FuzzyTree crossover for multi-valued stock valuation
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- @Article{Lin:2007:IS,
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author = "Ping-Chen Lin and Jiah-Shing Chen",
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title = "FuzzyTree crossover for multi-valued stock valuation",
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journal = "Information Sciences",
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year = "2007",
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volume = "177",
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number = "5",
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pages = "1193--1203",
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month = "1 " # mar,
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note = "Including: The 3rd International Workshop on
Computational Intelligence in Economics and Finance
(CIEF'2003)",
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keywords = "genetic algorithms, genetic programming, Multi-valued
stock valuation, Intrinsic value, Fuzzy number",
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DOI = "doi:10.1016/j.ins.2006.08.017",
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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
Citations