Portfolio management using value at risk: A comparison between genetic algorithms and particle swarm optimization

2009 ◽  
Vol 24 (7) ◽  
pp. 766-792 ◽  
Author(s):  
V. A. F. Dallagnol ◽  
J. van den Berg ◽  
L. Mous
Keyword(s):  
At Risk ◽  
Genetic Algorithms ◽  
Particle Swarm Optimization ◽  
Portfolio Management ◽  
Value At Risk ◽  
Particle Swarm ◽  
Swarm Optimization

Particle Swarm Optimization Algorithm as a Tool for Profiling from Predictive Data Mining Models

2015 ◽  
pp. 406-434 ◽  
Author(s):  
Goran Klepac
Keyword(s):  
Particle Swarm Optimization ◽  
Portfolio Management ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Optimal Values ◽  
Input Variables ◽  
Predictive Data Mining ◽  
Customer Profiles

This chapter introduces the methodology of particle swarm optimization algorithm usage as a tool for finding customer profiles based on a previously developed predictive model that predicts events like selection of some products or services with some probabilities. Particle swarm optimization algorithm is used as a tool that finds optimal values of input variables within developed predictive models as referent values for maximization value of probability that customers select/buy a product or service. Recognized results are used as a base for finding similar profiles between customers. The presented methodology has practical value for decision support in business, where information about customer profiles are valuable information for campaign planning and customer portfolio management.

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

Entropy ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. 1239
Author(s):  
Fatih Ecer ◽  
Sina Ardabili ◽  
Shahab S. Band ◽  
Amir Mosavi
Keyword(s):  
Genetic Algorithms ◽  
Particle Swarm Optimization ◽  
Population Size ◽  
Correlation Coefficient ◽  
Multilayer Perceptron ◽  
Gaussian Function ◽  
Particle Swarm ◽  
Series Data ◽  
Output Function ◽  
Swarm Optimization

Predicting stock market (SM) trends is an issue of great interest among researchers, investors and traders since the successful prediction of SMs’ direction may promise various benefits. Because of the fairly nonlinear nature of the historical data, accurate estimation of the SM direction is a rather challenging issue. The aim of this study is to present a novel machine learning (ML) model to forecast the movement of the Borsa Istanbul (BIST) 100 index. Modeling was performed by multilayer perceptron–genetic algorithms (MLP–GA) and multilayer perceptron–particle swarm optimization (MLP–PSO) in two scenarios considering Tanh (x) and the default Gaussian function as the output function. The historical financial time series data utilized in this research is from 1996 to 2020, consisting of nine technical indicators. Results are assessed using Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and correlation coefficient values to compare the accuracy and performance of the developed models. Based on the results, the involvement of the Tanh (x) as the output function, improved the accuracy of models compared with the default Gaussian function, significantly. MLP–PSO with population size 125, followed by MLP–GA with population size 50, provided higher accuracy for testing, reporting RMSE of 0.732583 and 0.733063, MAPE of 28.16%, 29.09% and correlation coefficient of 0.694 and 0.695, respectively. According to the results, using the hybrid ML method could successfully improve the prediction accuracy.

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