The infinite monkey theorem states that a monkey hitting keys at random on a typewriter keyboard for an infinite amount of time will eventually bang out some sort of recognizable text, such as the complete works of William Shakespeare.

Along the same lines, one might argue that Exchange-Traded Funds, or ETFs, are in alternatives’ form at least a version of the monkey-at-typewriter theorem, by virtue that a synthetic composition of securities combined into something that is purportedly a hedge fund can mimic, if not the best, but the same strategy and returns as a hedge fund manager — eventually.

All this monkey business theory stems from Rye Brook, New York-based IndexIQ’s release of the IQ ARB Merger Arbitrage ETF (ticker: MNA), which began trading this past Tuesday.

How it works: the ETF relies on a fixed rule book – the main requirement being that an acquirer must have offered a premium to a target company’s market price.

“To date, investors have not had broad access to capitalize on mergers and acquisitions activity in an ETF,” Adam Patti, IndexIQ’s CEO, said in a statement. “The Merger Arbitrage ETF is a hedged strategy designed to take advantage of price disparities that exist in merger activity and strengthen investor portfolios by buying below the target price and realizing the capital appreciation if the deal closes at or above the target price.”

In other words, rather than hiring experts at exorbitant prices to sift through deals and choose which to bet on, and paying a hefty fee in the process, one can buy an ETF of essentially the same thing, or so the theory goes.

So are ETFs about to replace hedge funds? Not exactly.

For starters, the M&A ETF hasn’t been around long enough to go through some unexpected turbulence – like when a deal collapses or when circumstances lead to a revaluing of a bid. This chart (below) is actually a pro-forma visual of the ETF, since it was only launched in the past week.

IndexIQ’s other hedge fund ETF, the Macro Tracker ETF, has weathered a bit more — four months to be exact — and isn’t hitting out of the ballpark.

What’s more, the ETF doesn’t have the same nimbleness of being able to slip in and out of positions that a hedge fund does; an ETF usually takes new positions just once a month, meaning it could miss the juiciest part of a trade.

On the flip side, ETFs are liquid and cheap, allowing investors to move money in and out as simply as transferring money into or out of a bank account, something no limited partnership hedge fund can lay claim to.

Of course, one could also argue that you get what you pay for. ETFs like IndexIQ’s rely on public data on hedge funds to build a portfolio of other ETFs that approximates a basket of hedge-fund strategies – a far cry from building a repertoire of deals through connections and know-how and then strategically betting on their intrinsic value and direction over time.

A monkey and a typewriter. With 1000 U.S. ETFs and counting, perhaps one day soon there will be a transparent nicely-packaged ETF that will replace real hedge funds.

The caveat: the strategies, whatever they may be, had better not be too complicated. Enough for a monkey to handle — eventually.

Ah, the good old days – when the hedge fund “secret sauce” was revealed and suppliers set about developing the products that would bring it to investors the world over under the moniker “hedge fund cloning”.  But as Swiss researchers Erik Wallerstein, Nils Tuchschmid and Sassan Zakerc note in a recent paper called “How do hedge fund clones manage the real world?”:

“Some years ago hedge fund replication was a much discussed topic on the hedge fund horizon. A credit crunch and some hedge fund Ponzi schemes later, the attention has turned elsewhere.  2008 performance of broad hedge fund indices where dismal at best. This did not bode well for selling pitches to persuade investors to turn to funds which replicate this performance.”

However, the trio goes on to argue that the $2 billion hedge fund replication business is far from dead.  Hedge fund replicas, they say, “…have several unique and interesting features, many which where attractive during the crises of 2008.”

They analyze the recent performance of 21 hedge fund clones from 17 companies covering the full spectrum of replication techniques from factor replication to distributional replication to mechanical replication.  (see the “Alternative Beta and Hedge Fund Replication” category at the right side of this page for extensive coverage of these topics).

Here’s how the 21 fund stack up from March 2008 to May 2009 (chart based on data in paper)…

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By: Ranjan Bhaduri, CAIA, AllAboutAlpha.com Editorial Board (with Hannah Wendling, AlphaMetrix)

The 2005 hurricane season stands out as one of the most brutal in recent memory in the United States.  Many will remember it as the only year in recent memory where we ran out of names (one for each letter of the alphabet) and had to dip into the Greek letters (Hurricanes “Alpha” “Beta” “Gamma” etc.)

That season caused an estimated $124 billion in damage in the US, $45.7 billion of which was insured. Hurricane Katrina alone saw the loss of many lives as well as $100 billion in damage, $34 billion of which was insured. That hurricane season led to customer claims far beyond the capacity of the U.S. insurance industry. Four years later, the insurance industry is still starved for capital and leading insurers in states like Florida are phasing out their policies altogether. With this year’s hurricane season getting under way, insurance rates are rising with no end in sight over the next five years.

Hurricanes now reaching Chicago

One possible solution to this growing insurance crisis may lie in derivatives. The Chicago Mercantile Exchange (CME) has developed a way to offset hurricane-related risk by transferring risk to the capital markets: As of last winter, it is now possible to trade contracts for hurricane futures and options. Having these contracts on a world-class exchange like the CME makes for a viable solution. The market participants include insurance and reinsurance firms, CTAs, hedge funds, energy companies, state governments, and utility companies.

