from index turnover, with updates every year

March 12, 2018

Capital Allocators Podcast with Ted Seides: Moneyball for Managers

 

Learn how to enhance your investment results in this great podcast from Ted Seides and his guests, Clare Flynn Levy from Essentia Analytics and Cameron Hight from Alpha Theory.

This conversation covers the founding of these two respective businesses, the mistakes portfolio managers commonly make, the tools they employ to help managers improve, and the challenges they face in broader adoption of these modern tools. The good news is the clients of Essentia Analytics and Alpha Theory have demonstrated improvement in their results after employing these techniques. If you ask Clare and Cameron, you may develop a whole new appreciation about the potential for active management going forward.

 

LevyHight-FINAL

 

By creating a disciplined, real-time process based on a decision algorithm with roots in actuarial science, physics, and poker, Alpha Theory takes the guessing out of position sizing and allows managers to focus on what they do best – picking stocks.

In this podcast, you will learn how Alpha Theory allows Portfolio Managers convert their implicit assumptions into an explicit decision-making process. 

 

To learn how this method could be applicable to your decision-making process:

 

LISTEN NOW

 


 

 

August 18, 2017

Man Versus Model of Man: Lewis Goldberg

I recently read an article by Jason Zweig and saw a reference to Lewis Goldberg’s, “Man Versus Model of Man” paper on Expert Studies in the 1970 Psychological Bulletin. There are hundreds of published studies that have a similar theme. Give an expert any and all available data that they want and ask them to make a judgement germane to their field of expertise (examples include Oncologist – how long will a patient live, Parole Board – who is most likely to recidivate, Wine Expert – price of wine at auction, etc.)

The experts tell the scientist which variables are most important in their decision and the scientist goes off and builds a model and compares the model’s results to the forecasts of the “experts.” Over the past 60 years, hundreds of expert studies have been performed and show that the model beats or ties the expert 94% of the time (1).

There was one of Goldberg’s quote about the use of models versus clinical decision making made me laugh:

Such an enterprise, originally viewed with considerable disdain by clinical psychologists, has recently weathered a period of intense controversy (Gough, 1962; Meehl, 1954; Sawyer, 1966), and may soon become a reasonably well accepted procedure in psychology—if not in medicine, stock forecasting, and other professional endeavors.

Consequently, it now seems safe to assert rather dogmatically that when acceptable criterion information is available, the proper role of the human in the decision-making process is that of a scientist: (a) discovering or identifying new cues which will improve predictive accuracy, and (b) constructing new sorts of systematic procedures for combining predictors in increasingly more optimal ways.

This quote was written 46 years ago yet clinical judgement still dominates psychology, medicine, and stock forecasting. Given the evidence, it is hard to argue against model-based decision making or man + model, but expert judgement still dominates.

The experts that will dominate the future (and are already beginning to do so) are the ones that embrace models as an extension of their own expertise. Models do not replacement human judgement. The parameters models are built upon are determined by experts. Experts also are required to intuit when exceptions to the model are necessary.

My belief is that Lewis Goldberg’s prediction will come true in the next decade as computing power, statistical techniques, software, and zeitgeist have grown to a point where Man + Machine will become the rule instead of the exception.

Here’s a few other great quotes from Lewis Goldberg’s article:

- Mathematical representations of such clinical judges can often be constructed to capture critical aspects of their judgmental strategies.

- The results of these analyses indicate that for this diagnostic task models of the men are generally more valid than the men themselves. Moreover, the finding occurred even when the models were constructed on a small set of cases, and then man and model competed on a completely new set.

- Ten years of research on the clinical judgment process have demonstrated that for many types of common clinical decisions and for many sorts of clinical judges, a simple linear regression equation can be constructed which will predict the responses of a judge at approximately the level of his own reliability. For documentation of this assertion and for details of the methodology, see Hoffman (1960), Hammond, Hursch, and Todd (1964), Naylor and Wherry (1965), and Goldberg (1968). While such regression models have 424 LEWIS R. GOLDBERG been utilized (probably somewhat inappropriately) to explain the manner in which clinicians combine cues in making their diagnostic and prognostic decisions (see Green, 1968; Hoffman, 1968), there is little controversy about their power as predictors of the clinical judgments

 

