Bayesian Network Based Prediction Algorithm of Stock Price Return

This paper describes the stock price return prediction using Bayesian network. The Bayesian network gives the probabilistic graphical model that represents previous stock price returns and their conditional dependencies via a directed acyclic graph. When the stock price is taken as the stochastic variable, the Bayesian network gives the conditional dependency between the past and future stock prices. In the present algorithm, the stock price return distribution is transformed to the discrete values set by using Ward method, which is one of the clustering algorithms. The Bayesian network gives the conditional dependency between the past and future stock prices. The stock price is determined from the discrete value set of the stock prices so that its occurrence probability is maximized. Finally, the present algorithm is compared with the traditional time-series prediction algorithms in the TOYOTA motor corporation stock price prediction. The present algorithm show 20% better than the time-series prediction algorithms.