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Stock Market prediction using Multi Expression Programming

by Crina Grosan, Ajith Abraham, Sang Yong Han, Vitorino Ramos
Progress in Artificial Intelligence (2005)

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.

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