Please note the following System Requirements. Further, please limit the number of open applications (particularly price streaming applications) while logged in to Alpha Theory™.

Recommended System Specifications
Processor: Dual Core or Quad-Core 2.4GHz or faster
Browser: Google Chrome 30+
Screen Resolution: 1280 x 1024 or greater
Internet Access: Business Class High-Speed

Minimum System Requirements
Processor: Intel Pentium-M 2.0Ghz or equivalent
Browser: Google Chrome, Mozilla Firefox, Internet Explorer 9+ (without Compatibility View), Safari
Screen Resolution: 1024 x 768 or greater
Internet Access: High-Speed

Subscribe to Alpha Theory content

Alpha Theory Blog - News and Insights

27 posts categorized "Risk Management"

July 05, 2017

Jason Zweig on Being Your Own Quant

Until the day you are relieved of your cognitive biases, it is important to frequently remind yourself of your mental fallibility so that you can be on guard for their effects. Jason Zweig’s “How to Be Your Own Quant” is perfect for that task. Here are a few gems from the short article:

 - Take a hint from hedge-fund manager Magnetar Capital LLC, which is seeking to “take what was in our head and our database and make rules out of it” — measuring intuitions, testing them for reliability and then basing decisions on them. Human judgment is inconsistent. People are good at knowing what matters, but not very good at always looking at it the same way.

 - Presented with identical information under different circumstances, we come to different conclusions about it. The judgments of everyone from accountants to physicians and weather forecasters will vary depending on such factors as mood, time of day and how many other demands they have on their attention.

 - Decades ago, the psychologist Lewis Goldberg showed that if you determine which factors experts consider most important in coming to a conclusion, you can program a computer to size up a ​decision based on those — and only those — factors. The computer’s predictions using the experts’ criteria turned out to be more accurate than the experts’ own predictions, because the computer always interprets the same evidence the same way.

June 01, 2017

The Value of Price Targets


We analyze the difference in ROIC (Return on Invested Capital) for the portion of client portfolios with price targets and the portion of client portfolios without price targets. In examining exposures, we find a long bias in the portion of the portfolio with research versus the portion of the portfolio without research making the Total measurements less meaningful. To neutralize this distortion, we look at differences on a long and short basis. We find that the portfolios with price targets outperform by 16% on an annualized ROIC basis (average of long and short improvement).

We also look at optimal position sizing suggested by the Alpha Theory algorithm and find that optimizing position sizes that have price targets adds an additional 5% for long positions.

Screen Shot 2017-06-01 at 2.32.50 PM

Table 1


Alpha Theory tracks investment results for clients on a daily basis. We’re able to segment the daily returns for portfolios into two categories, securities with price targets and securities with no price targets. We’re then able to calculate an average daily return across our client base, segmenting into these two categories. All of our calculations are done on an ROIC basis for comparability purposes. Removing the exposure effect would result in returns of a portfolio that is 100% allocated to either category. For comparison, we also calculate returns based on optimal position sizing recommendations as well as returns on the ACWI, an all-world index.


Figure 1 shows the cumulative returns over the time for the period of the analysis, where we break out client portfolios by the portion with and without price targets. We also look at how the optimal portfolio would have performed, which is a portfolio composed of securities with optimal position sizing output from Alpha Theory. Alpha Theory uses price target inputs to recommend position sizing based on probability-weighted return calculated from those price targets. Alpha Theory does not recommend position sizing on securities without research, so the comparison would be best made between optimal returns and the portion of the portfolio with price targets. We find that the portion of the portfolio with no price targets significantly underperforms the price target portfolio, by 11.5% on an annualized basis.  What we also see is that the price target portfolio, if sized optimally (where not already done so), would have increased performance even further, by 3.3%. Decomposing the exposures on the price target and non-price target portfolio reveals that the average net exposure for the price target portfolio is 40% and the average net exposure on the non-price target portfolio is -12%. This is an interesting divergence, as it tells us that managers are more likely to initiate a short position without research than a long position without research. With the non-price target portion running such a low net exposure, we would expect average returns to be roughly zero, as longs and shorts balance each other over a large sample. A clearer picture would be one where we break out the long and short portions.

