STOCK PRICE FORECASTING BASED ON BACK PROPAGATION NEURAL NETWORK AND MARKOV CHAIN

Abstract

The stock market is a nonlinear dynamics system with enormous information, which is difficult to predict effectively by traditional methods. The model of stock price forecast based on BP Neutral-Network is put forward in this article. The paper try to find the way how to predictive the stock price. Exhaustive method is used for the hidden layer neurons and training method determination. Finally the experiment results show that the algorithm get better performance in stock price prediction. © (2014) Trans Tech Publications, Switzerland.