Pemodelan Data Return Saham PT. Bank Republik Indonesia dengan Self-Exciting Threshold Autoregressive dan Algoritma Genetika

Nonlinear time series model is a time series model applied to data that has the nonlinear pattern. One of the nonlinear time series models is Self-Exciting Threshold Autoregressive (SETAR). The SETAR model is a time series model that data modeling is done by dividing data into multiple regimes, wher...

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Autor principal:	Maulida Nurhidayati
Formato:	article
Lenguaje:	EN
Publicado:	Department of Mathematics, UIN Sunan Ampel Surabaya 2018
Materias:	Nonlinear; SETAR; Grid Search; Genetic Algorithm; Stock Return Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/df86699fa1ff4de8be3d7912ad5aacd6
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spelling	oai:doaj.org-article:df86699fa1ff4de8be3d7912ad5aacd62021-12-02T14:53:31ZPemodelan Data Return Saham PT. Bank Republik Indonesia dengan Self-Exciting Threshold Autoregressive dan Algoritma Genetika2527-31592527-316710.15642/mantik.2018.4.1.16-21https://doaj.org/article/df86699fa1ff4de8be3d7912ad5aacd62018-05-01T00:00:00Zhttp://jurnalsaintek.uinsby.ac.id/index.php/mantik/article/view/286https://doaj.org/toc/2527-3159https://doaj.org/toc/2527-3167Nonlinear time series model is a time series model applied to data that has the nonlinear pattern. One of the nonlinear time series models is Self-Exciting Threshold Autoregressive (SETAR). The SETAR model is a time series model that data modeling is done by dividing data into multiple regimes, whereas each regime following an autoregressive (AR) model. The division of the regime based on the score of the delay and threshold of the data itself. The number of SETAR model parameters not only resulted from the best model search process but also resulted in a SETAR model that is not yet optimum. Based on these findings, this study used Genetic Algorithm (GA) to produce the best and optimum SETAR model. In this research, using SETAR simulation data modeling and return data of Bank Rakyat Indonesia (BRI) were performed. The method used to model the data is Grid Search (GS) and Genetic Algorithm (GA). The result of analysis of SETAR simulation data shows that GA method gives better modeling result than GS method. The GA motive AIC value for the amount of 200 data is -3.976178 which is smaller than the AIC GS method of 1.361723. For the amount of data of 500 AIC values, GA method is also smaller than AIC GS method. In BRI stock return data, GA method also gives better modeling result compared to GS. It is marked by the GA AIC method value of -11147.66 less than -11146.26 which is the AIC method of GS. Thus, the result of analysis of SETAR model simulation data and BRI stock return shows that GA method gives better modeling result compared to GS method based on generated AIC value.Maulida NurhidayatiDepartment of Mathematics, UIN Sunan Ampel SurabayaarticleNonlinear; SETAR; Grid Search; Genetic Algorithm; Stock ReturnMathematicsQA1-939ENMantik: Jurnal Matematika, Vol 4, Iss 1, Pp 16-21 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Nonlinear; SETAR; Grid Search; Genetic Algorithm; Stock Return Mathematics QA1-939
spellingShingle	Nonlinear; SETAR; Grid Search; Genetic Algorithm; Stock Return Mathematics QA1-939 Maulida Nurhidayati Pemodelan Data Return Saham PT. Bank Republik Indonesia dengan Self-Exciting Threshold Autoregressive dan Algoritma Genetika
description	Nonlinear time series model is a time series model applied to data that has the nonlinear pattern. One of the nonlinear time series models is Self-Exciting Threshold Autoregressive (SETAR). The SETAR model is a time series model that data modeling is done by dividing data into multiple regimes, whereas each regime following an autoregressive (AR) model. The division of the regime based on the score of the delay and threshold of the data itself. The number of SETAR model parameters not only resulted from the best model search process but also resulted in a SETAR model that is not yet optimum. Based on these findings, this study used Genetic Algorithm (GA) to produce the best and optimum SETAR model. In this research, using SETAR simulation data modeling and return data of Bank Rakyat Indonesia (BRI) were performed. The method used to model the data is Grid Search (GS) and Genetic Algorithm (GA). The result of analysis of SETAR simulation data shows that GA method gives better modeling result than GS method. The GA motive AIC value for the amount of 200 data is -3.976178 which is smaller than the AIC GS method of 1.361723. For the amount of data of 500 AIC values, GA method is also smaller than AIC GS method. In BRI stock return data, GA method also gives better modeling result compared to GS. It is marked by the GA AIC method value of -11147.66 less than -11146.26 which is the AIC method of GS. Thus, the result of analysis of SETAR model simulation data and BRI stock return shows that GA method gives better modeling result compared to GS method based on generated AIC value.
format	article
author	Maulida Nurhidayati
author_facet	Maulida Nurhidayati
author_sort	Maulida Nurhidayati
title	Pemodelan Data Return Saham PT. Bank Republik Indonesia dengan Self-Exciting Threshold Autoregressive dan Algoritma Genetika
title_short	Pemodelan Data Return Saham PT. Bank Republik Indonesia dengan Self-Exciting Threshold Autoregressive dan Algoritma Genetika
title_full	Pemodelan Data Return Saham PT. Bank Republik Indonesia dengan Self-Exciting Threshold Autoregressive dan Algoritma Genetika
title_fullStr	Pemodelan Data Return Saham PT. Bank Republik Indonesia dengan Self-Exciting Threshold Autoregressive dan Algoritma Genetika
title_full_unstemmed	Pemodelan Data Return Saham PT. Bank Republik Indonesia dengan Self-Exciting Threshold Autoregressive dan Algoritma Genetika
title_sort	pemodelan data return saham pt. bank republik indonesia dengan self-exciting threshold autoregressive dan algoritma genetika
publisher	Department of Mathematics, UIN Sunan Ampel Surabaya
publishDate	2018
url	https://doaj.org/article/df86699fa1ff4de8be3d7912ad5aacd6
work_keys_str_mv	AT maulidanurhidayati pemodelandatareturnsahamptbankrepublikindonesiadenganselfexcitingthresholdautoregressivedanalgoritmagenetika
_version_	1718389481643442176

Pemodelan Data Return Saham PT. Bank Republik Indonesia dengan Self-Exciting Threshold Autoregressive dan Algoritma Genetika

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