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Alpha Theory Blog - News and Insights

54 posts categorized "Portfolio Optimization"

January 05, 2018

2017 Year in Review

 

Alpha Theory’s product helps investment managers reduce emotion and guesswork in position sizing. The result is reduced errors and improved returns. For six consecutive years, Alpha Theory clients have outperformed their peers (see table below – we use the benchmark of Major Equity Hedge Index because 86% of Alpha Theory clients are hedge funds). Our clients have consistently outperformed their competitors, more than doubling their returns over the period.

 

Graph1

*Totals are not including 2017 data

In 2017, our average client generated 18.9% returns and, when it is released, I anticipate that we’ll beat the Hedge Index again. These results are consistent with other blog posts we’ve written highlighting our clients in 3rd party rankings: Reuters / WSJ / Novus.

 

NEW 13-F ANALYSIS

This year, we expanded our analysis through a new 13-F dataset with all publicly filing funds. The upside of using this dataset is it enables us to compare results against every reporting fund in 2017. The downside is it only includes the US equity long positions. The results indicate that once again, Alpha Theory clients outperform their peers.

The average Alpha Theory client performance in 2017 (13-F data) was 27.6% vs 19.9% for all others (3013 total funds with over 20 positions). That’s almost one full standard deviation higher (8.8% standard deviation) than the mean and has a Z-Score of 2.03 (statistically significant above the 95% confidence level).

Even more interesting was the individual performance results of our clients, one Alpha Theory client was the 2nd best performing fund in 2017 (this client thanked us more than once for our contribution to their success) and four clients landed in the top 40 performers.  We also had six of the top 100, and 10 of the top 200. Statistically, we’d anticipate less than 1% in all categories because Alpha Theory clients are less than 1% of all funds. Instead, as in previous periods, there is a concentration of Alpha Theory clients amongst the top performers.

Graph2

Simply put, Alpha Theory clients outperform their peers. The traits these firms share are discipline, intellectual honesty, and process focused. They gravitate to Alpha Theory because it is their tool kit to implement and measure that process.

 

PROCESS EQUALS PERFORMANCE

Alpha Theory clients use process to reduce the impacts from emotion and guesswork as they make position sizing decisions. Alpha Theory highlights when good ideas coincide with largest position sizes in the portfolio. This rules engine codifies a discipline that:

1. Centralizes price targets and archives them in a database

2. Provides notifications of price target updates and anomalies

3. Calculates probability-weighted returns (PWR) for assets and the portfolio as a whole.

4. Enhances returns

5. Mitigates portfolio risk 

6. Saves time

7. Adds precision and rigor to sizing process

8. Real time incorporation of market and individual asset moves into sizing decisions.

DISCIPLINED USAGE REDUCES RESEARCH SLIPPAGE

Alpha Theory’s research not only suggests that adoption of the application by itself leads to improved performance, but actual usage intensity further enhances results.

Usage intensity is determined by:

1. Percent of Positions with Research

2. Correlation with Optimal Position Size

3. Login Frequency

 

Graph3

1.Measured as the annualized ROIC where data was available, for a sample of 48 clients, 12 for each quartile

 

OPTIMAL POSITION SIZING REDUCES RESEARCH SLIPPAGE

Comparing clients’ actual versus optimal returns shows:

HIGHER TOTAL RETURNS
ROIC is 4.5% higher.

IMPROVED BATTING AVERAGE
Batting Average is 8% higher. Explanation: many of the assets that don’t have price targets or have negative PWRs are held by the fund but recommended as 0% positions by AT. Those positions underperform and allow AT’s batting average to prevail.

 Graph4

1.Measured as the average full year return for clients where full year data was available, adjusted for differences in exposure, net of trading costs

2.Before trading costs

 

ALPHA THEORY CLIENTS OUTPERFORM NON-CLIENTS
Alpha Theory clients have outperformed Major Equity Hedge Indices every year since Alpha Theory started collecting historical data. While our clients are a self-selecting cohort who believe in process and discipline; process orientation goes hand-in-hand with Alpha Theory software that serves as a disciplining mechanism to align best risk/reward ideas with rankings in the portfolio.

 Graph5

PRICE TARGETING REDUCES RESEARCH SLIPPAGE

Alpha Theory has further found that ROIC for assets with price targets is 5.6% higher than for those without price targets. 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 of the assumptions that went into them.  Said another way, the requirements of calculating a price target and the questions that targets foster are central to any good process.