Screen Shot 2017-06-01 at 2.39.03 PM

Figure 1

Figure 2 shows the same data for the long portion of the portfolio. We find that the long price target portfolio outperforms the long non-price target portfolio by 3.4% on an annualized basis. Sizing optimally (where not already done so) adds an additional 1.0% to the annualized return.

Screen Shot 2017-06-01 at 2.39.38 PM

Figure 2

We then look at the same data for the short portfolio in Figure 3. We find that the price target portfolio outperforms by 0.9%, on an annualized basis. Sizing optimally (where not already done so) would have added an additional 3.2%.

Screen Shot 2017-06-01 at 2.41.13 PM

Figure 3


Our assumption for why securities with price targets outperform those without is that price targets inform investors of value, make explicit the logic around their decisions, allow for optimal position sizing to be calculated and have a higher level of research rigor.

Investing in assets without first calculating price targets is deleterious to returns. This result is intuitive to most managers but hopefully this gives empirical evidence that will prevent future positions going into the portfolio without the critical step of defining risk-reward.

May 15, 2017

Changing The Course Of Active Management — The Concentration Manifesto

Is this the end of active portfolio management? You would think so if you listen to pundits. But I see it differently. I believe we have reached a critical juncture that will ultimately redefine the space for the better — where the winners will search for ways to constantly refine their process to maximize their edge.

At Alpha Theory, we are also searching for ways for our clients to maximize edge. To that end, about a year ago, while doing some research on the impact of “crowdedness” in portfolio sizing, my team and I discovered that crowded names consistently outperformed less crowded names. That made us wonder; in general, do holdings with bigger position sizes outperform those with a smaller position size? After digging through the numbers from a cross section of 60 funds totaling over $70 billion in assets under management, we found empirical evidence that they did.

We knew we were on to something. We then isolated our clients’ highest expected return positions to see if they were the best returns. They were. With all of this demonstrated skill and ability, the question remained: why do active managers underperform? The simple answer: low conviction positions negated most of the performance they generated with the high conviction names.

The Concentration Manifesto is my attempt to get a critical dialogue started between managers and allocators to ultimately improve the active management process. As you will see, the solution is simple, but not easy. It will require that both sides cast aside outdated thinking and embrace the notion that concentration is in their best interest. But by encouraging these important discussions, I believe we will be solidifying the long term survival of the active management industry.

I hope you find the analysis insightful and valuable and I look forward to being part of the conversation.





April 17, 2017

Investor Bias Seen in Data

By Cameron Hight and Justin Harris


Alpha Theory’s Analytics Department studies clients’ historical data to provide useful insights. Over time, we have identified patterns that point to certain investor biases. Typically, biases are highlighted by deviations between actual and optimal position sizes. Said another way, biases occur when managers size positions different than what the risk-reward would suggest.


Here are a few examples:


1. NOT ADJUSTING POSITION SIZE AFTER A BIG PRICE MOVE: One of the most common biases we see in the data, is that after large positive price changes, managers are less likely to cut exposure, even though the probability-weighted return has diminished due to the move. The potential damage from this willful ignorance is compounded by a much larger position with a lower expected return. The typical behavior of investors is to let winners run, however, we’ve found that to be sub-optimal for fundamental funds.

The first step to alleviating this bias is to force re-underwriting names when they reach an unacceptable PWR. If the new assumptions justify the size, then all is good. If not, then the manager knows there is some bias that is causing them to stay in the position. Forcing re-underwriting at critical levels ensures that checks and balances are in place so that profits are kept and not lost on reversals.


2. NOT SIZING UP GOOD PROBABILITY-WEIGHTED RETURN WHEN INITIATING A POSITION: When analysts input price targets into Alpha Theory, and a manager decides to act on that information, what we’ve seen in the data is a tendency to build a position over time. We’ve found, on average, this is detrimental to returns. Slowly scaling into a high conviction and high probability weighted return name causes investors to miss some of the return potential.


