Hedge Fund Clones: A Much Safer Way to Play

Written by: Martina Cassani, Cass Business School, London and Daniel Giamouridis, Athens University of Economics and Business, Athens

Martina Cassani is a student at Cass Business School in London. Daniel Giamouridis is at the Department of Accounting and Finance in the Athens University of Economics and Business, Athens, Greece. He is also Senior Visiting Fellow at Cass Business School, City University, London, and Research Associate at EDHEC Risk and Asset Management Research Centre, EDHEC Business School, Nice, France.

Introduction

In the last couple of decades, we have witnessed a growing interest in hedge funds. Assets invested in the hedge fund industry have been increasing at a steady pace from about $40 billion in 1990 to just over $1.4 trillion in the second quarter of 2009 (with a peak of about $1.9 trillion in the second quarter of 2008)1. While there have been cases of hedge funds delivering substantial profits to investors, recent studies (see Weidenmueller and Verbeek, 2009) indicate that the portion of these profits that can be attributed to ‘skill’ has — on average — deteriorated over the years. This observation suggests that hedge fund performance in recent years has been dominated by compensations for bearing certain risks, ie, ‘betas’ rather than ‘skill’ or ‘alpha’. Moreover, the hedge fund industry’s poor records on liquidity and transparency, as well as the sky-high fees, have been adding to the ongoing scepticism of investors with respect to the benefits of hedge fund investing. This has given rise to the question of what could be a good-value investment vehicle for one wishing to gain exposure to this asset class.

For about a decade, academics and practitioners have looked at replication strategies as a good-value investment in the hedge fund space. These are investment strategies that aim to produce the investment returns of the broad hedge fund industry, without having to invest in the funds themselves. The resulting portfolios are usually termed ‘hedge fund clones’ and are dynamically managed portfolios of liquid assets. While there are already products — ‘plain vanilla’, as well as derivatives — that can be traded with major investment banks and asset management companies, hedge fund replication professionals report rising interest across institutional investors and forecast a substantial increase in business. In this article, we will review the motivation behind hedge fund clones, discuss the benefits and criticisms that have been put forward, present the current status of the hedge fund clones business, and investigate the performance of selected plain vanilla products.

Motivation, recent research, benefits and scepticisms

The motivation for seeking hedge fund exposure through hedge fund clones is two-fold. On the one hand, investing in individual funds is generally associated with high costs, moderate to low liquidity, lack of transparency, and barriers to entry. A recent article in the Financial Times2 quoted that ‘…clients need transparency and liquidity, they do not like lock-ups or want high fees’. These are perhaps the most critical advantages of hedge fund clones.

On the other hand, the evidence has been increasing that the ‘alpha’ of the average hedge fund or fund of funds manager is very poor and not persistent (for example, see Agarwal and Naik, 2000, and Fung, et al, 2006), and that a large fraction of broad-based performance of hedge funds is due to risk premia (for example, see Hasanhodzic and Lo, 2007, and Giamouridis and Paterlini, 2009).

Collectively, these observations motivate the development of non-discretionary, rule-based investment strategies that utilise liquid assets such as futures, total return swaps, ETFs, and other instruments to replicate the broad-based performance of hedge funds. The structure of these strategies (ie, rule-based) allows minimal charges of about 50 to 100 bps per year as it currently stands. Investors are aware of the eligible list of market underlyings (ie, the possible investments), therefore, the strategies are fully transparent. And the liquidity of the replicating portfolio can be as high as daily, given the liquidity of the traded underlyings.

Recent research has proposed two approaches for the construction of the replicating portfolio: moment matching (for example, see Kat and Palaro, 2005a,b) and factor-based replication (for example, see Hasanhodzic and Lo, 2007). The former seeks to match the moments of the return distribution of the target and the replicating portfolio. The latter is based on a portfolio of assets whose weights are computed with the objective that the tracking error, with respect to replicated portfolio, is minimal. A third approach is based on the implementation of a generic version of a given strategy, which, however, may not be that different from a typical hedge fund (for example, see Mitchell and Pulvino, 2001). A recent paper by Tancar and Viebig (2008) provides a comprehensive overview of the methodologies that have, thus far, been presented in the literature. More recent works propose refinements to the above ideas. Amenc, et al (2009), for example, propose non-linear and conditional hedge fund replication models. Giamouridis and Paterlini (2009) propose modifications to the portfolio construction problem for more stable performance.

