Failure of Genetic-Programming Induced Trading Strategies: Distinguishing between Efficient Markets and Inefficient Algorithms

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@InCollection{Chen:2007:chen,
  title =        "Failure of Genetic-Programming Induced Trading
                 Strategies: Distinguishing between Efficient Markets
                 and Inefficient Algorithms",
  author =       "Shu-heng Chen and Nicolas Navet",
  booktitle =    "Computational Intelligence in Economics and Finance:
                 Volume II",
  publisher =    "Springer",
  year =         "2007",
  editor =       "Shu-Heng Chen and Paul P. Wang and Tzu-Wen Kuo",
  pages =        "169--182",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-540-72820-7",
  bibsource =    "OAI-PMH server at citeseerx.ist.psu.edu",
  contributor =  "CiteSeerX",
  language =     "en",
  oai =          "oai:CiteSeerXPSU:10.1.1.144.5068",
  URL =          "http://www.loria.fr/~nnavet/publi/SHC_NN_Springer2007.pdf",
  URL =          "http://www.springer.com/computer/ai/book/978-3-540-72820-7",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.144.5068",
  DOI =          "doi:10.1007/978-3-540-72821-4_11",
  abstract =     "Over the last decade, numerous papers have
                 investigated the use of Genetic Programming (GP) for
                 creating financial trading strategies. Typically, in
                 the literature, the results are inconclusive but the
                 investigators always suggest the possibility of further
                 improvements, leaving the conclusion regarding the
                 effectiveness of GP undecided. In this paper, we
                 discuss a series of pretests aimed at giving more
                 clear-cut answers as to whether GP can be effective
                 with the training data at hand. Precisely, pretesting
                 allows us to distinguish between a failure due to the
                 market being efficient or due to GP being inefficient.
                 The basic idea here is to compare GP with several
                 variants of random searches and random trading
                 behaviors having well-defined characteristics. In
                 particular, if the outcomes of the pretests reveal no
                 statistical evidence that GP possesses a predictive
                 ability superior to a random search or a random trading
                 behavior, then this suggests to us that there is no
                 point in investing further resources in GP. The
                 analysis is illustrated with GP-evolved strategies for
                 nine markets exhibiting various trends.",
}

Genetic Programming entries for Shu-Heng Chen Nicolas Navet

Citations