{"title":"联合利华印度尼西亚Tbk公司股价预测模型的比较研究","authors":"Maya Citra","doi":"10.55299/ijcs.v2i1.220","DOIUrl":null,"url":null,"abstract":"The purpose of this study is to know the comparison of forecasting models in predicting the stock price of PT. Unilever Indonesia Tbk. In this study, there are 2 forecasting models, namely ARIMA and GARCH forecasting. The population in this study is data on the daily closing price of PT. Unilever Indonesia Tbk for the period January 2018 to June 2021, so the sample in this study is 1090 time series data. The results showed that the best forecasting model to predict the stock price of PT. Unilever Indonesia Tbk, namely ARIMA (1,1,1) and GARCH (1,1). In the ARIMA model (1,1,1) there are assumptions that are not met, namely the assumption of homoscedasticity or in the model there is an element of heteroscedasticity so that the GARCH (1,1) model with MAPE 1.91% is selected as the best forecasting model to predict stock prices of PT. Unilever Indonesia Tbk.","PeriodicalId":202357,"journal":{"name":"International Journal of Community Service (IJCS)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Comparative Study of Stock Price Forecasting Models PT. Unilever Indonesia Tbk Using Arima and Garch\",\"authors\":\"Maya Citra\",\"doi\":\"10.55299/ijcs.v2i1.220\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The purpose of this study is to know the comparison of forecasting models in predicting the stock price of PT. Unilever Indonesia Tbk. In this study, there are 2 forecasting models, namely ARIMA and GARCH forecasting. The population in this study is data on the daily closing price of PT. Unilever Indonesia Tbk for the period January 2018 to June 2021, so the sample in this study is 1090 time series data. The results showed that the best forecasting model to predict the stock price of PT. Unilever Indonesia Tbk, namely ARIMA (1,1,1) and GARCH (1,1). In the ARIMA model (1,1,1) there are assumptions that are not met, namely the assumption of homoscedasticity or in the model there is an element of heteroscedasticity so that the GARCH (1,1) model with MAPE 1.91% is selected as the best forecasting model to predict stock prices of PT. Unilever Indonesia Tbk.\",\"PeriodicalId\":202357,\"journal\":{\"name\":\"International Journal of Community Service (IJCS)\",\"volume\":\"37 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-06-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Community Service (IJCS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.55299/ijcs.v2i1.220\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Community Service (IJCS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.55299/ijcs.v2i1.220","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
本研究的目的是了解预测模型在预测PT. Unilever Indonesia Tbk股价时的比较。本研究采用ARIMA和GARCH两种预测模型。本研究的人口是2018年1月至2021年6月期间PT. Unilever Indonesia Tbk的每日收盘价数据,因此本研究的样本是1090个时间序列数据。结果表明,预测PT. Unilever Indonesia Tbk股价的最佳预测模型为ARIMA(1,1,1)和GARCH(1,1)。在ARIMA模型(1,1,1)中存在不满足的假设,即均方差假设或模型中存在异方差因素,因此选择MAPE为1.91%的GARCH(1,1)模型作为预测PT. Unilever Indonesia Tbk股价的最佳预测模型。
Comparative Study of Stock Price Forecasting Models PT. Unilever Indonesia Tbk Using Arima and Garch
The purpose of this study is to know the comparison of forecasting models in predicting the stock price of PT. Unilever Indonesia Tbk. In this study, there are 2 forecasting models, namely ARIMA and GARCH forecasting. The population in this study is data on the daily closing price of PT. Unilever Indonesia Tbk for the period January 2018 to June 2021, so the sample in this study is 1090 time series data. The results showed that the best forecasting model to predict the stock price of PT. Unilever Indonesia Tbk, namely ARIMA (1,1,1) and GARCH (1,1). In the ARIMA model (1,1,1) there are assumptions that are not met, namely the assumption of homoscedasticity or in the model there is an element of heteroscedasticity so that the GARCH (1,1) model with MAPE 1.91% is selected as the best forecasting model to predict stock prices of PT. Unilever Indonesia Tbk.