Stock Price Prediction Using the Chaid Rule-Based Algorithm and Particle Swarm Optimization (PSO)

Stock prices in each industry are one of the major issues in the stock market. given the increasing number of shareholders in the stock market and their attention to the price of different stocks in transactions, the prediction of the stock price trend has become significant. many people use the share price movement process when comparing different stocks while investing, and also want to predict this trend to see if the trend continues to increase or decrease over time. in this research, stock price prediction for 1170 years -company during 2011-2016 (a sixyear period) of listed companies in stock exchange has been studied using the machine learning method (chaid rule-based algorithm and particle swarm optimization algorithm). the results of the research show that there is a significant relationship between earnings per share, e / p ratio, company size, inventory turnover ratio, and stock returns with stock prices. also, chaid rule-based algorithm has a good ability to predict stock prices.