A dynamic stock trading system based on a Multi-objective Quantum-Inspired Tabu Search algorithm

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@InProceedings{Chou:2014:ieeeSMC,
  author =       "Yao-Hsin Chou and Shu-Yu Kuo and Chun Kuo",
  booktitle =    "2014 IEEE International Conference on Systems, Man,
                 and Cybernetics (SMC)",
  title =        "A dynamic stock trading system based on a
                 Multi-objective Quantum-Inspired Tabu Search
                 algorithm",
  year =         "2014",
  pages =        "112--119",
  abstract =     "Recently evolutionary algorithms, such as the Genetic
                 Algorithm (GA), Genetic Programming (GP) and Particle
                 Swarm Optimisation (PSO), have become common approaches
                 used in financial applications to address stock trading
                 problems. In this paper, we propose a novel method
                 called the Multi-objective Quantum-inspired Tabu Search
                 (MOQTS) algorithm, which can be applied in a stock
                 trading system. Determining the best time to buy and
                 sell in the stock market and maximizing profits while
                 incurring fewer risks are important issues in financial
                 research. In order to identify ideal trading points,
                 the proposed trading system uses various kinds of
                 technical indicators as trading rules in order to cope
                 with different stock situations. The proposed algorithm
                 is used to identify the optimal combination of trading
                 rules as our trading strategy. Moreover, it makes use
                 of a sliding window in order to avoid the major problem
                 of over-fitting. In the experiment, the algorithm uses
                 both profit earned and other aspects, such as
                 successful transaction rate and standard deviation, to
                 analyse this system. The experimental results, in
                 relation to profit earned and successful transaction
                 rates in the U.S.A stock market, outperform both the
                 traditional method and the Buy & Hold method which are
                 common benchmarks in the field.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/SMC.2014.6973893",
  ISSN =         "1062-922X",
  month =        oct,
  notes =        "Also known as \cite{6973893}",
}

Genetic Programming entries for Yao-Hsin Chou Shu-Yu Kuo Chun Kuo

Citations