联合利华印度尼西亚Tbk公司股价预测模型的比较研究

Maya Citra
{"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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Promotion the Dangers of Smoking in Adolescents at SMAN 8 Padang Sidempuan in 2022 Implementation of Continuity of Care in New Born and Toddlers in the Era of the Covid 19 Pandemic In Napa Village, Angkola Selatan District, Tapanuli Selatan Regency Empowerment of Health Cadres in Utilizing Local Foodstuffs through Modisco Corn Processing to Increase Breast Milk Production for Postpartum Mothers Simulation of the Implementation of Nursing Discharge Planning to Reduce Patient Recurrence Rates in Hospitals The Effort Handling by the Police to follow Criminal crash Running in the City of Medan
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1