Kat: Why Accurately Replicated Hedge Fund Indices Won’t Do You Much Good

“Kat’s Meow” – An occasional guest column for AllAboutAlpha

By: Professor Harry Kat, Cass Business School, City University (London)

Judging by the number of investment banks and asset managers jumping on the bandwagon, the media attention and the number of forthcoming conferences on the subject, hedge fund replication has definitely arrived in 2007. However, since it is all a bit new and all a bit much so suddenly, many investors seem to be somewhat confused and unable to ask the right questions. This brief note is meant to provide some guidance in this area.

A little bit of history first. Initially, hedge funds were sold on the promise of superior performance – the story being that hedge fund managers’ long experience and proven investment skills were a virtual guarantee for superior returns. Especially high net worth investors were sensitive to these arguments and fuelled much of the early growth of the industry. Starting in the late 1990s, hedge fund performance took a turn for the worst, however, with every next year being worse than the year before. The story changed accordingly. No longer were hedge funds sold purely on the promise of superior performance, but more and more on the basis of a diversification argument, pointing at hedge funds’ relatively low correlation with stocks and bonds and the beneficial effects on risk and return from including hedge funds in the traditional investment portfolio. Especially institutional investors proved sensitive to these arguments and started to pour large amounts of money into the sector, in the process making up for the outflow of some of the early private investors. Nowadays, there aren’t many serious investors left who still expect hedge funds to provide them with superior returns. The hedge fund game these days is all about diversification.

Now let’s have a closer look at hedge fund replication. What are the attractions of replicated hedge funds? The main attraction is that there are no expensive managers to pay. Since the bulk of the hedge fund and fund of funds managers (we estimate 80%) are unable to make up for the fees that they charge, that is a very good thing. In addition, replicas solve a range of other problems as well, including liquidity, transparency and capacity problems, just to name a few. Altogether, this makes for a very attractive package: hedge fund returns at lower cost and without the usual hassle. The above almost sounds too good to be true, and unfortunately in many cases it is. Research has shown that the models used by the big name hedge fund replicators (see later) are unable to accurately replicate individual hedge funds as well as most hedge fund indices.

The only indices that can be replicated with reasonably accuracy are the indices that contain so many different funds that there is nothing hedge fund-like or ‘alternative’ about them. Since replication accuracy is on top of their wish list, these are the indices that the main hedge fund replicators aim to replicate.

At the time of writing only one replication product is really live: the Merrill Lynch Factor Index. Goldman Sachs announced a very similar product, to be called the Goldman Sachs Absolute Return Tracker Index, last year, but hasn’t actually launched it yet. Recently, JP Morgan announced that in the second quarter of this year it intends to launch what will be called the JP Morgan Alternative Beta Index. State Street Global Advisors as well as some others also look set to launch hedge fund replication products.

In what follows I will concentrate on the Merrill Lynch Factor Index. Not because I have an axe to grind with Merrill, but simply because there is not yet enough information about the forthcoming Goldman, JP Morgan and State Street products. It is, however, quite likely that these products will be very similar to the Merrill product (although the designers and marketers of these products will of course claim otherwise).

The ML Factor Index aims to replicate the HFRI Composite index, a basket of 1800 hedge funds following all kinds of strategies. Since it is extremely diversified, the peculiarities of the various strategies simply diversify away. The result is an index with mainly traditional risk exposures and very little is â€˜alternative’ about it. This is exactly why this index can be accurately replicated. It is, however, also exactly why investors should not be interested in this index. They are already exposed to all these risks and adding more of the same to a portfolio doesn’t provide any diversification benefits.


To illustrate the above point, Figure 1 shows the evolution of the S&P 500, the HFRI Composite index, the ML Factor Index and the HFRI Fund of Funds index since January 2003. Comparing the plots for the three hedge fund indices with that for the S&P 500 we see that the differences are only small. None of the three hedge fund indices seems to contain many ‘alternative’ ingredients. To a large extent, they simply mimic the S&P 500. This is also evident from the correlation coefficients. Over the period studied, both the HFRI Composite and the ML Factor Index had a correlation of 0.76 with the S&P 500. Instead of buying into the ML Factor Index for 100bps you might as well buy S&P 500 futures for 1bp and add the 99bps difference to your expected return.