While consensus is gathering pace for the benefits of hedge fund clone investing, it is important that some challenges are also acknowledged. Amenc and Schroder (2008) and Tancar and Viebig (2008) provide a discussion of the scepticisms over hedge fund clones. Perhaps the most obvious is that hedge fund clones focus on average performance and the technologies used will not be able to replicate ‘star’ managers. One other challenge is the fact that clones are, typically, based on liquid, exchange-traded or ‘plain vanilla’ derivative instruments and, therefore, they may not be able to replicate the entire risk/return spectrum of mangers trading, in many instances, complex derivatives, and they will only produce a truncated version of a hedge fund. Another argument claims that hedge fund managers are flexible, and are able to switch positions in a very opportunistic manner. Clones, which are based on historical data, will not be able to adapt accordingly.

Empirical investigation

The aim of our empirical investigation is to study the performance of products launched over the recent years. We believe it is a critical exercise, provided that meaningful performance data is currently available. Amenc, et al (2008) list a number of hedge fund replicating products (about 30), while our own more up-to-date investigation yielded a few more (about 50). For economy of space, we limit our empirical investigation to five products that are benchmarked against widely-used hedge fund indices. These include: Citi’s Hybrid Absolute Return Portfolio, Deutsche Bank’s Absolute Return Beta Index, and JP Morgan’s Alternative Benchmark Index, all tracking Hedge Fund Research’s Fund of Funds Composite Index; Meryll Lynch’s Factor Index replicating Hedge Fund Research’s Hedge Fund Composite Index; and Credit Suisse’s Long/Short Equity Replication Index tracking the Credit Suisse/Tremont Long/Short Equity Hedge Fund Index.

Data for our analysis was obtained from Bloomberg and was collected monthly for the period January 2003 to May 2009. Table 1, on the page opposite, tabulates information for the five clones. Given that all five products were, in fact, launched post-2006 about half (at best) of the data is pro-forma. We split the data set in various sub-samples. The first covers the period January 2003 to December 2008. The second sub-sample covers the period August 2007 to December 2008, a period when the asset management sector experienced substantial losses and unprecedented levels of volatility. Finally, the third sub-sample extends from January 2009 to May 2009, the period when the capital markets began to show signs of recovery.

Our investigation involves an assessment of the risk/return and performance attributes of the clones, relative to their benchmarks. We also perform a detailed correlation analysis between the clones and the target indexes.

Unconditional analysis: Clones vs. benchmarks

To study the performance of the clones, and compare it with the performance of their benchmarks, we employ a number of standard metrics for assessing the risk/return profile of quantitative investment portfolios. Our comparison involves measures of risk, return and performance.

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The Value of Liquidity and Option Timing From a Simple Game

Written by: Niall Whelan, ScotiaBank, Toronto and Ranjan Bhaduri, AlphaMetrix Alternative Investment Advisors, Chicago

Niall Whelan is the director of analytics and structured transations at ScotiaBank in Toronto. Ranjan Bhaduri is the managing director and the head of research at AlphaMetrix Alternative Investment Advisors in Chicago.

Introduction

In a previous paper, Bhaduri and Whelan (2008), we presented a simple model of hedge fund liquidity. That paper explored the fact that not being able to pull one’s money out of an investment instantaneously at a fair price can have a powerful impact on the portfolio. Real world examples include the 1993 Metallgesellschaft debacle (Smithson, 1998) and the recent difficulties experienced by the Bank of Montreal (Perkins and Stewart, 2007), as they attempt to exit from some thinly traded OTC energy derivatives. As we pointed out in our earlier paper, liquidity is a growing issue in the hedge funds arena; increased regulatory pressure has led various hedge funds to extend their lock-up period to avoid more scrutiny. Locking up investments represents a loss of liquidity to the investors and, despite its growing importance, very little quantitative work has been done to understand it in the context of hedge funds. In this article, we explore the impact of liquidity in a simple model.

To address this question in a tractable, heuristic setting we created a model which is intended as an analogy to the liquidity question. It is not difficult to understand that the value of liquidity can also be thought of as an option value, since liquidity gives the holder of a position the right, but not the obligation, to act, which is the classic feature of an option. Whether one chooses to think in terms of liquidity value or option value is largely dictated by context. At the end of this paper, we make the connection between the two interpretations explicit in the context of our specific model.

