Prediction of Jakarta Composite Index Using Neural Network Model and Genetic Optimization

Researches related to prediction of stock price data have been developing rapidly. Likewise, the modeling techniques used for predictive purposes are also increasing along with advances in the field of computing. This study applied neural network model in predicting the Jakarta Composite Index data...

Full description

Saved in:
Bibliographic Details
Published inJournal of physics. Conference series Vol. 1655; no. 1; pp. 12096 - 12102
Main Authors Santoso, R, Warsito, B, Yasin, H
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.10.2020
Subjects
Genetic algorithms
Neural networks
Optimization
Physics
Jakarta Indonesia
Indonesia
Online AccessGet full text

Cover

More Information
Summary:Researches related to prediction of stock price data have been developing rapidly. Likewise, the modeling techniques used for predictive purposes are also increasing along with advances in the field of computing. This study applied neural network model in predicting the Jakarta Composite Index data as a case of time series. The optimization method used was genetic algorithm. This method is included in one of the heuristic techniques. Unlike standard optimization methods, genetic algorithms do not use gradients as a basis for search techniques. Parameters in the neural network model are obtained from the process of decoding chromosomes from generation to generation. In comparison, the two gradient-based optimization methods were also applied, i.e Conjugate Gradient and Gradient Descent. The results showed the superiority of genetic algorithms compared to other optimization methods in out-sample prediction whereas, the in-sample prediction of gradient-based optimization methods achieve better precision.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1655/1/012096