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Update May, 2006
Hedge Funds Update
Hedge funds have once again been in the news, with Federal Reserve Bank of Atlanta holding a conference dedicated to the issues they raise, and the European Central Bank publishing a commentary on them in its latest Financial Stability Review. One of the most controversial subjects in the world of hedge funds is the extent to which existing indexes provide an accurate picture of the risks and returns from these investment strategies. The essential problem is that hedge funds self select when they will start reporting their results, and to which index they will contribute them. This creates a number of potential biases including selection (only successful funds will choose to report), survivorship (the indexes don't reflect the results of funds that stop reporting) and backfill (when firms start reporting, the index is "backfilled" with the fund's results up to that point, which often aren't matched by subsequent performance). Many academic papers have asserted that the net impact of these biases is a substantial overstatement of hedge fund returns, and an understatement of risk. In particular, a paper by Malkiel and Saha ("Hedge Funds: Risk and Return") that found a 4.5% over statement of average annual return has been a particular target of criticism by hedge fund supporters. Their counterargument is that survivorship bias is actually quite low, because it isn't just failing hedge funds that stop reporting, but also successful funds that are closed to new investors (the apparent logic being, "why report to an index if you aren't seeking new funds from investors?"). At the FRB Atlanta forum, Malkiel and Saha responded with another paper ("Why Do Hedge Funds Stop Reporting Their Performance") that found funds that ceased to report are more likely to be failing than delivering high returns.
Another issue that came up at the conference was the lower returns on so-called "investable" hedge fund index products, compared to those on the broader hedge fund indexes that they seek to replicate. Two explanations were offered, and both make sense. The first is that, because they allow more frequent capital withdrawals than the typical hedge fund, the investable index products should logically have lower returns because they carry less liquidity risk. The second point is that these investable products are based on larger hedge funds, and hedge funds' performance (like mutual funds') tends to decline with size (e.g., because great $25 million dollar investment ideas are easier to find and profitably execute than great $1 billion investment ideas).
Another set of papers looked at just how successful hedge funds have been at delivering alpha - that is, returns above some type of investable asset class benchmark index. In "How Smart Are The Smart Guys?", Griffin and Xu make a number of interesting observations: the median equity hedge funds trades twice as much as the median mutual fund, its portfolio has a lower correlation to the market index than its median mutual fund peer, and it has a more pronounced tendency to prefer small value stocks. However, the authors cannot prove that "hedge funds in general are any better at long stock picking or timing sectors than mutual funds," leading them to "question the ability of long-equity hedge funds to add value." They acknowledge, however, that "hedge fund firms seem to have more differential ability in stock picking than mutual funds," lending support to the widely held belief that the most talented active managers have moved from mutual to hedge funds because of the superior potential compensation they provide for active managers who are either very lucky or very skilled. In a related paper ("Sources of Hedge Fund Returns: Alphas, Betas, and Costs"), Ibbotson and Chen find that, in aggregate (and this varies across styles), underlying asset class returns (beta) account for the majority of hedge fund net returns after manager fees (5.4%, versus average alpha of 3.7%).
As always, the problem lies in identifying hedge fund managers who are truly skilled at generating alpha. In this regard, two other papers provide a sobering perspective. In "A Portrait of Hedge Fund Investors", Baquero and Verbeek show that while investors are quite quick to pull their money from underperforming funds (unlike the average investor in actively managed mutual funds), they show no reliable ability to pick winning managers in advance. In "Hedge Funds: Performance, Risk and Capital Formation", Fung, Hsieh, Naik and Ramadorai find diminishing returns to scale as hedge funds grow in size, as well as declining risk adjusted returns as they have become more popular and attracted more managers and capital. Specifically, they find that Funds of Hedge Funds (their proxy for an investable index) have seen declining returns, and on average deliver zero alpha (though about one in five did manage to deliver statistically significant alpha). Another paper ("Do Funds of Funds Deserve Their Fees on Fees?" by Ang, Kropf, and Zhuo) argues that because of the services they provide investors, funds of hedge funds deserve the fees they charge. However, other commentators have argued that the so-called "multistrategy" fund (in which a single fund company utilizes multiple strategies) is a more efficient approach than the fund-of-funds structure with its higher level of fees. Time will tell who is right.
This brings us to the European Central Bank paper. Its primary concern is the potential impact of the growth of hedge funds on the stability of the financial system. It begins by noting both the rising correlation of returns between funds within different hedge fund categories (with equity market neutral and global macro notable exceptions to this trend) and the rising correlations between the categories themselves. For example, our own research shows that, in the two years ended April 30, 2006, the average monthly return correlation between hedge fund categories in the value weighted Tremont Hedge Index has risen by .27 compared to the five years ended in April, 2004. In the eyes of the ECB, "the increasingly similar positioning of individual hedge funds within broad hedge fund investment strategies is another major risk for financial stability which warrants close monitoring despite the essential lack of any possible remedies. This risk is further magnified by evidence that broad hedge fund investment strategies have also become increasingly correlated, thereby further increasing the potential adverse effects of disorderly exits from crowded trades. It is difficult to gauge what could cause correlated sell-offs and how damaging these could be, but one possible trigger could be an abrupt end of the recent global search for yield possibly induced by the tightening of global liquidity conditions. A further slowdown of inflows into hedge funds or even widespread redemptions could also exert pressures on individual hedge funds to liquidate increasingly less liquid holdings, as more hedge funds seem to be venturing into less liquid markets in order to earn [additional returns from] the associated liquidity premium."
What is an investor to make of commentaries like these? The first lesson is that, as is usually the case, when a system becomes homogenous - that is, when it ceases to contain sufficient diversity - it becomes very vulnerable to sudden and substantial changes. In the opinion of the European Central Bank, this is also true for financial systems, and the increasingly homogenous alpha generation strategies employed by many of the world's 8,000 plus hedge funds. The second lesson is that, despite the intense competition among hedge fund managers, there are still those who can generate alpha, whether that is due to superior insight into fundamental value or the psychology of other investors. As always, the problem is how to identify these skilled managers in advance. The latest research shows that people who invest in hedge funds are no better at this than mutual fund investors, though the former are quicker to cut their losses.
In our opinion, this leads to a third lesson - while uncorrelated alpha is a very welcome addition to any portfolio, it is so hard to consistently generate that an average investors long-term asset allocations to these strategies should be relatively small. As we have noted in other writing in this journal, the equity market neutral strategy seems to most consistently deliver alpha that has a very low correlation with returns on broad asset class indexes. That is why it is our preferred vehicle for a long-term policy allocation. In addition, if one wanted to "outsource" the tactical shifting of one's allocations between asset classes, it would be logical to add a second allocation to a global macro type strategy. Unfortunately, there are still relatively few investment vehicles available to individual investors that make it easy for them to implement these allocations. Most mutual funds that claim "market neutrality" fail to achieve it, and are, in fact, long/short funds with a long bias. They retain more exposure to overall equity market risk than we would like to see. As for global macro funds, there are still too few in existence, and those that are available often use too few asset classes. In the United States, the Pimco All Asset Fund (PASAX), with its long-term five percent target real return of remains our favorite by far.
Update July, 2005
Most of us have all had a similar experience. Maybe it was back in the 80s, when leveraged buyout funds first made their appearance. Maybe it was back in the 90s, when everyone wanted a piece of the venture capital action. Or maybe it was at a party last weekend, when cousin Charlie was waxing eloquent about how he's in hedge funds today. “And why aren't you?” he's bound to ask. If you've been wondering how to reply to that question, this article is for you.
