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 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.
The purpose of this research is predicting the stock prices using the Particle Swarm Optimization Algorithm and Box-Jenkins method. In this way, the information of 165 corporations is collected from 2001 to 2016. Then, this research considers price to earnings per share and earnings per share as main variables. The relevant regression equation was created using two variables of earnings per sha...متن کامل
Optimizing the Prediction Model of Stock Price in Pharmaceutical Companies Using Multiple Objective Particle Swarm Optimization Algorithm (MOPSO)
The purpose of this study is to optimize the stock price forecasting model with meta-innovation method in pharmaceutical companies.In this research, stock portfolio optimization has been done in two separate phases.The first phase is related to forecasting stock futures based on past stock information, which is forecasting the stock price using artificial neural network.The neural network used ...متن کامل
Background and Objectives: Stock price prediction has become one of the interesting and also challenging topics for researchers in the past few years. Due to the non-linear nature of the time-series data of the stock prices, mathematical modeling approaches usually fail to yield acceptable results. Therefore, machine learning methods can be a promising solution to this problem. Methods: In this...متن کامل
SELECTION OF SUITABLE RECORDS FOR NONLINEAR ANALYSIS USING GENETIC ALGORITHM (GA) AND PARTICLE SWARM OPTIMIZATION (PSO)
This paper presents a suitable and quick way to choose earthquake records in non-linear dynamic analysis using optimization methods. In addition, these earthquake records are scaled. Therefore, structural responses of three different soil-frame models were examined, the change in maximum displacement of roof was analyzed and the damage index of whole structures was measured. The soil classifica...متن کامل
In this paper optimal torque control (OTC) of stand-alone variable-speed small-scale wind turbine equipped with a permanent magnet synchronous generator and a switch- mode rectifier is presented. It is shown that with OTC method in standalone configuration, power coefficient could be reached to its maximum possible value, i.e. 0.48. An appropriate control algorithm based on turbine characterist...متن کامل
Stock Exchange Share Price is very hard to predict since there are no significant rules to estimate or predict that. Stock price prediction is one of the emerging field of research and many methods like technical analysis, statistical analysis, time series analysis etc are used for this purpose. Artificial Neural Network is a popular technique for the stock price prediction. Here we use Multila...متن کامل
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دوره 5 شماره 2
صفحات 1- 19
تاریخ انتشار 2020-04-01
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