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E.M.H. R.I.P. W.T.F?

In an ironic twist, the Economist suggests that financial engineering (and by extension the hedge funds built with its outputs) are built on the shoulders of the efficient market hypothesis.  Yet the much ballyhooed demise of the EMH should, in theory, pave the way for skill-based, alpha-centric returns.  In fact, the newspaper even makes the case for hedge funds in this weeks edition:

“…In 1980 Sanford Grossman and Joseph Stiglitz, another subsequent winner of a Nobel prize, pointed out a paradox. If prices reflect all information, then there is no gain from going to the trouble of gathering it, so no one will. A little inefficiency is necessary to give informed investors an incentive to drive prices towards efficiency…”

Confirmation that fund of funds investment is a trailing indicator

Hedge Funds Review reports on S&P’s latest fund of funds asset flow data, observing that “…Investors quick to respond to disappointing returns have been much slower to react to improving performance.”

Is new 130/30 ETF active or passive?

While it’s originators, Andrew Lo and Pankaj Patel call it a “passive” strategy, the model upon which the new Proshares 130/30 ETF is built looks a lot different that a typical passive index.   Says Lo: “The quantitative model behind our 130/30 Large-Cap index is based on extensive, robust research on the real-world factors contributing to stock performance.”

(btw,did you know Lo’s first science project was to make a battery out of a lemon.  Amazing but true.)

When is a rebound not actually a rebound?

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Ponzi. Not since the term “hedge fund” was first used has there been such disagreement over financial terminology.  Suddenly, Ponzi schemes are all over the news.  Forbes’ noted last week in a story called “Everything is Coming up Ponzi” that “authorities are bagging mini-Madoffs left and right.“  Last Thursday, the US government took action against two more “Ponzi schemes” (neither starting with an “M”).  One was allegedly perpetrated by an 82 year old man against elderly members of the Catholic community.   And the other was allegedly orchestrated by a Philadelphia man who convinced investors to part ways with $50 million.

So are these two new Ponzi schemes really Ponzi schemes or is the SEC frantically trying to make up for previous lapses to show that they are back on top of things?

“Little Ponzi’s”

If Time Magazine’s Ari Officer is right then finding Ponzi schemes will be like shooting fish in a barrel for the SEC.  In fact, Officer writes that there is actually a Ponzi scheme in every hedge fund.  The reason is that like all investment funds, hedge funds routinely have a lot of unrealized gains and losses.  The extra challenge faced by hedge funds, however, is that the unrealized gains and losses are locked up in less liquid assets.

As a result, writes Officer, hedge funds sometimes need to rely on new investments to pay out old investors.  After all, why would a fund liquidate a position – particularly one that the manager feels is trading at a discount – when the fund already has a whack of new cash from subscriptions.

In fact, a fund doesn’t even have to do anything wrong to fall under Officer’s broad definition of Ponzi scheme:

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While the Edhec Risk and Asset Management Research Centre is bigger, smarter and better–connected than AllAboutAlpha.com, both organizations share the same genetic blueprint. Edhec’s Lionel Martellini confirmed this fact this morning in his introduction to day two of the university-affiliated organization’s annual hedge fund conference in London. Edhec sees alternative investments as a (the) central issue in institutional portfolio management and believes that we need stronger links between research and practice.

Some would say that this overstates the importance of emerging asset classes. But Martellini points to two unique aspects of alternative investments that are fundamentally unique: non-normality and data integrity. The introduction of higher moments such as skew and kurtosis le to an explosion of data since correlation was now joined by co-skew and co-kurtosis. These co-moments add exponentially to the amount of information required for portfolio construction. To compound things, monthly data that is susceptible to “smoothing” makes alternative investment research a whole new ball game for academics and practitioners.

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“One of the great things about hedge funds is that they have provided a field day for academic researchers to write scholarly articles on their risks and returns. Yet, for all of this scholarship, a practical roadmap to hedge funds has remained elusive. Until now.”

So begins a new 168 page treatise aimed at dispelling the “myths” surrounding hedge funds and educating investors.  The document, called “Road Map to Hedge Funds” was released this morning by the Alternative Investment Management Association (AIMA) and is squarely aimed at an institutional audience (the report is also bylined by The CAIA Association, UBS, and CalPERS).  Co-authors Alexander Ineichen of UBS (see related posts & interview) and Kurt Silberstein of CalPERS cover just about every question an institution might have about this poorly understood category of investments.  In his foreword to the document, alternative investment pioneer Mark Anson describes it as a:

“…pragmatic, user-friendly book that will go a long way to breaking down the myths of hedge funds while providing the user the ability to construct an intelligent hedge fund portfolio. Along the way, the psychobabble of the hedge fund world is eschewed to provide a commonsense guide in commonsense language that all can understand.”

