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Alpha Theory Blog - News and Insights

34 posts categorized "Risk-Adjusted Return"

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

August 11, 2016

The Power of Process — How Fundamental Investors Benefit from Quantitative Thinking


A recap of speech given on August 3rd, 2016 at Evercore ISI Quantitative Symposium

Why do Fundamental Investors need to think more Quantitatively? 90%+ of fundamental managers we’ve interviewed do not have their 5 best ideas as their 5 largest positions. The primary reason for this is:

    1. QUALITY MEASUREMENT: Fundamental investors generally do not have a repeatable process for measuring idea quality

    2. POSITION SIZING: Fundamental investors generally do not have a repeatable process for sizing positions

Quantitative investors “score” or measure the quality of an idea and use that score, in concert with portfolio constraints, to size positions. Most fundamental investors try to do this heuristically and fail. The failure has been overlooked for years because:

    1. CLOSE ENOUGH: Fundamental investors are smart and they can get pretty close in their heads. Yes, there will be big mistakes at times when emotions get in the way, but that’s more the exception than the rule.

    2. WIDE MARGINS: Since the publishing of “Security Analysis” after the Great Depression, fundamental investors have been able to take advantage of “Mr. Market” by holding true to fundamental investing axioms.

With every passing decade, fundamental margins shrink and are quickly approaching a point where “close enough” is no longer sufficient to generate positive returns. Many fundamental investors and allocators are recognizing this trend and seeking out ways to be more precise and maximize their fundamental advantage. Process is the key.



If you want to see how an industry can be transformed by process, look to the recent revolution in sports. Sports managers are just like great fundamental investors. They try to add great players (investments) to their team (portfolio) to maximize their chance of winning (generating positive alpha). Moneyball created a process around each step by making assumptions explicit and measuring their impact on the desired outcome. It’s not any more complicated than that. This simple concept revolutionized all of sports in a matter of 20 years. Investing is in the early years of Moneyball adoption and, if sports is an apt proxy, it will change rapidly.


Over the past 60 years (dating back to Paul Meehl’s “Clinical versus Statistical Prediction” paper), scientists have studied the judgement of experts. 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 (ex. Oncologist – how long will a patient live, Parole Board – who is most likely to recidivate, Wine Expert – price of wine at auction, etc.) The one request, is that they tell the scientist which variables are most important in their decision.

The scientist goes off and builds an improper (equal weights all factors) or proper (regressed) model and compares their model to the forecasts of the “experts.” Over the hundreds of expert studies for 60 years, the expert beats the simple model a paltry 6% of the time. And when the expert does forecast more accurately, it is usually by a very small margin.

We are not as good as we think we are at making complicated decisions. But we’re very good at determining the variables that matter. The logical conclusion from those facts is that we need to follow Bob Jones of System Two’s advice (spoke after my presentation at the ISI event with a similar message):

    1. Decompose a complex decision into its critical components

    2. Evaluate each individually

    3. Combine algorithmically1

(1) Weights used are not critical in most cases


The importance of process is evident in our analysis of client data. Below, we show positions where explicit price targets and probabilities were forecast outperformed those positions without forecasts.


In the graph 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.


We also show below, that had our clients followed their own model their performance would have been 13% vs 7%. Our clients know the variables that matter and following their own process would improve outcomes. Sounds just like the conclusions from the Expert Studies mentioned earlier.



“Objectivity is gained by making assumptions explicit so that they may be examined and challenged, not by vain efforts to eliminate them from analysis.” – Richards Heuer, Psychology of Intelligence Analysis

I believe the changes required to be more process oriented are completely intuitive to investors but require repetition and time before habits are formed. The catalytic change that ignites the whole process is the simple switch from implicit to explicit assumptions.


Step 1: EXPLICIT FORECASTS: Some investors chafe at price targets because they smack of “false precision.”  These 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.  Said another way, price targets improve the investment process because they foster great questions and force the team to be able to defend the methodology behind their calculations.

Step 2: EXPECTED RETURN: Apply probabilities to forecasts and convert them into expected returns. In Moneyball, the statistic that proved best at predicting win percentage was On-Base Percentage. In investing, it is Expected Return. It is the underpinning of good decision making in many scientific fields: actuaries, odds makers, poker players, physicists, etc. and is a requirement for making good decisions as an investor.

Step 3: TURN QUALITATIVE INTO QUANTITATIVE: Just like in the Expert Studies example, define the variables that are important in your decisions, analyze them independently, and combine them algorithmically.

