%0 Journal Article %1 Chen:2007:N %A Chen, Yuehui %A Yang, Bo %A Abraham, Ajith %D 2007 %J Neurocomputing %K Ensemble Flexible GP-like Particle Stock algorithm, algorithms, evolutionary genetic index learning, neural optimisation, programming, structure-based swarm tree tree, %N 4-6 %P 697--703 %R doi:10.1016/j.neucom.2006.10.005 %T Flexible neural trees ensemble for stock index modeling %V 70 %X 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.

@article{Chen:2007:N, 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.}, added-at = {2008-06-19T17:35:00.000+0200}, author = {Chen, Yuehui and Yang, Bo and Abraham, Ajith}, biburl = {https://www.bibsonomy.org/bibtex/2191ee9746f13ca848dafa303b038d13a/brazovayeye}, doi = {doi:10.1016/j.neucom.2006.10.005}, interhash = {4d380b871b024b870d637791af939314}, intrahash = {191ee9746f13ca848dafa303b038d13a}, journal = {Neurocomputing}, keywords = {Ensemble Flexible GP-like Particle Stock algorithm, algorithms, evolutionary genetic index learning, neural optimisation, programming, structure-based swarm tree tree,}, month = {January}, note = {Advanced Neurocomputing Theory and Methodology - Selected papers from the International Conference on Intelligent Computing 2005 (ICIC 2005), International Conference on Intelligent Computing 2005}, number = {4-6}, pages = {697--703}, timestamp = {2008-06-19T17:37:49.000+0200}, title = {Flexible neural trees ensemble for stock index modeling}, volume = 70, year = 2007 }