Regular AllAboutAlpha.com readers will recognize a lot of the thought leaders quoted in the “book”: Larry Summers and Sandy Grossman from the recent “unnamed event” in Boston (see related posts), Peter Bernstein author of Capital Ideas Evolving (see AAA review), Nassim Taleb (see related posts), Lars Jaeger (see related posts), Andrew Lo (see related posts), Larry Siegel (see related post), and of course, Ineichen himself, who is also the author of Asymmetric Returns.

But beyond the usual alternative investment crowd, the document makes liberal reference to names more commonly associated with “traditional” thinking: Benjamin Graham, Winston Churchill, Mark Twain, Albert Einstein and Warren Buffett (yes, even the man who says hedge funds can’t even beat the S&P 500 over the next 10 years – see related post).

If you are really into the topics covered on this website, you may find the document to be a little basic (it’s designed for an “intermediate” audience).  After all, we eat Anson’s “psychobabble” for breakfast.  But the document does a great job of bringing together all of the salient topics in one easy-to-read form.

It’s not all hedge fund cheer leading either.  For example, page 14 makes reference to the fact that hedge fund returns have been lower this decade than they were in the 80’s and 90’s and page 16 shows how wildly disparate industry size estimates have become.

But the most interesting part of the document is its myth-busting.  For example, for those out there who think hedge funds are taking over the world, check out this chart from the report…

See the two tiny bars on the right?  The entire hedge fund industry is smaller than the assets under management of a single firm (likely UBS we’re guessing).  This, despite what some have described as tsunami of institutional assets flowing into hedge funds over recent years.   According to data cited in the report (below), the tsunami wouldn’t even show up on the chart above.

As relatively active investors, hedge funds may indeed have a disproportionate influence in smaller, less liquid corners of the capital markets.  But this report clearly dispels the notion that they dominate capital markets in general.

Other myths addressed in the report include:

“Myth: Hedge funds gamble”

“…We do not think that there is an award for the most hilarious remark about hedge funds. If there were such an award, a strong contender for first prize would be the institutional investor who was quoted saying: “No, we don’t [currently invest in hedge funds]! It is completely obvious that hedge funds don’t work. We are not a casino.” The irony, of course, is that it is the long-only investor who depends most on luck and not the diversified hedge fund investor…”

“Myth: Hedge funds always hedge”

“…Returns are a function of taking risk. Hedge funds do not hedge all risk. If all risks were
hedged, there would be no return. The difference between hedge funds and long-only
managers is that hedge funds hedge certain risk while consciously being exposed to risk where
they expect a reward from bearing the risk…”

“Myth: Hedge funds are risky”

“Hedge funds, examined in isolation, are risky…To most investors, it is regarded as unwise not to diversify idiosyncratic risk. It should be similarly unwise not to diversify risk to a single hedge fund. Note that many critics of hedge funds do not distinguish between systematic and nonsystematic risk when demonising hedge funds…”

“Myth: Hedge funds are speculative”

“The misunderstanding of hedge funds being speculative comes from the myopic conclusion that an investor using speculative instruments must automatically be running speculative portfolios.”

“Myth: Hedge funds charge high fees”

“The attractive incentives in the hedge fund industry are regarded as one of the main drivers of high returns of hedge funds since it attracts managers who have – or are supposed to have – superior investment skill…

“…a higher proportion of the hedge fund manager’s capital is invested in positions about which the manager holds conviction (so) the management fee paid by the investor is based on a portfolio that consists of positions that are 100% managed actively.”

“Myth: Hedge fund generate strong returns in all market conditions”

“…hedge fund strategies are sometimes correlated with equities in a down market and sometimes not. If hedge funds are correlated with the stock market on the way up, that is actually fine with most people…”

“Myth: the lesson of LTCM is not to invest in hedge funds”

A picture says a thousand words…

And the list of myths goes on…

“Myth: hedge funds increase systemic risk of financial markets”

“Myth: selling short is the opposite of going long”

“Myth: there is no absolute return revolution”

The section on hedge fund myths ends with the following observation:

“There is still a lot of mythology with respect to hedge funds. Much of it is built on anecdotal evidence, oversimplification, myopia or simply a misrepresentation of facts. Although hedge funds are often branded as a separate asset class, a point can be made that hedge fund managers are simply asset managers utilising other strategies than those used by relative return long-only managers. The major difference between the two is the definition of their return objective; hedge funds aim for absolute returns by balancing investment opportunities and risk of financial loss. Long-only managers, by contrast, define their return objective in relative terms. They aim to win what Charles Ellis calls the loser’s game – that is, to beat the market.”

Despite the media sensationalism surrounding hedge funds, this is probably a good example of the “common sense” to which Mark Anson refers in his foreword.  So we highly recommend that you download the document (here), load up your printer with 200 sheets of fresh paper, hit print and grab a coffee.  At the very least, you’ll have something to read on the commute home this week.  Just don’t read it while you’re driving.

Okay.  So the truth is out, hedge funds have long equity exposure.  Our back-of-the-envelope analysis of the HFRI Index last week showed that all strategies – particularly “Equity Hedge” had a positive correlation with equity markets.

