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

Writer: Davoodi Kasbi، Aliasghar ؛ Azinfar، Kaveh ؛

Corresponding Author: Dadashi، Iman ؛

Advances in Mathematical Finance and Applications Spring 2020, Volume 5 - Issue 2 Ranking: ب/ISC (‎17 page(s) - From 197 to 213 )

Keywords: Stock price Particle swarm optimization algorithm Chaid rule-based algoritm

Abstract:

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.

Machine summary:

In this re- search, 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 Opti- mization Algorithm). Today, due expansion of economic activity, financial markets and the prosperity of investing in equity markets, especially stock exchanges by natural and legal persons, access to accurate and timely information and their accurate and realistic analysis are the most important tool for making the right decisions and obtaining expected benefits and using fi- * Corresponding author Tel. 3 Literature Review In recent years, different models have been used to predict stock prices by researchers, since artifi- cial intelligence techniques that include neural networks, genetic algorithms, and fuzzy logic have achieved successful results in solving complex problems, have been used mostly in this regard [28]. In this paper, different data mining algorithms that have been used to predict share prices in the stock market up to now, have been compared and reviewed. Therefore, in order to review the generality of the models presented, the error rate is obtained to predict the depend- ent variable of the stock price in each three years for the testing company-years (the company-years that were discarded by the 10-fold cross-validation method and the algorithm has not seen them so far). Investigating the Effect of Internal Factors on Stock Price Change in Investment Companies Accepted in Tehran Stock Exchange, Accounting Research, 2017, 6(4), P.

Download citation file :
(پژوهیار, , , )

Download PDF
Downlaod HTML

Shortlink:

Page: