Size – The Limiting Factor

By Cliff Stanton and Jeremy Frank

April 2016

Product proliferation is a constant in the investment industry. At the time of this writing, there are 8,143 separate and distinct open-end mutual funds, 553 closed-end funds, and 1,870 exchange traded funds registered in the United States, according to Morningstar. In addition, there are thousands of separate accounts, hedge funds and private equity funds. John Bogle, Founder and Chairman of the Vanguard Group, put it best when he said, “For better or worse, the selection of mutual funds has become an art form. Indeed, it is fair to say that choosing a mutual fund has come to require the same assiduous analysis as selecting an individual common stock.”

At 361 Capital we can relate to that perspective, because as most of our clients know, our roots (now distant) are in the hedge fund-of-fund world. As such, we are acutely aware of the daunting task that confronts advisors selecting funds/strategies on behalf of their clients. With that in mind, from time to time we like to highlight due diligence concepts to aid advisors in their vetting process. In this month’s Alternatives Briefing, we bring attention to the need to measure and monitor capacity, and we provide some insight into how we approach that question as it relates to our funds.

As the alternative mutual fund space continues to evolve, clear winners are beginning to emerge, and with that, asset flows are increasingly more concentrated, going to fewer and fewer funds. Of course with greater flows come larger funds, and the question of a strategy’s capacity becomes ever more important. Capacity, roughly defined, is the asset level under which a money manager is able to add value, and if exceeded, is detrimental to future returns due to increasing costs. While explicit costs typically decline as assets increase, the cost of liquidity to fill ever-increasing position sizes and/or opportunity costs associated with failing to fill a position, increase with size. It is our belief that most of the strategies under the alternatives umbrella have significantly less capacity than do most of their traditional, long-only peers, and as such, this is an area that demands attention. To learn more about our approach to measuring capacity, please see the Education section below.

The Data

Equities rebounded strongly in March, with the S&P 500 gaining nearly 7% and the MSCI EAFE advancing by more than 6.5%. The strong performance of the S&P brought both the year-to-date and 12-month returns into the black, while international markets continued to post a loss over these time frames. Given the strong equity market performance, it is no surprise that long/short equity was the best performing alternative category during the month, though it still remains in negative territory on both a year-to-date and 12-month basis. Managed futures suffered during the month, losing more than 3%. While slightly positive year-to-date, the category’s performance over the last 12 months has been nearly -6%, as reversals in commodities, the dollar and momentum stocks all weighed on performance. While March was positive for most categories, the last 12 months have been difficult for most alternative strategies, with equity market neutral being the only category with a positive return.


Despite recent negative performance, managed futures has continued to garner assets. The category gained nearly $1 billion during March, bringing year-to-date net asset flows to more than $4.6 billion. Multialternative funds experienced outflows for the first time in quite a while, shedding more than $500 million during the month. This was mainly driven by a large outflow and subsequent shuttering of one of the category’s larger funds. Long/short equity outflows slowed, losing only $200 million. Several funds have begun to raise assets at a rather quick pace, while outflows have slowed for the funds that have suffered the most over the last 18 months.

Alternatives in the News

SEC Rule Regarding Use of Derivatives by Registered Investment Companies

The comment period for the SEC’s proposal on the use of derivatives came to an end during the month, and there were plenty of comments. AQR and Federated, among many others, both put together quality pieces touching on the clear pitfalls of the rule as proposed. Additionally, PIMCO’s chart explaining elementary risk dynamics was both amusing and informational. As we have stated before, there are really two main issues with the proposed rule. First, it will impact ordinary fixed income funds that use derivatives to manage duration, sometimes utilizing very low risk and therefore high notional value futures. The second issue, which AQR did a great job of summarizing and which we’ve commented on previously, relates to the fact that limiting notional values, irrespective of risk, forces strategies such as managed futures to concentrate in higher risk assets where they can get more potential return per unit of notional value. This will have the unintended consequence of increasing risk in these funds and lowering return expectations, as they now have a more limited universe. While many reputable firms posted thoughtful comments, it was somewhat entertaining (and a little scary) to go through comments from retail investors regarding leveraged ETFs. This article sums up the tone of most of the comments, as it seems many are going to get rich or die trying. But all things considered, after processing the comment letters, we assume there will be changes to the rules as proposed.

Morningstar Expands Alternative Fund Categories

Effective April 29, 2016, Morningstar will be expanding its alternative fund categories to include option writing and long/short credit. This is a much welcomed delineation of strategies that have been lumped into large, heterogeneous categories, making peer group comparisons difficult. Hopefully this trend will continue. DailyAlts

Three Things to Consider Before Hitting the Sell Button

Points that can’t be made too often. A must read post, as are all the links. Attain

Education

As mentioned in the intro, there is a limit to the amount of money that can be successfully managed within a given strategy. Sometimes this number may be in the $10s of billions and sometimes the most that can be efficiently managed may be $100 million or less. With such wide variance in capacity across strategies, it’s helpful to think about the process for deciphering the capacity of an investment strategy.

When we discuss capacity, what we are alluding to is the impact a manager is having when trading. If a manager is trying to buy $1 million of a stock that trades $10 million a day, the impact will be greater than if that same stock traded $100 million per day. In addition to the size of individual trades, the frequency of trading must be considered, even if the impact per trade is small, it can add up over multiple trades.

While it’s not surprising that the size of a trade is directly related to impact, quantifying the impact is complicated. We always begin with an assumption that we are having impact as we grow. However, in order to quantify this, data must be collected. That may sound easy, but in practice there are many variables that need to be taken into account, such as:

  1. 1. The time frame over which trading occurs
  2. 2. The type of trading algorithms employed
  3. 3. The percent of volume being traded
  4. 4. The randomness of market moves independent of our trading
  5. 5. The known market behaviors unaffected by our trading (e.g. markets tend to mean revert at
  6.     the end of day after large moves)

Once data has been collected, transaction cost models can be created that account for most of the above issues. Given it takes time to gather enough live trading data to draw statistically significant conclusions, it is a long-term process that is continually updated as more data is collected.

While going through the entire modeling process is beyond the scope of this discussion, we’ll dig into one of the issues outlined above – the randomness of market moves. A nagging question whenever doing impact analysis is whether we are moving the market or if the market is just moving? One simple methodology we utilize in an attempt to answer this question is to track a highly correlated reference index while we are trading, one that we know we are not impacting. Deviations in the paths of the reference index and the index we are trading may help us identify when and to what extent we are having an impact.

But to continue, once a realistic transaction cost forecasting model has been developed, the next question is what level of slippage is unavoidable? Once this amount has been identified, capacity per instrument can be backed into using the transaction cost models. These per instrument capacity estimates can be rolled up into an overall “capacity” number for the strategy. Additionally, once impact has been identified, it provides an attractive target for adding alpha, as any execution improvement is essentially zero variance alpha.

While this was just a cursory overview into the thought processes behind calculating the capacity of a strategy, we hope it demonstrates the multifaceted problem that asset managers face, and in doing so, provides investors with lines of questioning to pursue during the due diligence process.

And Lastly

Our favorite comment to the SEC regarding leveraged ETFs:

And along the same lines: