Stock price prediction using the Chaid rule-based algorithm and particle swarm optimization (pso)

Advances in Mathematical Finance and Applications

مقاله 5، دوره 5، شماره 2، تیر 2020، صفحه 197-213   

نوع مقاله: Research Paper

شناسه دیجیتال (DOI): 10.22034/amfa.2019.585043.1184

نویسندگان

Aliasghar Davoodi Kasbi¹؛ Iman Dadashi^{* 2}

¹Department of Accounting, Babol Branch, Islamic Azad University, Babol, Iran

²Department of Accounting, Babol Branch, Islamic Azad University, Babol, Iran

چکیده

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 com-paring 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 six-year 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, particle swarm optimization (pso) algorithm has a good ability to predict stock prices.

کلیدواژه‌ها

stock price؛ particle swarm optimization algorithm؛ Chaid rule-based algoritm

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