Let's start with some basic definitions. At the highest level, a “hedge fund” is a privately organized pool of capital run by a professional investment manager. At last count, there were about 10,000 hedge funds managing $1 trillion in investor funds.
One of the most important features of these funds is that they are not cheap to own. In order to attract the best active managers, a typical hedge fund charges investors an annual fee equal to two percent of assets, and pays twenty percent of all returns (above a certain amount) to the fund manager. In order to justify these fees, hedge funds often make two promises to their investors. First, that their returns will be high, and second that they will have a low correlation to the returns on other asset classes. Let's begin by taking a closer look at how these funds theoretically generate the high returns they promise.
We'll begin by noting that since hedge funds are actively managed products, their superior returns ultimately must be grounded in the ability of a skilled manager to make a superior forecast, in comparison to his or her competitors. In turn, these forecasts must be based on some combination of superior information and/or a superior model for making sense of the public and private information available to the fund manager.
“Hedge Funds” are not a separate asset class, per se. Rather, they are a collection of diverse investment strategies that are applied in different asset classes (e.g., bonds, equity, commodities, etc.). These investment strategies broadly fall into three categories. “Statistical Arbitrage” managers start by calculating the statistical relationship between different types of assets. When those relationships depart from historical norms, these managers attempt to profit by acquiring (“going long”) the asset that they believe to be underpriced, while selling (“going short”) an equal amount of the asset they believe to be overpriced. When the underlying relationship returns to its historical norm, the arbitrage manager reverses these trades to realize his or her profit. Because these price differences are typically quite small, Arbitrage funds typically use large amounts of leverage (i.e., debt) on top of their investors' funds to magnify them. Provided asset prices return to their long-term relationship, the Statistical Arbitrage strategy can make attractive returns. However, if these price relationships remain out of line - or worse, get even more out of line - then Statistical Arbitrage strategies can lose very large amounts of money, very quickly. Just ask anyone who invested in Long Term Capital Management, and watched it blow up (and almost take down the banking system with it) in 1998.
Now what about the argument that arbitrage funds have a low correlation to returns on other asset classes? As long as the statistical relationships don't get too far out of line with their historical norms, the low correlation argument probably holds. However, when they get significantly out of line, it is probably because significant negative events are also taking place in other asset classes. When these events occur (e.g., the Russian debt default in 1998 that caused the spread between emerging markets debt and U.S. Treasury Bonds to dramatically widen), they will typically cause the correlation between the statistical arbitrage strategy and some asset class returns (e.g., high yield and emerging markets debt) to sharply increase.
“Directional” hedge fund seek to make profits by going long assets they have concluded are undervalued, and/or short assets they believe to be overvalued. These long and short positions could be in different asset classes (in the case of “Global Macro” funds), in one asset class (e.g., “Equity Long/Short” or “Emerging Markets” funds) or in different securities issued by companies subject to unusual events (e.g., “Event Based” or “Merger Arbitrage” funds). The hedge fund manager's valuation of these securities is typically based on some combination of fundamental analysis of the expected cash flows and risks from the assets themselves, or analysis of the expected future moves of other investors. The long and short positions are typically not offsetting; hence a directional manager has a “net long” or “net short” exposure, that tends to be correlated with the returns on the relevant asset classes. Obviously, making the wrong valuation judgment causes a Directional manager to lose money. The actual amount of money lost, however, depends on what the hedge fund has been investing in. For example, a quarter percent change in interest rates has a lot bigger impact on the value of a long-term bond than it does on the value of a short-term bond. In this example, the hedge fund manager can magnify the potential return on her interest rate forecast by changing the maturity of the bonds he or she holds. Moreover, the actual “directional bet” may itself not be a symmetrical one. For example, consider a hedge fund that invests in catastrophe bonds. These are typically issued by companies that provide insurance against low probability, but high cost events (e.g., like a hurricane). If the specified event does not occur within a given period, the bondholders receive their principal back, plus a very attractive return. However, if the event occurs, and is sufficiently costly to the insurance company, the bondholders may lose their principal. In this case, on the upside, if our Directional hedge fund manager's hurricane forecast is accurate, he and his investors will make a nice return. However, if his forecast is wrong, the downside losses can be much larger.
Finally, “Market Neutral” strategies are based on the difference between systematic risk, which is common to all securities in an asset class, and company-specific risk. A Market Neutral manager seeks to profit from his or her superior ability to analyze company-specific risk, without taking systematic (also known as “beta”) risk. For example, an equity market neutral manager might invest $100 each in ten companies, while selling short a $1,000 of equity index exchange traded funds. The net return on this investment would be pure “alpha” - that is, return for taking company specific, rather than market risk. In theory, it should also be uncorrelated with the return on the underlying asset class. In practice, however, the relationship between systematic and company-specific risk is not as cut and dried as it is in theory. For example, consider an investment in a company that improves its operations to the point that it shifts from being included in a value sub-index to membership in the market's growth sub-index, at the same time that the balance of investor sentiment shifts from favoring value to favoring growth. Clearly, the return realized by a hedge fund manager who owns this stock will contain elements that are both company-specific and systematic. In short, in practice it is very hard to be perfectly equity market neutral (see, for example, “Are Market Neutral Hedge Funds Really Market Neutral?” by Andrew Patton).
One more point needs to be made, which applies to all hedge fund styles. As more money has been invested in hedge funds, the logical question to ask is how managers will seek to match their past returns in a much more competitive environment. Two answers present themselves, and neither is reassuring. The first is to make investments that involve more risk. The second is to employ more leverage to magnify the impact of declining “basic” returns. Both raise the chances of experiencing serious losses, unless an investor carefully controls his or her risk exposure.
Some very smart people are worried about this. For example, in their recent paper “Systematic Risk and Hedge Funds,” Chan, Getmansky, Haas and Lo conclude that “the hedge fund industry may be heading into a challenging period of lower expected returns, and that systematic risk is currently on the rise.” Similarly, in its March, 2005 Quarterly Review (“Time Varying Risk Exposures and Leverage in Hedge Funds”), the Bank for International Settlements concluded that “painting a comprehensive picture of the hedge fund industry is virtually impossible given the data available.” It also found that “hedge funds that reportedly belong to different style families, and thus presumably follow different investment strategies, have at least some commonality in their risk exposures.” Moreover, “to the extent that hedge funds engage in investments that have payoffs that resemble derivative instruments, their returns will be non-linearly related to the returns on the underlying market risk factors.”
Let's now move on to another point that is too often overlooked in the excitement over the prospective returns from investing in hedge funds (which remind us of Charles Revlon's comment about the cosmetics business: “we're selling hope.”). Most studies show that in the world of hedge funds, the difference between top and bottom quartile managers' returns is quite large. This is taken as evidence of “inefficiency,” or substantial differences in managers' skill and access to information. However, even in inefficient markets, alpha is still a zero sum game. This is an important point that investors too often overlook. Mathematically, there is a weighted-average return from investing in the universe of all hedge, buyout, or venture capital funds, that is ultimately related to the amount of systematic (i.e., beta) risk they bear. In any year, some funds will deliver returns above this average (generating positive alpha) while others will deliver returns below it (negative alpha).
Investors in hedge funds face the same challenges as investors in actively managed mutual funds: How to identify truly skilled investment managers? And how to be sure that these managers will not capture (via fees and expenses) all the alpha they create? As you know, this implies a successful forecast on the part of the investor choosing from multiple hedge fund managers. And any manager selection forecasting skill necessarily depends on the investor having either superior information and/or a superior model. Paying an investment consultant (or, alternatively, a “fund of fund” manager, which makes sub-investments in a number of hedge funds) to make this choice only changes the nature of the forecasting problem (while making it more expensive for the investor). However, the forecasting problem does not go away.