So what can an investor seeking truly uncorrelated returns do about this?  After all, it’s quite possible that a hedge fund could produce alpha, but deliver it to investors with a side helping of over-priced beta.  Short bias managers, for example, are often said to produce a positive alpha even though they lose their shorts year after year.  It’s cases like this that make the term “absolute returns” a misnomer (see related post).

A new paper by the Edhec Risk and Asset Management Research Centre illustrates the ways that a fund of hedge funds can mitigate itself from these not-so-hidden factor exposures.

To begin with there’s that pesky equity exposure.  The following graph from the report shows the contribution of the MSCI World (blue line) to the volatility of the HFRI Fund of Funds Index:

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BY TRISTRAM LETT, MANAGING DIRECTOR, ABSOLUTE RETURN STRATEGIES, INTEGRA CAPITAL CORPORATION -   Benchmark hedging, a process designed to reduce the investment risk in a benchmark, is not new. However, doing it successfully and efficiently is. This article has two parts: in the first we explore the process of benchmark hedging and how it is possible to efficiently implement the process. As well, we look at the benefits it provides. We then move on to put this into an important context-the use of hedge funds in institutional portfolios. This has some unexpected conclusions for those who have recently become interested in this topic. We then circle back and compare the strategy being proposed at the outset to the use of hedge funds and outline both the similarities and differences of the two approaches.

Suffice it to say at the moment, benchmark hedging is of interest for a very simple reason-it frees up risk budget. Who would not want that flexibility? Investors then have more choice and more choice improves their welfare. The extra risk budget can be spent in whole or in part, or hoarded. Whatever one does with it, it is from the perspective of improving their circumstances. An obvious benefit to plan sponsors is the increased investment flexibility it provides.

Distributional Replication

A recent development in portfolio management called distribution replication promises a viable solution to providing a means to efficiently and cheaply allow plan sponsors to remove risk from their investment portfolios without impairing their return. Lest one think that talking about return distributions is unnecessarily abstract, consider this thought. When investors are hiring an investment manager, exactly what are they hiring? It is the manager’s distribution and the underlying processes that create it. Hiring a manager is mostly about liking the mean, the standard deviation and other statistical characteristics of the manager’s return distribution. Distribution replication is all about synthetically creating distributions that replicate desired statistical characteristics.

The means to accomplish this was initially developed by Professor Harry Kat and a former PhD student of his, Dr. Helder Palaro. The Kat/Palaro (K/P) process can create a custom distribution of returns on demand through a dynamic trading strategy using futures on well known, very liquid financial markets such as equities, bonds, currencies and commodities. Dynamic trading strategies structured to replicate option payoffs have been around for over 20 years. What makes the K/P method unique is its ability to target correlation to some benchmark or bogey.

The ability to target correlation is a significant step forward because it allows investors to place powerful diversification features in their portfolios. The question now is what use can we put this distribution creation process to?

Benchmark Hedging

Most of the risk in institutional investment portfolios is benchmark risk. It can be expressed as a composite beta created from the weighted betas held in the asset mix policy of the fund. The average Canadian pension fund, for example, has a benchmark comprised of the following weighted betas: 40% DEX Universe Index, 35% TSX 60 Index, 12.5% MSCI EAFE Index and 12.5% S&P500 Index, all expressed in Canadian dollars.

One point that needs to be made clear is the amount and kind of investment risk that exists in the average Canadian benchmark. On a capital allocation basis, 60% of the benchmark is exposed to equity risk. However, on a risk allocation basis over 90% of the investment risk is equity risk. This fact alone should be sufficient for institutional investors who wish to examine ways to reduce the risk in the benchmark.

Many plan sponsors set tracking error limits (risk budgets) around the various betas to accommodate active management to earn incremental return. The benchmark portfolio has a return distribution that should meet the plan’s liability funding requirements in as efficient a manner as possible. It has a mean, a standard deviation, skew and kurtosis that describe it. Over the past 28 years, from January 1980 until December 2007, the average Canadian pension benchmark, rebalanced monthly to its initial weights, had an annual average return of 11.5%, a standard deviation of 9.0%, skew of -0.40 and excess kurtosis of 1.8. If it is possible to create a similar portfolio synthetically through a dynamic trading strategy with no correlation to the benchmark portfolio, has something valuable been created?

Indeed it has. This portfolio can be implemented as an overlay to hedge the benchmark portfolio, by creating a portfolio invested 50% in the benchmark and 50% in the synthetic portfolio. This would have significant risk reducing properties, because of the lack of correlation between the two portfolios. This diversification would free up risk budget, which can be spent or hoarded, or some combination.

Examine the following set of efficient frontiers to appreciate what is occurring.

We used the monthly seven-year data set from October 2000 to September 2007 to run the out-of-sample test.  We specified that the distribution of returns was to have a standard deviation of 7.0%, similar skew and kurtosis as the benchmark and zero correlation with the benchmark. The lower efficient frontier represents that of all the assets that Canadian pension funds invest in. We assume that the average asset mix is on the efficient frontier where indicated, though that is not necessarily true. During this period, it had the indicated risk and return coordinates.

