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.
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.