PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Nonparametric kernel-based sequential investment strategies
Laszlo Gyorfi, Gábor Lugosi and Frederic Udina
Mathematical Finance Volume 16, pp. 337-356, 2006.


The purpose of this paper is to introduce sequential investment strategies that guarantee an optimal rate of growth of the capital under minimal assumptions on the behavior of the market. The new strategies are analyzed both theoretically and empirically. The theoretical results show that the asymptotic rate of growth matches the optimal one that one could achieve with a full knowledge of the statistical properties of the underlying process generating the market, under the only assumption that the market is stationary and ergodic. The empirical results show that the performance of the proposed investment strategies measured on past {\sc nyse} and currency exchange data is solid, and sometimes even spectacular.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
ID Code:2880
Deposited By:Gábor Lugosi
Deposited On:22 November 2006