The upper efficient frontier is derived from mixing a like portfolio in all cases but with zero correlation in a 50/50 combination with all other asset mix combinations. Point A represents where the average Canadian asset mix, now hedged 50/50 with the synthetic portfolio, sits. Over this period, the combined portfolio had a 5.2% standard deviation with a 7.4% average return. The higher return is peculiar to the sub-period and is not the portfolio expectation over the longer term. Theoretically the hedged portfolio should have the same mean, but a lower risk.

The plan sponsor now has a range of choices. It can stay at this position and enjoy the lower total fund volatility relative to its benchmark. Or, it can “spend” some or all of its 1.8% freed-up risk budget (7.0% – 5.2%) to further improve its performance. We tested this by increasing the standard deviation of the hedging portfolio by 50%, leaving all other characteristics constant. This had the effect of increasing the risk of the hedged benchmark to 7.0%, the risk in the original benchmark, but it also led to an increase in performance of 1.6% to 9.0% (Point B). At the margin, each unit of additional risk from the initial hedged position produced 0.88% of incremental return.

The range of options shows the benefit from benchmark hedging. This flexibility born from employing a zero correlated replicant has great value to any investor.

The Synthetic Hedging Portfolio

The synthetic hedging portfolio is constructed from very liquid futures exposures in major markets. The most common ones used are Eurodollars (LIBOR), five-year Treasury Bonds, 10-year Treasury Bonds, S&P 500, Russell 2000, FTSE 100, DAX, Nikkei 225 and GSCI. Most of the positions are long, but some are short from time to time to ensure the creation of the desired risk profile and correlation.  Never is the portfolio net short. Small trades are made daily as it adjusts itself to the changing risk characteristics that emerge in the marketplace in order to maintain its position versus the specified characteristics and correlation.

Two very important implications result from the structure of the hedging portfolio, plus a number of ancillary benefits. First, the exposures used are all markets which earn risk premia and therefore have a distribution of returns with a positive mean. In other words, they are beta-based distributions.

Second, the correlation is far less likely to “phase lock” like the correlations with alpha-based strategies. This is one of the more problematic characteristics of well-diversified portfolios of alpha generators: whenever there is a market crisis, all the carefully crafted diversification disappears just when it is needed the most. Other benefits of the synthetic portfolio are its complete transparency, its immediate liquidity, its low fees and the lack of headline risk.

What is Alpha?

Hedge funds are being carefully considered by institutional investors for inclusion in their portfolios. However, a number of their characteristics are proving daunting for them. Lack of transparency and liquidity, high fees, phase-locking correlation and potential headline risk are the most often cited. Hedge funds are marketed as sources of alpha.  Below we explore the nature of alpha because it is often misconceived. The role of alpha in a portfolio is not unlike what the benchmark hedging process is accomplishing.  Alpha is not generally thought of as a risk management tool, but that is its central role in portfolio management.

Alpha is not always well understood. Regard the yellow distribution (Figure below), and ask the question, “what is it that investors will pay fees of 2 and 20 for in the alpha distribution?” This distribution has a mean of 0%, a standard deviation of 3.5% and a correlation with almost all other assets/strategies of zero.

Now regard the gray distribution (Figure below). This distribution has a mean of 10%, a standard deviation of 14% and a correlation greater than 50%, with many other assets/strategies. The yellow distribution can be acquired for a fee of 2 and 20, the gray for a fee of under 10 basis points.

Which one would you want in your investment program?

Surprisingly, both, because the lack of correlation that accompanies pure alpha makes it a superior diversifying asset, particularly with the higher-risk beta component.  Dr. Martin Leibowitz said at a recent conference, “any uncorrelated asset in a portfolio is gold.” Because we seek this and wish to employ it in portfolios, it means that we are all risk managers. As long as the alpha earns a premium to the risk-free rate, we are better off.

But as Hamlet noted in his oft-quoted soliloquy, “ay, there’s the rub.”  Alpha is not lying in fields waiting to be harvested. It is very difficult to create, thus it is very special. And thus it has special value. Look back at Figure 2, the alpha distribution. Note again that its mean is zero. This means that the production of alpha is a zero sum game.  For every winner, there must be a loser. The only way winners gain from losers is through the anonymity of the marketplace because no loser would willingly take a loss. Investors are trying to position themselves on the right-hand side of the distribution. Doing so involves a high degree of selection risk.

As noted earlier, the alpha distribution of returns also has a number of characteristics that makes fiduciaries uncomfortable. Lack of liquidity, lack of transparency, gating, high fees and headline risk all are difficult for fiduciaries to countenance in the context of the selection risk they are taking.

Essentially what has occurred through the advanced dynamic trading strategy of Kat/Palaro is the creation of a portfolio from blue distributions that has the desirable characteristic of a positive mean and the even more valuable characteristic of the alpha (yellow) distribution of zero correlation with everything. This makes it a superior risk management tool to alpha. But is it alpha?

The following scatter gram plots the synthetic hedging fund against the average Canadian pension fund benchmark.

It is readily apparent there is no correlation. The broken regression line confirms the near zero beta and the positive alpha. From a statistical point of view it is alpha created from dynamically managing betas.