Step 4: DEFINE RULES: Every investor has rules that guide their decision making. Make those rules explicit and construct a framework to measure your adherence to them.

Step 5: MAXIMIZE TRANSFER COEFFICIENT: Make sure all of your rules and assumptions are being transferred into the portfolio. To do that, create a model that expresses all of your rules and assumptions as a position size. This allows you to compare your a priori self with your actual decisions. Said another way, it creates an unemotional version of you. Said yet another way, it creates a system that looks at every position brand new every day and asks the question, “if I were investing in this asset for the first time today, what position size would I take?”

Step 6: ANALYZE RESULTS: Once you’ve done the first five steps, you can measure your explicit assumptions and model for correctness.

Step 7: REFINE PROCESS: Take the results from Step 6 and draw conclusions for ways to improve forecasts, inputs, and the model.


These steps are straightforward. The adoption of this process is critical to success in the future where the edge for fundamental investors has dramatically shrunk. The difference between two equally skilled analytical minds will be the process applied to maximize that analytical prowess. The future of fundamental investing is clear if Moneyball is a true harbinger of things to come. Embrace the benefits of process today and be at the vanguard of investing. Ignore the benefits of process and slowly lose to competitors more adaptable to change. 



July 18, 2016

Superforecasting – Alpha Theory Book Club

Alpha Theory hosted its first ever book club on July 12th with over 40 portfolio managers, analysts, and allocators coming together to discuss “Superforecasting” by Phil Tetlock. We were lucky enough to have two Superforecasters, Warren Hatch and Steve Roth, moderate and perform forecasting exercises with the group. We spent 2 ½ hours together and only scratched the surface on applying Superforecasting to investing.

Here are a few key takeaways:

1. RAW TALENT: On average, our group had the attributes of Superforecasters with high Active Open-Mindedness (3.99 out of 5) and high Fluid Intelligence (8 out of 10 – this is the highest score that the Good Judgement folks have seen).

Active Open Mindedness



Fluid Intelligence


2. IDENTIFYING TALENT: There are identifiable attributes that can be used in hiring and have a profound impact on forecasting skill (40% - see chart below).

Screen Shot 2016-07-18 at 4.02.10 PM

3. DEVIL’S ADVOCATE: Firms should appoint a Devil’s Advocate for each investment to expand critical thinking (someone to ask the question, “I see your downside is $40. How is that if the 52-Week Low is $22 and the trough multiple would put it at $25?”)

4. OUTSIDE VIEW: Firms should require an Outside View for every investment idea (“While everyone I’ve spoken to says this deal will close, only 20% of deals with one party under SEC investigation close.”)

5. REFINEMENT: New information should always be incorporated in forecast (think Bayesian).

6. POSTMORTEM: An Accuracy Score should be calculated for every investment and should frame the conversation of “what did we do well?” and “what did we do poorly?”.

7. TEAMS MAKE BETTER FORECASTS: Team dialog generally improves forecasting accuracy.

8. FORECAST CULTURE: Firms should embrace “forecast” as part of their vernacular and conversations should revolve around how information impacts the forecast.

9. MEASURE TO BE BETTER: We all forecast, but we rarely measure. That fact needs to change if we really want to improve.


The October Alpha Theory Book Club topic will be “Success Equation: Untangling Skill and Luck” by Michael Mauboussin. Mr. Mauboussin will moderate and highlight the book’s application to investing. Contact your Alpha Theory representative if interested in attending.


June 21, 2016

How Good Are My Analysts? Building a Better Hedge Fund Through Moneyball & Superforecasting

Traditionally, measuring hedge fund analyst skill has been an opaque process mired in ambiguity and subjectivity.  It is often misconstrued and tainted by portfolio manager influence in the form of sizing decisions, liquidity constraints and other non-analyst determinants.  But, in the same way Moneyball revolutionized evaluating baseball player value by prioritizing on-base percentage over batting average, Alpha Theory has distilled the key indicator for predictive aptitude. Alpha Theory invented the Alpha Theory Accuracy Score to introduce radical transparency into the rating of forecasting skill for hedge fund analysts.

P&L is Yesterday’s Batting Average

Using the Moneyball analogy, quantitative disruption of baseball player evaluation changed the way players are paid by isolating the player skill that contributes most to team wins. Using that data, managers now pay athletes in proportion to the amount of that winning skill they individually possess.  As such, the key metric for baseball player value evolved from batting average, to the more predictive on-base percentage, or OBP. 