(1) “Comparative Efficiency of Informal and Formal Prediction Procedures” – William Grove and Paul Meehl, published in Psychology, Public Policy, and Law (1996)

July 28, 2017

Crist on Value

There is a paper, famous in value investing circles, called Crist on Value. It is a chapter from a book written by horse handicapper Steven Crist who opines on the short-comings of the average horse bettor. Its popularity amongst value investors is due to its sage advice that can readily be applied to investing. The article is of moderate length and a must read for any fundamental investor. I’ve taken the liberty of highlighting a few of quotes pertinent to our profession:

- “How often have you or a fellow track-goer opined that you're a pretty good handicapper but you really need to work on your betting strategies or your so-called money management? The problem with this line of thinking is that it suggests betting is some small component of the game, which is like pretending that putting is a minor part of championship golf.” INVESTOR CORROLARY: Investors that believe they are good stock pickers but just don’t get the position size right.

- “Even a horse with a very high likelihood of winning can be either a very good or a very bad bet, and the difference between the two is determined by only one thing: the odds. A horseplayer cannot remind himself of this simple truth too often, and it can be reduced to the following equation: Value = Probability x Price." INVESTOR CORROLARY: Investments decision process requires three components: profit from win, cost from loss, and probabilities of each.

- “Now ask yourself honestly: Do you really think this way when you're handicapping (in probability-weighted returns)? Or do you find horses you "like" and hope for the best on price? Most honest players will admit they follow the latter path. This is the way we all have been conditioned to think: Find the winner, then bet. Know your horses and the money will take care of itself.” INVESTOR CORROLARY: Every investment requires story and value. With both, you don’t have an investment.

- “Sticking to your guns is easier said than done, but it is the only way to win in the long run. The horseplayer who wants to show a profit must adopt a cold-blooded and unsentimental approach to the game that is at variance with both the "sporting" impulse to be loyal to your favorite horses and the egotistical impulse to stick with your initial selection at any price. This approach requires the confidence and Zen-like temperament to endure watching victories at unacceptably low prices by such horses.” INVESTOR CORROLARY: If you’re human, you’re subject to bias and emotion. Define rules and procedures in advance that highlight discrepancies between your actions and your rules.

- “I cannot argue in good conscience that Two Item Limit had precisely a 60 percent chance of victory as opposed to 57 or 63 percent, and I doubt that such calibration is in fact achievable. It is, however, possible through experience to get close enough that if you demand sufficient value to cover the margin of error, you should outperform the competition-your fellow horseplayers.” INVESTOR CORROLARY: Coming up with probabilities for stock price outcomes are even more subjective, but that doesn’t mean you can skip the exercise. Play around with a range of outcomes and figure out what is “too conservative” and “too aggressive” to give you comfort for where the probability should be.

 - “If every horseplayer but you were a certifiable idiot, betting at random on names and colors, you would win every day. Conversely, if the only people betting into the pool were the small number of professionals who make a living this way, your chances for long-term victory would be slim.” INVESTOR CORROLARY: How does your competition in the stock market stack up today?

- “Your opportunity for profit at the racetrack consists entirely of mistakes that your competition makes in assessing each horse's probability of winning.” INVESTOR CORROLARY: While we look at the move to passive as a negative; more passive money should increase the number of opportunities for active investors.

- “There is no shame in passing a race because you just don't see any value in it. Nor should you force yourself to play a race in which you have no confidence in your own odds line.” INVESTOR CORROLARY: Good ideas are hard to find, but worth the wait.

- “Recognize the difference between picking horses and making wagers in which you have an edge. The only path to consistent profit is to exploit the discrepancy between the true likelihood of an outcome and the odds being offered.” INVESTOR CORROLARY: Probability-weighted return is the arbiter of all decisions.

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.

 

SE

 

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

Resources

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

February 22, 2016

How Do Hedge Funds Become Better Forecasters? - A collaborative study between Novus and Alpha Theory.

We believe that one of the few untapped frontiers in Alpha Generation is measuring and putting process around forecasting.  Alpha Theory co-authored “How Do Hedge Funds Become Better Forecasters?” with our friends at Novus to explore a few ways investors can improve their process and forecasting acumen.

