基于lstm的深度学习股票预测模型及预测优化模型

IF 2.3 Q3 MANAGEMENT EURO Journal on Decision Processes Pub Date : 2021-01-01 DOI:10.1016/j.ejdp.2021.100001
Akhter Mohiuddin Rather
{"title":"基于lstm的深度学习股票预测模型及预测优化模型","authors":"Akhter Mohiuddin Rather","doi":"10.1016/j.ejdp.2021.100001","DOIUrl":null,"url":null,"abstract":"<div><p>A new method of predicting time-series-based stock prices and a new model of an investment portfolio based on predictions obtained is proposed here. For this purpose, a new regression scheme is implemented on a long-short-term-memory-based deep neural network. The predictions once obtained are used to construct an investment portfolio or more specifically a predicted portfolio. A large set of experiments have been carried on stock data of NIFTY-50 obtained from the National stock exchange of India. The results confirm that the proposed model outperforms various standard predictive models as well as various standard portfolio optimization models.</p></div>","PeriodicalId":44104,"journal":{"name":"EURO Journal on Decision Processes","volume":"9 ","pages":"Article 100001"},"PeriodicalIF":2.3000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2193943821001175/pdfft?md5=959b7546dae30fbd745909766e9fc3b5&pid=1-s2.0-S2193943821001175-main.pdf","citationCount":"0","resultStr":"{\"title\":\"LSTM-based Deep Learning Model for Stock Prediction and Predictive Optimization Model\",\"authors\":\"Akhter Mohiuddin Rather\",\"doi\":\"10.1016/j.ejdp.2021.100001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>A new method of predicting time-series-based stock prices and a new model of an investment portfolio based on predictions obtained is proposed here. For this purpose, a new regression scheme is implemented on a long-short-term-memory-based deep neural network. The predictions once obtained are used to construct an investment portfolio or more specifically a predicted portfolio. A large set of experiments have been carried on stock data of NIFTY-50 obtained from the National stock exchange of India. The results confirm that the proposed model outperforms various standard predictive models as well as various standard portfolio optimization models.</p></div>\",\"PeriodicalId\":44104,\"journal\":{\"name\":\"EURO Journal on Decision Processes\",\"volume\":\"9 \",\"pages\":\"Article 100001\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2021-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2193943821001175/pdfft?md5=959b7546dae30fbd745909766e9fc3b5&pid=1-s2.0-S2193943821001175-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"EURO Journal on Decision Processes\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2193943821001175\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MANAGEMENT\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"EURO Journal on Decision Processes","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2193943821001175","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MANAGEMENT","Score":null,"Total":0}
引用次数: 0

摘要

本文提出了一种新的基于时间序列的股票价格预测方法和一种新的基于预测结果的投资组合模型。为此,在基于长短期记忆的深度神经网络上实现了一种新的回归方案。一旦获得预测,就用于构建投资组合,或者更具体地说,用于预测投资组合。对从印度国家证券交易所获得的NIFTY-50股票数据进行了大量的实验。结果表明,该模型优于各种标准预测模型和各种标准投资组合优化模型。
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
LSTM-based Deep Learning Model for Stock Prediction and Predictive Optimization Model

A new method of predicting time-series-based stock prices and a new model of an investment portfolio based on predictions obtained is proposed here. For this purpose, a new regression scheme is implemented on a long-short-term-memory-based deep neural network. The predictions once obtained are used to construct an investment portfolio or more specifically a predicted portfolio. A large set of experiments have been carried on stock data of NIFTY-50 obtained from the National stock exchange of India. The results confirm that the proposed model outperforms various standard predictive models as well as various standard portfolio optimization models.

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
CiteScore
2.70
自引率
10.00%
发文量
15
期刊最新文献
Editorial Board Editorial: Feature issue on fair and explainable decision support systems Corrigendum to “Multi-objective optimization in real-time operation of rainwater harvesting systems” [EURO Journal on Decision Processes Volume 11 (2023) 100039] Multiobjective combinatorial optimization with interactive evolutionary algorithms: The case of facility location problems Performance assessment of waste sorting: Component-based approach to incorporate quality into data envelopment analysis
×
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