Specifically, OBP has a 92 percent correlation with runs scored compared to batting’s 81 percent, making it more predictive.  Also, OBP’s 44 percent correlation year-to-year is more persistent than the 32 percent correlation of batting.  The predictive reliability and performance consistency make OBP a superior metric to forecast wins for baseball teams.  OBP’s disruption of batting average is an apt metaphor for the way Alpha Theory’s Accuracy Score will transform analyst ranking and assessment today.      

In 2016, analysts are still primarily rated by the profits and losses their investments generate for the fund, or P&L.  But making money on an investment is a misleading measure of analyst skill.  Beyond its tendency to be distorted by portfolio manager discretion, P&L performance, both good and bad, often masks the integrity and quality of investment processes.  Thus, P&L often misleads portfolio managers into thinking lucky analysts are actually skilled and vice versa.

For example, take these two analysts:

How good is my

Looking at the table above and using P&L to measure skill, Analyst #1 would be exceptional and Analyst #2 would be sub-par.  But Analyst #1 and #2 had the same forecasts, so their forecasting skill is actually identical.  P&L does not translate into forecast skill because analysts do not have ultimate control over position sizing; the portfolio manager does!

More Science, Less Art                                                                                                                         

Inspired by the ideas presented in the groundbreaking book, Superforecasting: The Art and Science of Prediction, Alpha Theory’s Accuracy Score delivers quantitative insight into a qualitative blind spot for portfolio managers.  Authored by Wharton Professor Phillip Tetlock and Dan Gardner in 2015, Superforecasting applies a Brier Score-inspired approach to quantifying predictive skill.  The Brier Score was created by meteorological statistician, Glenn Brier, in 1950 and measures the accuracy of probabilistic outcomes.  Superforecasting applies Brier’s methodology to only binary, or yes/no, outcomes.  

The New Standard

Alpha Theory’s Accuracy Score is an algorithmic solution that measures analysts’ predictive skill over a 0 - 100 percent range, where 100 is the best.  Scores are calculated on a per-forecast basis and then averaged per analyst.  The Accuracy Score algorithm transforms point estimate price targets and probability forecasts into an implied probability distribution, enabling each forecast to be independently scored.  By distributing multi-faceted outcomes across a range of probabilities, the Accuracy Score can measure forecasting skill for any price along the distribution.

The distribution of scores across our Alpha Theory clients is shown below.  The results follow a normal distribution, which further validates the Accuracy Score’s efficacy in rating analysts’ ability to forecast future price movements.

Screen Shot 2016-06-21 at 9.41.12 AM

Good forecasts are the most essential component of fund success and critical when portfolio managers are sizing positions.  Using a data-driven approach to determine which analysts make the best forecasts allows managers to apply those forecasts with greater confidence, leading to better position sizing and superior performance.

The Good Judgement Project

In 2011, the Intelligence Advanced Research Projects Activity, a U.S. government research organization, sponsored a geopolitical forecasting tournament that would span 4 years. The IARPA tournament enlisted tens of thousands of forecasters and solicited more than 1 million forecasts across nearly 500 questions related to U.S. national security.

A group called the Good Judgement Project entered the competition, engaged tens of thousands of ordinary people to make predictions, and the won the tournament. The GJP’s forecast accuracy was so persistent that IARPA closed the tournament early to focus exclusively on them. In fact, GJP was able to find a select group of “Superforecasters” that generated forecasts that were "30 percent better than intelligence officers with access to actual classified information.” 

Ways to Improve Forecasting Skill

The main findings of the GJP and the book that followed are especially relevant to investors. The research in Superforecasting indicates that predictive accuracy doesn’t require sophisticated algorithms or artificial intelligence.  Instead, forecast reliability is the result of process-oriented discipline.  

This process entails collecting evidence from a wide variety of sources, thinking probabilistically, working collaboratively, keeping score and being flexible in the face of error. According to the book, the 10 traits that most Superforecasters possess are: 

    1.  Intelligence - above average, but genius isn’t required

    2.  Quantitative - not only understand math but apply it to everyday life

    3.  Foxes, not hedgehogs - speak in terms of possibilities, not absolutes

    4.  Humility - understand the limits of their knowledge

    5.  System 2 Driven - use the logic-driven instead of instinct-driven portion of their brain

    6.  Refute fatalism - life is not preordained

    7.  Make frequent and small updates to their forecast based on new information

    8.  Believe that history is one of many possible paths that could have occurred

    9.  Incorporate internal and external views


Accountability = Profitability

Organizations cannot improve without systematic and data-driven assessments of their personnel.  Take Bridgewater Associates, for example.  One of the primary factors driving the persistent outperformance of Ray Dalio’s storied fund has been the institutional commitment to radical transparency and accountability.  Similarly, Alpha Theory’s Accuracy Score illuminates blind spots and holds analysts accountable through the precise measurement of predictive skill. For funds that lack the time, inclination or internal resources to create their own probabilistic forecast-grading models, Alpha Theory’s Accuracy Score fills the void.