3. UNDISCIPLINED APPROACH: Our data has shown that managers who are more disciplined (i.e. have more of their portfolio with price target coverage and size closer to optimal position sizing) tend to outperform those who don’t. Unfortunately, running complex sizing algorithms through our heads is not something we do well. What we see in the data is that positions without explicit price targets underperform. Be it hubris or any other number of reasons, it’s almost always detrimental to returns.


4. DIVERSIFYING: Our research shows that the largest positions in client portfolios outperform smaller names by a big margin, mostly because the batting average on top holdings is high. Most clients nullify this benefit by taking on many more names in the portfolio at much lower probability-weighted returns. We’ve done research which shows that concentrated portfolios outperform diversified portfolios by 2.2% on an alpha basis (run as a Monte Carlo study using batting averages calculated for various portions of client portfolios 2011-2016). The cost of diversification is a loss of alpha without a commensurate improvement in risk protection.


For 2016 returns, if clients sized using the suggested Optimal Position Size, they would have been better off by 5.1%. Clearly we recognize that not every position was able to be sized optimally, but even if half of that difference could have been captured, there was a lot of money left on the table. The biases above highlight why some of the difference occurs. It’s hard to beat an unemotional version of yourself, especially when we’re not psychologically built for the game.

March 13, 2017

Ted Seides - Alpha Theory Book Club


On March 7th, Alpha Theory hosted a book club with over 30 portfolio managers, analysts, and allocators coming together to discuss Ted Seides’ book, “So You Want to Start a Hedge Fund?”. We were lucky enough to have Ted present and answer questions about the capital raise environment, investment process best practices, hiring, keeping investors happy, etc.


Here are a few takeaways:


1. CAPITAL RAISE ENVIRONMENT: It’s hard out there and isn’t getting any easier. Allocators are getting pressure from their investors about their hedge fund investments.

2. INVESTING ENVIRONMENT: Once again, it’s hard out there and isn’t getting any easier. There are more smart managers than ever looking at the same ideas.

3. FEES: Fee pressure will continue and managers will be asked for fee strategies which better align the interests of the investor and the manager.

4. DURATION DISCONNECT: There has been, and probably always will be, a disconnect between the duration that a manager is judged and the duration in which a manager manages their portfolio. The best thing a manager can do is be open and honest about their challenges so that investors get comfortable with volatility of performance numbers.

5. TURNOVER: Managers should be quick to remove “bad fit” analysts, even if they’re going to get push-back from investors over changes with the team.

6. STASIS: Many hedge funds have a “set it and forget it” mentality towards culture, personnel, and investment process. Many great corporations have advanced human capital strategies and hedge funds can leverage that knowledge to build superior organizations (i.e. Bridgewater or Point72).

7. COACHES: To prevent stasis, it is important to read and sometimes bring in outside help. There are experts in team building, time management, bias mitigation, decision science, investment process, etc.

8. RUNNING A BUSINESS IS HARD: Most hedge fund managers don’t have the luxury of just picking stocks. They’re charged with hiring/firing, raising capital, investor relations, human resources, picking accountants, selecting offices, etc. All the things that a CEO of a company deals with plus managing a fund. The reason portfolio managers are so busy is because they have two full time jobs.

9. THE BET: As most know, Ted was the other side of the famous 10-year bet with Warren Buffett pitting the S&P 500 against a basket of hedge fund allocators. Ted still fully believes that hedge funds can outperform in the right environments (i.e. market is overbought).


Thanks to all those that attended and contact Alpha Theory if you would like to learn more about attending future book clubs.


February 24, 2017

Stock Picking is Hard


Stock picking has never been so hard.


From a recent interview with Charlie Munger of Berkshire Hathaway:

“In the old days, I frequently talk to Warren about the old days, for years and years and years what we did was shoot fish in a barrel. It was so easy we didn’t want to shoot fish while they were moving. We waiting until they slowed down and shot at them with a shot gun. It’s gotten harder and harder. Now we get little edges. It isn’t any less interesting. And we do not make the same returns we made when we’d pick this low hanging fruit off trees that offered a lot of it.”