To recall our model, we consider a hat with b black balls worth -$1 each and w white balls worth $1 each. On each turn, the player chooses whether to draw a ball from the hat and gains $1 if a white ball is drawn, and loses $1 if a black ball is drawn. The selection is done without replacement of balls and the player can elect to stop playing whenever they like. We would like to determine the expected payoff of this game. The role of liquidity is that the player can choose to exit as they please. We showed in Bhaduri and Whelan (2008) that the value of this game exceeds the intrinsic value of the hat itself, which is w – b. Subsequent to that paper, we learned of the seminal work of Shepp (Shepp, 1969) who analysed the same game and attained the same expected payoff. (Interestingly, the motivation related to accounting for biases in ESP experiments arising from preferential stopping.) The work we present here extends from that by considering higher moments and the full distribution of values, as well as relating it to topics in finance.

In this article, we focus on analyses of the payoff and risk-return profiles. We work almost exclusively in the asymptotic limit of large hat size. This is for the three reasons: i) that limit is tractable; ii) it is most meaningful since it relates to long time horizons; and iii) it is indicative of what happens even for small hats, as we showed in Bhaduri and Whelan (2008).

First, we determine the expected value of the game and then introduce the concept of a ‘strategy’ of play. We then determine the standard deviation of the returns from playing, which permits an exploration of the Sharpe Ratio. We then generalise the analysis by deriving the entire distribution of returns by way of the characteristic function. We find highly non-normal value distributions, which argues against use of the Sharpe Ratio. We explore use of the Ω function (Keating and Shadwick, 2002) applied to this problem. We conclude the paper with a brief reinterpetation of the problem as an asset option. This then singles out one of the strategies as corresponding to the risk neutral value.

The game

We start by presenting some numerical results. Each cell in Table 1 represents the expected value of employing an optimal strategy to the corresponding hat. Here, optimal strategy is defined as playing if the expected value is greater than zero, and stopping if the expected value is zero. The table indicates how to effectively play the game and can obviously be extended to larger hats.

Consider the hat with six black balls and four white balls, highlighted in Table 1.

Intuitively, one might think that it is not worth playing since there are more black balls than white balls. However, we observe the counterintuitive result that the expected value is positive (equal to 1/15). Thus, it makes sense to play even with a significant preponderance of black balls. The reason for the positive value is that the right to stop playing at any time overcomes the negative imbalance of black balls to white balls. The player’s advantage of getting to choose to stop picking balls at any time is clearly large. We identify this to be a liquidity/option effect.

The best way to calculate the expected value of a given hat is by iteration, as we argued in Bhaduri and Whelan (2008). Owing to the fact that the probabilities of getting white or black balls is directly related to their numbers, the recursion relation is:

The expression above, together with the boundary conditions that E(0,w) = w and E(b,0) = 0, completely specifies the problem. Starting with the boundary cases, we can solve recursively for arbitrary values of w and b. These are the results presented in Table 1 below.

Asymptotic analysis of the value

It is natural to inquire what happens as the number of balls gets larger. For this purpose, it is useful to reparameterise as N = b+w and r = b/N. In particular, N ∞ will play the role of an asymptotic parameter as we hold r fixed. When expressed in the new variables, we call the expected value EN(r). We can think of EN(r) as a function of r, parameterised by N. The recursion relation in the new representation is:

where r1 = b/(b+w –1) and r2 = (b–1)=(b+w–1). The leading order expression (in N) are r1 ≈ r+r/N and r2 ≈ r–(1–r)/N.

Does Independent Credit Research Add Value?

Written by: Julien Rerolle and Cédric Rimaud, Spread Research, France

Spread Research is an independent credit research company dedicated to providing value-added analysis on the credit worthiness of high-yield corporates. Over the last five years, Spread Research has developed a robust analytical framework to analyse the rating of high-yield debt issuers, publishing both corporate rating indications and investment recommendations for the benefit of the investment community.

Introduction

Asset management firms, hedge funds and other institutional investors typically receive three kinds of investment advice: first, from the investment banks that are keen to promote their trading and investment banking operations; second, from the rating agencies’ credit ratings, for which the issuers have to pay annual fees, and which are often used in the guidelines of an investment mandate; and third, from their own research analysts, who often rely on the research of the investment banks and the rating agencies, as well as other publicly-available information. This article first reviews the current debate on the independence of each of these players as providers of fundamental research and then presents a short study on the track record of a specific provider of independent credit research, showing that independent credit research can add significant value over time.

