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Alpha Theory Blog - News and Insights

30 posts categorized "Risk-Adjusted Return"

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)

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

ALPHA THEORY - 2016 YEAR IN REVIEW

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.

1

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:

2

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

2017 NEW YEAR’S RESOLUTIONS

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:

CREATE PROCESS ----> BUILD HABITS ----> ACHIEVE 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.

2

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!

 

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

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.

PROOF OF THE NEED FOR CHANGE

MONEYBALL

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.

EXPERT STUDIES

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

EMPIRICAL EVIDENCE

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.

Picture1

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.

Picture2

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.

Picture3

SO WHAT DO WE PROPOSE?

“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.

Picture4

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.

Step 8: REPEAT WITH IMPROVED PROCESS

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

 

 1

Fluid Intelligence

2

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.

10. BOOK CLUBS ARE COOL!!!

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

    10. CONSTANTLY SEARCH FOR WAYS TO IMPROVE THEIR FORECASTING PROCESS

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.