If there is any good news, it is that in the world of hedge funds, (and unlike the world of mutual funds), past performance may be a useful guide to future results. For example, in “The Life Cycle of Hedge Funds”, Mila Getmansky found that for most hedge fund categories, performance increased with size, but at a decreasing rate. One reason for this is provided in the paper “Analyst Industry Diversification and Earnings Forecast Accuracy” by Dunn and Nathan. They found that as analysts covered a broader range of industries, their forecasting accuracy declined. To put it differently, what has been called the “fundamental law of active management” states that alpha is a function of forecasting skill times the number of opportunities for its application. Dunn and Nathan's findings suggest that this may need to be modified, given the apparent negative relationship between these two variables (a point that is also consistent with many findings from cognitive psychology research). In other words, while big and successful funds may benefit from better access to deal flow, increased size may actually cause their forecasting skills to weaken.
To answer the second question - the probability of earning risk adjusted returns (after those hefty manager fees) greater than those available in publicly traded asset classes - we must turn to the thorny question of how to measure hedge fund performance.
The aggregate indexes for hedge fund performance all suffer from substantial shortcomings. The first is the so-called “self-selection bias.” This refers to the fact that funds report their returns voluntarily. Logically, this probably biases the results towards the more successful funds. This problem is compounded by the fact that hedge funds may report to multiple competing index providers.
The second problem is known as the “backfill bias.” This refers to the fact that when a fund joins an index, it provides a year or two of previous returns. Research has shown that subsequent results are almost always lower. Hence, if backfilled data are included, index average returns will be biased upwards.
The third problem is the “survivorship bias.” This refers to a situation in which funds that merge, close, or stop reporting have their results dropped from the index. Again, this biases returns upward, and risk downward.
The fourth problem is the “stale pricing bias.” When the reported price of an infrequently traded security is determined not by a market transaction, but rather by an appraisal (often by the fund manager), a number of distortions typically result. First, the returns on the security (and of the fund itself) display a higher correlation over time than is the case with most publicly traded securities. Second, this causes reported standard deviations to appear artificially low. It also artificially depresses the reported correlation of return with other asset classes. (For more information on these biases, see “Do Hedge Funds Hedge?” by Asness, Krail, and Liew, and “Asset Allocation Effects of Adjusting Alternative Assets for Stale Pricing” by Andrew Connor).
The fifth problem is known as “style drift.” Researchers have found that a hedge fund's self-categorization of its investment strategy (e.g., Equity Market Neutral), when regressed against different asset class returns, shows that another approach is being used (e.g., equity long/short, which entails substantial systematic risk exposure).
A number of index providers have attempted to eliminate some, if not all of these biases. For example, the value weighted CSFB/Tremont Hedge Fund Indexes do not allow backfill data, and they are corrected for survivorship bias. Over the 1994-2004 period, the real returns on this index (and on two key sub-style indexes) are as follows:
| Annualized Quarterly Data | All Hedge Funds | Equity Market Neutral | Global Macro | Public Market Equity |
| Average Annual Real Return | 11.8% | 7.1% | 16.1% | 9.5% |
| Standard Deviation of Returns | 9.4% | 5.0% | 13.3% | 18.1% |
| Skewness of Returns (Asymmetry) | (.26) | (.18) | .18 | (.26) |
| Kurtosis of Returns (Size of Tails) | .24 | (.72) | .20 | (.10) |
We include Equity Market Neutral and Global Macro in this table because they are based on two clear strategies for generating alpha: security selection (in the case of EMN), and asset class timing (in the case of GM). Their correlation with each other is .43. As you can see, the two hedge fund styles, plus the overall index, have historically delivered attractive aggregate returns per unit of risk, as measure by standard deviation (volatility). You can also see that at this aggregate level, the distributions of hedge fund returns are close to normal. Skewness refers to whether the distribution is tilted to the left (negative skew, or annual returns below the average more likely) or right (positive skew). A skew greater than .5 or less than (.5) is considered a significant departure from normality. Kurtosis measures the extent to which more returns are located in the tails of the distribution (i.e., at either extreme) relative to a normal distribution. A positive kurtosis value implies a higher than normal percentage of extreme annual returns. Kurtosis of more than 1.0 or less than (1.0) is considered a significant departure from normality.
However, we stress that these figures are aggregates for a given hedge fund style. At the level of an individual hedge fund, annual returns can be (and often are) very non-normally distributed. What this table says is that when these funds are combined, their returns come close to a normal distribution with attractive risk and return characteristics.
This raises an obvious question: Can you invest in a hedge fund index product?
Many hedge funds are organized as limited partnerships, with the investment manager as the general partner. These LP investments are generally only available to “qualified” investors”, who can produce evidence of a minimum level of income or net worth. In addition, the minimum investment in a hedge or private equity partnership has traditionally been quite large. However, in recent years these have been falling. For example, some partnerships now accept minimum investments of $25,000. Even smaller minimums are often available if the investment advisors, who combine different people's contributions to reach the LP's minimum investment.
In addition, a growing number of closed end and even open ended mutual funds (OEICs or unit trusts in Europe) now claim to be using “hedge fund-like” strategies to manage their investments (e.g., Hussman Strategic Growth Fund and the Pimco All Asset Fund in the United States, which are similar to equity market neutral and global macro hedge funds). Retail hedge funds are available in some countries. A good example of this is the Tremont Capital Opportunity Trust in Canada (TT.UN), which invests in a broad mix of underlying hedge fund strategies. However, these funds of funds are not cheap; the Tremont's expenses are on the order of 3.0% per year. This has created an opportunity for the introduction of lower cost products that track hedge fund indexes. One example of a hedge fund index product in the United States is the RYDEX Sphinx fund (which has a $25,000 minimum). Another example, Rydex' Structured Beta Funds, is discussed in this month's product and strategy notes. Elsewhere, in many countries CSFB offers similar products that track the CSFB/Tremont investable hedge fund indexes. And in Germany, Hansainvest has recently launched an index fund that tracks the MSCI HedgeInvest Index.
Finally, in some countries, equity linked debt instruments also have been issued which promise return of principle, plus payments that are tied to the return on a hedge fund interest. A good example of these is a note issued by Societe Generale Bank in Canada, and the Isle of Man, whose return is tied to the MSCI Hedge Invest Index. Rabobank has launched a similar product in Europe. To our knowledge, there are no investable products based on a buyout, venture capital, or combined private equity index.
However, the performance so far of investable hedge fund index products confirms the problem of fund returns declining with size that was first raised in the theoretical literature. Since these index products invest in relatively large underlying hedge funds, their performance has tended to lag that of the broad hedge fund index, which contains a large number of smaller funds. For example, the year to date nominal return through June, 2005 on the CSFB/Tremont Hedge Fund Index was 1.34% (in U.S. dollars), while the return on the MSCI HedgeInvest Index was .17%, while the return on the Standard and Poor's Hedge Fund Index (SPHINX) was .13%, and the CSFB Tremont Investable Hedge Fund Index was up .19%.
