Modeling of Indonesia Composite Index using Artificial Neural Network and Multivariate Adaptive Regression Spline (retracted)

The Indonesian Composite Stock Price Index is an indicator of changes in stock prices are a guide for investors to invest in reducing risk. Fluctuations in stock data tend to violate the assumptions of normality, homoscedasticity, autocorrelation, and multicollinearity. This problem can be overcome...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Mutia Yollanda, Dodi Devianto, Putri Permathasari
Formato:	article
Lenguaje:	EN
Publicado:	Department of Mathematics, UIN Sunan Ampel Surabaya 2019
Materias:	artificial neural network multivariate adaptive regression spline indonesia composite index Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/37fb8e879d424f3c85ae0eca88156dcf
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:37fb8e879d424f3c85ae0eca88156dcf
record_format	dspace
spelling	oai:doaj.org-article:37fb8e879d424f3c85ae0eca88156dcf2021-12-02T16:54:10ZModeling of Indonesia Composite Index using Artificial Neural Network and Multivariate Adaptive Regression Spline (retracted)2527-31592527-316710.15642/mantik.2019.5.2.112-122https://doaj.org/article/37fb8e879d424f3c85ae0eca88156dcf2019-10-01T00:00:00Zhttp://jurnalsaintek.uinsby.ac.id/index.php/mantik/article/view/521https://doaj.org/toc/2527-3159https://doaj.org/toc/2527-3167The Indonesian Composite Stock Price Index is an indicator of changes in stock prices are a guide for investors to invest in reducing risk. Fluctuations in stock data tend to violate the assumptions of normality, homoscedasticity, autocorrelation, and multicollinearity. This problem can be overcome by modelling the Composite Stock Price Index uses an artificial neural network (ANN) and multivariate adaptive regression spline (MARS). In this study, the time-series data from the Composite Stock Price Index starting in April 2003 to March 2018 with its predictor variables are crude oil prices, interest rates, inflation, exchange rates, gold prices, Down Jones, and Nikkei 225. Based on the coefficient of determination, the determination coefficient of ANN is 0.98925, and the MARS determination coefficient is 0.99427. While based on the MAPE value, MAPE value of ANN was obtained, namely 6.16383 and MAPE value of MARS, which was 4.51372. This means that the ANN method and the good MARS method are used to forecast the value of the Indonesian Composite Stock Index in the future, but the MARS method shows the accuracy of the model is slightly better than ANN.Mutia YollandaDodi DeviantoPutri PermathasariDepartment of Mathematics, UIN Sunan Ampel Surabayaarticleartificial neural networkmultivariate adaptive regression splineindonesia composite indexMathematicsQA1-939ENMantik: Jurnal Matematika, Vol 5, Iss 2, Pp 112-122 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial neural network multivariate adaptive regression spline indonesia composite index Mathematics QA1-939
spellingShingle	artificial neural network multivariate adaptive regression spline indonesia composite index Mathematics QA1-939 Mutia Yollanda Dodi Devianto Putri Permathasari Modeling of Indonesia Composite Index using Artificial Neural Network and Multivariate Adaptive Regression Spline (retracted)
description	The Indonesian Composite Stock Price Index is an indicator of changes in stock prices are a guide for investors to invest in reducing risk. Fluctuations in stock data tend to violate the assumptions of normality, homoscedasticity, autocorrelation, and multicollinearity. This problem can be overcome by modelling the Composite Stock Price Index uses an artificial neural network (ANN) and multivariate adaptive regression spline (MARS). In this study, the time-series data from the Composite Stock Price Index starting in April 2003 to March 2018 with its predictor variables are crude oil prices, interest rates, inflation, exchange rates, gold prices, Down Jones, and Nikkei 225. Based on the coefficient of determination, the determination coefficient of ANN is 0.98925, and the MARS determination coefficient is 0.99427. While based on the MAPE value, MAPE value of ANN was obtained, namely 6.16383 and MAPE value of MARS, which was 4.51372. This means that the ANN method and the good MARS method are used to forecast the value of the Indonesian Composite Stock Index in the future, but the MARS method shows the accuracy of the model is slightly better than ANN.
format	article
author	Mutia Yollanda Dodi Devianto Putri Permathasari
author_facet	Mutia Yollanda Dodi Devianto Putri Permathasari
author_sort	Mutia Yollanda
title	Modeling of Indonesia Composite Index using Artificial Neural Network and Multivariate Adaptive Regression Spline (retracted)
title_short	Modeling of Indonesia Composite Index using Artificial Neural Network and Multivariate Adaptive Regression Spline (retracted)
title_full	Modeling of Indonesia Composite Index using Artificial Neural Network and Multivariate Adaptive Regression Spline (retracted)
title_fullStr	Modeling of Indonesia Composite Index using Artificial Neural Network and Multivariate Adaptive Regression Spline (retracted)
title_full_unstemmed	Modeling of Indonesia Composite Index using Artificial Neural Network and Multivariate Adaptive Regression Spline (retracted)
title_sort	modeling of indonesia composite index using artificial neural network and multivariate adaptive regression spline (retracted)
publisher	Department of Mathematics, UIN Sunan Ampel Surabaya
publishDate	2019
url	https://doaj.org/article/37fb8e879d424f3c85ae0eca88156dcf
work_keys_str_mv	AT mutiayollanda modelingofindonesiacompositeindexusingartificialneuralnetworkandmultivariateadaptiveregressionsplineretracted AT dodidevianto modelingofindonesiacompositeindexusingartificialneuralnetworkandmultivariateadaptiveregressionsplineretracted AT putripermathasari modelingofindonesiacompositeindexusingartificialneuralnetworkandmultivariateadaptiveregressionsplineretracted
_version_	1718382850611347456

Modeling of Indonesia Composite Index using Artificial Neural Network and Multivariate Adaptive Regression Spline (retracted)

Ejemplares similares