A methodology to avoid over-diversification of funds of equity funds An implementation case study for equity funds of funds in bull markets

Nadège Ribau-Peltre (), Pascal Damel () and An Lethi ()
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Nadège Ribau-Peltre: CEREFIGE - Centre Européen de Recherche en Economie Financière et Gestion des Entreprises - UL - Université de Lorraine
Pascal Damel: LGIPM - Laboratoire de Génie Informatique, de Production et de Maintenance - UL - Université de Lorraine
An Lethi: LGIPM - Laboratoire de Génie Informatique, de Production et de Maintenance - UL - Université de Lorraine

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Abstract: Funds of funds are funds that invest primarily in units of other funds. They have developed in Europe since the end of the 1990s. They exist because no fund manager can be excellent in all fields (all sectors, all geographical areas ...) and because it can therefore be interesting to compose funds from shares of funds managed by different management companies. As there is a higher diversification in funds of funds, they can be attractive at first glance. But studies have pointed out that they have some disadvantages, the main one being over-diversification. In this paper, we will review the literature on the issue of over-diversification by showing the consequences this overdiversification may have on the management and performance of funds of funds. Using Markowitz's mean-variance optimization method, we will on the one hand show that by building funds of funds from a panel of 551 equity funds, the efficient frontier is made up of funds of funds comprising from 1 to 11 funds with an average of 7.44 funds. This empirical study thus shows that the efficient frontier is composed of portfolios comprising a number of funds significantly below the professional standard (20 to 30 funds). Over-diversification and the accumulation of the resulting costs are therefore not a necessity. On the other hand, we will show that the mean-variance optimization method can be improved by DCA clustering techniques. A prior clustering of the initial database makes it indeed possible to reduce (by almost 10 in our example) the size of the database on which the Markowitz's meanvariance optimization is applied. The efficient frontier deriving from this reduced database is almost equivalent in terms of risk-adjusted performance as the one deriving from the initial database, while avoiding computational problems generated during the optimization process on wide databases (especially when including regulatory constraints).

Keywords: funds of funds; Mean-Variance optimization; market expectations; over-diversification; clustering method (search for similar items in EconPapers)
Date: 2018-07-05
Note: View the original document on HAL open archive server: https://hal.science/hal-03027770v1
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Published in 9th International Research Meeting in Business and Management, Jul 2018, Nice, France

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