Flexible neural trees ensemble for stock index modeling

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@Article{Chen:2007:N,

• author = "Yuehui Chen and Bo Yang and Ajith Abraham",
• title = "Flexible neural trees ensemble for stock index modeling",
• journal = "Neurocomputing",
• year = "2007",
• volume = "70",
• number = "4-6",
• pages = "697--703",
• month = jan,
• note = "Advanced Neurocomputing Theory and Methodology - Selected papers from the International Conference on Intelligent Computing 2005 (ICIC 2005), International Conference on Intelligent Computing 2005",
• keywords = "genetic algorithms, genetic programming, Flexible neural tree, GP-like tree structure-based evolutionary algorithm, Particle swarm optimisation, Ensemble learning, Stock index",
• DOI = "doi:10.1016/j.neucom.2006.10.005",
• 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