Abstract
Bayesian network is the is one of the probabilistic graphical models that represents a set of random variables and their conditional dependencies via a directed acyclic graph. In this study, Bayesian network is applied for the stock price forecast. The present method classifies stock price into three situations; normal value, positive and negative outliers. Different networks are defined by data in three situations in order to forecast the stock price. The effectiveness of the present method is discussed in the numerical example.
