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Nonparametric nearest neighbor based empirical portfolio selection strategies
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László Györfi
Published/Copyright:
September 25, 2009
Abstract
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.
Published Online: 2009-09-25
Published in Print: 2008-03
© by Oldenbourg Wissenschaftsverlag, Budapest, Germany
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Articles in the same Issue
- Editorial
- A lattice model with incomplete information: A credit risk application
- Optimal portfolios with Haezendonck risk measures
- Mean and covariance matrix adaptive estimation for a weakly stationary process. Application in stochastic optimization
- Nonparametric nearest neighbor based empirical portfolio selection strategies
Keywords for this article
sequential investment;
universally consistent portfolios;
nearest neighaor estimation
Articles in the same Issue
- Editorial
- A lattice model with incomplete information: A credit risk application
- Optimal portfolios with Haezendonck risk measures
- Mean and covariance matrix adaptive estimation for a weakly stationary process. Application in stochastic optimization
- Nonparametric nearest neighbor based empirical portfolio selection strategies