To this end, Alpha Theory is exploring areas of collaboration with the leadership of Good Judgment Inc. (a spin-off from the Good Judgement Project in “Superforecasting”).  As the competitive landscape for investment capital tightens, discretionary managers must leverage probabilistic data to survive.  Alpha Theory’s Accuracy Score is a mission-critical asset that can help funds compete in the current investment landscape, improving operating inefficiencies and better aligning analyst pay with their intrinsic value to the firm.

May 31, 2016

All in a Day’s Work – Mental Capital Allocation

A Portfolio Manager acts as the final arbiter of what is a good idea and what is not. The Portfolio Manager’s acumen is what investors are ultimately paying for. They need to know each position almost as well as the analyst to be able to properly position it against the rest of the positions in the portfolio. The problem is that most PMs don’t have the time to carry that responsibility because they’re invested in too many positions.

We can prove that PMs don’t know their positions well enough by breaking down a PM’s performance over the course of a year. Let’s assume that the average portfolio manager has 2,500 work hours a year to dedicate to stock analysis (50 weeks times 50 hours). The amount of time dedicated to stock analysis is determined by calculating the percentage of time dedicated to raising capital, communicating with existing investors, running the business, and staring at the P&L.

If the typical portfolio consists of 100 positions and has 50% turnover, then the PM had to understand 150 positions over the course of the year. Now, let’s assume that a PM evaluates two ideas for each position they put in the portfolio. That doubles the number of positions analyzed to 300.

All in a day

The result is 6 hours (basically a day) per year for each position in the portfolio. A PM is ultimately in charge of answering a simple question, “Do I believe my analyst’s assumptions?” 6 hours is hardly enough time for due diligence of that sort.

Don’t take my word for it. Do the math for yourself:

    1. HOURS: Measure your yearly hours using the steps above

    2. ACTUAL POSITIONS: Measure historical average positions per year

    3. ANALYSIS TIME: Measure the time it takes to properly analyze a company (take an example of work you felt really good about)


    5. RESULT: If Actual Positions is greater than Theoretical Max Number of Positions, then you have too many.

Self-reflection is a key to success because it shows us the proper steps to improve. Reflect on how you allocate your Mental Capital and take the time to figure out if you have the time.

April 25, 2016

The Misperception of False Precision for Price Targets and Probabilities

“Objectivity is gained by making assumptions explicit so that they may be examined and challenged, not by vain efforts to eliminate them from analysis.” – Richards Heuer, Psychology of Intelligence Analysis.

Some investors chafe at price targets because they smack of “false precision.”  These 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.  Said another way, price targets improve the investment process because they foster great questions and force the team to be able to defend the methodology behind their calculations.

If “false precision” is the concern, then probabilities are doubly damned. They not only require precise estimation, but are considerably more subjective than price targets. The problem with this argument is that probability is an assumption in the process even if you don’t make it explicit. No investor believes they’re 100% right, so there is always a chance of success and failure. In an implicit process, probabilities are expressed with words like “pretty confident” or “high likelihood”, but they are still an estimate of probability. One that is easily misunderstood and difficult to judge. For the same reason that you should require explicit price targets, you should require explicit probabilities.

Not only do explicit price targets and probabilities help investors make better decisions in the short term, they also allow firms to use historical analytics to measure where they are making mistakes and find ways to avoid them.  Great PM’s should be able to leverage information like:  My analysts assume they’ll be right 74% of the time but are actually only right 51% of the time. I will adjust ask them to adjust their assumptions accordingly. 

To make the differences more concrete, I’ve created an example conversation. Problems are better framed when price targets and probabilities are explicitly defined. Compare these conversations and choose the one that you’d prefer as a portfolio manager (I’ve bolded the differences in the two pitches):

Implicit: I think we should add Baidu to the portfolio because overall earnings are being depressed because their non-search business lines are losing money. The current search business is solid, as Baidu holds a natural monopoly in China like Google does in the U.S.A. and their prospects are even better because their users are rapidly moving from 3G to 4G mobile, which will dramatically increase search demand. Their two money-losing lines are independently worth $10+ billion, but are currently not assigned value. The risk is that they can’t monetize the two non-search businesses and they become big money losers. I think there is a high likelihood this is double based on the sum-of-the-parts over the next year or so and has limited downside based on net cash and conservative earnings and multiple. The Risk-Reward is compelling.

