The huge capital inflow into hedge funds has motivated this study. Whereas the mean-variance community likes the diversification benefits provided by hedge funds, "searching for alpha " (Ineichen, 2002) is the major force behind their increasing popularity. The second component of the return, the exposure to market beta, is cheaply available by investing in traditional asset classes, such as the index funds. In this paper we focus on a specific point for hedge fund investments: the selection of hedged equity funds, which cover (a) long-short equity, (b) dedicated short bias as well as (c) equity market neutral. This style has the largest market share in the hedge fund sector. We concentrate our investigations on hedged equity funds for the following reasons: 1. The huge capacity. Insomuch as the investment universe is the whole equity market the considered funds are not expected to suffer decreasing returns with increased number of investors in the market, as it is anticipated for event-driven and relative-value hedge funds. 2. The excellent liquidity. It allows more favorable leveraging schemes for widely

and Tremont TASS (Europe) Limited for providing the hedge fund data. 2 STOCKS, BONDS AND HEDGE FUNDS: NOT A FREE LUNCH! We study the diversification effects from introducing hedge funds into a portfolio of stocks and bonds. Our results show that in terms of skewness and kurtosis equity and hedge funds do not combine very well. Although the inclusion of hedge funds significantly improves the portfolio’s mean-variance characteristics, it can also be expected to lead to significantly lower skewness as well as higher kurtosis. This means that the case for hedge funds is not a free lunch but includes a definite trade-off between profit and loss potential. Our results also emphasize that to have at least some impact on the overall portfolio, investors will have to make an allocation to hedge funds which by far exceeds the typical 1-5 % that many institutions are currently considering. 3 Hedge funds are often said to provide investors with the best of both worlds: an expected return similar to equity with a risk similar to that of bonds. When past

Over the recent past, stylized facts have not yielded a synthesis regarding the predictability of returns for alternative investment assets such as hedge funds. Recent studies on alternative asset return predictability have added to the ambiguity. These studies suggest that classification prediction methods may dominate more traditional return-level prediction methodology. This paper examines the predictive accuracy of three alternate radial basis function neural networks when applied to the returns of thirteen Credit Swiss First Boston/Tremont (CSFB) hedge fund indices. We provide evidence that the Kajiji-4 RBF neural network dominates within the RBF topology in the prediction of hedge fund returns by both level and classification. The results also show that the Kajiji-4 method is capable of near perfect directional prediction.

Limited for supplying the hedge fund data. 2 DIVERSIFICATION AND YIELD