Title :
Stock Market Prediction Using Multi Expression Programming
Author :
Crina Grosan;Ajith Abraham;Vitorino Ramos;Sang Yong Han
Author_Institution :
Department of Computer Science, Babe?-Bolyai University, Kog?lniceanu 1, Cluj-Napoca, 3400, Romania. cgrosan@cs.ubbcluj.ro
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 artificial neural network trained using Levenberg-Marquardt algorithm, support vector machine, Takagi-Sugeno Neuro-Fuzzy model and 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 markets","Artificial neural networks","Intelligent systems","Artificial intelligence","Machine intelligence","Genetic programming","Support vector machines","Takagi-Sugeno model","Boosting","Testing"
Conference_Titel :
Artificial intelligence, 2005. epia 2005. portuguese conference on
Print_ISBN :
0-7803-9365-1
DOI :
10.1109/EPIA.2005.341268
