We are accustomed to judging the value-added of investment funds in somewhat of a vacuum.  That is to say, we do so without regard to our own judgments about where markets will go in the future.   All that seems to really matter is the risk adjusted performance of a fund vs. its benchmark.  If that benchmark goes up by, say, 10%, we hope our managers beat it by, say, 3%.  If it goes down by 10%, we still hope to beat it by 3%.  Despite the “absolute return” moniker conferred upon hedge funds, they too aim to simply beat their appropriate index, whether it be the HFRI convert arb index in the short term or the S&P 500 over the long run.

But a paper released last month by Brian Jacobsen (of Wells Fargo Funds Management – although the views expressed are Jacobsen’s) proposes a different way to looking at the value of active management.  Instead of looking to traditional measures such as alphas and information ratios, Jacobsen looks at the up-capture and down-capture ratios of mutual funds to determine value-add.

He argues that investors buy mutual funds based on a given set of expectations about markets.  If the investor thinks markets are going to rise significantly, she might seek out a fund with an out-sized up-capture ratio.  Conversely, if she thinks markets will fall or remain flat, she might seek out a fund with a small down-capture ratio, regardless of the up-capture potential.  In other words, the relative importance of historical up-capture and down-capture ratios will change depending on the investor’s expectations.  As a result, writes Jacobsen,

“Depending on the investor’s expectations, a manager may be more or less valuable.   Active management is, thus, context and investor dependent.”

He examines the up-capture and down-capture ratios of nearly 800 US equity mutual funds with a benchmark of the S&P500.  Since no one knows the future direction of markets (or it would be priced in already), you’d expect investors to expect a 100% up-capture if they are expected to bear the burden of 100% down-capture (ignoring the effect of Prospect Theory for a moment).  In fact, such a security would be the S&P 500.

But what if a fund had a zero down-capture ratio.  In other words, what if the manager was really really good – like Madoff-good – at truncating their return distribution at zero and never losing money.  What kind of up-capture ratio would an investor want in that case?  Zero?  That wouldn’t be much of an investment.

Using some basic assumptions made by Jacobsen, investors should – in theory – demand roughly a 25% up-capture ratio even in the presence of no apparent downside (red dots below – what he calls the “fair value” of the mutual fund as an “option”).  But the 787 mutual funds he studied seem to have up-capture ratios that slightly exceed “fair value” (blue dots in chart below from paper).

By eschewing low downside for higher upside, investors are kind of saying “Security of low down-capture be damned!  I want to ride this market up!”  It’s as if greed trumps fear (could it be?)

This makes intuitive sense when you think about.  Why assume any risk unless the upside is bigger than the downside.

Jacobsen calls the amount by which funds deliver greater than fair value up-captures an “investor surplus” (similar to the “consumer surplus” in economics).  As of December 26, 2009, the average investor surplus, based on three year trailing data, was 0.026991 (i.e. the up-capture ratio delivered by mutual funds was, on average 2.7 percentage points higher than “fair value”).

The bottom line, according to Jacobsen, is one in which Wells Fargo – or any fund manager – may find solace:

“A manager can add value to a portfolio in ways besides just picking winning stocks—it is valuable to avoid holding a losing stock…Even if a manager does not beat a particular benchmark—which is a retrospective assessment—that does not mean that it was not—prospectively—valuable to invest with the manager.”

The voluntary nature of hedge fund reporting has made many commentators see hedge fund indices and, by extension, hedge fund industry performance, with a somewhat jaundiced eye.  Since hedge fund databases are designed as surreptitious marketing vehicles for hedge funds, the worst performing funds are likely to be absent from the indices.

But others counter that the best performing funds are also likely to abstain from voluntary reporting since they believe it might be viewed as kind of tacky by its rarefied clientele. More…

We review a lot of academic research here at AllAboutAlpha.com.  Some of it can be a little dense and we’re the first to admit that we often can’t follow the mathematical nuance or arcane calculus contained in some of it.  Still, we try to distill these papers down to a simple lesson or two that can be communicated in the 5 minutes or so that you have to read our daily posts.