So where does this leave us? Should you invest in hedge funds? As previously noted, potential hedge fund investors face the problem of identifying a skilled fund manager. If you have no confidence you can do this, there are a growing number of index products that are available, that track not just broad hedge fund indexes, but increasingly sub-styles like Equity Market Neutral and Global Macro. The argument in favor of investing in hedge funds index products runs like this: (1) I know I lack the skill to pick top quartile hedge fund managers. (2) However, I have the risk capacity to pursue higher returns than are available from my well-diversified, low-cost beta (asset class index fund) portfolio. (3) Managers of traditional “long-only” actively managed mutual funds are charging me relatively high prices for a mix of systematic (beta) and unsystematic (alpha) returns that are often times highly correlated with the returns on other parts of my portfolio. (4) By investing in an Equity Market Neutral hedge fund style index, I can obtain, at a relatively low cost, some (close to) pure alpha return that should have a low correlation with the rest of my portfolio. The same is true for a Global Macro hedge fund index product. (5) I am also willing to accept the risk of a decline in forecasting skill (and therefore returns) as the hedge funds that underlie the index products in which I am investing grow in size, and the overall hedge fund market becomes more competitive.
In sum, for most of us, investing in hedge funds will never be a fast and easy path to riches. On the other hand, for those investors with sufficient risk capacity, investable hedge fund indexes provide a low cost alternative with a reasonable probability of adding some uncorrelated returns to their portfolios. However, these investment will remain marginal ones (or a bit of frosting on the cake, if you will), to be made only after most of one's portfolio (and risk capacity) has been allocated to a broad mix of asset class index funds.
January 2005
We suspect that many of our readers have recently had the same experience we have. You’re at a party, and, after the weather, sports, politics, and house prices, people start talking about investing. These days it seems almost inevitable (or sadly unavoidable, depending on your perspective) that, relatively early on in the conversation someone will try to impress everyone by rather loudly noting (let’s face it, most women don’t do this) that they’re "in hedge funds." As we shortly thereafter make our excuses, and head off to get another drink, many of us naturally wonder to ourselves (if not to others), "should I be ‘in hedge funds’ too?"
Clearly, this is a timely question to ask. As the Financial Times recently noted in its January 27th issue, "Investors poured a record US $60 billion into hedge funds worldwide in 2003, lifting the industry’s capital to between $725 and $750 billion." And since many hedge funds leverage this capital with debt, the total value of the investments they control is a multiple of this amount. The FT went on to note that "there was also a big leap in the number of new funds during [2003], with only a handful of funds closing." TASS [a producer of a leading hedge fund index] estimates there were about 1,000 new funds launched in 2003, taking the total to about 6,700. Of these, about 1,700 are funds of funds."
It also seems likely that more and more investors will be thinking about hedge funds. Not only are more hedge fund index products being launched, but more professional financial advisers are considering their use in client portfolios. As investmentadvisor.com recently noted, "a recent study, "Asset Gathering in Intermediary Channels", conduced by Financial Research Corp and the Financial Planning Association, found that 62% of the 635 advisers surveyed planned to increase their usage of hedge funds over the next three to five years."
Given this growing interest in hedge funds, we have analyzed how they might fit into our target real return portfolios. Before we begin, let’s start with a quick review of what hedge funds are, and the investment strategies they follow.
The definition of a hedge fund used by the Presidential Working Group on Financial Markets is "any pooled investment vehicle that is privately organized, administered by professional investment managers, and not widely available to the public." Hedge fund managers have much more lucrative compensation arrangements than mutual fund managers. A typical hedge fund receives an annual management fee equal to 1% to 2% of the fund’s assets, and can earn an incentive fee equal to 15% to 20% of the fund’s profits above a certain minimum level of return. Most hedge funds also include what is called a "high water mark" provision, which requires that past year’s losses be made up before this incentive fee takes effect. Given the attractiveness of this package, it should come as no surprise that many of the best mutual fund managers have left their old jobs to manage hedge funds.
A more interesting question is how hedge fund managers make money for their investors. Hedge funds are not a true asset class, in the sense that we usually use that term. Asset classes represent some type of claim on real productive assets that share common characteristics. The return on an asset within an asset class has two components: compensation for the risk of the asset class itself (also known as "beta risk"), and compensation for risks unique to the specific asset under consideration (also known as "alpha risk"). When you hold a diversified portfolio of assets from within the same asset class, the alpha risks (and the returns associated with holding them) cancel each other out, and you are left with non-diversifiable risk (also known as beta or systemic risk), and the return for holding it. When you diversify your portfolio across asset classes, the beta risk is reduced, but not eliminated.
In contrast to a true asset class, the broad term "hedge funds" refers to a very diverse collection of actively managed investment strategies which aim to maximize the return for holding alpha risk in one or more asset classes. As we have discussed in the past, there are two fundamental sources of superior active investment management performance: a manager can have better information than other investors, and/or he can have a better model for making sense of information that is available to all investors. This holds for both active mutual fund and hedge fund managers. The difference between them, however, lies in how they make use of whatever advantage they have.
In principle, an advantage can affect either where you invest, and/or how you invest. By where you invest, we mean the allocation of your investments between different asset classes, and, within those classes, between different regions (or countries), styles (e.g., momentum vs. value), sectors, and individual securities. By how you invest, we mean the extent to which you take directional bets on whatever assets you are investing in (that is, the extent to which you take long or short positions), and the extent to which you try to magnify your gains by using leverage to increase the size of your investment positions. This leverage can come either from the use of debt (e.g., margin borrowing), or derivatives (options, futures, etc.) which you can purchase for less than their full face value.
Mutual fund managers are far more limited in how they invest than are hedge fund managers. First, many mutual fund managers are expected to stay within a certain "style" category (e.g., large cap growth). As a result, their performance is usually measured relative to the relevant "style benchmark" (e.g., the S&P 500 growth index). In contrast, hedge fund managers are generally allowed to invest in a wider range of asset classes, and, as important, their performance is usually measured relative to an absolute return target (e.g., at least 12% per year), rather than any index (although that is changing, with performance versus a hedge fund index increasingly used as hedge fund investing becomes more popular).
Second, mutual fund managers are generally prohibited from taking short positions in the stocks in the stocks in which they invest. In contrast, hedge fund managers are allowed to take short positions. Practically, this "long only" constraint means that mutual fund managers can only make money from investing in assets that they believe to be undervalued, while hedge fund managers can make money from both undervalued and overvalued situations.
Third, mutual fund managers are generally limited in the amount of leverage (be it in the form of debt or derivatives) they can use to magnify their returns. From a regulatory point of view, there is a good reason for this: using leverage is a risky strategy, that magnifies not only gains, but losses as well (remember Long Term Capital Management?). Presumably, sophisticated "accredited investors" understand this risk, and are willing to take it when they invest in hedge funds, which can and do use leverage.
Now that we know, in general terms, how hedge funds make money, let’s look in somewhat more detail at the different strategies they employ.
The first major group of strategies used by hedge funds are known as "event-based" investing. "Event Driven" funds try to make their money by taking long or short positions based on their forecast about the outcome of an expected event. For example, some of these funds invest in the securities of companies involved in merger and acquisition transactions, while others invest in the debt and equity of firms facing serious financial problems. At the end of 2003, Event Drive hedge funds accounted for 17% of the assets in the CSFB/Tremont Investable Hedge Fund Index.
The second major investing strategy used by hedge funds is arbitrage. In the traditional meaning of the term, arbitrage was a low risk strategy, in which one simultaneously bought an asset in a market in which it appeared underpriced, while selling the same or a very similar asset in another market in which it appeared overpriced. As practiced by hedge funds, however, this strategy is considerably higher risk, and often involves holding open long and short (and highly leveraged) positions in assets whose alleged similarity occasionally turns out not to be the case (just ask the people who ran Long Term Capital Management).
