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An Approach to Ranking the Hedge Fund Industry

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Abstract

Due to the complexity and heterogeneity of hedge fund strategies, the evaluation of their performance and risk is a challenging task. Starting from the standard mutual fund industry, the literature has evolved in the direction of refining traditional measures (e.g. the Sharpe Ratio) or introducing new ones. This paper develops an approach, based on the Principal Component Analysis, to uncover the relevant information for performance measurement and combine it into a unique rank.

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Notes

  1. 1.

    MSCI World Index and Barclays Euro Aggregate (BEA) are chosen to represent correlation with Equity and Bond market.

  2. 2.

    It should be pointed out that maximum drawdown is an empirical measure, without any statistical consistency.

  3. 3.

    A different solution can be found in Bramante and Zappa (2011).

  4. 4.

    Kurtosis are translated to zero.

  5. 5.

    These are the two widely recognized hedge fund index providers in the industry.

  6. 6.

    Since the ability of this method in summarizing common patters depends on whether data contain strongly correlated variables, average partial correlation between variables was computed across the three considered years. Above all, the largest ones are between the three “Cornish Fisher” indicators and within the risk variables (Annualized Volatility, Negative Semi Deviation and Value at Risk 5 %).

  7. 7.

    A varimax rotation was performed.

  8. 8.

    Similar results, referred to the remaining two scenarios, are omitted.

  9. 9.

    30 % is arbitrarily chosen. However, empirical simulations show that 30 % of asset allocation in hedge funds seems to be closed to the optimum, in terms of the distance from the efficient frontier.

References

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Acknowledgments

The development of this paper benefited significantly from the input and support of Alessandro Cipollini and Antonio Manzini.

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Correspondence to Riccardo Bramante .

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Bramante, R. (2013). An Approach to Ranking the Hedge Fund Industry. In: Giudici, P., Ingrassia, S., Vichi, M. (eds) Statistical Models for Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00032-9_8

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