Graph6*Long-only as many short positions are hedges and have no price targets

 

December 15, 2017

Superforecasting for Investors: Part 2

Alpha Theory hosted a book club on December 6th with portfolio managers, analysts, and allocators coming together to discuss “Superforecasting” by Phil Tetlock. We were lucky enough to have a Superforecaster, Warren Hatch, moderate and perform forecasting exercises with the group. We spent 2 hours together and only scratched the surface on applying Superforecasting to investing.

 

Here are a few key takeaways:

1. COMMON ATTRIBUTES OF SUPERFORECASTERS:

INTELLIGENCE: Above average but genius isn’t required

QUANTITATIVE: Not only understand math but apply it to everyday life

FOXES, NOT HEDGEHOGS: Speak in terms of possibilities, not absolutes

INTELLECTUALLY HUMBLE: Understand the limits of their knowledge

SYSTEM 2 DRIVEN: Use the logic-driven instead of instinct-driven portion of their brain

DO NOT BELIEVE IN FATALISM: Life is not preordained

CONSTANTLY REFINE: Make frequent small updates to their forecast based on new information (but not afraid to make big changes when warranted)

COUNTERFACTUALS: Believe that history is one of many possible paths that could have occurred

OUTSIDE VIEW: Incorporate the internal and external views

GROWTH MINDSET: CONSTANTLY SEARCH FOR WAYS TO IMPROVE THEIR FORECASTING PROCESS

 

2. IDENTIFYING TALENT: There are identifiable attributes that can be used in hiring and have a profound impact on forecasting skill

 

Active Open Mindedness*

   image from alphatheory.typepad.com

Fluid Intelligence*

image from alphatheory.typepad.com

 

* At a prior book club, we measured participants and the results showed they 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 Judgment  folks have seen).

Active Open Mindedness (i) and Fluid Intelligence (a) are two measurable traits that managers can use to select talent. In the chart below, the improvement impact of the definable attributes equates to about 40% of their forecasting skill over standard forecasts.

image from alphatheory.typepad.com

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

 

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

 

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

 

9. CLUSTERING: Break complex topics into individual components that are better able to be forecast and use the combination of the smaller forecasts to forecast the more complex. (ie. Will AAPL break $200 is a complex forecast that can be broken down into Will iPhone X ship more than 400m units? / Will Samsung’s technology outpace Apple’s? / etc.)

 

10. INDEXING: Individual clustering questions can be weighted to come up with a forecast for the complex topic instead of using simple equal weighting.

 

11. DIVERSITY OF FORECASTS MATTER: Forecasts made from similar perspectives are less accurate than those made from multiple perspectives (see Boosting below).

 

12. BOOSTING: If you have three forecasters with different perspectives that all arrive at a 70% probability of an event occurring then the actual probability is greater than 70%.

 

13. GISTING: We didn’t get to spend much time here, but the idea is that complex subjects, reports, presentations, etc. can be distilled down into gists that the team votes on and refines into supergist. Full understanding is never just quantitative or qualitative. Superforecasting is quantitative. Supergisting attempts to provide the qualitative piece. 

 

14. HYBRID FORECASTING COMPETITION: IARPA, the defense agency that sponsored the forecasting tournament that launch the Superforecasters (Good Judgment) is sponsoring a new Man+Machine Forecasting Tournament. For those interested in Forecasting and Machine Learning, this is your spot: https://www.iarpa.gov/index.php/research-programs/hfc

 

November 10, 2017

Predictably Insightful: Recap of the Behavioral Alpha Conference

 

This is a picture of me and Dan Ariely, author of “Predictably Irrational” and five other great books on decision pitfalls we all fall into. Dan was the keynote speaker at Behavioral Alpha 2017 an event put on by our friends at Essentia Analytics and we were proud to help sponsor.

 

Behavioral Alpha

 

The day was packed with great speakers including:

- Dan Ariely: “Behavioral Finance in Practice” 

- Denise Shull talking about “Your Senses, Feelings & Emotions are the Ultimate Dataset”

- Clare Flynn Levy: "Applying Behavioral Finance to Your Own Investment Process" 

- Fireside Chat with Mark Baumgartner: “Why Asset Allocators Care About Behavioral Analysis” 

- Cameron Hight: “Mistakes Managers Make & How to Fix Them”

- Peer Idea Exchange: Paul Sonkin and Paul Johnson: “Pitching the Perfect Investment:

- Managing the Tensions Between Analysts and Managers” 

- Dave Winsborough: “How the Collective Personality of Your Team Affects Performance”

 

Here’s a quick recap of some of the takeaways:

Dave Winsborough discussed ways that we can build better teams by understanding the goal we’re trying to accomplish, the needed components to accomplish that goal, and measuring the team participants to make sure that the team has all of the necessary components. It’s a relatively straightforward idea that should be applicable to almost any team.