But occasionally, we come across academic-style articles written by practitioners.  Not surprisingly, these tend to be a little more, well, practical.  Here’s a great example… More…

Like beauty and art, alpha is in the eyes of the beholder.  That’s because the interpretation of a return as being alpha depends entirely on the viewer’s perspective: their benchmark.  For example, if an investor sought a fund with a large cap US mandate, but her manager decided to invest solely in large cap US technology companies,  then her returns could contain a healthy portion of (negative or positive) alpha.  But if that same investor sought a large cap US technology fund, those very same returns could be described as nothing more than large cap US tech beta.

To a great extent, the measurement of alpha and beta is all about attributing investment success (or lack thereof) to either the investor or the manager.  And since alpha costs more than beta, the measurement of alpha and beta also determines the division of investment spoils between investor and manager.

Nowhere is this more critical than in the hedge fund industry, where alpha and beta are often so difficult to define.  As Raj Gupta, Hossein Kazemi and Edward Szado of the University of Massachusetts point out in a research study released last week: More…

The illiquidity of alternative investments often used to explain their risk-adjusted out performance (see previous coverage on this topic  here).  But what if some of that risk adjusted out performance was actually the result of so-called “return smoothing”?  Critics of hedge funds suggest that hedge fund managers have an incentive to mis-report returns in order to make themselves look good.   Are the critics right?  And if so, is the “illiquidity premium” really just a result of the mis-valuation of illiquid investments?

These are the questions tackled by a new paper written by Gavin Cassar of Wharton and Joseph Gerakos of the University of Chicago’s Booth School of Business.  Cassar and Gerakos use a database of hedge fund due diligence reports (the same one used in this study) to measure the serial correlation of hedge fund returns across funds with different valuation policies.

The study contains a table that you might find interesting if you ever have to conduct due diligence on a hedge fund.  Reproduced below, it shows the percentage of funds in each hedge fund strategy that use each of several different NAV calculation sources: More…

The number of true-tested, without-fail methods to tell which way stocks will go on any given trading day is as long as the Brooklyn Bridge.

From watching action in the post-trade overnight markets to measuring ticks in pre-trade market futures to gauging the depth, breadth and ‘vol’ of the Asian markets, there are many ways to skin that cat.

How successful they are is anyone’s guess (though certainly everyone would be rich if they were indeed successful). But a recent stab at measuring some U.S. lending spreads by some market watchers does appear to at least show whether or not the market is behaving rationally – and in turn provide a heads-up of sorts on when a particular sector is going to change course.

As the theory goes, by examining the S&P Securities Lending Spread Indices, which measure the spread between the Fed Funds Open Rate and the Rebate Rate, or cost of borrowing, equities in the U.S. markets, one can get a rough idea of whether a particular sector is about to take a dive, or vice versa. S&P launched these indices in September at a pretty granular level for U.S. stock sectors.

Without getting too technical, the premise is as follows: When one sees a positive spread, it means the Rebate Rate is below the Fed Funds Open Rate, and borrowers are paying lenders to borrow stock. In other words, the market is working accurately.

Conversely, when one sees a negative spread, it means that lenders are paying borrowers for those borrowers to keep their stock out on loan. This has been happening since the credit crisis last year, but it is old news. More…

Let’s say your active equity manager beats the index one month or one quarter.  Fluke?  Maybe so.  How about a few months in a row or a few quarters in a row?  The chance of them having horseshoes in their pockets becomes less likely as the number of months and quarters grows.  But how many months does it take for you to know if your manager’s alleged “alpha” is truly alpha (i.e. skill) and how much is dumb luck.  Furthermore, does this time horizon depend on the volatility of the fund itself?

These questions and others are addresses by a newly released paper by Sassan Zaker of Julius Bear.  Zaker describes investor’s willingness to accept (and reward) out performance of any kind as a “free put option” given to the manager.