Of course there will be those who argue this is not alpha, as it was created by a passive, mechanical strategy.  We are sympathetic to those arguments, but in the end who really cares? Certainly not the investor. If he can get a distribution of returns with a positive mean, controlled variance, stable zero correlation, total transparency and liquidity, and reasonable fees, why would he care what greek letter it bore?

Institutional investors now have a choice of how to manage benchmark risk in their portfolios. They can seek out pure alpha sources, mindful of the inherent selection risk they are employing in their portfolios to remove some of the beta risk. Or, they can use the synthetic portfolio creation process of Kat and Palaro to do the same thing but without the attendant baggage that hedge funds bring to the party.

Each has drawbacks which plan sponsors must decide between if they plan to go this route. The K/P process, because it is proprietary, will never be fully transparent to its users. Comfort can only be gained by talking to other users and doing simulations. However, this is now a process with which institutional investors are familiar when hiring managers. In the end, the value to plan sponsors in hedging their benchmark beta risk is significant. Gaining the flexibility to better tune their investment program in order to meet their objectives is the result.

This article originally appeared in the Summer 2008 edition of the Canadian Investment Review.

Harry Kat

Professor of Risk Management at the Cass Business School at City University, London. Before returning to academia, he was Head of Equity Derivatives Europe at Bank of America in London, Head of Derivatives Structuring and Marketing at First Chicago in Tokyo and Head of Derivatives Research at MeesPierson in Amsterdam.

The Alternative Investment Research Centre (Cass School)
Contact Information (Cass)
Research (SSRN)
Relevant Postings (AllAboutAlpha.com)

Alternative Investment Research Centre, Cass Business School

Director: Harry Kat, harry [at] airc [dot] info

From the Institute’s Website: The Alternative Investments Research Centre (AIRC) aims to generate practical answers to practical questions concerning alternative investments and their role in investment portfolios. The AIRC does so by acting as a clearing-house for data and research ideas between the alternative investment industry and academia. With the help of its corporate members, the AIRC builds and maintains databases for the various types of alternative investments and allows its associate researchers to access these data for qualifying research projects.  More…

Working Papers

Related AllAboutAlpha Content

Regular readers may remember our survey of attitudes toward 130/30 funds last August.  Since that survey, several others (e.g. Merrill Lynch, Vodia Group) have come up with similar numbers – about 15-17% of institutions actively investing in 130/30 and another 25-30% considering investments in the next 12 months.  Today, Kathryn Wilkens tells us of a recent survey of practitioners (members of the Chartered Alternative Investment Analyst Association) on this topic in our regular installment of “Alternative Viewpoints”.

A true advocate of alternative investments herself, Kathryn Wilkens received a Ph.D. in finance at the University of Massachusetts in 1998, and received a Center for International Securities and Derivatives Markets (CISDM) fellowship in 1997.  She was on the CAIAA’s advisory board from the program’s inception in 2002 though 2005, and is now the CAIAA’s Director of Curriculum.

Alternative Viewpoints – powered by CAIA

Special to AllAboutAlpha.com by: Kathryn Wilkens, Ph.D., CAIA, Director of Curriculum, the Chartered Alternative Investment Analyst Association, Amherst, Massachusetts

Last month, 440 CAIA members responded to a survey on 130/30 funds and I presented the results at Terrapinn’s 130/30 conference in Santa Monica.  The survey questions were structured around two main themes frequently discussed here at AllAboutAlpha.com:

• What is the most appropriate benchmark for 130/30 funds?
• What best describes your opinion about 130/30 funds?

When I posed the first question to the attendees at the Terrapinn conference, all but one responded that a standard long-only equity index such as the S&P 500 index or the Russell 2000 is the appropriate benchmark for 130/30 funds.  Yet Dow Jones, Credit Suisse, and S&P have all recently developed 130/30 indices (see related AAA postings Dow Jones joins the 130/30 Index Parade, S&P follows CS into 130/30 index business, 130/30 Indices: True indices or like playing chess against a computer?)  Two of these indices are classified as a type of Strategy Index with another being a so-called fundamental index.

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After the huge interest in last week’s commentary by clean tech investor Investeco on alternative beta in alternative energy, we dispatched our intrepid “fly on the wall” (see his previous posting from London) to one of the biggest events on the clean tech investing calendar, the Wall Street Green Trading Summit.  We asked him to report back on any new markets that were ideally suited for an alpha-centric investor.  Here’s what he found…

Dear Alpha Male,

(Thanks for the assignment on this side of the pond.  Much easier on the wings than the last gig.)

Anyone who thinks alpha is finite and non-renewable ought to check out the market inefficiencies the burgeoning clean tech sector.  According to the International Emission Trading Association, the international carbon market was worth approx. $70 Billion US in 2007  and according to New Carbon Finance the US market alone could equal $1 trillion by 2020.

