Modelling and trading the London, New York and Frankfurt stock exchanges with a new gene expression programming trader tool

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@Article{journals/isafm/Karathanasopoulos17,
  author =       "Andreas Karathanasopoulos",
  title =        "Modelling and trading the London, New York and
                 Frankfurt stock exchanges with a new gene expression
                 programming trader tool",
  journal =      "Int. Syst. in Accounting, Finance and Management",
  year =         "2017",
  number =       "1",
  volume =       "24",
  pages =        "3--11",
  keywords =     "genetic algorithms, genetic programming, Gene
                 Expression Programming",
  bibdate =      "2017-05-28",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/isafm/isafm24.html#Karathanasopoulos17",
  DOI =          "doi:10.1002/isaf.1401",
  abstract =     "The scope of this manuscript is to present a new
                 short-term financial forecasting and trading tool: the
                 Gene Expression Programming (GEP) Trader Tool. It is
                 based on the gene expression programming algorithm.
                 This algorithm is based on a genetic programming
                 approach, and provides supreme statistical and trading
                 performance when used for modelling and trading
                 financial time series. The GEP Trader Tool is offered
                 through a user-friendly standalone Java interface. This
                 paper applies the GEP Trader Tool to the task of
                 forecasting and trading the future contracts of
                 FTSE100, DAX30 and S&P500 daily closing prices from
                 2000 to 2015. It is the first time that gene expression
                 programming has been used in such massive datasets. The
                 model's performance is benchmarked against linear and
                 nonlinear models such as random walk model, a
                 moving-average convergence divergence model, an
                 autoregressive moving average model, a genetic
                 programming algorithm, a multilayer perceptron neural
                 network, a recurrent neural network a higher order
                 neural network. To gauge the accuracy of all models,
                 both statistical and trading performances are measured.
                 Experimental results indicate that the proposed
                 approach outperforms all the others in the in-sample
                 and out-of-sample periods by producing superior
                 empirical results. Furthermore, the trading
                 performances are improved further when trading
                 strategies are imposed on each of the models.",
}

Genetic Programming entries for Andreas S Karathanasopoulos

Citations