Explicit: I think we should add Baidu to the portfolio because overall earnings are being depressed because their non-search business lines are losing money. The current search business is solid, as Baidu holds a natural monopoly in China like Google does in the U.S.A. and their prospects are even better because their users are rapidly moving from 3G to 4G mobile, which will dramatically increase search demand. Their two money-losing lines are independently worth $10+ billion but are currently not assigned value. The risk is that they can’t monetize the two non-search businesses and they become big money losers. I think there is a 70% chance the stock could hit $350 based on sum-of-the-parts over the next year and has downside to $150 based on net cash and conservative earnings and multiple. The Probability-Weighted Return is 50%.

Your discretion as a portfolio manager is not lessened by explicit assumptions. In fact, the explicit assumptions are examined and challenged in a way that is not allowed by the vagaries of implicit assumptions. What “high likelihood” means to you may be different than your analyst. Your interpretation of “limited downside” may be different. Explicit assumptions aren’t designed to make you guess at the unknowable. They are designed to make sure the investment case can withstand the stress of being explicitly defined. If they can’t, you are investing at your own peril.

This post is an extension of the 2014 post – Explicit Lyrics – Why Implicit Assumptions Are Dangerous.

March 21, 2016

Ruminations on Risk

We present this month an interview with Alpha Theory Advisors head Benn Dunn.  Alpha Theory Advisors is the consulting arm of Alpha Theory, providing investment process engineering and thought leadership, outsourced risk management, research leadership and tactical portfolio management guidance to numerous alternative investment firms currently managing approximately $6Bn in AUM across all asset classes. Prior to joining Alpha Theory, Benn served as the Head of Risk Management at the CR Intrinsic Investors unit at S.A.C. Capital Advisors and Chief Risk Officer at Weiss Multi-Strategy.

  1. What is making this market environment so different from others where mid- to high-single digit pullbacks have occurred? Index performance does not appear outright disastrous but we have heard of many hedge funds seeing drawdowns disproportionate and unexpected relative to their exposure levels and typically conservative stance vis-à-vis directional bets.

Things have been changing rapidly versus last year, in that there are many hedge fund-specific issues, even outside of macro and market dynamics, starting to occur.  We are seeing a perfect storm where multi-manager platforms that tend to run (by mandate) market-neutral had levered up in some cases very substantially, and then saw their risk models come apart given an increasingly damaging – and self-perpetuating – unwind in momentum but also other segments.

Crowded growth names like LinkedIn, Tableau and others during earnings season saw declines of as much as 50% in a day – unprecedented in their stock histories – after reporting ugly results.  A widely circulated note from Cornerstone in January essentially fit this increasingly talked-about narrative (that momentum was set to unwind), and that only contributed to the perfect storm.  The short of it is that many risk models, which had until last week been predicated on being market-neutral, factor-neutral, sector-neutral, etc., no longer held up as such.

When this kind of thing starts to happen, the primary and pervasive response is to cut gross exposure.  A big part of the stealth correction and behind-the-scenes damage in the early part of this year can be attributed to reductions in gross, and the result was that crowded names with “small exits” became very dangerous.

  1. What advice, as a result, are you giving clients?

While the contrarian in me suggested some buying of beaten-up but quality assets, I also suggested clients be somewhat defensive, and indeed keep gross exposure in check, for a few reasons.  There is still a very unclear macro environment at the moment, whether it’s the Fed, China, energy company debt problems, Mid-East geopolitics, etc.  The list of reasons the market could go down remains much longer than the list of reasons the markets should go up.

In addition, asset allocators, who tend to avoid over-staying their welcomes and sometimes redeem first and ask questions later, seem in some cases to be pulling out of the multi-manager platforms that were supposed to behave more neutrally.  So that has added to what seemed for too much of this year to be a self-perpetuating risk environment, and the reduction in gross exposure that can have knock-on effects.  One note I saw out of Morgan Stanley in February highlighted that gross exposure started the year at the highest it had been since 2008.  And yet we have been hearing some very well-known and substantial market-neutral platforms have experienced drawdowns that are very atypical for their style and approach.

