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A Method for Comparing Hedge Funds

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  • Uri Kartoun

Abstract

The paper presents new machine learning methods: signal composition, which classifies time-series regardless of length, type, and quantity; and self-labeling, a supervised-learning enhancement. The paper describes further the implementation of the methods on a financial search engine system to identify behavioral similarities among time-series representing monthly returns of 11,312 hedge funds operated during approximately one decade (2000 - 2010). The presented approach of cross-category and cross-location classification assists the investor to identify alternative investments.

Suggested Citation

  • Uri Kartoun, 2013. "A Method for Comparing Hedge Funds," Papers 1303.0073, arXiv.org, revised Mar 2013.
    • Handle: RePEc:arx:papers:1303.0073
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    References listed on IDEAS

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    1. Nandita Das, 2003. "Hedge Fund Classification using K-means Clustering Method," Computing in Economics and Finance 2003 284, Society for Computational Economics.
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