Within the overall arbitrage strategy group, "Convertible Arbitrage" funds try to make money by taking advantage of pricing differences between a company’s convertible bonds (that is, bonds that have the option of being converted into equity shares at a later date) and its outstanding shares. For example, a hedge fund might buy a company’s convertibles while selling short its stock, assuming the latter was perceived to be overvalued. The profit on the strategy would come from both the interest earned on the bond, plus the profit earned on the short sale of the stock (when you sell a stock short, you receive a price for the shares today, but promise to deliver them at a later date. If the shares have declined in price by that date, you can buy the shares you need to deliver for a price that is lower than what you have received for them). However, because the profit margins on these convertible arbitrage trades are usually small, hedge funds in this category generally use substantial amounts of leverage to magnify their returns. At the end of 2003, approximately 11% of the total amount invested in hedge funds tracked by the CSFB/Tremont Index was invested in funds in this category.
"Fixed Income Arbitrage" funds try to profit by taking advantage of pricing differences between similar fixed income securities (buying the undervalued one, and shorting the overvalued one). Again, because the profit margins on individual transactions are small, these funds typically use large amounts of leverage. Long Term Capital Management was in this category, and provides a vivid example of how high leverage can quickly lead to a hedge fund’s demise if its view of the market proves incorrect. Fixed Income Arbitrage funds accounted for 10% of the total amount invested in the hedge funds tracked by CSFB/Tremont.
A third type of hedge fund is often included in the arbitrage category, but its fit there is awkward at best. The managers of "Equity Market Neutral" funds essentially hunt for pure alpha. That is, they take long and short positions in different companies depending on their view of those companies expected future performance relative to the overall market. However, they do not take any overall equity market (beta) risk, as they hedge it away by using derivative contracts, or by taking offsetting long and short positions. At the end of 2003, Equity Market Neutral funds accounted for 10% of the total capital of the CSFB/Tremont Hedge Fund Index.
The majority of money invested in hedge funds, however, is not in any of the strategies we have already discussed, but rather in what are broadly called "directional strategies." These funds try to earn high returns by taking large directional bets, in the expectation that overvalued assets they are short will fall in price and undervalued assets they are long will rise in price. Because directional trading typically generates higher profit margins per transaction, these funds generally use less leverage than the arbitrage funds.
"Long/Short Equity" funds are different from market neutral funds in that the long and short positions they take may be of different sizes. Long/Short funds may either invest in a broad range of asset classes, or be more narrowly focused (e.g., a biotechnology hedge fund). At the end of 2003, they accounted for 13% of the CSFB/Tremont Index.
"Global Macro" funds hunt for alpha using a market timing approach. They take long and short positions across a very broad range of asset classes and markets around the world, depending on their view of their respective future returns. These funds may also use substantial amounts of leverage on a tactical basis to increase the potential payoffs from some of their directional bets. Famous hedge funds, such as George Soros’ Quantum Fund or Julian Robertson’s Tiger Fund are in this class. They accounted for 13% of the CSFB/Tremont Index at the end of 2003.
"Managed Futures" funds invest in listed financial and currency futures, and their managers are usually called commodity trading advisors, or CTAs. They often employ momentum strategies. These funds accounted for 10% of the hedge fund assets tracked by CSFB/Tremont at the end of 2003.
"Emerging Markets" funds try to make money through superior market timing and security selection in markets that are often less liquid than those of developed countries. They accounted for 3% of the total hedge fund assets tracked by CSFB/Tremont at the end of 2003.
"Dedicated Short Bias" funds have greater than fifty percent of their assets invested in short equity market positions. Because of the difficulty of making money over the long term taking this approach (given that the economy grows, and markets rise, in far more years than they fall), dedicated short funds accounted for only 2% percent of total hedge fund assets at the end of 2003.
Finally, hedge funds which employed multiple investing strategies accounted for 11% of hedge fund assets at the end of 2003.
Now let’s move on to our analysis of how hedge funds fit into an investor’s portfolio.
In conducting this research, our first problem was the quality of the available data on hedge fund returns. To put it mildly, it is questionable at best. Because this data underlies much of the current enthusiasm for investing in hedge funds, it is critical that people understand its limitations.
To begin with, there are at least ten different indexes that claim to track the performance of the hedge fund universe. However, many of these indexes are constructed using different methodologies (e.g., how they classify different hedge fund strategies, whether they use equal or market capitalization based weighting, and whether they require audited results from the funds they include). Just to make things more interesting, hedge fund managers themselves decide whether or not to report their results to an index provider. For example, a fund with a poor performance record may choose not to report its results. At the other extreme, a fund with an outstanding performance record, which is closed to new investors, also may choose not to report its results. This is called "self-selection bias."
Moreover, reporting funds provide their results to different index providers. As a result, no index comes close to covering the entire hedge fund universe. But the problems don’t end there. When a hedge fund initially decides to report its results to an index provider, it delivers not only its current and future returns, but also a history of its past returns as well. Unfortunately, in the case of an indexed hedge fund product, you can only invest in a fund after it has been added to the index. In other words, what counts from an index investor’s point of view is performance after a fund has been added to an index, not before it. The extent to which fund returns are lower after they join an index than they were before this point is called "backfill bias."
Finally, the treatment of funds that leave an index can also create bias in the reported index returns. If either the returns of these funds are removed from the index database after they stop reporting, or if (in the case of failing funds) their final returns are not obtained, then the reported index returns can be biased upwards. This is known as "survivorship bias."
While a number of authors have examined the potential impact of these different biases, one of the best papers we’ve read on the subject is "A Reality Check on Hedge Fund Returns" by Posthuma and Van der Sluis. They directly examined the backfill bias in the TASS database (which contains over 3,000 hedge funds) over the period 1996 to 2002 (previous studies had only estimated the size of the problem). The authors found that more than half the reported returns in the database were backfilled. They went on to create a proxy for a truly investable index by (a) using only non-backfilled returns, (b) including the returns from funds which left the index; and (c) using two different approaches to estimate the final returns from failing funds (one assumed that investors received all their money back, while they second assumed a 50% loss of capital). When constructing their index, the authors equally weighted each hedge fund’s return. This is the practice used by almost all the major hedge fund index vendors, except CSFB/Tremont, which weights funds’ returns by their assets under management (in line with the way most other asset class indexes are constructed).
Posthuma and Van der Sluis found that due to backfill bias, average annual hedge fund index returns were overstated by 4.35% during the 1996 – 2002 period (10.73% before the bias was removed, versus 6.34% after). By strategy, the impact of backfill bias ranged from a high of 6.34% for Long/Short Equity to 3.13% for Global Macro, 2.60% for Equity Market Neutral, and 2.45% for Event Driven. However, these returns assumed that investors suffered no loss after a fund left the investable index. When the authors assumed that such funds incurred a 50% loss of capital, the overall return on the index declined to 7.43%, and the backfill bias rose to 7.24% -- essentially leaving the overall investable index return equal to zero. Moreover, the authors also found that the net impact of these biases also distorted (unfavorably) various measures of risk. Let’s look at these. Standard deviation (also known as volatility) measures the extent to which returns are disbursed around their average. In general, investors who are risk averse prefer lower levels of standard deviation, and seek to maximize the amount of return per unit of volatility they take on. Posthuma and Van der Sluis found that backfill and survivorship biases artificially lowered their hedge fund index’s reported standard deviation. Skewness measures the extent to which positive or negative returns are more probable. In a positively skewed distribution (which risk-averse investors prefer), positive returns are more probably than negative ones. A normal (bell curve) distribution has skewness equal to zero, because positive and negative returns are equally probable. In this case, the authors found that removing the survivorship and backfill biases made hedge fund returns’ skewness more negative. Finally, kurtosis measures the "peakedness" of the distribution of returns, relative to what would be found in a normal distribution. Positive kurtosis means the distribution has "fatter tails" than a normal distribution. Practically, positive kurtosis means that extreme returns – both positive and negative (skewness tells you which is more likely) – are more likely than they would be if returns were normally distributed. Investors’ kurtosis preference depends on the skewness of the distribution. If it is negative, risk-averse investors dislike positive kurtosis, because it means that big negative returns are more likely than big positive ones. On the other hand, if a distribution is positively skewed, then a risk averse investor may prefer somewhat higher than normal kurtosis, which would raise the probability of realizing big positive returns.