“I used to say, ‘you have to marry the best person that will have you.’ That’s a rule of life. You have to get by on the best advantage you can get. Things have gotten so difficult in the investment world.


From a recent article on investing by Ben Carlson of CNBC:

Michael Mauboussin calls this the paradox of skill. Mauboussin says, "It's not that managers have gotten dumber. It's precisely the opposite. The average manager is more skillful than in past years. The paradox of skill says that when the outcome of an activity combines skill and luck, as skill improves, luck becomes more important in shaping results." How many institutional investors bother to ask themselves if the investment managers they are investing with are lucky or truly exhibit skill?

Active managers are competing against many more managers these days than they did in the past. There are roughly 300,000 investment professionals worldwide (portfolio managers and analysts) working for hedge and mutual funds (Alpha Theory estimate). There are 43,000 exchange listed public companies5. That works out to about 7 analysts for every stock! Asset prices become more efficiently priced when lots of smart people pay attention. With those odds, it is no wonder that there is a dearth of good ideas.


From Daniel Chambliss’s paper on “The Mundanity of Excellence”:

“Superlative performance is really a confluence of dozens of small skills or activities, each one learned or stumbled upon, which have been carefully drilled into habit and then are fitted together in a synthesized whole.”

“Excellence is accomplished through the doing of actions ordinary in themselves, performed consistently and carefully, habitualized, compounded together, added up over time.”

It has never been more important to do the little things that lead to success. Alpha Theory’s dominant beneficial attribute is the process discipline it instills in our clients. Our clients have outperformed the HFRI Index for each of the last five years (as far back as we have data) by an average of 3%. I believe their discipline is a big part of what makes them excellent. As good as they are, they can be better. If they would have strictly followed their models, their performance would have been 6% higher. There is alpha out there for the good stock pickers but it requires discipline and a desire to be excellent.


January 31, 2017


Client Outperformance

For the fifth year in a row (as far back as we have data) our clients have outperformed the HFRI Equity Hedge Index. To date, our clients’ compound return is 20% greater than the index.


As good as our clients are, they would have been even better if they followed the optimal position sizes they built inside of Alpha Theory:


On average, Alpha Theory suggests a lower gross exposure. So, to compare on an apples-to-apples basis, we look at Return on Invested Capital. For 2016, the Optimal ROIC was 13.3% versus 6.5% actual. That’s a difference of 6.8%.

Let’s put that difference into perspective. Our clients manage over $100B using Alpha Theory. On an ROIC basis, 6.8% of additional return on $100B is $6.8B. Assuming 20% performance fees, our managers left almost $1.4B of income on the table.

In 2016, 84% of our clients would have performed better if they would have followed optimal position sizing.

Betting the Forecasting Edge

Lastly, 2016 was the best year on record for the correlation between our clients’ forecasts and actual returns. The correlation between expected and actual returns was 0.19 for 2016. While this may seem low, one would expect a correlation near zero if selected randomly. For every year since 2012, with the exception of 2015, the correlation between expected and actual returns has been positive.

We believe this is a strong indication of predictive power in analysts’ forecasts. If analysts’ forecasts were random, then optimal position size would not beat actual returns with such regularity.

There are many ways to try and improve but few are as easy as creating a discipline around position sizing. The evidence is clear, if a firm has any edge, then creating a repeatable process to bet that edge is the difference between good and great.

Additional Portfolio Metrics

Screen Shot 2017-01-31 at 6.56.58 PM



December 28, 2016


By Emma Vosburg and Cameron Hight


Every year, people make New Year’s resolutions and every year, people break them. Creating positive habits that reinforce resolutions is the difference between the people that keep their resolutions and those that break them.

Creating process that works for you is the key to forming habits that lead to accomplishing your goals. You must commit to the process. For investors, a lack of systematic investment process means it will be difficult to consistently outperform your competitors. Alpha Theory is process in a box!

