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Alpha Theory Blog - News and Insights

51 posts categorized "Portfolio Optimization"

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.

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.

October 12, 2015

Do Price Targets Matter in Volatile Markets? (And, Why Alpha Theory Should Be a Starting Point Even in Turbulent Times)

This blog was co-authored with Alpha Theory's Customer Relations Manager, Dana Lambert.

    “Stock prices will continue to fluctuate – sometimes sharply – and the economy will have its ups and downs.  Over time, however, we believe it is highly probable that the sort of businesses we own will continue to increase in value at a satisfactory rate.” – Warren Buffett, famed investor

    “While many have portrayed the current environment as a highly risky time to invest, these individuals are likely confusing risk with volatility.  We believe risk should be determined based on the probability that an investor will incur a permanent loss of capital.  As market values have declined substantially, this risk has actually diminished rather than increased. “– Bill Ackman, Pershing Square 3Q08 Investor Letter

The recent market environment has proven challenging for many funds, including Alpha Theory clients. The market has been volatile, but the real challenge is directionality.  As of September 28, the S&P was down 11% over the prior 49 trading days, with 30 of the 49 days being down.  Alpha Theory clients generally benefit from pure volatility (large ups and downs without a direction) because they are buying on dips and selling on rises (mean-reversion).  The problem with a uniformly down directional market is that clients are continually getting indications to add to their longs and trim their shorts – the proverbial “catching the falling knife”.  Although Alpha Theory can not overcome persistent negative correlation between scenario estimates and outcomes – in other words inaccurate research – it does offer three options to help clients deal with these circumstances.

OPTION #1 - RAISE PREFERRED RETURN. When the price of an asset falls, its probability-weighted return (PWR) rises.  When the PWR rises, the normal action is to increase your position size.  But when all asset prices fall, all PWRs rise and thus the longs become more attractive and the shorts less so.  This suggested increase in long exposure may not be tenable and there may be a general skepticism regarding the price targets. In this situation, a manager can raise the preferred return for longs and thus raise the ‘hurdle rate’ required to be a full position in his or her fund (i.e., before you required only a 40% PWR to be a full position, but in this market environment you require 60%).  This will immediately lower long exposure and only suggest adding to the best ideas.  In the extreme example of February 2009, clients raised their hurdle rates to 70% or 80% and were able to see quickly numerous compelling ideas and how to shift capital appropriately.

OPTION #2 - RELATIVE INDEX ADJUSTMENT. As the market falls, the “market multiple” decreases – which has ripple effects through the price targets in Alpha Theory.  For those who cannot re-underwrite all of their targets for the new market paradigm, the application offers an easy-to-use feature called ‘Relative Index Adjustment’.  This basically adds back the move of the market to an asset’s expected return, and the following would be an illustrative example.  If the market is down 11%, then most assets’ prices will also be down and their suggested position sizes will increase.  Now let’s turn on the Relative Index Adjustment.  If every asset is down 11%, commensurate with the market move, then Alpha Theory will adjust the prices so that there is no change (-11% Stock Move minus -11% Market Move = 0% change) and thus no suggested change in position size.  The beauty of this system is that you can turn it on and off and the Market Move is calculated since the last price target update.  So if an analyst updates a price target, the Market Move gets set back to zero because the analyst would take into account the new “market multiple.”

OPTION #3 - REUNDERWRITE CONSERVATIVE PRICE TARGETS.  Fundamental investors recognize that there is no absolute intrinsic value for each asset because their assumptions are subjective.  There is, however, a range of assumptions that span from aggressive to conservative.  Down markets imply that pushing your assumptions to the conservative end of the spectrum may be appropriate.  After doing this, you can see which assets are still suggested buys and which are not.  The confidence imbued by using the most conservative assumptions allows you to be aggressive with add and trim decisions. 

A few views to help isolate where to start the re-underwriting process are: 

  • Performance view: shows those assets that have suffered the most on an absolute and relative basis
  • Group by Risk/Reward within 10%: groups the assets that are within 10% of Reward and 10% of Risk targets

  Do Px Targets Matter 1

                        Do Px Targets Matter 2

 

While consideration of the aforementioned steps certainly is appropriate as you develop conviction about downward directionality for the market, it is also worth noting that volatile markets can often be followed by periods of relative calm and distinct upwardly-biased directionality – and of course this has been the pattern for the past several years now.  So where in one week an analyst or PM sees a 1-year target as likely to be unachievable, the next week suddenly the expected return gap narrows considerably.  In short, just when you may be losing faith in your targets, they can quickly fall back into an attainable range.

