Flexible neural trees ensemble for stock index modeling
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gp-bibliography.bib Revision:1.8028
- @Article{Chen:2007:N,
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author = "Yuehui Chen and Bo Yang and Ajith Abraham",
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title = "Flexible neural trees ensemble for stock index
modeling",
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journal = "Neurocomputing",
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year = "2007",
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volume = "70",
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number = "4-6",
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pages = "697--703",
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month = jan,
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note = "Advanced Neurocomputing Theory and Methodology -
Selected papers from the International Conference on
Intelligent Computing 2005 (ICIC 2005), International
Conference on Intelligent Computing 2005",
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keywords = "genetic algorithms, genetic programming, Flexible
neural tree, GP-like tree structure-based evolutionary
algorithm, Particle swarm optimisation, Ensemble
learning, Stock index",
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DOI = "doi:10.1016/j.neucom.2006.10.005",
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abstract = "The use of intelligent systems for stock market
predictions has been widely established. In this paper,
we investigate how the seemingly chaotic behaviour of
stock markets could be well represented using flexible
neural tree (FNT) ensemble technique. We considered the
Nasdaq-100 index of Nasdaq Stock MarketSM and the S&P
CNX NIFTY stock index. We analysed 7-year Nasdaq-100
main index values and 4-year NIFTY index values. This
paper investigates the development of novel reliable
and efficient techniques to model the seemingly chaotic
behaviour of stock markets. The structure and
parameters of FNT are optimised using genetic
programming (GP) like tree structure-based evolutionary
algorithm and particle swarm optimization (PSO)
algorithms, respectively. A good ensemble model is
formulated by the local weighted polynomial regression
(LWPR). This paper investigates whether the proposed
method can provide the required level of performance,
which is sufficiently good and robust so as to provide
a reliable forecast model for stock market indices.
Experimental results show that the model considered
could represent the stock indexes behaviour very
accurately.",
- }
Genetic Programming entries for
Yuehui Chen
Bo Yang
Ajith Abraham
Citations