Financial Time Series Prediction and Evaluation by Genetic Programming with Trigonometric Functions and High-Order Statistics

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@PhdThesis{RSchwaerzel,
  author =       "Roy Schwaerzel",
  title =        "Financial Time Series Prediction and Evaluation by
                 Genetic Programming with Trigonometric Functions and
                 High-Order Statistics",
  school =       "Department of Computer Science, The University of
                 Texas at San Antonio",
  year =         "2006",
  address =      "USA",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.298.6260",
  URL =          "http://www.cs.utsa.edu/uploads/theses/RSchwaerzel.pdf",
  URL =          "http://www.cs.utsa.edu/research/dissertations/",
  size =         "139 pages",
  abstract =     "This research describes an extension of the
                 traditional application of Genetic Programming in the
                 domain of the prediction of daily currency exchange
                 rates. In combination with trigonometric operators, we
                 introduce a new set of high-order statistical functions
                 in a unique representation and analyse their
                 performance using daily returns of the British Pound
                 and Japanese Yen. In addition, the same experimental
                 design and analysis is applied to ten other financial
                 time series from two different domains. We will
                 demonstrate that the introduction of high-order
                 statistical functions in combination with trigonometric
                 functions will outperform other traditional models such
                 as ARMA models and Genetic Programming with the basic
                 function set. We use the Akaike Information Criterion
                 for the selection of the best ARMA model for our
                 benchmark testing. Performance will be measured on hit
                 percentage, average percentage change, and profit. The
                 t-Test is applied to derive confidence intervals and to
                 evaluate the significance of our results.",
  notes =        "Supervising Professor: Dr. Tom Bylander

                 'We have shown that the Genetic Programming models
                 outperformed the Akaike selected ARMA model and the
                 simple Buy and Hold Strategy using the HIT, the APC,
                 and the Profit performance measures'

                 UKP GBP v Yen JPY",
}

Genetic Programming entries for Roy Schwaerzel

Citations