Directional markets that move quickly are challenging for many reasons.  It is easy to throw up your hands and rationalize that “price targets don’t matter” or “our research is wrong”.  It is hard to restrain those emotions and redouble your efforts to find the value that has been exposed in the quick, volatile relocation of asset prices. To do so requires a rigorous, disciplined process that begins with retesting assumptions (i.e., raising return hurdles, adjusting for the market move, and setting more conservative targets).  If, after re-underwriting price targets and portfolio inputs, Alpha Theory is still recommending upsizings, then you can feel confident in your actions … even in a volatile, directional market.

August 24, 2015

Impact of Minimum and Preferred Return Settings on Optimal Position Size

We welcome our very own Customer Relations Manager, Dana Lambert, for this guest post:

During the on-boarding phase and in meetings with clients, we often like to point out how minimum and preferred return settings influence optimal position size (OPS), as well as the right framework to think about these settings.

As a starting point, Alpha Theory was designed to derive OPS using a set of linear relationships between OPS and risk-adjusted return (RAR) and – if used – liquidity, beta and other thresholds.  The way to think about this is straightforward enough, starting with the RAR bounds.  If the parameters are set to 0% and 10% for minimum and maximum position sizes, respectively, and 0% and 50% for minimum and preferred returns, then the following will be the case.

(1)    For a stock with a 50% or greater risk-adjusted return, all else equal, Alpha Theory will recommend a 10% position size.

(2)    For shares with a 0% return, all else equal, the application will suggest a 0% position size.

(3)    At a 25% return, the midpoint of the risk-adjusted return range, Alpha Theory will show a 5% position recommendation, the midpoint of the position size range.

Following is a graphical depiction of the linear relationship with OPS on the y axis and RAR on the x axis:

The ‘all else equal’ implies that no other portfolio-level constraints or Checklist items are in use.  In short, Alpha Theory employs a linear scale to determine the optimal position weighting as well as for liquidity thresholds, beta, and confidence. 

(For example, assuming the same minimum and maximum position size thresholds, and $10m and $20m average daily volume bounds for minimum and preferred liquidity: any stock with below $10m in liquidity will see a 0% position recommendation, a $20m or greater liquidity name will see no cut to OPS from the liquidity parameters, and a stock with $15m in ADV will see a 5% position suggestion – assuming the 50% or greater return threshold is already met.  Further, stocks with a beta of 2.0 will see half the optimal position size recommendation versus stocks with a beta of 1.0 while betas of 0.5 will result in a doubling of OPS.  Finally, for each 10% degradation in confidence, OPS will also fall 10%.)

What this implies is that by establishing a range for both RAR and position size, we’re establishing the degree of sensitivity for this linear relationship, or the slope of the line in our diagram above.  In the example above, each 10% increment in RAR leads to a 2% jump in OPS.  Of course, the next logical question is how one goes about determining minimum and preferred RAR, which involves more consideration for most versus setting the minimum and maximum position size (which most portfolio managers have long ago determined without substantial deliberation).

In setting the minimum RAR, we consider it best practice to use the 0% bound.  The reason is simple, in that as long as there is some positive return available in a stock, we do not want Alpha Theory to suggest a zero weighting in that name.  The reality is that as expected return approaches zero (say, 3% then 2% then 1% return left), the OPS will be suggesting some nominal position anyway, so risk will be minimized.

In establishing the preferred RAR level, one place to begin is to look across all of one’s stock-by-stock expected returns and note what the average or even just predominant trend is in RAR levels.  If one’s entire book shows itself to offer no single stock with greater than say, a 20% probability-weighted return, then a 50% preferred return threshold is almost certainly too high.  Another way to think about preferred return is to determine the 95% confidence interval of the RAR range.  Find a preferred return that is close to but not ultimately the highest RAR in the book.

On the other hand, too low a preferred RAR may indeed make position size recommendations too sensitive to changes in expected return.  So if one has the original 0% and 10% position limits from our first example, but RAR bounds are changed to 0% and 20% (rather than 0% and 50%), then only a 4% change in expected return will be required to change the position recommendation by 2%, a much higher ratio than the original 10% RAR change needed (2%/10% or 1:5 in the original example versus 2%/4% in the current example or 1:2).  Clearly a 4% change in expected returns occurs much more frequently and easily than a 10% change, so the swing in OPS will be much more pronounced when a lower return delta exists.  For this reason, we find ourselves recommending to many clients who have too low a preferred return threshold – and as a result who are seeing major and/or unsettling OPS changes – that they widen their RAR range, and in effect lessen the optimal position size sensitivity.

