PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Internal regret in on-line portfolio selection
Gilles Stoltz and Gábor Lugosi
Machine Learning Journal Volume 59, pp. 125-159, 2005.

Abstract

This paper extends the game-theoretic notion of internal regret to the case of on-line potfolio selection problems. New sequential investment strategies are designed to minimize the cumulative internal regret for all possible market behaviors. Some of the introduced strategies, apart from achieving a small internal regret, achieve an accumulated wealth almost as large as that of the best constantly rebalanced portfolio. It is argued that the low-internal-regret property is related to stability and experiments on real stock exchange data demonstrate that the new strategies achieve better returns compared to some known algorithms.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:655
Deposited By:Gilles Stoltz
Deposited On:29 December 2004