In their study of hedge fund returns, Posthuma and Van der Sluis found that the backfill and survivorship bias tended to depress reported kurtosis. Similar findings on the impact of survivorship and backfill bias on reported average returns, standard deviation, skweness and kurtosis were also reported by Professor Ross Barry of Macquarie University in his paper "Hedge Funds: A Walk Through the Graveyard." Last but not least, we should also mention that Posthuma and Van der Sluis found that there was "no persistence between the returns of the backfilled hedge fund returns and the non-backfilled returns." In other words, what is true of mutual funds also seems to be true in the hedge fund world: you can’t use past performance to pick future winners.
Impressive as it was, Posthuma and Van der Sluis’ study left out another important bias. The returns that hedge fund managers report each month to various index providers are based, in part, on changes in the market value of the assets in which they have invested. However, if those assets are sufficiently illiquid (as would be the case for example, with some distressed debt, exotic derivative instruments, or privately placed equity), it can be very difficult to obtain accurate market prices for them each month. As a result, estimated values are frequently used, in an approach that is not dissimilar to the way residential real estate is often re-valued by appraisers during the long period in between market transactions. In both cases, the appraisal approach leads to a higher degree of correlation between asset prices in succeeding periods than is normally found in liquid markets. Statistically, this is called "autocorrelation." Practically, the autocorrelation bias in some hedge fund returns causes reported standard deviations to be lower than what their "true" value probably is.
Perhaps the greatest limitation of most studies of hedge fund performance is the relatively short periods they cover. More than anything else, this is a function of the length of the available hedge fund index data series, which generally only go back as far as 1994. A number of researchers have tried to overcome this limitation by using regression modeling to artificially create a longer series of hedge fund performance data. In practice, this involves regressing the performance of a hedge fund (or hedge fund index) against the values of a number of other independent variables that have longer data series. If the model produces a reasonably good fit between the actual and predicted hedge fund performance, then the historical values of the independent variables can be used to project back into the past a longer series of estimated hedge fund returns. Of course, developing these regression models is not without its challenges, including which independent variables to use, and the actual form of the model itself (e.g., should it be a simple linear model or a more complex polynomial one?).
One of the most interesting approaches of this type is described in the paper "Risks and Portfolio Decisions Involving Hedge Funds" by Agarwal and Naik. They started with the observation that "a large number of equity oriented hedge fund strategies exhibit payoffs [return distributions] resembling a short position in a put option on the market index, and therefore bear significant left tail risk." As we shall see, this is a very important point to keep in mind.
For those readers who are a little unclear about what it means to be short a put option, it means that you are, in essence, an insurance company. In selling (or, as it is known, "writing") a put option you have promised the other party that, for a specified period of time (say the next 360 days), you stand ready to purchase a specified quantity of an asset (say, 1,000 shares of the exchange traded index fund that tracks the Russell 3000 Index) at a specified "strike price" (say, $65 per share). In exchange for making this promise (or, to be more accurate, taking on this risk), the purchaser pays you a premium. Now let’s think about what happens next. Suppose that over the next year, the R3000 ETF never trades below $65/share, and the holder of the option you have sold therefore chooses not to exercise it. At the end of the year, you add the option premium you earned to your other gains and losses to calculate the total return on your investment portfolio. You notice that said return is higher because of the option premium, so you decide to do the same thing again next year. And again, the ETF never trades below $65/share. So you do it again for a third year, and, again, nothing happens. Now think about how what you have done has affected the three-year portfolio returns you have reported to your key investors (say, your spouse).
First, the option premiums you earned raised your reported average return. Second, because those premiums were constant over three years, they reduced the reported standard deviation of your reported returns. Third, if in any year the option premium turned what otherwise would have been a negative return into a positive one, they made the skewness of your returns look more positive. Finally, because you never had to pay out under the insurance contract (er, the put option), you had no big negative returns – so selling the put option had no affect on the reported kurtosis of your returns.
By now, I rather suspect you’re getting the larger picture: Agarwal and Naik’s finding that "a large number of equity oriented hedge fund strategies exhibit payoffs [return distributions] resembling a short position in a put option on the market index, and therefore bear significant left tail risk" is potentially a ticking time bomb for all those people (think back to whichever self-style "hedge fund guru" you most recently encountered) who believe that, in essence, hedge funds are a free lunch that can magically improve a portfolio’s risk/return tradeoff.
This really isn’t news, however. The hints have been there for quite a while. It wasn’t just Long Term Capital Management that was brought down by the Russian Debt Crisis in 1998; a lot of other hedge funds went out of business then too. And, as we will demonstrate below, despite their acknowledged shortcomings, the existing hedge fund return indexes still give an indication that this is a very different animal from other asset classes.
But let’s for the moment go back to our investor who had discovered the joys of selling put options. Say he does it again in year four, but this time the market tanks, and the R3000 ETF drops to $25/share. At this price, the holder of the put option exercises it, which forces our intrepid investor to pay $65,000 for 1,000 ETFs that are only worth $25,000. Given his track record over the previous three years, that big a loss seems like it will take quite a bit of explaining to his investors (ouch!). On the other hand, a statistical analysis of his reported returns will finally reflect the large risk (in the form of greatly increased standard deviation and kurtosis, as well as negative skewness) that was lurking all along in his investment strategy. The moral of the story is simple. Investing is like the rest of life: if something seems too good to be true, it probably is too good to be true.
But back to Agarwal and Naik’s paper. When they used their regression model (which included option payoffs as some of its factors) to extend their estimated hedge fund index return data series back to 1927, they made another disturbing discovery. Hedge funds’ recent performance seems to be significantly better than their long-term performance. More specifically, they found that their projected average historical hedge fund returns were significantly lower, and their standard deviation higher than those estimated using just their more recent performance. Seems like another caution flag to us.
With these shortcomings in mind, we set out to explore the potential impact of using hedge funds in model portfolios with different compound annual real return objectives.
Our first step was to choose an index to use. We decided on the CSFB/Tremont Index, because it is (a) has a history dating back to 1994; (b) is relatively free of survivorship bias, and (c) is the only major index that is asset weighted. The latter factor makes it more comparable with the other returns series we use in our analysis.
Our next step was to develop inputs to use in our simulation optimization model. We began by looking at both the overall index, and a number of strategy sub-indexes, including Equity Market Neutral, Global Macro, and Event Driven. We chose the first two because our past analyses had found them to be potentially valuable additions to a portfolio, in terms of their impact not only on returns, but also on standard deviation, correlation, skewness and kurtosis (for a paper which also reaches this conclusion, see "Fund of Funds Portfolio Selection" by Davies, Kat, and Lu). We chose Event Driven because it provides a good contrast with the first two strategies.
