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Alpha Theory Blog - News and Insights

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.