As Figure 2 shows, the forthcoming JP Morgan product suffers from the same deficiency. Similar to the ML Factor Index, the JP Morgan Alternative Beta Index tends to move in virtual lock step with the HFRI Composite and Fund of Funds indices.


A second indication of the lack of ‘alternative’ elements in the current replication products can be obtained by looking at the composition of the ML Factor Index portfolio, which is shown in Table 1. From Table 1 we see how traditional the ML Factor Index portfolio really is. It contains the S&P 500, USD, MSCI EAFE, MSCI Emerging Markets, Russell 2000 and 1-month Libor. Nothing alternative about it. The result is evident: a high correlation with traditional asset classes and therefore little or no diversification benefits. This clearly shows what focussing on replication accuracy does: it leads one to replicate those indices that are most traditional in nature. Unfortunately, these indices are also the least interesting from a diversification perspective.


So maybe it is better to forget about replication accuracy altogether and just invest in a replication strategy of which we know upfront that it doesn’t work? Doing so, we at least have a chance of ending up with something that adds value in a portfolio context. Although this is true, the problem with such a fatalistic approach is that it leaves an awful lot to chance. With an index that can be replicated we can study the track record of the index to get an idea of the kind of returns we can expect. With an index that can’t be replicated, we don’t have that luxury. In that case we can only look at the backtested strategy results, which will necessarily cover a significantly shorter period of time than the track record of the index that is being replicated.

Another area where hedge fund replication products fail is the stability of the risk profile generated. Since the prime focus is on replication accuracy instead of a stable bottom-line risk profile, the risk profiles of the resulting portfolios can be quite unstable. Figure 3 for example, shows the weights of the various assets making up the ML Factor Index portfolio. From this graph it is obvious that the composition, and thereby the risk profile, of the replicating portfolio is capable of changing very substantially over time. For example, in February 2002 the portfolio was 20% long S&P 500, but in July 2005 it was 20% short S&P 500.

What does the future hold for hedge fund replication? Given the global distribution and marketing power of Merrill, Goldman, JP Morgan and State Street, there is no doubt that they will manage to raise serious money with these hedge fund replication products. Investors buying into these products with the aim to diversify their traditional portfolios, however, are likely to be disappointed as there is very little ‘alternative’ about these products, resulting in very high correlation with traditional asset classes and correspondingly low diversification benefits. Caveat Emptor!

– Harry M. Kat, March 3, 2007 

Professor Harry Kat is an occasional contributor to AllAboutAlpha.com. The opinions expressed herein are those of Professor Kat and not necessarily those of Alpha Male or AllAboutAlpha.com.

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  1. Noël Amenc and Lionel Martellini
    May 13, 2013 at 9:06 am

    In the present article, a number of comments are made about a paper we published in the European Financial Management Journal (Amenc, N., L. Martellini, J.-C. Meyfredi and V. Ziemann, 2010, Passive hedge fund replication — Beyond the linear case, European Financial Management Journal, 16, 2, 191-210.) We feel that some of these comments deserve a response, as they might be misleading for the reader who is not overly familiar with the research.

    The contribution of our paper published in the European Financial Management Journal is to extend Hasanhodzic and Lo (2007) by assessing the out-of-sample performance of various non-linear and conditional hedge fund replication models. In a nutshell, our ambition was to improve the performance results in Hasanhodzic and Lo (2007) by considering more sophisticated models and we were quite surprised to find that going beyond the linear case does not necessarily enhance the replication power.

    The article “Hedge Fund Replication: A Re-examination Of Two Key Studies” claims that our paper “includes some very helpful analysis […] but whose conclusions are undermined by selective omission. For instance: Even though there was over two years of live data from replication indices that showed strong results with high correlation through the crisis, the authors neglect to include this and focus instead on re-doing the Lo analysis with the admittedly incomplete five factor set.” We indeed decided not to analyze live performance data from passive replication products, and this for two reasons. First of all, this was not the focus of our paper, which instead was (as mentioned above) to try and improve over linear replication models. Secondly, we did not feel that two years was a sufficiently long sample to allow for any meaningful statistical analysis. It is our belief that showing coherence in the scope of a research project paper, and avoiding meaningless statistical analysis, should be called something other than “selective omission.”