The following table shows summary real return data covering the 1994 –2003 period.
| A$ | Aggregate Index | Equity Mkt. Neutral | Global Macro | Event Driven |
| Average Real Return | 9.58% | 4.78% | 14.46% | 8.38% |
| Std. Deviation | 12.38% | 10.04% | 15.86% | 9.76% |
| Average/Std. Deviation | 0.77 | 0.48 | 0.91 | 0.86 |
| Skewness | (0.06) | 0.50 | 0.11 | (0.02) |
| Kurtosis | (-.27) | (0.82) | (0.06%) | (0.86) |
| C$ | Aggregate Index | Equity Mkt. Neutral | Global Macro | Event Driven |
| Average Real Return | 9.36% | 8.57% | 13.10% | 9.46% |
| Std. Deviation | 8.38% | 6.02% | 12.51% | 6.53% |
| Average/Std. Deviation | 1.12 | 1.42 | 1.05 | 1.45 |
| Skewness | 0.30 | (0.21) | 0.01 | (1.18) |
| Kurtosis | (0.07) | (0.08) | 0.98 | 3.49% |
| Euro | Aggregate Index | Equity Mkt. Neutral | Global Macro | Event Driven |
| Average Real Return | 8.18% | 7.40% | 11.89% | 8.28% |
| Std. Deviation | 13.71% | 9.94% | 16.79% | 11.67% |
| Average/Std. Deviation | 0.60 | 0.74 | 0.71 | 0.71 |
| Skewness | 0.33 | 0.16 | 0.40 | (0.73) |
| Kurtosis | 0.33 | (0.29) | 1.08 | 2.00 |
| Yen | Aggregate Index | Equity Mkt. Neutral | Global Macro | Event Driven |
| Average Real Return | 11.37% | 10.56% | 15.17% | 11.46% |
| Std. Deviation | 16.87% | 12.25% | 20.74% | 14.34% |
| Average/Std. Deviation | 0.67 | 0.86 | 0.73 | 0.80 |
| Skewness | (0.46) | (0.34) | (0.53) | (0.89) |
| Kurtosis | 2.83 | 1.61 | 4.24 | 2.57 |
| UK Pound Sterling | Aggregate Index | Equity Mkt. Neutral | Global Macro | Event Driven |
| Average Real Return | 7.19% | 6.41% | 10.86% | 7.28% |
| Std. Deviation | 11.81% | 8.34% | 14.94% | 10.16% |
| Average/Std. Deviation | 0.61 | 0.77 | 0.73 | 0.72 |
| Skewness | 0.46 | 0.47 | 0.56 | (1.23) |
| Kurtosis | 1.74 | 1.14 | 1.12 | 6.31 |
| US$ | Aggregate Index | Equity Mkt. Neutral | Global Macro | Event Driven |
| Average Real Return | 8.86% | 8.07% | 12.58% | 8.95% |
| Std. Deviation | 8.52% | 3.08% | 12.18% | 6.04% |
| Average/Std. Deviation | 1.04 | 2.62 | 1.03 | 1.48 |
| Skewness | 0.06 | 0.10 | (0.60) | (3.36) |
| Kurtosis | 1.61 | 0.41 | 1.89 | 22.18 |
These tables are interesting for a number of reasons. First, most studies done to date have used hedge fund returns in U.S. dollars (the currency in which over 80% of hedge funds report their returns). As you can see, the U.S. dollar table confirms the findings from many of these studies that, at the level of the aggregate index, hedge funds’ impressive ratio of average return/standard deviation also requires the acceptance of quite a high level of kurtosis (i.e., a greater probability of experiencing extreme returns). The U.S. dollar table also show the relative attractiveness of the Equity Market Neutral strategy, and the unattractive skewness and kurtosis characteristics of the Event Driven strategy.
What is equally interesting, however, is the way exchange rate changes affect the perception of these strategies’ results when they are expressed in different currencies. In general, the relationship between the Equity Market Neutral and Event Driven strategies remains the same. An exception to this, however, is the table showing real hedge fund returns expressed in Australian dollars, where the Global Macro approach comes out best.
Despite the attractiveness of Equity Market Neutral relative to the aggregate hedge fund index, we chose to use the latter in our asset allocation analysis because the only hedge fund index products available thus far are based on this measure. While we performed a sensitivity check to get a rough idea of the impact of moving away from the aggregate index, we did not, in this analysis, include Equity Market Neutral and Global Macro as separate asset classes.
In our analysis, we first used our simulation optimization model to develop optimal target return portfolios using inputs based on historical data. For hedge funds, we used returns from 1994 to 2003; for the other asset classes we used returns from 1973 to 2002. The correlation matrix we used, however, covers only the 1994 to 2003 period. Our second step was to repeat the portfolio construction process using our estimated future returns for each asset class as inputs. We then combined the historical and forward looking portfolios, weighting the former 67% and the latter 33%.
For hedge funds, our examination of comparable historical data showed a rather close relationship between the return on the aggregate hedge fund index and the return on the Wilshire 5000 U.S. equity index, albeit with a substantially lower standard deviation. The following table shows this data:
| Currency |
Average Hedge Fund Index Real Return (1994-2003)
|
Average Wilshire 5000 Real Return
(1994 - 2003) |
| A$ | 9.58% | 8.43% |
| C$ | 9.36% | 9.57% |
| Euro | 8.18% | 8.39% |
| Yen | 11.37% | 11.58% |
| UK Pound Sterling | 7.19% | 7.39% |
| US $ | 8.86% | 9.06% |
Given this historical data, we set our future hedge fund real return assumptions equal to the expected local currency real return on the Wilshire 5000 Index.
For the sake of brevity, the rest of this section will focus on our analysis of the impact of including hedge fund index products in U.S. Dollar portfolios whose objective is a minimum compound annual real rate of return. The inputs for this analysis are summarized in the following table:
| US$ |
Hist Ret
|
Fut Ret
|
Std Dev
|
RB
|
DB
|
FB
|
CP
|
C
|
DE
|
RE
|
EE
|
HF
|
| Real Bonds | 2.30% | 2.50% | 2.50% | 1.00 | 0.43 | 0.35 | -0.06 | 0.26 | -0.16 | -0.13 | -0.14 | -0.04 |
| Dom Bonds | 3.80% | 4.00% | 5.40% | 1.00 | 0.30 | 0.01 | 0.04 | -0.06 | -0.11 | -0.16 | 0.16 | |
| For Bonds | 9.50% | 3.61% | 11.20% | 1.00 | 0.02 | 0.17 | -0.02 | 0.23 | -0.05 | -0.18 | ||
| Comm Prop | 7.90% | 3.70% | 9.80% | 1.00 | 0.10 | 0.32 | 0.29 | 0.33 | 0.23 | |||
| Commodities | 8.10% | 8.10% | 18.30% | 1.00 | 0.07 | 0.14 | 0.12 | 0.17 | ||||
| Dom Equity | 7.30% | 6.20% | 16.30% | 1.00 | 0.79 | 0.73 | 0.54 | |||||
| For Equity | 7.00% | 5.57% | 17.20% | 1.00 | 0.71 | 0.42 | ||||||
| EM Equity | 9.60% | 7.50% | 24.00% | 1.00 | 0.52 | |||||||
| Hedge Funds | 8.86% | 6.20% | 8.52% | 1.00 |
Given the relatively questionable quality of the hedge funds return data we used, we capped the maximum allowable allocation to hedge funds at twenty percent of the target return portfolios.
