Stock price forecast using Bayesian network

Bayesian network is a probabilistic graphical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph. This paper describes the price earnings ratio (P/E ratio) forecast by using Bayesian network. Firstly, the use of clustering algorithm transforms the continuous P/E ratio to the set of digitized values. The Bayesian network for the P/E ratio forecast is determined from the set of the digitized values. NIKKEI stock average (NIKKEI225) and Toyota motor corporation stock price are considered as numerical examples. The results show that the forecast accuracy of the present algorithm is better than that of the traditional time-series forecast algorithms in comparison of their correlation coefficient and the root mean square error.

► Bayesian network is applied for forecasting Nikkei stock average price and Toyota motor corporation stock price. ► Bayesian network models the stochastic dependency between past stock prices to predict the future stock price. ► The present method is compared with the time-series forecast algorithms such as AR, MA, ARMA and ARCH models. ► The computational accuracy of the present algorithm is 15–20% better than the time-series forecast algorithms.