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Genetic Programming in Statistical Arbitrage

  • Philip Saks
  • Dietmar Maringer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4974)

Abstract

This paper employs genetic programming to discover statistical arbitrage strategies on the banking sector in the Euro Stoxx universe. Binary decision rules are evolved using two different representations. The first is the classical single tree approach, while the second is a dual tree structure where evaluation is contingent on the current market position. Hence, buy and sell rules are co-evolved. Both methods are capable of discovering significant statistical arbitrage strategies.

Keywords

Genetic Programming Sharpe Ratio Statistical Arbitrage Trading Rule Dual Tree 
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-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Philip Saks
    • 1
  • Dietmar Maringer
    • 1
  1. 1.Centre for Computational Finance and Economic AgentsUniversity of Essex 

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