There is a PDF version of this post available for download here.

Summary­­

  • Long/short portfolios are helpful tools for quantifying the value-add of portfolio changes, especially for active strategies.
  • In the context of fees, we can isolate the implicit fee of the manager’s active decisions (active share) relative to a benchmark and ask ourselves whether we think that hurdle is attainable.
  • Bar-belling low fee beta with high active share, higher fee managers may actually be cheaper to incorporate than those managers found in the middle of the road.
  • However, as long as investors still review their portfolios on an itemized basis, this approach runs the risk of introducing greater behavioral foibles than a more moderated – yet ultimately more expensive – approach.

After a lecture on cosmology and the structure of the solar system, William James was accosted by a little old lady.

"Your theory that the sun is the centre of the solar system, and the earth is a ball which rotates around it has a very convincing ring to it, Mr. James, but it's wrong. I've got a better theory," said the little old lady.

"And what is that, madam?" Inquired James politely.

"That we live on a crust of earth which is on the back of a giant turtle,"

Not wishing to demolish this absurd little theory by bringing to bear the masses of scientific evidence he had at his command, James decided to gently dissuade his opponent by making her see some of the inadequacies of her position.

"If your theory is correct, madam," he asked, "what does this turtle stand on?"

"You're a very clever man, Mr. James, and that's a very good question," replied the little old lady, "but I have an answer to it. And it is this: The first turtle stands on the back of a second, far larger, turtle, who stands directly under him."

"But what does this second turtle stand on?" persisted James patiently.

To this the little old lady crowed triumphantly. "It's no use, Mr. James – it's turtles all the way down."

— J. R. Ross, Constraints on Variables in Syntax 1967

The Importance of Long/Short Portfolios

Anybody who has read our commentaries for some time has likely found that we have a strong preference for simple models.  Justin, for example, has a knack for turning just about everything into a conversation about coin flips and their associated probabilities.  I, on the other hand, tend to lean towards more hand-waving, philosophical arguments (e.g. The Frustrating Law of Active Management[1] or that every strategy is comprised of a systematic and an idiosyncratic component[2]).

While not necessarily 100% accurate, the power of simplifying mental models is that it allows us to explore concepts to their – sometimes absurd – logical conclusion.

One such model that we use frequently is that the difference between any two portfolios can be expressed as a dollar-neutral long/short portfolio.  For us, it’s long/short portfolios all the way down.

This may sound like philosophical gibberish, but let’s consider a simple example.

You currently hold Portfolio A, which is 100% invested in the S&P 500 Index.  You are thinking about taking that money and investing it entirely into Portfolio B, which is 100% invested in the Barclay’s U.S. Aggregate Bond Index.  How can you think through the implications of such a change?

One way of thinking through such changes is that recognizing that there is some transformation that takes us from Portfolio A to portfolio B, i.e. Portfolio A + X = Portfolio B.

We can simply solve for X by taking the difference between Portfolio B and Portfolio A.  In this case, that difference would be a portfolio that is 100% long the Barclay’s U.S. Aggregate Bond Index and 100% short the S&P 500 Index.

Thus, instead of saying, “we’re going to hold Portfolio B,” we can simply say, “we’re going to continue to hold Portfolio A, but now overlay this dollar-neutral long/short portfolio.”

This may seem like an unnecessary complication at first, until we realize that any differences between Portfolio A and B are entirely captured by X.  Focusing exclusively on the properties of X allows us to isolate and explore the impact of these changes on our portfolio and allows us to generalize to cases where we hold allocation to X that are different than 100%.

Re-Thinking Fees with Long/Short Portfolios

Perhaps most relevant, today, is the use of this framework in the context of fees.

To explore, let’s consider the topic in the form of an example.  The iShares S&P 500 Value ETF (IVE) costs 0.18%, while the iShares S&P 500 ETF (IVV) is offered at 0.04%.  Is it worth paying that extra 0.14%?

Or, put another way, does IVE stand a chance to make up the fee gap?

Using the long/short framework, one way of thinking about IVE is that IVE = IVV + X, where X is the long/short portfolio of active bets.

But are those active bets worth an extra 0.14%?

First, we have to ask, “how much of the 0.18% fee is actually going towards IVV and how much is going towards X?”  We can answer this by using a concept called active share, which explicitly measures how much of IVE is made up of IVV and how much it is made up of X.

Active share can be easily explained with an example.[3]  Consider having a portfolio that is 50% stocks and 50% bonds, and you want to transition it to a portfolio that is 60% stocks and 40% bonds.

