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Alternative Investments
Investing in Hedge Funds: Adding Value through Active Style Allocation Decisions
Authors: Lionel Martellini, Mathieu Vaissié and Volker Ziemann
Date: October 2005
Size: 994636 Bytes

One of the by-products of the bull market of the 90’s has been the consolidation of hedge funds as an important segment of financial markets. It was recently announced that the value of the hedge fund industry worldwide had passed the $1 trillion mark for the first time, with approximately 7,000 hedge funds in the world, around 1,000 of which were launched in 2003.

One of the key reasons behind the success of hedge funds in institutional money management is that such alternative investment strategies seem to provide diversification benefits with respect to other existing investment possibilities. In an attempt to fully capitalize on such beta benefits in a top-down approach, investors or (funds of hedge funds) managers must be able to rely on robust techniques for optimization of portfolios including hedge funds. Standard mean-variance portfolio selection techniques are known to suffer from a number of shortcomings, and the problems are exacerbated in the presence of hedge funds. Most importantly it can be argued that the following two aspects require specific care. First, because hedge fund returns are not normally distributed, a mean-variance optimization would be severely ill-adapted. Secondly, the problem of parameter uncertainty needs to be carefully addressed, as the lack of a long history and the non-availability of high frequency data imply that parameter estimation is a real challenge in the case of hedge fund returns.

While both problems (non-trivial preferences about higher moments of asset return distribution and the presence of parameter uncertainty) have been studied independently, what is still missing for active style allocation in the hedge fund universe is a model that would take into account both of these two aspects. Our contribution is precisely to introduce an optimal allocation model that incorporates an answer to both challenges within a unified framework. To this end, we introduce a suitable extension of the Black-Litterman Bayesian approach to portfolio construction that allows for the incorporation of active views about hedge fund strategy performance in the presence of non-trivial preferences about higher moments of hedge fund return distributions.

In a nutshell, we suggest the following approach. We first generate “neutral” views on expected hedge fund returns based on the desire to match a benchmark portfolio composition, where the benchmark is designed based on minimizing the portfolio Value-at-Risk. For this purpose, we use an asset pricing model that incorporates investors’ preferences not only on expected return and volatility, but also on higher moments of hedge fund return distributions. Next, we present a simple factor analysis that allows us to obtain a bullish, a bearish or a neutral view concerning the expected return. The next step involves blending such active views with the neutral views, applying a Bayesian statistical approach similar to that introduced by Black-Litterman. Finally, we generate optimal allocations to hedge funds that are consistent with this mixture of neutral and active views.

We also present a numerical application illustrating how investors can use a multi-factor approach to generate such active views and dynamically adjust their allocation to various hedge fund strategies while staying coherent with a long-term strategic allocation benchmark. We were able to show that the active style selection process, combined with the Black-Litterman portfolio selection method, allows for significant outperformance without a large increase in tracking error. In particular, the implementation of the process led to a 100 basis points excess return over the period January 1997 to December 2004 for a small tracking error (0.86%), leading to a 1.17 information ratio. A more aggressive portfolio version, based on an increase in the parameter defining the relationship between neutral and active views, led to outperformance of almost 200 basis points for a 1.37 tracking error, leading to a 1.41 information ratio. We also show that the optimal design of a hedge fund portfolio based on active allocation decisions to various alternative strategies leads to a significant improvement in the Omega function, a relevant risk-adjusted measure of performance.

The bulk of the message conveyed in this paper is straightforward and has important potential implications for the hedge fund industry: it is only by taking into account the exact nature and composition of an investor’s existing portfolio, as opposed to regarding hedge fund investing from a stand-alone approach, that institutional investors can truly customize and maximize the benefits they can expect from investing in these modern forms of alternative investment strategies. Overall the results in this paper strongly suggest that significant value can be added in a hedge fund portfolio through the systematic implementation of active style allocation decisions, both at the strategic and tactical levels. While this fact has long been recognized by market participants, the lack of reliable asset allocation tools has not facilitated the implementation of effective top-down approaches to investment in hedge funds. In this study, we argue that such techniques are actually already available and we show that a suitable extension to the Black-Litterman model can be used to implement active views on hedge fund style performance in a meaningful and consistent approach that avoids the pitfalls of standard optimization procedures.