Application of Bayesian Network to stock price prediction

Eisuke KITA, Masaaki Harada, Takao Mizuno

Abstract


Authors present the stock price prediction algorithm by using Bayesian network. The present algorithm uses the networktwice. First, the network is determined from the daily stock price and then, it is applied for predicting the daily stock pricewhich was already observed. The prediction error is evaluated from the daily stock price and its prediction. Second, thenetwork is determined again from both the daily stock price and the daily prediction error and then, it is applied for thefuture stock price prediction. The present algorithm is applied for predicting NIKKEI stock average and Toyota motorcorporation stock price. Numerical results show that the maximum prediction error of the present algorithm is 30% inNIKKEI stock average and 20% in Toyota Motor Corporation below that of the time-series prediction algorithms such asAR, MA, ARMA and ARCH models.


Full Text: PDF DOI: 10.5430/air.v1n2p171

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Artificial Intelligence Research

ISSN 1927-6974 (Print)   ISSN 1927-6982 (Online)

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