 

CLICK HERE TO DOWNLOAD THE ARTICLE

 

Selected Quotes from the Article:

“Many investors chafe at price targets because they smack of “false precision". Those investors are missing the point because the key to price targets is not their absolute validity but their explicit nature which allows for objective conversation about the assumptions that went into them.”

“Unlike real life, investors can track every investment choice they have ever made. Being able to analyze statistically significant trends on a complex and numerate datasets is a huge advantage and is a crucial tool in avoiding the confirmation biases that anecdotal thinkers lean on when rationalizing decisions.”

“Developing a process orientation isn’t about stifling fluidity or gut feel. It is about recognizing that intuition is actually an informal process. By being able to document and empirically study past behaviors, all investors can understand flaws in their internal process.”

January 30, 2015

The Worst Year Ever for Hedge Funds (Novus Article)

Our friends at Novus put together an interesting article called “The Worst Year Ever for Hedge Funds” that used statistics to analyze which market environments are easiest for fundamental investors to generate alpha. The conclusion was that 2014 was the hardest year ever for hedge funds to generate alpha because the dispersion between winner returns and loser returns were the tightest in hedge fund history (read the article for a thorough explanation). I will not attempt to articulate any deeper, for doing so would basically require me to read you the article. What I will attempt is a few basic observations:

1)      I really hope the premise of this article is correct and 2014 marks a nadir for hedge fund outperformance. As my partner, Benn Dunn, keeps saying, “what hedge funds really need in 2015 is a good down 5% for the S&P 500.”

2)      For the hypothesis of this article to hold in practice, there is an assumption of luck. Basically, if we pick random portfolios, 2014 was the toughest year to produce outperformance. If alpha generation were pure skill, the evidence for 2014 wouldn’t stand-up because the skillful manager would buy the very high performers and short the very low performers…even though there were a limited number. The truth lies somewhere in between.

3)      If there is a prospective element (these variables are forecastable) then a manager could use them to determine leverage (i.e. when it is easy, lever up, when it is like 2014, lever down). However, I’m finding it difficult to find variables in the analysis that are forecastable or are even stable. If they were at least stable, then you can use a tight current spread to suggest lower leverage, but the spreads can change rapidly.

Overall, this is a great read and made me think critically about disaggregating alpha. If you haven’t read the author Joe Peta’s book “Trading Bases”, you should. Finally, I’ll leave you with my favorite quote from the article, an excerpt I chose for clearly selfish reasons given Alpha Theory’s focus on position sizing:

    “At Novus we’ve examined the performance of many hundreds of hedge funds and we’ve found that, by far, the most important skill to possess is the ability to size positions effectively. That’s because unlike Exposure Management, and to a lesser degree Security Selection, the ability to size positions efficiently is the most persistent and consistent alpha-generating skill that a portfolio manager can possess.”

March 21, 2014

Dynamic factor modeling reveals hidden risks

GUEST POST FROM BENN DUNN, President of Alpha Theory Advisors:

Damian Handzy, CEO of Investor Analytics, and I developed the concept of dynamic factor modeling in this latest article on Risk.net.  We argue that traditional 3rd party vendor models do not accurately reflect many firm’s investment processes and leave measurable risk hidden.  Using beta as a common language between risk and portfolio managers, we recommend leveraging the literally thousands of listed instruments and funds to ease the process of risk measurement and hedging.

Click Here to full the article on Risk.net.

November 05, 2013

Less Correlation Gives Stock Pickers Opportunity

We’d like to welcome our first blog from Benn Dunn who runs our Risk Consulting practice. I’m a little biased, but I believe that Benn is one the smartest risk minds in investing today. Check out this article on Risk.net where he is quoted on the topic of correlation in portfolio management.

While lower correlations across asset classes and within markets are generally thought of as positive for security selection, the path to lower correlations can often be confusing.  Traditional risk models deliver confusing and difficult to interpret results during these regime shifts.  Fortunately, Alpha Theory is not dependent on trailing correlations when making portfolio construction recommendations. 

Click here to read the full article on Risk.net

 

May 01, 2013

The Checklist: Evolution of an Effective Decision Process Part 2

As a continuation to The Checklist: Evolution of an Effective Decision Process Part 1 we now turn our attention to money managers and their checklists. 

