A Method for Comparing Hedge Funds

Uri Kartoun

Papers from arXiv.org

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.

Date: 2013-02, Revised 2013-03
New Economics Papers: this item is included in nep-fmk
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1303.0073

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