Training Multilayer Perceptron with Genetic Algorithms and Particle Swarm Optimization for Modeling Stock Price Index Prediction

Prediction of Stock Price using Particle Swarm Optimization Algorithm and Box-Jenkins Time Series

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...

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 pred...

A Neuro-Fuzzy Expert System Trained by Particle Swarm Optimization for Stock Price Prediction

In today’s competitive markets, prediction of financial variables has become a critical issue. Especially in stock market analysis where a wrong prediction may result in a big loss in terms of time and money, having a robust prediction is a crucial issue. To model the chaotic, noisy, and evolving behavior of stock market data, new powerful methods should be developed. Soft Computing methods hav...

Multilayer Perceptron: Architecture Optimization and Training

Evaluation of Differential Evolution and Particle Swarm Optimization Algorithms at Training of Neural Network for Stock Prediction