Divergent estimation error in portfolio optimization and in linear regression
Abstract
The problem of estimation error in portfolio optimization is discussed, in the limit where the portfolio size N and the sample size T go to infinity such that their ratio is fixed. The estimation error strongly depends on the ratio N/T and diverges for a critical value of this parameter. This divergence is the manifestation of an algorithmic phase transition, it is accompanied by a number of critical phenomena, and displays universality. As the structure of a large number of multidimensional regression and modelling problems is very similar to portfolio optimization, the scope of the above observations extends far beyond finance, and covers a large number of problems in operations research, machine learning, bioinformatics, medical science, economics, and technology.
Keywords
Cite
@article{arxiv.0710.1855,
title = {Divergent estimation error in portfolio optimization and in linear regression},
author = {Imre Kondor and Istvan Varga-Haszonits},
journal= {arXiv preprint arXiv:0710.1855},
year = {2009}
}
Comments
5 pages, 2 figures, Statphys 23 Conference Proceeding