Still, converting the sun’s energy in alpha isn’t like taking candy from a baby.  Such enormous growth might suggest that, in terms of generating alpha, low hanging fruit should be plentiful.  However, this was not the message to be taken from this gathering.  There may still be plenty of opportunities for generating alpha in emissions trading, but unless you’re exploiting opportunities in places like Russia and the Ukraine, you’re going to have to work harder to get it than ever before. The vast capital (intellectual and financial) getting into the sector has already eaten up the lowest hanging of the fruit.

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Here is a sample of the news stories we didn’t get a chance to explore in detail this week.  As usual, all of them can be found on the Alpha-ticker above or in the news items section of AllAboutAlpha.com (free registration may be required for a few of these).

Morgan Stanley says Alpha/Beta Separation “the way of the future”. The AllAboutAlpha site partner lays out its alpha-centric philosophy telling IPE that the pension industry is about to experience a “second wave” of LDI strategies based on the separation of alpha and beta.

Dutch Insurer Aegon splits portfolio into alpha and beta segments are farms each one out to a different manager. According to the firm’s press release, “By managing the parts separately from one another, better risk-return ratios are possible. This way, more sources of value added will be available and a greater focus can be created in the portfolio. Separating the US share portfolio has created an increase of a yearly average of the total return of 2.5% without any risk increase.”

More…

Regular readers may remember the name William Shadwick (see related posting).  Widely known as the developer of the Omega Function and Omega Metrics®, Shadwick won the 2007 Journalism Award of the Investment Management Consultants Association ,jointly with Ana Cascon, for a paper in which they lifted the veil on some of their powerful new statistics for finance.  A prominent mathematician, he was responsible for establishing the Fields Institute for Research in Mathematical Sciences before entering the finance industry in 1998.  He is the founder of Omega Analysis, a quantitative research firm in London.

Shadwick, who believes in using sophisticated tools and avoiding unnecessary complexity, issues a general warning about the hidden assumptions in quantitative models below. He has also been watching the ongoing debate between Harry Kat and Lars Jaeger and tells AllAboutAlpha, I’m afraid it doesn’t offer much comfort to either the Jaeger or the Kat schoolI think that over-modeling has had some severely negative consequences and it’s about time people started to pay more attention to the gap between theory and reality.

That’s all very well in practice, but it will never work in theory!
(Financial models should come with health warnings)

Special to AllAboutAlpha.com by: Dr. William Shadwick, Omega Analysis

The title of this piece comes from a joke about a highly qualified financial engineer’s reaction to a well-proven trading strategy. It illustrates the tension between theory and practice that we have all seen as quantitative methods become ever more common at trading desks and in investment management.

In general, the rise of quantitative tools in finance has been highly beneficial but the widespread use of models has been a decidedly mixed blessing. In science, the constant development of theories expressed as mathematical models to be tested, rejected, confirmed or refined through observation and experiment is the main source of progress in our understanding of the physical world.  This process is also crucial for engineering and technology where it is the key to predicting future events and controlling them to our advantage.

It was inevitable that this paradigm would eventually be adopted in economics and finance too.  In the half century since Markowitz put portfolio construction on a quantitative footing there has been a steady growth in the use of increasingly sophisticated and complex models and statistical techniques in investment management.

The nature of the financial markets is such that this growth in models has not been accompanied by the sort of testing that the field science demands.  Finance academics simply cannot perform experiments like those upon which the sciences rely, and they are also severely constrained in the type of observations they can make.  Data and information about what goes on in reality (as opposed to theory) is, and is likely to remain, in very short supply in comparison to the sciences.
The end users of the models in finance are not intent on understanding how markets may be explained.  That is the goal of academic research.  Instead, they want to employ theory and models to produce profits.  Like physical engineering, which has had its share of collapsing bridges, financial engineering has therefore led to many accidents.

For example, the current mess in the credit markets would not have been possible without the extensive and inappropriate rise of sophisticated models.  The results of mis-priced risk have now been cascading through the financial system for several months and show no sign of abating.

Assumptions = Hidden Models

A mathematical model can be thought of as a process that states:

If assumption A is satisfied, then input of B is guaranteed to be followed by output of C.

A robust model is one in which:

If A is close enough to being satisfied and the input is close enough to B then a result close to C is guaranteed.

The most basic requirement for the use of mathematical models – in any context – is that they are appropriate for the job.  The model tells you nothing about what happens if assumption A is not even close to being satisfied.  In this case, no matter how diligently one applies the model with the expectation of getting from B to C, the process cannot be trusted.  If the model is not robust, the difference in output may even be very dangerous.

Hidden Model: Return distributions are independent and identically distributed

The increased use of quantitative methods means that models are now almost ubiquitous and are often present but hidden.  For example, almost every hedge fund investor or manager has used the square root of 12 rule to produce an annualized volatility figure from a sample of monthly returns.  But how many people remember to check the assumption upon which the rule is based? The returns must be independent draws from the same distribution (i.i.d., or independent and identically distributed) for this rule to be justified.

This is an example of a hidden model.  It is not the returns of a hedge fund that we are talking about but the model of returns of a hedge fund.  Does anybody really believe that hedge fund returns have no auto correlation?   Does anybody really believe that there is an unchanging distribution from which the returns are drawn?

