FuzzyTree crossover for multi-valued stock valuation

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@Article{Lin:2007:IS,
  author =       "Ping-Chen Lin and Jiah-Shing Chen",
  title =        "FuzzyTree crossover for multi-valued stock valuation",
  journal =      "Information Sciences",
  year =         "2007",
  volume =       "177",
  number =       "5",
  pages =        "1193--1203",
  month =        "1 " # mar,
  note =         "Including: The 3rd International Workshop on
                 Computational Intelligence in Economics and Finance
                 (CIEF'2003)",
  keywords =     "genetic algorithms, genetic programming, Multi-valued
                 stock valuation, Intrinsic value, Fuzzy number",
  DOI =          "doi:10.1016/j.ins.2006.08.017",
  abstract =     "Stock valuation is very important for fundamental
                 investors in order to select undervalued stocks so as
                 to earn excess profits. However, it may be difficult to
                 use stock valuation results, because different models
                 generate different estimates for the same stock. This
                 suggests that the value of a stock should be
                 multi-valued rather than single-valued. We therefore
                 develop a multi-valued stock valuation model based on
                 fuzzy genetic programming (GP). In our fuzzy GP model
                 the value of a stock is represented as a fuzzy
                 expression tree whose terminal nodes are allowed to be
                 fuzzy numbers. There is scant literature available on
                 the crossover operator for our fuzzy trees, except for
                 the vanilla subtree crossover. This study generalises
                 the subtree crossover in order to design a new
                 crossover operator for the fuzzy trees. Since the stock
                 value is estimated by a fuzzy expression tree which
                 calculates to a fuzzy number, the stock value becomes
                 multi-valued. In addition, the resulting fuzzy stock
                 value induces a natural trading strategy which can
                 readily be executed and evaluated. These experimental
                 results indicate that the fuzzy tree (FuzzyTree)
                 crossover is more effective than a subtree (SubTree)
                 crossover in terms of expression tree complexity and
                 run time. Secondly, shorter training periods produce a
                 better return of investment (ROI), indicating that
                 long-term financial statements may distort the
                 intrinsic value of a stock. Finally, the return of a
                 multi-valued fuzzy trading strategy is better than that
                 of single-valued and buy-and-hold strategies.",
}

Genetic Programming entries for Ping-Chen Lin Jiah-Shing Chen

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