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Alpha Theory Blog - News and Insights

48 posts categorized "Portfolio Optimization"

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.

 

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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

 

 

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

September 12, 2016

The Day the Music Died

For the past few years, I have had a significant edge in my fantasy football leagues because of my pre-draft preparation.  Each year, I would go online to do my preparation before the draft and pull data from multiple sources and look around for novel analytical approaches to player selection which I could combine with the data I was able to source.  Unfortunately, it looks like the years of my fantasy football spreadsheet’s domination has come to an end. I knew this day would come. Each year the data and analysis I was running sourcing seemed to get better and better. This year, I found 95% of my bespoke spreadsheet already prepared by someone else online at: http://apps.fantasyfootballanalytics.net/.  Undoubtedly, several of my competitors found it too.

Key attributes of my fantasy football sheet:

1. Improvement over Average (also called Value over Replacement) – this is by far the most important attribute. It basically states how much better any particular player is to the average player that will be drafted at the same position. Without IOA, the players with the highest potential points will always pop to the top, which is inaccurate because you can’t fill a team with just one position. For example, a QB may be forecast to score 350 points over the year and a WR is forecast to score 325. On that basis, the QB looks like the favorite. But the average drafted QB will score 280 and the average drafted WR will score 180 points. That means that the WR will add 145 points (325-180) against other teams vs the QB which will only add 70 points (350-280).

2. Multiple Sources – I pull projections from multiple sites to create a “wisdom of crowds” improvement.

3. Forecast Risk – measure standard deviation of projections from multiple sites to see how likely a player was to hit the average I was using in my model.

4. Average Draft Position – pulled Average Draft Position from multiple sources to compare my ranking to what was likely to happen.

5. Over/Undervalued to Average Draft Position – this allowed me to see if a player that my sheet really liked, could be selected in a later round because folks on average were drafting him later.

6. Dropoff – used to quickly see how big the difference is between the player and the next best player at the same position.

1

Now let’s compare to FFAnalytics.net:

2

The only thing my sheet has is a Strength of Schedule adjustment (#7) and I can just add that in after I download the data.

My reasons for writing this blog are two-fold. One, I want everyone to feel sorry for me that my edge is gone. Two, we all should recognize that with each passing year, data becomes easier to access and manipulate. If there are things in your business you consider your “edge” (competitive advantages), do a careful assessment of the landscape and forecast the probability that it can be undermined by data ubiquity, data analytics tools, or machine learning (artificial intelligence). Old businesses are being disintermediated in a matter of a few years by the likes of Google, Alibaba, Amazon, Uber, AirBNB, etc. Asset managers would be well served to take a hard look at what parts of their spreadsheets/processes and analytics are no longer novel and which are likely to persist and contribute as their true “edge”.    Without an edge in your processes or analytics, there is a limited likelihood that they will see an “edge” in their returns. 

September 01, 2016

THE CROWDING CONUNDRUM

Crowding has received quite a bit of attention lately due to the big drawdown in crowded names starting in the summer of last year. Because of that drawdown, we’ve seen clients increasingly include crowdedness as a negative risk factor when sizing positions. But how dangerous is crowding?

Proof #1 – Crowdedness (Alpha Theory)

At the behest of Benn Dunn, the head of Alpha Theory’s Investment Process Advisory Practice, we set out to quantify the impact of crowded names in client portfolios1. Here’s the shocking news: they helped. In fact, crowded longs grew 50% faster than the overall S&P 500 since 2011 and longs and shorts outperformed on an alpha basis.

Screen Shot 2016-09-01 at 2.21.31 PM

Our clients are among the best and brightest in the hedge fund space, so we were pleased to see there is a positive correlation between return and position size. In this case, crowding is the best form of “wisdom of crowds”. See below that Crowded Longs returned 154% since 2011 while the S&P 500 was up 85%.

Picture1

Proof #2 – Crowdedness (Novus)

Other sources show similar results. Below is a graph and table from “The Novus “4C” Indices” which shows a dramatic outperformance of Crowded names for funds2

Picture2

CROWDING – THE BULL CASE by Cameron Hight

Crowding outperformance can be interpreted as stock picking skill in its broadest form. If crowdedness is a measure of forecasted idea quality, then our clients did a great job of picking the winners. It suggests that active managers are good at picking a few names they truly believe in. Said another way, they show both selection and sizing skill.

We recognize that our clients are generally superior performers in both stock selection and position sizing. Alpha Theory clients are up 54% since 2012 versus 25% for the HFRI Equity Hedge Index. That being said, they have underperformed the S&P 500’s return of 85%. I believe that the best managers would be able to consistently outperform if they would concentrate on their best ideas.

The problem is that most managers retard returns by building large portfolios that dilute the skill of their best ideas with the luck of the rest. In fact, by distracting themselves with a portfolio of lower quality ideas, they not only drag down performance, they reduce the amount of mental capital they can allocate to their high quality ideas.

CROWDING – THE BEAR CASE by Benn Dunn

While “Hedge Fund Crowding” has often had negative connotations in the popular press, it has also historically been a significant source of alpha for hedge funds piling into the same names.  One explanation is pretty straightforward. When exceptional buyside analysts do deep qualitatively and quantitatively differentiated work, their views get disseminated to the broader hedge fund community through idea dinners, conferences, casual conversations, and quarterly letters. The idea spreads and those names begin to outperform as additional hedge funds crowd into the names and investment theses are borne out. Another reason for outperformance is hedge fund inflows. We saw the impact of inflows into equity hedge funds through the middle of last year.

The flip side of crowding is the narrowness of the exit when everyone heads for the door at the same time.  We saw the first hints of this in certain segments of the high beta growth universe in March/April of 2014 and a bit of replay when the Peoples Bank of China took their first steps towards a currency float last August.  The first quarter of 2016 makes these more recent episodes look trivial by comparison. 

It is now widely acknowledged through 13F filings that significant equity gross exposure came out of hedge funds in Q1 with the most widely held names suffering disproportionate drawdowns relative to what traditional downside risk measures would predict.  It would seem that the tailwind of positive inflows into the equity hedge fund universe is at best abating if not reversing altogether which would indicate that crowding will be a less significant source of alpha in the near future.  Going forward, one of the most important questions an analyst or PM needs to ask when adding a position to the portfolio is “who is the marginal buyer of this stock?”  If the answer is “another hedge fund” then the hurdle rate for getting that name in to the portfolio needs to be much higher.

Conclusion

Is it the great idea alone or the crowding into it that causes outperformance? It is almost certainly a bit of both. In fact, without each other to positively reinforce the other, the outperformance would typically not materialize. However it happens, the positive impacts of crowding and conviction are real and hopefully this article provides some positive counterpoints for a factor that has been largely maligned.

1 The methodology consisted of constructing three mock portfolios, Long (top 10 most crowded longs), Short (top 10 most crowded shorts), Total (top 10 longs and top 10 shorts). The positions were equal weighted. The portfolio was rebalanced once a month at the beginning of the month and represents the time period 02/01/2011 to 08/01/2016. Each portfolio was simulated as a 100% gross exposure portfolio.

2 Crowdedness Index is comprised of the most crowded stocks in hedge funds. A crowding score is assigned to each security in our database and serves as the ranking factor for the Index. The score is based on the number of managers invested in the stock and the liquidity of the hedge funds in each security. The two factors are components in the crowded score. In other words, these stocks are a blend of “illiquid” and “popular." Since they are rather illiquid, when managers try to sell they can drive the price down considerably in a short period of time. But overall, the Index is not a great short candidate since it outperforms the market by a wide margin over the long term.

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.

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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.