FuzzyTree Crossover for Multi-Valued Stock Valuation

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Misc{pingchen_lin_paper,
  author =       "Pin-Chen (P. C.) Lin and Jiah-Shing Chen",
  title =        "FuzzyTree Crossover for Multi-Valued Stock Valuation",
  howpublished = "Tutorial at Computational Intelligence in Economics
                 and Finance, Summer Workshop",
  year =         "2004",
  month =        aug,
  keywords =     "genetic algorithms, genetic programming, Stock
                 Valuation, Intrinsic Value, Multi-Value, Fuzzy Number",
  URL =          "http://www.aiecon.org/conference/efmaci2004/pdf/pingchen_lin_paper.pdf",
  size =         "15 pages",
  abstract =     "Stock valuation is very important for fundamental
                 investors to select undervalue stocks to earn excess
                 profit. However, it may be difficult to use stock
                 valuation results because different models generate
                 different estimates on 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. 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 little literature available on the crossover
                 operator for our fuzzy trees except the vanilla subtree
                 crossover. This study generalizes the subtree crossover
                 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.
                 Experimental results indicate that the FuzzyTree
                 crossover is more effective than subtree crossover in
                 terms of expression tree complexity and run time.
                 Second, shorter training periods produce better ROI. It
                 indicates long-term financial statement may distort the
                 intrinsic value of a stock. Finally, the return of
                 multi-valued fuzzy trading strategy is better than that
                 of single-valued and Buy-and-Hold strategy. We suggest
                 that more attention should be put on the multi-valued
                 stock valuation approach.",
  notes =        "Pin-Chen (P.C.) Lin = Ping-Chen Lin",
}

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

Citations