Genetic Programming-based trading system: An application on the NASDAQ 100 stock index

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@InProceedings{gaila_genetic_2012,
  author =       "Maria Gaila and Vassilios Vassiliadis and 
                 Nikolaos Kondakis and George Dounias",
  title =        "Genetic {Programming}-based trading system: {An}
                 application on the {NASDAQ} 100 stock index",
  booktitle =    "IMAEF-2012",
  year =         "2012",
  editors =      "Spyridon Symeonides and Nikos Benos and Yorgos
                 Goletsis",
  address =      "Ioannina, Greece",
  month =        "21-22 " # jun,
  organisation = "Department of Economics of the University of
                 Ioannina",
  keywords =     "genetic algorithms, genetic programming, artificial
                 intelligence, technical indicators, trading system",
  URL =          "http://mde-lab.aegean.gr/images/stories/docs/CC79.pdf",
  size =         "2 pages",
  abstract =     "Nowadays, the vast amount of socio-economic and market
                 information play an important role in the formation of
                 any financial market's characteristics and overall
                 behaviour. As a consequence, the uncertainty and
                 complexity of the financial markets immensely increase.
                 Based on the aforementioned, a crucial task for
                 potential traders is to identify market trends and
                 detect potential investment opportunities. What is
                 more, individually traditional trading strategies based
                 on technical indicators, such as certain statistical
                 and econometric forecasting methods, have proven
                 inadequate to adapt to the rapidly evolving market
                 conditions. Conversely, when combining such indicators,
                 there is a higher possibility of more promising
                 results. The field of Artificial Intelligence provides
                 a range of metaheuristic algorithms for dealing with
                 complex tasks, as the above mentioned. Specifically, in
                 this study an intelligent algorithm based on the
                 principles of Darwinian evolution, namely Genetic
                 Programming, is proposed. The main aim of the study is
                 to combine a number of technical indicators and other
                 financial heuristics, with the use of Genetic
                 Programming, in order to detect potential market
                 signals for trading. One of the main characteristics of
                 Genetic Programming is its ability to manipulate
                 complex technical rules/heuristics in a way that
                 optimizes the investors expected outcome. The proposed
                 trading system is applied to the NASDAQ 100 stock
                 index. Particularly, the dataset comprises daily
                 adjusted closing prices of the stock index, for the
                 period January 1985 to December 2011. Regarding the
                 experimental set-up, the entire dataset is divided into
                 three sub-periods: training, validation and forecasting
                 (trading) interval. The algorithmic trading system is
                 applied to the training interval in order to provide a
                 number of technical rules. The quality (fitness) of
                 these rules is then tested in the validation period,
                 based on the criterion of profit maximization. Finally,
                 the fittest rule is applied to the forecasting time
                 period, which consists of unknown data.",
  notes =        "http://www.econ.uoi.gr/imaef2012/programme.php

                 http://mde-lab.aegean.gr/research-material",
}

Genetic Programming entries for Maria Gaila Vassilios Vassiliadis Nikolaos Kondakis Georgios Dounias

Citations