A Method for Comparing Hedge Funds
Statistical Finance
2013-05-14 v2 Information Retrieval
Machine Learning
Machine Learning
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.
Cite
@article{arxiv.1303.0073,
title = {A Method for Comparing Hedge Funds},
author = {Uri Kartoun},
journal= {arXiv preprint arXiv:1303.0073},
year = {2013}
}