Dynamic Stock Trading System based on Quantum-Inspired Tabu Search Algorithm

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@InProceedings{Kuo:2013:CEC,
  article_id =   "1437",
  author =       "Shu-Yu Kuo and Chun Kuo and Yao-Hsin Chou",
  title =        "Dynamic Stock Trading System based on Quantum-Inspired
                 {Tabu} Search Algorithm",
  booktitle =    "2013 IEEE Conference on Evolutionary Computation",
  volume =       "1",
  year =         "2013",
  month =        jun # " 20-23",
  editor =       "Luis Gerardo {de la Fraga}",
  pages =        "1029--1036",
  address =      "Cancun, Mexico",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/CEC.2013.6557680",
  abstract =     "Many heuristic methods or evolutionary algorithms such
                 as Genetic Algorithm (GA) and Genetic Programming (GP)
                 are common approaches used in financial applications.
                 Determining the best time to buy and sell in a stock
                 market, and thereby maximising the profit with lower
                 risks are important issues in financial research.
                 Recent researches have used trading rules based on
                 technical analysis to address this problem. These rules
                 can determine trading times by analysing the value of
                 technical indicators. In other words, we can make
                 trading rules by analysing the value of technical
                 indicators. A simple example of a trading rule would
                 be, if one technical indicator's value achieves the
                 pre-defined value, then we can buy or sell stocks. A
                 combination of trading rules would become a trading
                 strategy. The process of making trading strategies can
                 be formulated as a combinatorial optimisation problem.
                 In this paper, a novel method which can be applied to a
                 trading system is proposed. First, the proposed system
                 uses the Quantum-inspired Tabu Search (QTS) algorithm
                 to find the optimal combination of trading rules.
                 Second, it uses sliding window to avoid the major
                 problem of over-fitting. The experiment results of
                 earning profit show much better performance than other
                 approaches. Especially, the proposed method outperforms
                 Buy & Hold method which is a common benchmark in this
                 field.",
  notes =        "Also known as \cite{6557680}

                 CEC 2013 - A joint meeting of the IEEE, the EPS and the
                 IET.",
}

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

Citations