Fund-of-Funds Construction by Statistical Multiple Testing Methods

Abstract

Fund-of-funds (FoF) managers face the task of selecting a (relatively) small number of hedge funds from a large universe of candidate funds. We analyse whether such a selection can be successfully achieved by looking at the track records of the available funds alone, using advanced statistical techniques. In particular, at a given point in time, we determine which funds significantly outperform a given benchmark while, crucially, accouting for the fact that a large number of funds are examined at the same time. This is achieved by employing so-called multiple testing methods. Then, the equal-weighted or the global minimum variance portfolio of the outperforming funds is held for one year, after which the selection process is repeated. When backtesting this strategy on two particular hedge fund universes, we find that the resulting FoF portfolios have attractive return properties compared to the 1/N portfolio (that is, simply equal-weighting all the available funds) but also when compared to two investable hedge fund indices.