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Stock Market Prediction using Multi Expression Programming

57. Crina Grosan, Ajith Abraham, Sang Yong Han, Vitorino Ramos, Stock Market Prediction using Multi Expression Programming, in ALEA´05, Workshop on Artificial Life and Evolutionary Algorithms at EPIA´05 - Proc. of the 12th Portuguese Conference on Artificial Intelligence, C. Bento, A. Cardoso and G. Dias (Eds.), IEEE Press, pp. 73-78, 2005.

PDF file: paper (169 Kb)

Abstract: The use of intelligent systems for stock market predictions has been widely established. In this paper we introduce a genetic programming technique (called Multi-Expression programming) for the prediction of two stock indices. The performance is then compared with an artifcial neural network trained using Levenberg-Marquardt algorithm, support vector machine, Takagi-Sugeno neuro-fuzzy model, a difference boosting neural network. We considered Nasdaq-100 index of Nasdaq Stock MarketSM and the S&P CNX NIFTY stock index as test data.

Keywords: Stock Market Prediction, Multi Expression Programming, Nasdaq-100, CNX NIFTY stock index.

Cited by:

º William Wilson, Phil Birkin and Uwe Aickelin, "The Motif Tracking Algorithm", in International Journal of Automation and Computing 04(1), January 2007.

º Zhang Jianwei, Zhang Yingjiang, Wang Zongyue, Lin Zhiyi and Huang Zhang Can, "Study on Multi-Expression Programming", in Journal of Wuhan University of Technology (Information & Management Enginnering), Vol.29, No.2 pp. 57-61, 2007.

º William O. Wilson, Phil Birkin and Uwe Aickelin, "Price Trackers Inspired by Immune Memory", Proc.s of the 5th Int. Conf. on Artificial Immune Systems (ICARIS 2006), LNCS, Springer-Verlag, Oeiras, Portugal, 2006.

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[...] Interactions among many sporuliferous and ubiquitous abstractions may lead to increasing reality [...] V. Ramos, 2001.
http://www.laseeb.org/vramos + http://www.chemoton.org. Vitorino Ramos (Nov. 2007).