  1. What can stop the bleeding or draws a line in the sand?

The downside is sustained until CROs (Chief Risk Officers) get their funds’ risk levels back in line and some of these multi-managers are done cutting gross exposure.  Some funds will eliminate individual sector groups for violating drawdown limits, so capital exposed comes down to risk levels that are within appropriate limits.  And finally, some stocks may go to equity values that are below any rational level associated with even liquidation values.

  1. What might happen at the macro level to help the dust settle?

One silver lining here of late is the weakening dollar.  If the narrative was China being forced to weaken its currency and emerging markets hurting due to the strengthening dollar, then these problems start to get alleviated as the dollar backs off its recent strength.  Oil prices go up, along with other commodities.  Of course while all this helps, there are some big market-neutral platforms that saw drawdowns of as much as 5%-20% for January, and this on top of at least one immediately prior weak year.  So many allocators will pull back from these platforms and try to high-grade their books even as some of the dust is settling.

  1. If the contrarian in you has you tempted to do something, what is it?

The contrarian in me is wanting to back up the truck and buy equities of companies without debt maturities in the near term or significant debt at all, where there is a sustainable business model, positive free cash flow, no need for access to capital markets.  These are stocks that represent babies being thrown out with the bath water.  The next trick is whether a fund has something closer to permanent capital – or at least locked-up capital, because that allows a fund to wait for and survive the bottom and benefit from the inevitable reversion to the mean.

  1. If a fund does not have permanent capital and is somewhat levered and net long exposed, what is the most appropriate advice?

One has to de-gross or take down overall exposure.  Of course, this exacerbates the downside among crowded names, where everyone is selling the same things at once.  But the problem with a levered fund is that risk becomes existential, there is actual business risk for a fund, and my job as a CRO is to prevent that above all.

  1. Should funds reduce net long exposure or should they focus on gross?

Over the past couple of months gross represents risk.  When the multi-strategy funds are unwinding or their longs have sold off well more than their shorts, they can't just cover their shorts and let themselves get off-balance on that score.  So they are just de-grossing; they cannot organically take one side of the book down without doing so on the other side.  What this means is that even with an up tape, funds can still get hurt badly if they own some crowded names.

  1. Are there some over-arching themes to be aware of in addition to all this?

It’s been talked about for some time now that liquidity has become extremely limited among some stocks in the market, especially as you go down the market cap ladder.  This causes exits to be very narrow and even if only one fund has to unwind its book, there are simply not enough incremental buyers to take on the stock for sale if the selling has to be fast and sizeable in nature.

There can be secondary and even tertiary effects where the fund exiting a set of names has overlap with names of a second fund that cause the second fund to become stressed, which in turn could impact a third fund holding only a couple of the second fund’s assets.

For a fund with longer-dated capital, this kind of forced or artificial liquidation can represent an opportunity.  Going through the big holdings of what are known to be highly stressed funds and correlating to market volumes and trader scuttlebutt to build a sense for completions to unwinds, name-by-name, is not an uncommon practice.

  1. For funds that lack a CRO (or the means to pay for one), what advice would you give?

There are available tools that can help a fund measure its exposure to any number of factors; even Bloomberg can be of help.  One should be aware of some common mistakes and pitfalls from a risk standpoint: beta, thematic or industry mismatch.  Being long software and short semis – even if beta matched – can prove to be a sector mismatch; one can be too heavily long growth and short cyclicals without being completely aware of such nuances and potential consequences.  There are things that can go wrong on the earnings front that can shift how the fund appears to a risk model, even overnight.

  1. What other risk variables are commonly overlooked?

The Barra model now counts so many factors (I think there are something like 64 industry groups alone).  There is country exposure, basis risk, and even temporal variables to be aware of.  For instance, the short energy/long consumer trades people put on may appear appropriate at first, but the consumer names only see their up move resulting from low energy prices with an extreme lag (up to6 months to a year).  A fund can have balance sheet mismatches, where one is long companies that need access to capital and short some less levered ones.  Or, a fund could be long a few European banks and short some U.S. financials, where the market’s treatment of those different categories cause downside from a geographic mismatch.

  1. What aspects of Alpha Theory can a PM make use of to be more risk-aware and mindful of exposures?

The Confidence Checklist is certainly one element of the application that can allow users to score or grade for risk factors of any kind.  Re-underwriting one’s price targets – even to incorporate recessionary earnings scenarios or market multiples – can also be a potential help.  Just focusing on individual company outlooks and prospects for their own sake and ignoring some of the noise can be a constructive exercise that Alpha Theory forces.  Analysts should be making these company-by-company assessments regularly.

