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Daily Stock Prediction Using Neuro-genetic Hybrids
Yung-Keun Kwon and Byung-Ro Moon
School of Computer Science & Engineering
Seoul National University
Shilim-dong, Kwanak-gu
Seoul, 151-742 Korea
{kwon,moon}@soar.snu.ac.kr
Abstract. We propose a neuro-genetic daily stock prediction model. Traditional indicators of stock prediction are utilized to produce useful input features of neural networks. The genetic algorithm optimizes the neural networks under a 2D encoding and crossover. To reduce the time in processing mass data, a parallel genetic algorithm was used on a Linux cluster system. It showed notable improvement on the average over the buy-and-hold strategy. We also observed that some companies were more predictable than others.
LNCS 2724, p. 2203 ff.
lncs@springer.de
© Springer-Verlag Berlin Heidelberg 2003