Fund managers fall into similar traps of overconfidence about their ability to manage the complex system of investing. Dr. Gawande discusses investing (Chapter 8) by analyzing three public equity managers that employee an explicit checklist and a study by Geoff Smart, which analyzed 51 venture capital managers. The evidence, although not a large sample size, is compelling. The public managers attribute the checklist to being a primary component of their success and a distinct competitive advantage.

“The checklist doesn’t tell the manager what to do. It is not a formula. But the checklist helps him be as smart as possible every step of the way, ensuring that he’s got the critical information he needs when he needs it, that he’s systematic about decision making, that he’s talked to everyone he should. With a good checklist in hand, he was convinced he and his partners could make decisions as well as human beings are able. And as a result, he was also convinced they could reliably beat the market.” “They (Checklists) improve their outcomes with no increase in skill. That’s what we are doing when we use the checklist.” “When he first introduced the checklist, he assumed it would slow his team down, increasing the time and work required for their investment decisions. He was prepared to pay that price. The benefits of making fewer mistakes seemed obvious. And in fact, using the checklist did increase the up-front work time. But to his surprise, he found they were able to evaluate many more investments in far less time overall.” 1

And even though their competitors have noticed their success and asked them for their secret, when told that the key is a checklist, they turn up their nose.

“In the money business, everyone looks for an edge. If someone is doing well, people pounce like starved hyenas to find out how. Almost every idea for making even slightly more money— investing in Internet companies, buying tranches of sliced-up mortgages, whatever— gets sucked up by the giant maw almost instantly. Every idea, that is, except one: checklists. I asked one of the equity managers how much interest others have had in what he has been doing these past two years. Zero, he said— or actually that’s not quite true. People have been intensely interested in what he’s been buying and how, but the minute the word checklist comes out of his mouth, they disappear. Even in his own firm, he’s found it a hard sell.” “I find it amazing other investors have not even bothered to try,” he said. “Some have asked. None have done it.” 1

The evidence from the VC community is even more compelling because the results are more statistically significant (51 managers) and are empirical. Mr. Gross categorized managers into five different categories by how they made decisions. One of the classifications was “Airline Captains” which meant they used checklists extensively to make decision.

“Smart next tracked the venture capitalists’ success over time. There was no question which style was most effective— and by now you should be able to guess which one. It was the Airline Captain, hands down. Those taking the checklist-driven approach had a 10 percent likelihood of later having to fire senior management for incompetence or concluding that their original evaluation was inaccurate. The others had at least a 50 percent likelihood. The results showed up in their bottom lines, too. The Airline Captains had a median 80 percent return on the investments studied, the others 35 percent or less.” 1

Smart’s study was performed over ten years ago and his findings are known by the VC community. You would think these fact-based results would change behavior, but as Gawande found when asking about Smart about behavior changes for VC managers:

“But when I asked him (Geoff Smart), now that the knowledge is out, whether the proportion of major investors taking the more orderly, checklist-driven approach has increased substantially, he could only report, “No. It’s the same.” We don’t like checklists. They can be painstaking. They’re not much fun. But I don’t think the issue here is mere laziness. There’s something deeper, more visceral going on when people walk away not only from saving lives but from making money. It somehow feels beneath us to use a checklist, an embarrassment. It runs counter to deeply held beliefs about how the truly great among us— those we aspire to be— handle situations of high stakes and complexity. The truly great are daring. They improvise. They do not have protocols and checklists.” 1

The Checklist Manifesto is well worth the day or so it takes to read. The real question, are you willing to adopt a Checklist mentality? Will you take the time to check the boxes? Are you disciplined enough to stop yourself from making decisions unless those boxes have been checked? I know that if I were back on the buyside, I would take out my mini-checklist and expand on it, formalize, and refine as we improve our process. I’ve seen firsthand the importance of making decisions explicit. But I think Dr. Gawande says it best:

“Instead they (pilots) chose to accept their fallibilities. They recognized the simplicity and power of using a checklist. And so can we. Indeed, against the complexity of the world, we must. There is no other choice. When we look closely, we recognize the same balls being dropped over and over, even by those of great ability and determination. We know the patterns. We see the costs. It’s time to try something else. TRY A CHECKLIST.” 1

 1Gawande, Atul (2009-12-15). The Checklist Manifesto: How to Get Things Right. Picador.