September 28, 2015

How I used Alpha Theory to help my girlfriend land her dream job

We welcome our very own VP of Business Development, Jason Cooper, for this guest post:

As the Head of Sales for Alpha Theory, I get to spend my days acting as a diagnostician/thought partner to wickedly intelligent people (Hedge Fund PMs and analysts), helping them unpack, assess and formalize their investment process to achieve greater discipline, speed their path-to-action and improve outcome quality and consistency. In short, I get to help smart decision-makers make even smarter decisions. We’re really good at what we do and it’s a ton of fun!

In my personal life, however, I’m less accomplished in this area. Like many (a/k/a all?) in committed relationships, I attempt, regularly and unsuccessfully, to provide objective and impactful counsel to my better-half on life decisions, large and small. For low-stakes decisions, my opinions are solicited and just-as-quickly dismissed. No (little) fuss. No (little) muss. As the stakes increase, however, so does the likelihood that I’ll receive “negative feedback” that in some cases can be quite severe (interestingly enough and somewhat counterintuitively, exercising the “no opinion” option actually can yield the highest severity negative feedback of all). Anyway, I’d pretty much resigned myself to a lifetime of the observed dynamics until I had a breakthrough – a “hack” if you will – worth sharing. The following is a true story. Identities have been withheld to insulate myself from any potential regulatory/legal liability or potential verbal/physical retaliation:

Recently my better half sought my advice on a high-stakes decision. As a mid-level employee for the online division of a large, listed retailer, she had become increasingly unhappy in her role so she began looking for new opportunities, quickly catching the attention of a hiring manager for a new economy darling who was eyeing her for a more senior position with significantly higher pay. Successfully (and discreetly) progressing through a grueling, multi-round interview process that has become something an urban legend in Silicon Valley circles, she was notified that she had been selected as one of the final candidates for a full-day, on-site, round-robin format selection interview. She got this notice on Friday afternoon and the interview was set for the following Monday. The problem? She had been preparing all quarter for a presentation to senior management scheduled on the same day as the interview, a presentation that had been on the calendar for months and was understood to significantly inform her annual performance review and, by extension, influence her career trajectory.

What to do? Option #1: Bag the presentation – at significant professional risk - for the chance at her dream job. Option #2: Miss the job interview, play it safe, nail her presentation and continue her unhappy existence until the next promotion?

The decision, emotionally charged, was long on variables and short on complete information. Rational thinking had been abandoned. I did my best to listen and suggest ways to frame the decision. Sparing the details, in sum, it didn’t go well. Finally, when all else seemed lost, I gasped “let Alpha Theory tell you what to do!” In my amazement, she immediately calmed, processed this unusual suggestion and agreed. We spent the next 30 minutes defining and structuring the scenarios, associated payoffs, probabilities, time horizons, other key assumptions (like what annualization methodology we’d use, how we’d account for reinvestment rate and the width of the potential distribution of returns, etc.) and how we’d judge the results and act (i.e. like a poker player). 

  • Positive expected return: Bet (prepare a colleague for the presentation and go to the interview).
  • Negative expected return: Fold (miss the interview and take your chances with the existing job).

I plugged everything into Alpha Theory. Here were our results (salary details have been altered):

     Help Girlfiend Land Job


Clear. Unambiguous. Unemotional. Actionable. Fast. Alpha Theory said “go to the interview”. I emailed her the screen shot above and, to my amazement, she instantly and calmly replied, “Okay. I’ll do it”, which was followed shortly thereafter by an enthusiastic one-line email reading, “I need Alpha Theory for my life!”

Of course you know how this story ends: she pushed off the presentation to a colleague and went to the interview, nailed it and got the offer two days later (which she accepted). This story illustrates the importance of frameworks and using them explicitly (vs. implicitly) to help drive a decision process that yields higher quality outcomes (with lower stress). This was a straight-forward, point-in-time binary decision (go vs. don’t go) and nonetheless was difficult to answer in the face of such high-stakes, so many subjective variables, the human condition (daily decision-fatigue, general fallibility). Can you imagine how much harder it is for investors who are trying to ‘solve for’ a similarly complex primary buy/sell decision but also the second order decision of “how much”? I can, because I see it every day and I’m amazed how many sophisticated investors effectively try to “white-knuckle it” using their mental heuristic when they don’t have to.