Zaker is the co-author of some interesting papers that raise questions about the alpha credentials of many hedge funds (see related AllAboutAlpha.com post).  His recent solo work titled “Alpha Uncertainty Principle,” begins with a noteworthy description of alpha (our emphasis):

“Alpha is the metric, goal, and justification of the active asset management industry, yet there is little agreement on whether and how to integrate alpha in asset allocation. Despite multiple academic studies showing that, as a whole and after cost, the industry has little or negative alpha, as in Gruber [1996], alpha remains what investors demand and what the industry aspires to. Alpha divides the investment community into believers and skeptics. Its business significance stems from the fact that it is often equated with active management skill and as a differentiator justifies higher management fees.”

He goes on to argue that the uncertainty over what is real vs. perceived alpha can be thought of as the “implied cost of assuming alpha’s existence.”   In other words, if you have only a few data points and you believe the manager’s line that they represent true alpha, you are taking a risk that they may also represent simple luck.

Zaker writes that a fund’s information ratio determines the number of data points required before a manager can declare the existence of true alpha.  As intuition would suggest, the higher the information ratio, the fewer data points required before this declaration can be made (with 95% confidence) .

He also illustrates that higher volatility funds require more data points in order to determine if their returns are truly alpha (with 95% confidence).

The special challenge when measuring alpha in hedge funds, as Zaker points out, is that returns may be driven by any number of alternative beta factors – many of which might look like alpha.  His “Alpha Uncertainty Principle (AUP)” cautions against too many complex and “overlapping” sources of apparent alpha.  Since each alpha source may not actually be alpha, the more alleged alphas, the greater the chance that they are all an ex poste way of describing one thing: luck.

Drawing on the axiom first posited by 14-th century smart guy William of Ockham, he writes that this is akin to “Ockham’s Razor” – that, given two possible theories, “the one with the simplest explanation is to be preferred.”

He defines “Alpha’s Razor” in the following way:

“For any given level of alpha produced by different comparable active managers, the one with the least complexity should be regarded as the one with higher quality.”

Broadly speaking, the concept of the Alpha Uncertainty Principle  can be applied to several problems according to Zaker:

• Delineating alpha from beta: As a portfolio analysis technique, it can help identify true alpha.
• Setting fees: As a management technique, it can identify alpha-returns that are worthy of higher management or performance fees.
• Establishing monetary policy: Zaker even suggests that central banks’ controls of short term interest rates “unintentionally disrupt the supply-demand constellation for alpha, causing excessive alpha demand in the short term.”

While statistically-speaking, one prescient market call can look like alpha when a fund is regressed against its benchmark, intuition suggests that successive winning calls are required before an investor can begin to feel comfortable that the manager actually has any skill.

That intuition now has a new name: The Alpha Uncertainty Principle.

Although hedge funds in general have displayed an uncomfortably high equity beta over the past year, there have been two bright spots – strategies that follow-through on the promise of low market correlation (and therefore, assuming returns are positive, alpha).  Global Macro is one such category.  Market Neutral is the other.

When you think about it, this is somewhat counter-intuitive.  Market neutral funds tend to use more quantitative strategies and trade more often than their buy (/sell) and hold long/short cousins.  So you’d think that they rely heavily in market liquidity to get in and out of so many positions so quickly.

You might also think that market neutral funds would rely on funding liquidity to employ leverage.  When funding liquidity dries up, one might expect that market neutral funds would therefore suffer.

But it turns out that both of these assumptions may in fact be wrong.  A paper by Arjen Siegmann and Denitsa Stefanova of VU University Amsterdam seems to suggest that the more market neutral a fund, the less it is affected by liquidity shocks (market liquidity and funding liquidity).

Rather than relying on managers’ self-identify as “market neutral”, Siegmann and Deitsa divide the universe of equity hedge funds into 5 buckets based on their equity betas.  Then they regressed the monthly beta of the funds in these buckets against the TED spread (used as a proxy for funding liquidity) and against a measure of market liquidity called the “ILLIQ” measure (average daily price move over average daily volume).

It turned out that the higher the equity beta of the fund, the stronger the relationship of that funds’ beta to both of these liquidity measures.  Or put another way, the beta of market neutral funds actually seemed to be less dependent on market and funding liquidity – not more dependant (see charts below constructed with data from the paper). More…