A short review of some literature

In their investment process, asset managers rely on external sources to provide them with sufficient information to select the best investment opportunities. In a recent paper (‘Executive Comment: An Examination Of How Investor Needs Are Served By Various Ratings Business Models’, April 2009), Standard & Poor’s (S&P), one of the three main certified rating agencies, classifies these sources along three different business models: the ‘subscriber-fee model’, the ‘government utility model’ and the ‘issuer-fee’ model. The difference is mainly centred on the sources of the funds that finance this research: the investors, the taxpayers, or the corporations issuing public debt, respectively.

A lot of literature has been published on the effect of research on asset prices in the market for publicly-tradable securities. Beaver et al (2006) make the distinction between the purpose of the research between the ‘contracting’ role of certified rating agencies and the ‘valuation’ role of non-certified rating agencies. The ‘issuer-fee’ model takes its purpose in the need for issuers and investors to comply with listing requirements, as they are imposed by market regulators, and investment guidelines for mutual funds; therefore, it is of a ‘contracting’ nature. The ‘subscriber-fee’ model serves the need to perform an independent ‘valuation’ of securities before choosing to buy or sell them. The ‘government utility model’ would stand in between these two groups.

First, let us review the role of the rating agencies. In its July 2008 Summary Report of Issues Identified in the Commission Staff’s Examinations of Select Credit Rating Agencies, the Securities and Exchange Commission (SEC) has singled out the problem of the ‘issuer pay’ conflict, and has made several recommendations to ensure that changes are implemented to avoid that rating analysts be part of the fee discussions in the rating analysis. It does not, however, solve the fundamental issue of the independence of the analysis. Their fees are paid by the issuers of the debt instruments they rate; this is the first conflict of interest. Back in 1975, when the SEC created the Nationally Recognised Statistical Rating Organisation (NRSRO) status, ratings were remunerated by investors. Whether we should go back to the old model remains an open question. Those who followed blindly the ratings published by the rating agencies now understand that they must conduct their own research. Not doing so means being late to the game, at best. Some commentators, like Christopher Wright (2009), consider that the competition among credit rating agencies will bring benefits. He identifies three areas where new entrants can come a long way in asserting their competitive advantage: the timeliness of the information they provide, the quality of the research underpinning the credit rating and the management of their potential conflicts of interest.

As early as March 2005 in France, the national regulator, the Autorité des Marchés Financiers (AMF) gave its approval for the creation of an association of independent credit analysts, the Compagnie des Analystes Financiers Indépendents (CAFI), whose goal was to promote the independence of research in the investment industry. But the debate has taken a new dimension, following the recent financial crisis. On 23 April 2009, the European Parliament approved a set of rules on credit ratings, asking rating agencies to stop providing advisery services and disclose their models, among other things. It is clear that the oligopoly of the three main agencies (Fitch, Moody’s and S&P’s), whose work is paid for by the issuers, lends itself to unfair practices that new regulations ought to control. The financial crisis is, in part, a consequence of the improper validation of the credit-worthiness of sub-prime borrowers, repackaged into complex financial transactions and given a false sense of security through a model-implied rating. Discussions are also going on in the US, with the SEC reviewing ideas to regulate them.

Regarding the research published by investment banks, several studies on its impact on financial markets have also been conducted. For example, Johnston et al (2008) show the effect of sell-side research on the pricing of market securities and single out its existence as a reason for the speedier release of new information into the securities market. Such effect also highlights a second agency problem, this time at investment banks. The joint issues of the ‘soft commission’ system of investment banks, where investors pay for research through trading commissions and advisery mandates are not new. The fall of Worldcom and Enron, as well as many research papers published in the aftermath of these events, like Ljungqvist et al. (2005), have demonstrated the duplicity of sell-side recommendations coming from investment banks, which tend to follow their own relationships with the issuers, or the needs of the banks’ own trading desks in managing their risk positions. The large investment banks have willingly started to tighten their Chinese Walls and to segregate their research units from trading; the industry has been forced to pay large sums to independent research firms, although this has largely benefited the equity, rather than the debt, markets. Investors will probably remain suspicious, knowing the fees that the investment banks charge their clients for their advisery and syndication business.

