MGP-INTACTSKY: Multitree Genetic Programming-based learning of INTerpretable and ACcurate TSK sYstems for dynamic portfolio trading

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@Article{Mousavi:2015:ASC,
  author =       "Somayeh Mousavi and Akbar Esfahanipour and 
                 Mohammad Hossein Fazel Zarandi",
  title =        "MGP-INTACTSKY: Multitree Genetic Programming-based
                 learning of {INTerpretable} and {ACcurate} {TSK}
                 {sYstems} for dynamic portfolio trading",
  journal =      "Applied Soft Computing",
  year =         "2015",
  volume =       "34",
  pages =        "449--462",
  month =        sep,
  keywords =     "genetic algorithms, genetic programming, Multitree
                 genetic programming, TSK fuzzy rule based system",
  ISSN =         "1568-4946",
  DOI =          "doi:10.1016/j.asoc.2015.05.021",
  size =         "14 pages",
  abstract =     "In this paper, a Multitree Genetic Programming-based
                 method is developed to learn an INTerpretable and
                 ACcurate Takagi-Sugeno-Kang (TSK) fuzzy rule based
                 sYstem (MGP-INTACTSKY) for dynamic portfolio trading.
                 The MGP-INTACTSKY uses a TSK model with a new structure
                 to develop a more interpretable and accurate system for
                 dynamic portfolio trading. In the new structure of TSK,
                 disjunctive normal form rules with variable structured
                 consequent parts are developed in which the absence of
                 some input variables is allowed. Input variables are
                 the most influential technical indices which are
                 selected by stepwise regression analysis. The technical
                 indices are computed using wavelet transformed stock
                 price series to eliminate the noise. The proposed
                 system directly induces the preferred portfolio weights
                 from the stock's technical indices through time. Here,
                 genetic programming with the multitree structure is
                 applied to learn the TSK fuzzy rule bases with the
                 Pittsburgh approach. With this approach, the
                 correlation of different stocks is properly considered
                 during the evolutionary process. To evaluate the
                 performance of the MGP-INTACTSKY for portfolio trading,
                 the proposed model is implemented on the Tehran Stock
                 Exchange as an emerging market as well as Toronto and
                 Frankfurt Stock Exchanges as two mature markets. The
                 experimental results show that the proposed model
                 outperforms other methods such as the momentum
                 strategy, the multitree genetic programming-based crisp
                 system, the genetic algorithm-based first order TSK
                 system, the buy and hold approach and the market's main
                 index in terms of accuracy and interpretability.",
}

Genetic Programming entries for Somayeh Mousavi Akbar Esfahanipour Mohammad Hossein Fazel Zarandi

Citations