PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Nonparametric nearest-neighbor-based sequential investment strategies
Laszlo Gyorfi, Frederic Udina and Harro Walk
Statistics and Decisions Volume 26, Number 2, pp. 145-157, 2009. ISSN 0721-2631


In recent years optimal portfolio selection strategies for sequential investment have been shown to exist. Although their asymptotical optimality is well established, finite sample properties do need the adjustment of parameters that depend on dimensionality and scale. In this paper we introduce some nearest neighbor based portfolio selectors that solve these problems, and we show that they are also log-optimal for the very general class of stationary and ergodic random processes. The newly proposed algorithm shows very good finite-horizon performance when applied to different markets with different dimensionality or scales without any change: we see it as a very robust strategy.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:4953
Deposited By:Laszlo Gyorfi
Deposited On:18 March 2009