Returns on hedge fund investments or stock market prices are not random variables.  However the extent of apparent randomness in their behavior means that statistical tools are most appropriate for describing them and for making predictions of the future.  In the case of the square root of 12 rule, the prediction is the annual volatility expected over many years.  While nobody would feel that a sample of 3 annual returns would merit the calculation of an annual volatility, we’re happy to use a sample of 36 monthly returns and the model of returns as i.i.d. random variables to make the prediction.

The danger in such an assumption is that it can easily underestimate the true annual volatility of returns. This may produce serious strains on an investment program because the path by which the NAV goes from its initial value to its value 5 or 10 years later often matters a great deal.  It matters to a manager who may spend significant time without receiving a performance fee after a large loss or a series of smaller ones.  It matters to the investor who requires some of the proceeds of his investment for income during the period.  This is, of course, the reason for wanting an estimate of annual volatility in the first place.  (It is difficult to find an example of an investor who only needs to know that his investment NAV will rise in the long term while being unable to count on using the proceeds at any intervening time before the long term -   when, as Keynes said, we’re all dead.)

Hidden Model: Return are normally distributed

There are more dangerous assumptions than returns being independent and identically distributed.  One might also assume that they were normally distributed.   Probably everyone has heard the black swans argument about the importance of extreme events in markets and Mandelbrot and Taleb’s attacks on the reliance on normal distributions in finance theory.

I think they have greatly overestimated the number of academics who haven’t yet noticed that market returns aren’t normal. However there is no doubt that the persistence of press and industry descriptions of large market losses in terms of standard deviations (and ascribing an extremely low probability to such an event in consequence) indicates a widespread hidden assumption of normality.

This is dangerous for the obvious reason that it call lead to a feeling of safety where none exists. If you know that there is a 1 in 10 chance of a catastrophic loss instead of believing the chance to be 1 in 1000, the expected return you require for taking such a risk will be very different. There is no doubt that many of the estimates of loss responsible for the sub-prime debacle required exactly this sort of mis-pricing.

Hidden Model: Standard deviation is a proxy for risk

In great part, these dangers are a consequence of another hidden model, namely the use of standard deviation of returns as a proxy for risk. The realization that this model of risk is especially dangerous when applied to hedge funds has led both academics and finance practitioners to make use of skewness and kurtosis in an attempt at more sophisticated modeling of risk.

Skewness is intended to model asymmetry – the mismatch of upside and downside risk. Kurtosis is intended to model the likelihood of extreme events or fat tails.  Of course, certain assumptions must be satisfied for these models to perform as intended.   Dangers introduced by relying on these metrics are compounded by the great sensitivity of skewness and kurtosis to (even moderate) outliers.  These are not statistics meant for small samples.

Hidden assumptions in hedge fund replication

The extent to which distributional replicators will succeed in reproducing hedge fund returns will depend on the extent to which the noisiness of skewness and kurtosis can be managed.  An even more critical assumption (underpinning distributional replication) is that distributions with the same mean, variance, skewness and kurtosis must be very similar.  This is not true in general.  So replicators must depend on this assumption being satisfied – at least approximately – for the distributions that matter to them.

The use of standard deviation to describe risk is also an essential part of the risk-factor approach to hedge fund replication.  In fact, the term risk-factor itself equates risk with standard deviation of returns.  In this case, the (linear regression) model could be said to be hidden in plain sight, but it is no more easily remembered for that.

Does everyone who uses the term alpha really mean it to be interchangeable with an artifact of a particular model of returns?   For that matter, does everyone accept the hidden model in the statistician’s use of the word explain when he says that certain risk-factors explain some percentage of hedge fund returns?

It sounds rather different if he instead says that he has a model which (while nothing can be known about its actual similarity to a particular investment strategy or indeed how likely its assumptions are to be satisfied), manages to approximately reproduce the strategy’s mean, standard deviation, and correlation with a number of financial indices.

Bottom Line: Hidden assumptions should give rise to health warnings on quantitative models

Models are everywhere in quantitative finance but it is almost impossible to find any attendant statements regarding the assumptions upon which they are based.  Their purveyors should issue health warnings that tell the user that hidden assumptions are present and that failing to check that the assumptions are valid may be dangerous to investment health.
It is essential that we recognize the difference between finance and science.  In science, increasingly sophisticated mathematical techniques always produce better results over time.  But this need not be the case in finance.  Nevertheless finance can and should aspire to the status of an engineering discipline.

While you are unlikely to find health warnings on financial models any time soon, there are a few simple principles which can reduce the danger they present:

• It is far more important to look to simplicity (and common sense) than it is to look to increasing complexity as a means to better control investment outcome.
• A model whose robustness is unknown or unknowable should never be employed.
• Sophisticated tools should only be used if it is possible to verify that all required assumptions are satisfied (at least to a good approximation).  When this condition can be met, a simple application of a sophisticated technique is preferable to a complicated one.

Keeping these in mind will reduce the risk that financial models may pose to your investment health!

- William F. Shadwick, February 2008

Note: Shadwick is speaking tomorrow at Terrapinn’s Hedge Fund Replication and Alternative Beta Conference in London.