Hedge funds are expected to gain further ground versus traditional long-only funds and to become mainstream investment vehicles in the years to come. Much of their prominence has been linked to their capacity to generate active or alpha returns versus the predominantly beta inspired returns of their traditional colleagues. However, it seems that some of the mystique surrounding the performance wonders has been due to the inability of the industry to discern true alpha generation from exposure to alternative betas. Although exposure to the latter can be beneficial both in terms of return potential as well as from the viewpoint of diversification, it could be that investors can gain more cost effective exposure through other ways. Traditionally, performance evaluation for these skill-based strategies has been problematic, mainly driven by the lack of consistent performance data. For a review of the different difficulties, we refer to Ineichen (2002).
Separating beta risk exposure from alpha return is a hazardous exercise given the wide range of instruments and strategies employed by hedge funds. Hedge fund managers switch between asset classes, hold long as well as short positions, use leverage and use derivatives resulting in highly non-linear payoff structures. Hence, a multifactor model is the most obvious method to estimate hedge fund returns. A number of previous studies analyzed a variety of risk exposures of hedge funds. Among others, Fung and Hsieh (2002) adopt an asset-class multifactor model in the spirit of the style analysis of Sharpe (1992). Edwards and Caglayan (2001), on the other hand, employ the Fama-French style risk factors.
A further property of hedge funds is that they have a tendency to change risk exposures more often than traditional funds. Given the few investment restrictions and regulations, hedge fund managers are able to shift their exposures rapidly in response to a change in the risk-return tradeoff of the underlying investment opportunities. To enhance the understanding and assessment of this risk-return tradeoff in hedge funds, we analyze the dynamics of hedge funds' risk exposures through time. The importance of such time-varying risk exposures is also studied in Alexander and Dimitriu (2005), who use a regime switching model for hedge fund returns. We employ a multifactor model and estimate the risk exposures for fixed rolling-over windows. This analysis captures the time-variation in most investment strategies. Moreover, it also reveals any hidden risk exposures. A similar analysis has been conducted by McGuire et al.(2005) who employ a rolling-over estimation of Sharpe’s style analysis on monthly hedge fund returns. In contrast to their study, we use daily investable hedge fund return data. Given the possibility of frequent changes in the investment approach, high frequency data should better capture this time-variation and non-linearity. Moreover, we can expect that the investability of the daily hedge fund indices introduces a particular (liquidity-induced) risk-return tradeoff in the data that is not present in the risk return profile of monthly data.
In a second step, we analyze to what extent we can replicate (out-of-sample) the hedge fund return series. As noted by Asness (2004a) hedge fund strategies have a tendency to move over time from alpha to systematic beta trading strategies. By exploiting arbitrage opportunities, the market becomes more efficient and alpha returns quickly disappear. Moreover, once a strategy becomes common and widespread, its corresponding return is more likely a beta return and thus a reward for taking up this common systematic risk. In a competitive market, such beta return can survive as a risk premium, while true alpha disappears over time. This is exactly what the current hedge fund business is experiencing. Alpha strategies that become mainstream, such as convertible arbitrage, no longer generate sufficient arbitrage profits so managers tend to take more directional alternative beta exposure. However, this does not imply that there is no more scope for out-performance. A second source of alpha return, namely the timing of (alternative) beta exposure, could still be realized.
This could be one of the strengths of hedge funds. While the hedge funds’ beta returns are similar concepts as traditional beta returns, their structure is much more complex, and their strategies demand expert knowledge and skills. Successful market timing of alternative betas can, as such, be interpreted as another important source of alpha return. Furthermore, unconstrained hedge fund structures have the potential to produce better outcomes than the more constrained traditional formats (see Beckers and Smedts (2004)). To test whether successful market timing is present in hedge fund returns, we re-estimate the multifactor model assuming that the estimated coefficients correspond to portfolio weights. This analysis allows us to get a better understanding of the (predictive) success of a particular hedge fund strategy. In the current context of daily investable indices, this analysis is particularly interesting.
A hedge fund strategy that outperforms in an environment where daily liquidity is to be maintained, is clearly a very attractive investment. We find that several of the strategies indeed outperform the (static) replicated strategies. This is interpreted as evidence in favour of successful market timing by hedge fund managers.
The paper is organized as follows. In Section 2 we describe the hedge fund data used in the paper, and we briefly elaborate on potential biases. Section 3 explains the methodology of the asset-based multifactor model. Section 4 reports the estimation results of the factor model and presents the replication results. Finally Section 5 concludes.
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