Let us help you create a winning process and build the habits necessary to fulfill your New Year’s resolutions. Let’s look at a list of possible Alpha Theory New Year’s goals:


Screen Shot 2016-12-28 at 9.27.05 AM

Why make an Alpha Theory New Year’s resolution? Alpha Theory’s research not only suggests that adoption of the application by itself leads to improved performance, but actual usage further enhances results. In the table below, we show that clients who are more process oriented (as measured by having price targets, frequency of review, and diligence at updating position size based on their forecasts) outperformed our clients who were less diligent.


A systematic approach to accomplishing goals is valuable in every aspect of life. In 2017, create process that builds habits and allows you to achieve your goals. No excuses!


November 30, 2016

Using Analytics to Improve Investment Process

By Cameron Hight and Justin Harris


Data is taking a precedence in modern life as a necessity for those who want to improve. With specific relevance to asset management, finding insightful feedback in data is where managers can use unbiased facts to flesh out the inefficiencies in their investment process. As more and more managers rely on data to inform their investment process, the cost of not doing so increases. We, at Alpha Theory, have spent considerable time over the past year working with clients to help them hone their process, based on insights from data. Here are a few examples:


Alpha Theory uses analyst forecasts to optimize portfolio position sizing. Managers must therefore know what confidence they can put on analysts’ research, because optimizing on bad research could be deleterious to returns.  Said another way, managers need to know who to trust.

As a starting point, the average analyst in our system forecasts that they’ll make money on their investments 75% of the time compared to their actual success rate of 53%. Additionally, they forecast upside returns of 43% compared to their realized returns of 26%. Clearly there is a pervasive, systematic overconfidence. With that perspective, you can evaluate your own team.

Probability Analysis. One of the first areas to evaluate is the forecasted probability of making money compared to the actual percentage of investments that were profitable. In the graph below, we show a representative comparison of forecast versus actual success rates. As a manager, this is excellent information because in one glance, you see which analyst is most accurate and who has the most bias. The actionable information would be requesting that Jimmy and Matt cap their forecasted success percentage at 50% and reviewing specific names with Jim to see why he has such dramatic bias.

Screen Shot 2016-12-01 at 8.49.59 AM

Additional analyses could include long/short and sector breakout or alpha-based analysis. These additional insights may help clarify the analyst’s differences. The point is that you ask questions based on empirical data that help your analysts improve while, at the same time, giving the portfolio manager the real-data to back up hunches of analyst bias.

Performance Analysis. In the graph below, we show how price forecasts (bars) compare to actual outcomes (underlay). We see that, while Jim Braddock had a disappointingly low success rate (graph above), his price forecasts were excellent. In fact, because the asymmetry was positive, his ideas were net profitable even though only 25% of them made money. From a manager’s perspective, encouraging Jim to make more realistic probability forecasts, gives you data that could dramatically improve fund performance.

Screen Shot 2016-12-01 at 8.50.16 AM

Simple Graphs, Profound Insights. These two simple graphs provide many insights, as illustrated above. A few others to show the power of the picture:

1. Matt Doherty’s names should be given lower confidence given that he only makes money in 50% of his names and he loses more on his losers than he wins on his winners.

2. Ahtray Dahurt’s forecasts should receive higher confidence as his reward and risk price targets and probabilities are in line with actual results.

3. Jim Braddock’s forecasts are net profitable, but his reward probabilities are over inflated. There is an opportunity to profit from his ability to call names with extreme upside returns, but, in the near term, scaling back confidence while working with Jim to improve his batting or at least decrease his forecast probabilities would improve the forecast process.

These are a few of the many insights that Alpha Theory provides to clients to help them become better investment managers. Data provides insights that lead to action that result in an improved process. This chain of improvement is the benefit of capturing and analyzing data. Until we “see the data”, the answer may not be intuitive. This is why it is so important to create a data driven approach, focused on process improvements, that allow you to keep pace with the rapidly evolving data-driven world.


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