We always want to allow for personal preference and one’s own intuition to dictate the ‘business rules’ in Alpha Theory, and indeed there is not a ‘perfect’ level for most portfolio constraints.  But we also want users to understand fully the implications of their choices, especially for critical fund-level parameters that influence the OPS framework.  The relationship between RAR and position size is the starting point and key construct for the optimal position size calculation, and this post should serve to highlight how to consider one’s choices for these thresholds.

May 26, 2015

Eight of the 50 Top Performing Hedge Funds are Alpha Theory Clients

This is a continuation/republish of our March blog, “20% of Top 15 Hedge Funds are Alpha Theory Clients” where we highlighted that three of the Top 15 Hedge Funds over the past three years are Alpha Theory clients.

Our friends at Novus, who put out great research, released their first quarterly analysis of hedge fund performance. In their Q1 2015 report, eight Alpha Theory clients are in the Top 50 hedge funds. Based on the size of our client base, we would have expected less than one* Alpha Theory client to be on the list but our clients actually make up 16% of the Top 50 (8/50). While we recognize that one quarter does not a trend make, we have seen empirical and anecdotal evidence of our clients’ outperformance due to their focus on value, process, discipline, and unemotional decision making.  

There are two benefits reinforced by Alpha Theory which may explain why our clients are so prevalent on this list. The first is better position sizing. Performance results are a function of Stock Selection + Position Sizing. Position Sizing is often an instinctual assessment (“best guess”) of amalgamated information processed in a portfolio manager’s head. Making it even more complicated, pricing and information is constantly in flux.  Genius or not, the task of sizing positions through mental (instinctual) calculation is subject to error and bias. Position Sizing is less than optimal for funds using instinct based decision processes because their Transfer Coefficient (the correlation between the fund’s assessed idea quality and the position size) is much less than 1. For Alpha Theory clients, the Transfer Coefficient is much closer to 1 which maximizes the alpha tied to position sizes.

The second reason why Alpha Theory customers are outperforming their peers is process. Each of our customers develop a custom process that automatically highlights when positions are mis-sized, forces conversations when price objectives are breached, or creates a framework for discussing ideas based on their probabilistic outcomes. It is difficult to create discipline without process and our clients are able to do both and outperform because of it.

We’ve measured the performance improvements that come with better position sizing and they’re real. We’ve seen it from real-world analysis of our clients where performance improvements averaged over 7%. We’ve run Monte Carlo simulations which suggest that tightly coupling idea quality and position size can add 2-6% of additional alpha (this even assumes that price targets and probabilities can be off by up to 50%).

Everyone recognizes that position sizing is important and a growing number of hedge funds are starting to do something about it. Imagine two hypothetical hedge funds that have both performed the exact same research. The firm that has a better process to translate that research into a portfolio is going to win every time (assuming the research is reasonably accurate). Process and discipline will be the hallmark of many of the future hedge fund superstars. If you don’t have a structured process now, it is important to start soon. The move towards a more logic-driven decision process has already begun and the winners are starting to show up in the data.

* Assuming that there are approximately 10,000 hedge funds worldwide according to HFR.com.

March 25, 2015

20% of the Top Performing Hedge Funds are Alpha Theory Clients

A recent Wall Street Journal article measured the three year performance of hedge funds and three of the top fifteen are clients of Alpha Theory. That means that 20% of the top 15 performing hedge funds are Alpha Theory clients. There are approximately 10,000 hedge funds worldwide (according to HFR.com) so approximately 5% of our clients are in the top 15 versus the expected percentage of less than 1%. Two of our clients are in the top 10, or 3% versus the <1% expected percentage.

There are two benefits reinforced by Alpha Theory which may explain why our clients are so prevalent on this list. The first is better position sizing. Performance results are a function of Stock Selection + Position Sizing. Position Sizing is often an instinctual assessment (“best guess”) of amalgamated information processed in a portfolio manager’s head. Making it even more complicated, pricing and information is constantly in flux.  Genius or not, the task of sizing positions through mental (instinctual) calculation is subject to error and bias. Position Sizing is less than optimal for funds using instinct based decision processes because their Transfer Coefficient (the correlation between the fund’s assessed idea quality and the position size) is much less than 1. For Alpha Theory clients, the Transfer Coefficient is much closer to 1 which maximizes the alpha tied to position sizes.

