An Evolutionary Bootstrap Method for Selecting Dynamic Trading Strategies

  • Blake LeBaron
Chapter
Part of the Advances in Computational Management Science book series (AICM, volume 2)

Abstract

This paper combines techniques drawn from the literature on evolutionary optimization algorithms along with bootstrap based statistical tests. Bootstrapping and cross validation are used as a general framework for estimating objectives out of sample by redrawing subsets from a training sample. Evolution is used to search the large space of potential network architectures. The combination of these two methods creates a network estimation and selection procedure which aims to find parsimonious network structures which generalize well. Examples are given from financial data showing how this compares to more traditional model selection methods. The bootstrap methodology also allows more general objective functions than usual least squares since it can estimate the in sample bias for any function. Some of these will be compared with traditional least squares based estimates in dynamic trading settings with foreign exchange series.

Keywords

Foreign Exchange Hide Unit Sharpe Ratio Hill Climbing Foreign Exchange Market 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer Science+Business Media Dordrecht 1998

Authors and Affiliations

  • Blake LeBaron
    • 1
  1. 1.Department of EconomicsUniversity of Wisconsin - MadisonMadisonUSA

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