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 was a multilayer perceptron network using the error propagation learning algorithm.After predicting the stock price with the neural network, the forecast price data in the second phase has been used to optimize the stock portfolio.In this phase, a multi-objective genetic algorithm is used to optimize the portfolio, and the optimal weights are assigned to the stock and the optimal stock portfolio is created.Having a regression model, the design of the relevant genetic algorithm has been done using MATLAB software.The results show that the stock portfolio created by MOPSO algorithm has a better performance compared to the algorithms used in the article under comparison under all four risk criteria except the criterion of conditional risk exposure. In all models, except the conditional risk-averaged value model, the stock portfolios created by the MOPSO algorithm used in the research have more and more appropriate performance.
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
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...متن کامل
a comprehensive survey: applications of multi-objective particle swarm optimization (mopso) algorithm
numerous problems encountered in real life cannot be actually formulated as a single objective problem; hence the requirement of multi-objective optimization (moo) had arisen several years ago. due to the complexities in such type of problems powerful heuristic techniques were needed, which has been strongly satisfied by swarm intelligence (si) techniques. particle swarm optimization (pso) has ...متن کامل
A Comprehensive Survey: Applications of Multi-objective Particle Swarm Optimization (mopso) Algorithm
Numerous problems encountered in real life cannot be actually formulated as a single objective problem; hence the requirement of Multi-Objective Optimization (MOO) had arisen several years ago. Due to the complexities in such type of problems powerful heuristic techniques were needed, which has been strongly satisfied by Swarm Intelligence (SI) techniques. Particle Swarm Optimization (PSO) has ...متن کامل
Multi-Objective Optimization of Squeeze Casting Process using Genetic Algorithm and Particle Swarm Optimization
The near net shaped manufacturing ability of squeeze casting process requiresto set the process variable combinations at their optimal levels to obtain both aesthetic appearance and internal soundness of the cast parts. The aesthetic and internal soundness of cast parts deal with surface roughness and tensile strength those can readily put the part in service without the requirement of costly s...متن کامل
Multi-objective particle swarm optimization (MOPSO) is a search algorithm based on social behavior. Most of the existing multi-objective particle swarm optimization schemes are based on Pareto optimality and aim to obtain a representative non-dominated Pareto front for a given problem. Several approaches have been proposed to study the convergence and performance of the algorithm, particularly ...متن کامل
دوره 14 شماره 2
صفحات 89- 97
تاریخ انتشار 2021-07-01
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