If sell-side research provided by investment banks does provide investors with timely information and serve their ‘valuation’ function, ‘non-certified’ rating agencies and other providers of independent debt research ought to provide investors with a superior level of service; their research is objective and solely at the service of investors. This should, therefore, play to their investing clients’ advantage. We would like to explore next whether their track record supports the thesis that they add value over time and, in particular, if we compare them with the changes published by the rating agencies.

Based on the firm’s fundamental research, several layers of opinion are given for a bond or a loan:

1. a notation for the company’s transparency on financial information;
2. a corporate rating: Spread Research publishes its view on the credit rating for the next six months, incorporating any event risk that could affect the company;
3. an outlook for its rating;
4. an estimate of the recovery rate upon default, depending on the seniority of the debt instrument; and
5. an indicator for the liquidity and refinancing risk of the company.

Cayman Islands Funds: A New Gateway to Capital Markets in India

Written by: Dennis Ryan and Sonia Xavier, Conyers Dill & Pearman, Dubai

Conyers Dill & Pearman advises on the laws of the Cayman Islands, British Virgin Islands, Bermuda and Mauritius, and comprises 600 staff in 11 jurisdictions with more than 150 lawyers. The firm specialises in company and commercial law, commercial litigation and private client matters. Dennis Ryan and Sonia Xavier are associates in the firm’s Dubai office, and specialise in all aspects of corporate finance and the formation of investment entities.

Investment funds domiciled in the Cayman Islands have historically faced challenges when seeking to invest into Indian capital markets. One of the major hurdles in this regard has been addressed by the admission of the Cayman Islands Monetary Authority (CIMA) as an ordinary (ie, full) member of the International Organization of Securities Commissions (IOSCO) on 10 June 2009.

By way of background, the IOSCO Objectives and Principles of Securities Regulation were endorsed by its member regulators of various securities and futures markets in 1998 and, generally, are viewed by securities regulators as the key international benchmarks on sound principles and practices for securities regulation. Currently, IOSCO members regulate the vast majority of the world’s securities markets.

To access the Indian markets, an investment fund must register as a Foreign Institutional Investor (FII) with the Securities and Exchange Board of India (SEBI). In the past, since CIMA was not a member of IOSCO, SEBI often engaged in extensive due diligence and enquiries before allowing registration of a Cayman fund as an FII. As a result, few Cayman Islands funds have registered with SEBI. CIMA’s admission to IOSCO looks set to change this trend in favour of Cayman Islands funds.

The timing could not be better. With emerging markets competing to attract liquidity, the Cayman Islands, with over 9,000 CIMA registered investment funds, a proven track record with investors and an excellent and sophisticated service infrastructure, has a great deal to offer India and investors that wish to access its markets.

One remaining challenge is that the Cayman Islands do not currently have a tax treaty with India. Mauritius, on the other hand, has long been the preferred jurisdiction for investment in India as a consequence of the favourable double taxation agreement between those countries (the Mauritius-India DTAA), contributing to around 44% of foreign direct investment (FDI) into India.

Investment funds from non-tax treaty jurisdictions have developed a structure involving a wholly-owned Mauritius subsidiary for purposes of Indian investment. Typically, this structure requires a Cayman Islands (or other non-treaty jurisdiction) investment fund to register with SEBI as an FII. The Mauritius subsidiary fund will then be registered with SEBI as a sub-account of the FII, permitting it to invest directly in Indian securities, via SEBI.

The Mauritius fund will be set up as a Global Business Company Category 1 (GBC1) that is resident in Mauritius for tax purposes. As a Mauritius tax resident, this fund is subject to tax on income at the flat rate of 15%. However, it is entitled to claim a credit for foreign tax on income not derived from Mauritius against the Mauritius tax payable, resulting in an effective tax rate generally ranging between 3% and nil. As a tax-resident GBC1, the fund is also entitled to take advantage of Mauritius’ network of tax treaties, including the Mauritius-India DTAA.

Under the Mauritius-India DTAA, capital gains realised from the sale of Indian securities held by a Mauritius fund will be exempt from taxes in India and taxable in Mauritius, provided the Mauritius fund does not have a permanent establishment in India. Since Mauritius does not impose any capital gains tax on its residents, such gains would, ultimately, not be liable to any taxation. Additionally, there are no withholding taxes on dividends and proceeds paid by the Mauritius fund to its shareholders.

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