The following tables show the impact of including a hedge fund index as a possible investment alternative in portfolios with target compound annual real returns (in US Dollars) of 7%, 5%, and 3%.
| US$ | 7% Historical | 7% Future |
7% Weighted |
| Real Return Bonds | 5% | 0% | 3% |
| Domestic Bonds | 0% | 0% | 0% |
| Foreign Bonds | 40% | 0% | 27% |
| Commercial Property | 20% | 0% | 13% |
| Commodities | 5% | 20% | 10% |
| Domestic Equity | 5% | 50% | 20% |
| Foreign Equity | 0% | 0% | 0%q |
| Emerging Equity | 10% | 15% | 12% |
| Hedge Funds | 15% | 15% | 15% |
| Total | 100% | 100% | 100% |
| Expected Annual Return | 8.3% | 6.8% | N/A |
| Expected Std. Deviation | 6.9% | 12.9% | N/A |
| Probability of Achieving Target | 76.0% | 38.0% | N/A |
| US$ | 5% Historical | 5% Future |
5% Weighted |
| Real Return Bonds | 5% | 5% | 5% |
| Domestic Bonds | 30% | 0% | 20% |
| Foreign Bonds | 30% | 5% | 22% |
| Commercial Property | 10% | 0% | 7% |
| Commodities | 5% | 20% | 10% |
| Domestic Equity | 5% | 50% | 20% |
| Foreign Equity | 0% | 0% | 0% |
| Emerging Equity | 5% | 10% | 7% |
| Hedge Funds | 10% | 10% | 10% |
| Total | 100% | 100% | 100% |
| Expected Annual Return | 6.9% | 6.4% | N/A |
| Expected Std. Deviation | 5.3% | 11.7% | N/A |
| Probability of Achieving Target | 93.0% | 62.0% | N/A |
| US$ | 3% Historical | 3% Future |
3% Weighted |
| Real Return Bonds | 55% | 15% | 42% |
| Domestic Bonds | 15% | 20% | 17% |
| Foreign Bonds | 10% | 15% | 12% |
| Commercial Property | 10% | 10% | 10% |
| Commodities | 5% | 10% | 7% |
| Domestic Equity | 5% | 10% | 7% |
| Foreign Equity | 0% | 5% | 2% |
| Emerging Equity | 0% | 5% | 2% |
| Hedge Funds | 0% | 10% | 3% |
| Total | 100% | 100% | 100% |
| Expected Annual Return | 4.3% | 4.8% | N/A |
| Expected Std. Deviation | 3.2% | 5.7% | N/A |
| Probability of Achieving Target | 97.0% | 91.0% | N/A |
Finally, as another way of assessing the potential impact of adding hedge funds to these target return portfolios, we backtested them using data from 1994 – 2003. When we were doing this, we also ran a simple sensitivity analysis to test the potential impact of using index funds tied to something other than the aggregate hedge fund index. Specifically, while we kept our hedge fund allocations unchanged, we substituted a simple mix of 50% Equity Market Neutral Index return and 50% Global Macro Index return for the Aggregate Hedge Fund Index Return. In the following table, these results are labeled "w/EG". The results were encouraging, and indicate that a superior asset allocation solution probably could be achieved by using a mix of hedge fund style indexes, rather than the aggregate index. We will undertake a more comprehensive analysis when and if hedge fund style-based index products are introduced.
1994 –2003 Backtesting Analysis
| US$ | 3% Real Tgt | 3% Tgt w/HF | 3% Tgt w/EG |
| Average Real Return | 4.96% | 5.15% | 5.20% |
| Std. Deviation | 3.96% | 3.96% | 3.92% |
| Average/Std. Deviation | 1.25 | 1.30 | 1.33 |
| Skewness | 0.22 | 0.26 | 0.29 |
| Kurtosis | 1.29 | 1.48 | 1.50 |
| CAGR 1994-2003* | 4.71% | 4.92% | 4.92% |
| US$ | 5% Real Tgt | 5% Tgt w/HF | 5% Tgt w/EG |
| Average Real Return | 6.04% | 6.16% | 6.30% |
| Std. Deviation | 8.09% | 6.25% | 6.01% |
| Average/Std. Deviation | 0.75 | .99 | 1.05 |
| Skewness | -0.65 | -0.41 | -0.35 |
| Kurtosis | 1.11 | 0.76 | 0.63 |
| CAGR 1994-2003* | 5.45% | 5.75% | 5.92% |
| US$ | 7% Real Tgt | 7% Tgt w/HF | 7% Tgt w/EG |
| Average Real Return | 5.81% | 6.53% | 6.74% |
| Std. Deviation | 9.03% | 7.96% | 7.59% |
| Average/Std. Deviation | 0.64 | 0.82 | 0.89 |
| Skewness | -0.67 | -0.73 | -0.66 |
| Kurtosis | 1.47 | 1.69 | 1.49 |
| CAGR 1994-2003* | 5.13% | 5.98% | 6.24% |
Looking at these results, (as well as results denominated in five other currencies, which are not shown here) made a number of points clear. First, the potential impact of hedge funds seems to depend on a portfolio’s target real return. For the 3% target real return portfolios, the impact was minimal, and the impact of improved return/standard deviation was usually offset by higher kurtosis (the impact on skewness was usually minimal). The inclusion of hedge funds seemed to provide the greatest benefits to the 5% target real return portfolios, and more often than not, this did not require taking on more skewness and/or kurtosis-related risk. On the other hand, the benefits of hedge funds to the 7% target real return portfolios, while usually significant in the area of return/standard deviation, typically required a worsening of those portfolios’ skewness and kurtosis. Finally, as previously noted, even a simple replacement of the aggregate hedge fund index returns with a 50/50 mix of Equity Market Neutral and Global Macro style index returns in many cases significantly reduced hedge funds’ negative impacts while preserving many of their portfolio return and standard deviation benefits.
However, we reiterate that when it comes to investing in this area, a healthy degree of skepticism and caution are still warranted. Not only is the quality of the underlying data suspect, but a larger question remains unanswered. Can it really be that almost 7,000 hedge fund managers possess either the superior information or the superior models needed to consistently deliver superior returns over a long period of time, even as more and more money is pursuing the same general investment strategy? Wouldn’t this also imply the equally sudden development of an opposite class of traders who are somehow doomed to be consistent losers?
| Market Condition |
Normal
|
Inflation
|
Deflation
|
| Reasons to Invest in Absolute Value (Hedge Fund) Strategies |
Equity Market Neutral seems likely to boost returns while lowering portfolio risk Global Macro can provide additional returns through tactical asset allocation for a small portion of your portfolio, minimal additional risk |
Since all equity is a claim on residual cash flow, and since companies can eventually adjust their prices when faced with inflation, equity returns should suffer less than fixed rate bond returns
Unsettled environment should favor Global Macro strategy |
Unsettled environment should favor global macro strategy. |
| Reasons Not to Invest in Absolute Value (Hedge Fund) Strategies |
Liquidity is low, so not appropriate for investors who make regular withdrawls from portfolio. With large amount of new money flowing into hedge funds, historical risk/return relationships will probably worsen in the future |
Other asset classes (e.g., real return bonds, commodities, and residential property) provide better protection against inflation
Hedge funds haven't really been tested under these conditions |
Other asset classes – such as investment grade bonds – provide better protection against deflation than equity (including equity market neutral funds). |