Next time you or your significant other have a life decision, try using Alpha Theory. You may not achieve true objectivity and we can’t guarantee a right answer, but the structure and discipline will help drive a more ‘subjectively rational’ decision and maybe…just maybe…no significant other will be injured in the making of this decision.

July 18, 2014

Probability Inflation: The Risk of Ignoring Batting Average – Part 2

In my last post, I discussed how the probability of success in investing is grossly overestimated compared to historical batting average (funds assume 75% of success vs. their batting average of 55%). This causes miscalculations in portfolio allocation. In this post, I will discuss how Alpha Theory has been partially controlling for this phenomenon through our proprietary risk-adjusted return calculation (Alpha Theory Risk-Adjusted Return). Our method basically averages the arithmetic and geometric probability weighted returns. Note the difference between the Arithmetic and Geometric returns below.

Geometric return more heavily weights the downside node and reduces the risk-adjusted return for assets with high probabilities of extreme loss (see “Which Way is Up?” for further explanation). Not only does the geometric adjustment highlight the same gradation of the lower probabilities between the 75% and 55% arrays, it also is more intuitive to a portfolio manager. When surveyed, we found that portfolio managers would not size the top three positions equally when only shown the arithmetic return. There is an inherent heuristic that portfolio managers employ to control for this phenomenon. The problem is that heuristics are easily miscalculated and inconsistently applied.

Alpha Theory uses a hybrid Arithmetic/Geometric return to calculate the Alpha Theory Risk-Adjusted Return. This allows managers to properly account for portfolio impact and avoid heuristics. Downside is more impactful than upside in a compounding vehicle like a portfolio, so overemphasis of downside is practical. To properly allocate capital, it is critical for funds to first, make explicit forecasts of price targets and probability and then second, properly account for the asymmetry of upside and downside. This is difficult (impossible really) to accomplish without a systematic approach and we encourage every fund to capture and calculate risk-adjusted return for every investment in their portfolio.

In portfolio management, preventing loss is paramount. Using realistic probabilities, more closely in line with the fund’s historical batting average, and Alpha Theory Risk-Adjusted Return, will properly skew the portfolio towards those assets that have large asymmetry and little downside. In a compounding vehicle, like a portfolio, avoiding these “bad bets” will generate higher long-term geometric expected return, the ultimate goal of portfolio management.

June 17, 2014

Probability Inflation: The Risk of Ignoring Batting Average – Part 1

My company, Alpha Theory, started performing historical analysis of clients’ data about six months ago. We’ve only finished work on a dozen or so clients but one trend seems to be consistent, probability inflation. Let me explain what that means. Our clients use Alpha Theory to estimate the risk-adjusted return of each investment and use that information to properly allocate capital across their portfolio. Part of estimating risk-adjusted return is assigning probabilities to various potential outcomes. What we find is that clients generally have probabilities of success (scenarios where they make money) that fall in the 70-80% range. The issue is that their historical batting averages are more in the 50-60% range (batting average is how often they ACTUALLY make money on their bets).

"Of the almost 100 U.K. and U.S. fund managers in Investment Intelligence’s database, Chaban says, the best hit rate he’s seen is 64 percent; the median is just over 50 percent. " - Taras Chaban, chief executive officer of Investment Intelligence Ltd. (Bloomberg article)

Inflated probability of success causes two issues. One, the risk-adjusted return is inflated. Two, the probability of loss is too low which results in an underestimation of risk. The net effect is overly optimistic assumptions and bets on assets that are too risky.

Here’s an example. Below we have three potential investments with equal 75% probabilities of success. As you can see, the return and risk characteristics vary dramatically for each asset but the risk-adjusted return is a constant 30%. Imagine a portfolio manager deciding between this set of investment options with inflated probabilities and determining to weight all three positions equally.


Now imagine a portfolio manager, presented with the same investment choices, but with more realistic probabilities of success (55% instead of 75%). There is no way that these would be equally sized. In fact, #3 wouldn’t even be considered.


The return reduction for investment #1 is 8%, which is meaningful, but nothing like that for #3, which falls from 30% to -5%! Assuming a 55% probability of success creates an entirely different (and more realistic) set of investment possibilities for the portfolio manager to choose between. The complexion of the portfolio changes to lower probability of downside bets. I strongly encourage funds to use more realistic probabilities (average closer to historical batting average). If not, they will almost certainly suffer from overexposure to the #3s of the investment world.

Be on the lookout for next month’s post discussing Alpha Theory’s novel approach to calculating return and its relevance to the issue of Probability Inflation.