The second reason why Alpha Theory customers are outperforming their peers is process. Each of our customers develop a custom process that automatically highlights when positions are mis-sized, forces conversations when price objectives are breached, or creates a framework for discussing ideas based on their probabilistic outcomes. It is difficult to create discipline without process and our clients are able to do both and outperform because of it.

We’ve measured the performance improvements that come with better position sizing and they’re real. We’ve seen it from real-world analysis of our clients where performance improvements averaged over 7%. We’ve run monte-carlo simulations which suggest that tightly coupling idea quality and position size can add 2-6% of additional alpha (this even assumes that price targets and probabilities can be off by up to 50%).

Everyone recognizes that position sizing is important and a growing number of hedge funds are starting to do something about it. Imagine two hypothetical hedge funds that have both performed the exact same research. The firm that has a better process to translate that research into a portfolio is going to win every time (assuming the research is reasonably accurate). Process and discipline will be the hallmark of many of the future hedge fund superstars. If you don’t have a structured process now, it is important to start soon, as the move towards a more logic-driven decision process has already begun and the winners are starting to show up in the data.

February 27, 2015

Capitalizing on the Random Walk (Part 2)

I wrote a blog post 3 ½ years ago about the topic of trading around positions. See part #1 of Capitalizing on the Random Walk

 

        “Our trading models tend to buy stocks that are recently out of favor and sell those recently in favor. Thus, to some extent, our actions have the effect of dampening extreme moves in either direction, and, as a result, reducing volatility in those stocks.” - James Simons, Legendary Investor of Renaissance Technology

        “I made my money by selling too soon.” – Bernard Baruch, Legendary Businessman

        When asked how he had become so rich?  He replied, “I sold too early.” - JP Morgan, Famous Financier

 

A smart client of ours asked the question, “how often should we trade to maximize the benefit of trading around positions?” In an ideal world, you would buy at the nadir and sell at the apex of any straight-line price increase.

    Example of a stock that trades from $40 up to $50 down to $30 then back to $40. The net profit for not trading is 0%. The maximum profit is a trading gap that times a sell at the apex of the trading range ($50). The fund is assumed to have a maximum position size of 10% and the starting position size at $40 is 6.6%.

                       

What this example illustrates is that if the price goes down (time is irrelevant because this would apply for 1 tick, 1 day, 3 days, etc.) you would want your position to be at 0% and when it rises, you would want to be at a full position. Clearly that is not realistic, but to understand the mechanics of the system it is important to understand the extremes. The counter-extreme is to not trade at all and return 0% which is the worst outcome in a mean-reversion trading pattern. So somewhere in between lies the hybrid of ideal and practical. The exact point is different for different managers, but I would say that you should set Trade Triggers (colored highlight rules if you are an Alpha Theory user) that alert you when gaps are greater than 1% or 1.5% or 2%, whatever allows for maximum profit capture per unit of acceptable inefficiency. Basically, you need to create a heuristic like “we trade when assets are 1% away from optimal and the difference is at least 50%.” Here’s an example:

Alert if:

1)      OPS = 0%

2)      If %FromOptimal > 1% and Max(%fromOptimal/CPS, %fromOptimal/OPS) > 50%

 

ASSET #1:

% from Optimal = 2%

CPS = 1%

OPS = 3%

Max(2%/1%,3%/2%)=100%. This asset would be highlighted.

 

ASSET #2:

% from Optimal = 3%

CPS = 7%

OPS = 10%

Max(3%/7%,3%/10%)=43%. This asset would NOT be highlighted.

 

It is important to remember that while this method is sub-optimal if the stock ever trades above the selling price it is vastly superior to No Trades.

Finally, for those with tax considerations there is a different constraint. Basically, I think it ends up being a different heuristic. Let’s say we come up with a 6/1 rule of thumb. If you’re 6 months away you’ll trade 1% differences, 5 months = 2%, 4 months = 3%, 3 months = 4%, 2 months = 5%, and 1 month = 6%. I’m not sure that is perfect, but there is DEFINITELY a huge value in waiting for Long Term treatment if the fund is tax sensitive.