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A Comparative Study of Heuristic Conversion Algorithms, Genetic Programming and Return Predictability on the German Market

  • Esther MohrEmail author
  • Günter Schmidt
  • Sebastian Jansen
Part of the Studies in Computational Intelligence book series (SCI, volume 447)

Abstract

This paper evaluates the predictability of the heuristic conversion algorithms Moving Average Crossover and Trading Range Breakout in the German stock market. Hypothesis testing and a bootstrap procedure are used to test for predictive ability. Results show that the algorithms considered do not have predictive ability. Further, Genetic Programming is used to adapt the buying and selling rules of the investigated algorithms resulting in a new algorithm. Results show that a genetic programming approach does not lead to good new algorithms. We extend former works by using the Sortino Ratio as a measure of risk, and by applying competitive analysis.

Keywords

Genetic Programming Competitive Ratio Excess Return Trading Rule Genetic Programming Algorithm 
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 Berlin Heidelberg 2013

Authors and Affiliations

  • Esther Mohr
    • 1
    Email author
  • Günter Schmidt
    • 1
  • Sebastian Jansen
    • 2
  1. 1.Saarland UniversitySaarbrückenGermany
  2. 2.Banking and Financial ServicesUniversity of HohenheimStuttgartGermany

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