    A second criticism made in the article “Hedge Fund Replication: A Re-examination of Two Key Studies” is that we use a selection of useful factors for each strategy, as opposed to using a large identical set of factors for all strategies. This criticism is phrased as follows: “By starkly reducing the factor set, the authors essentially designed an experiment that was bound to fail.” Unfortunately, we have found that while including more factors improves the performance of replication models in-sample, it tends to hurt the out-of-sample performance of such models. In short, parsimony is a well-known necessary condition for out-of-sample robustness, and we feel it is misleading to claim that one only needs to add an increasingly large number of factors to generate satisfactory hedge fund replication performance.

    Finally, a comment is made that “It is difficult to read this paper without the sense that the authors, who are closely tied to the fund of hedge fund industry (and funded by Newedge), had a predetermined agenda.” We feel that this comment is out of place. EDHEC-Risk has always been known for publishing unbiased academic research and at no point in the research process did Newedge intervene to influence the results in any possible way. In the same way that we consider that professionals have the right to be taken seriously and to be criticized for what they write and not for what they are when they express scientific views, we believe that authors from the academic world deserve the same level of respect. If the only real argument for criticizing a research paper is to disparage the authors’ conduct with no evidence, then we do not think that this criticism is admissible. Just like we think it is logical to display the financial contributions to our research programs that the Institute receives from our sponsors (the authors are not beneficiaries), we also think it is logical for this concern for transparency and the sponsor’s desire to support transparent and independent research to be recognized and not denigrated.

  2. Andrew Beer
    August 15, 2013 at 12:05 pm

    Dear Professors Amenc and Martellini:

    My apologies for the delayed response. Thank you for taking the time to read and provide a critique of my note. I will respond in order:

    1. The reason I highlighted the live performance was that the extant replicators were largely successful at tracking industry returns in 2007-09. As a practitioner and researcher, this is valuable information. It was not a coincidence that the models had similar features — 24 month window length, 5-8 factors across major asset classes, etc. Those parameters were chosen because highly capable researchers at investment banks and asset management firms were conducting similar experiments and reached similar conclusions. Consequently, it seemed to me at the time that there was a lot of other research and live validation that a variant on the Hasandhozic/Lo experiment could work well. This seemed pertinent to me, but perhaps it wasn’t to you.

    2. We’ve found that more factors do in fact improve out of sample results, but up to a limit. Since I don’t have your research, I cannot comment on the specific results you refer to. I fully agree with the concept of parsimony, which we’ve embraced while other firms have added unnecessary factors for appearance of complexity. In addition — and this may simply be a semantic issue — I don’t see how going from 5 or 6 factors to one or two is an “extension” of the study. Instead of working to extend or modify the H/Lo factors, you seemed to scrap them entirely and start again with a much narrower pool — one or two factors for many sub strategies. At the time, I thought it was well established that one or two factor models didn’t work well. I still don’t understand this transition in your paper and it seems to me like you ended up with a very different study (perhaps “Can a two factor model explain hedge fund sub sector returns out of sample?”). As a practitioner, my interpretation of this was that the poor outcome was a foregone conclusion, and hence I questioned why it was included.

    3. I apologize for the insinuation. This was based on a rumor at the time of publication — I don’t remember the source, probably from one of the banks who of course had their own agenda. As a practical matter, though, I would argue that no research is unbiased. As a liberal arts major, I am trained to read into subtext, and my strong impression from reading your papers and the way they were structured was that you had a vested interest in arguing against what you describe as “passive” replication. If I misinterpreted this, then I apologize.

    If you would like to have a live debate on any of these issues, please feel free to contact me directly. And thank you again for taking the time to read and carefully respond to my submission.

    All the best,


  3. Prof Jim Liew
    May 5, 2015 at 11:19 am

    That definitely would be fun to see a live debate! :) Did you guys do it?

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