In essence, your second portfolio is equal to your first plus a portfolio that is 10% long stocks and 10% short bonds.  Or, equivalently, we can think of the second portfolio as equal to the first plus a 10% position in a portfolio that is 100% long stocks and 100% short bonds.

Through this second lens, that 10% number is our active share.

Returning to our main example, IVE has a reported active share of 42% against the S&P 500[4].

Hence, we can say that IVE = 58% IVV + 42% X.  This also means that 0.157% of the 0.18% fee is associated with our active bets, X.  (We calculate this as 0.18% - 0.04% x 58%.)

If we take 0.157% and divide it by 42%, we get the implicit fee that we are paying for our active bets.  In this case, 0.373%.

So now we have to ask ourselves, “do we think that a long/short equity portfolio can return at least 0.373%?”  We might want to dive more into exactly what that long/short portfolio looks like (i.e. what are the actual active bets being made by IVE versus IVV), but it does not seem so outrageous.  It passes the sniff test.

What if IVE were actually 0.5% instead?  Now we would say that 0.477% of the 0.5% is going towards our 42% position in X.  And, therefore, the implicit amount we’re paying for X is actually 1.13%.

Am I confident that an equity long/short value portfolio can clear a hurdle of 1.13% with consistency?  Much less so.  Plus, the fee now eats a much more significant part of any active return generated.  E.g. If we think the alpha from the pure long/short portfolio is 3%, now 1/3rd of that is going towards fees.

With this framework in mind, it is no surprise active managers have historically struggled so greatly to beat their benchmarks.  Consider that according to Morningstar[5], the dollar-weighted average fee paid to passive indexes was 0.25% in 2000, whereas it was 1% for active funds.

If we assume a very generous 50% active share for those active funds, we can use the same math as before to find that we were, in essence, paying a 1.75% fee for the active bets.  That’s a high hurdle for anyone to overcome.

And the closet indexers?  Let’s be generous and assume they had an active share of 20% (which, candidly, is probably high if we’re calling them closet indexers).  This puts the implied fee at 4%!  No wonder they struggled…

Today, the dollar weighted average expense ratio for passive funds is 0.17% and for active funds, it’s 0.75%.  To have an implied active fee of less than 1%, active funds at that level will have to have an active share of at least 30%.[6]

Conclusion

As the ETF fee wars rage on, and the fees for standard benchmarks plummeting on a near-daily basis, the only way an active manager can continue to justify a high fee is with an exceptionally high active share.

We would argue that those managers caught in-between – with average fees and average active share – are those most at risk to be disintermediated.  Most investors would actually be better off by splitting the exposure into cheaper beta solutions and more expensive, high active share solutions.  Bar-belling low fee beta with high active share, higher fee managers may actually be cheaper to incorporate than those found the middle of the road.

The largest problem with this approach, in our minds, is behavioral.  High active share should mean high tracking error, which means significant year-to-year deviation from a benchmark.  So long as investors still review their portfolios on an itemized basis, this approach runs the risk of introducing greater behavioral foibles than a more moderated – yet ultimately more expensive – approach.

 


 

[1] https://blog.thinknewfound.com/2017/10/frustrating-law-active-management/

[2] https://twitter.com/choffstein/status/880207624540749824

[3] Perhaps it is “examples” all the way down.

[4] See https://tools.alphaarchitect.com

[5] https://corporate1.morningstar.com/ResearchLibrary/article/810041/us-fund-fee-study--average-fund-fees-paid-by-investors-continued-to-decline-in-2016/

[6] We are not saying that we need a high active share to predict outperformance (https://www.aqr.com/library/journal-articles/deactivating-active-share). Rather, a higher active share reduces the implicit fee we are paying for the active bets.

Corey is co-founder and Chief Investment Officer of Newfound Research, a quantitative asset manager offering a suite of separately managed accounts and mutual funds. At Newfound, Corey is responsible for portfolio management, investment research, strategy development, and communication of the firm's views to clients.

Prior to offering asset management services, Newfound licensed research from the quantitative investment models developed by Corey. At peak, this research helped steer the tactical allocation decisions for upwards of $10bn.

Corey is a frequent speaker on industry panels and contributes to ETF.com, ETF Trends, and Forbes.com’s Great Speculations blog. He was named a 2014 ETF All Star by ETF.com.

Corey holds a Master of Science in Computational Finance from Carnegie Mellon University and a Bachelor of Science in Computer Science, cum laude, from Cornell University.

You can connect with Corey on LinkedIn or Twitter.

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