Denise Shull discussed ways we can become better in tune with our feelings and emotions with the idea of learning when and how to leverage those feelings. Learning how to identify our own emotions is a powerful first step towards being able to mute the negative emotions and take advantage of the positive (signals).

Much of the conference was on emotion and bias and how they cause us to make poor decisions. I completely agree, but that’s not my expertise. I spent much of my time talking the processes that help mitigate bias. This primarily involved making our assumptions and decision process explicit so that they can be judged and analyzed.

Dan Ariely gave several fascinating anecdotes like how casinos are the best at applying behavioral tools, how company internal satisfaction surveys have predictive power for stock performance, how Intuit is giving teams time and money to try bold new initiatives to help them get over the risk of projects that fail, a weight scale that doesn’t show your weight (but tracks it over time) is a much better way to lose weight than one that gives immediate feedback that is subject to good-habit-breaking volatility, and how people in the next to the lowest tax bracket are the ones most opposed to minimum wage hikes because it could push them into the lowest rung of society. His major takeaway was the bias and personality are tough to eliminate so you have to create habits, rules, and routinized behaviors that help us do the things we say we want to do (very Alpha Theory😉).

Clare Flynn-Levy showed how investors can make better decisions by capturing some basic information about themselves and their decisions. Taking the time to tie those data points together can help us better understand when we make good decisions and when we make poor decisions. By understanding these cues when they’re happening we can take advantage of the positive and avoid the negative.

Mark Baumgartner discussed his time at the Ford Foundation and Institute of Advanced Studies and some of the things he’s seen in the managers he evaluates. He said that about 10% of the managers he meets have some form of structured process around behavioral science, decision making, portfolio management, position sizing, etc. He believes that the primary value of a manager isn’t based on these processes, but he believes there is a lot of easy to pick up alpha form implementing process. He would like to see his managers embrace it more actively but says the industry moves glacially while the products that help improve the process are evolving very fast.

The room was full of managers and allocators. There was a self-selection bias, but the crowd truly embraced the concepts for how to be better using the behavioral science discussed during the day. In fact, the crowd asked amazing questions and one of my favorite parts of the day was from a member of the audience that was expanding on his thoughts about the difficulties of capturing alpha. He said the number of investors has increased from 5,000 to 1 million over 50 years. How do you reverse that trend when it is one of the highest paid professions, where you get to work with amazing people, research a broad range of interest, get to meet leaders in industry, academics, and government, and be exposed to an array of amazing ideas? If I’m ambitious and at the top of my class, why would I not pursue that profession.

Hmmmmm, maybe we can ask Dan Ariely if he has some creative way to change that behavior.

 

September 14, 2017

Asset Manager Reliance on Human Judgement vs Machine

Asset management is the industry most reliant on human judgement according to a recent Price Waterhouse study on Data Analytics.

 

Screen Shot 2017-09-14 at 10.14.59 AM

 

Asset managers rely on human judgement 3x more than the next industry. For an industry with some of the best and brightest, we seem to be far behind. There is no expectation that this will happen overnight, but at a bare minimum we need to be experimenting with ways to enhance our judgement with machines.

Alpha Theory has been doing just that for over 10 years and our clients have outperformed the average hedge fund by over 2x. Getting started is not hard. Adopting “machine” does not require a wholesale change as all of our clients operate with Man + Machine. What it does require is an acceptance that Man alone is generally inferior to Man + Machine and a cultural embrace of the “machine” as an enhancement to the daily judgements we all make.

The reliance on human judgement will fall over time for asset managers. Do not be the last the change.

 

 

August 18, 2017

Man Versus Model of Man: Lewis Goldberg

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

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

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

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

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

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

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

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

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

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

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

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

 

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

July 28, 2017

Crist on Value

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

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

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

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

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

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

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

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

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

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

June 01, 2017

The Value of Price Targets

Abstract

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

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

Screen Shot 2017-06-01 at 2.32.50 PM

Table 1

Data

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

Research

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

Screen Shot 2017-06-01 at 2.39.03 PM

Figure 1

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

Screen Shot 2017-06-01 at 2.39.38 PM

Figure 2

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

Screen Shot 2017-06-01 at 2.41.13 PM

Figure 3

Conclusion

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

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

May 15, 2017

Changing The Course Of Active Management — The Concentration Manifesto

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

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

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

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

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

 

DOWNLOAD FULL VERSION

 

 

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.