Comparative Study of Long Short-Term Memory (LSTM) and Quantum Long Short-Term Memory (QLSTM): Prediction of Stock Market Movement

Tariq Mahmood, Ibtasam Ahmad, Malik Muhammad Zeeshan Ansar, Jumanah Ahmed Darwish, Rehan Ahmad Khan Sherwani
{"title":"Comparative Study of Long Short-Term Memory (LSTM) and Quantum Long Short-Term Memory (QLSTM): Prediction of Stock Market Movement","authors":"Tariq Mahmood, Ibtasam Ahmad, Malik Muhammad Zeeshan Ansar, Jumanah Ahmed Darwish, Rehan Ahmad Khan Sherwani","doi":"arxiv-2409.08297","DOIUrl":null,"url":null,"abstract":"In recent years, financial analysts have been trying to develop models to\npredict the movement of a stock price index. The task becomes challenging in\nvague economic, social, and political situations like in Pakistan. In this\nstudy, we employed efficient models of machine learning such as long short-term\nmemory (LSTM) and quantum long short-term memory (QLSTM) to predict the Karachi\nStock Exchange (KSE) 100 index by taking monthly data of twenty-six economic,\nsocial, political, and administrative indicators from February 2004 to December\n2020. The comparative results of LSTM and QLSTM predicted values of the KSE 100\nindex with the actual values suggested QLSTM a potential technique to predict\nstock market trends.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"15 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuantFin - Statistical Finance","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.08297","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

In recent years, financial analysts have been trying to develop models to predict the movement of a stock price index. The task becomes challenging in vague economic, social, and political situations like in Pakistan. In this study, we employed efficient models of machine learning such as long short-term memory (LSTM) and quantum long short-term memory (QLSTM) to predict the Karachi Stock Exchange (KSE) 100 index by taking monthly data of twenty-six economic, social, political, and administrative indicators from February 2004 to December 2020. The comparative results of LSTM and QLSTM predicted values of the KSE 100 index with the actual values suggested QLSTM a potential technique to predict stock market trends.
长短期记忆(LSTM)与量子长短期记忆(QLSTM)的比较研究:预测股市走势
近年来,金融分析师一直在努力开发预测股价指数走势的模型。在巴基斯坦这样的经济、社会和政治局势下,这项任务变得极具挑战性。在这项研究中,我们采用了高效的机器学习模型,如长短时记忆(LSTM)和量子长短时记忆(QLSTM),通过 2004 年 2 月至 2020 年 12 月期间 26 个经济、社会、政治和行政指标的月度数据来预测卡拉奇证券交易所(KSE)100 指数。LSTM 和 QLSTM 预测的 KSE 100 指数值与实际值的比较结果表明,QLSTM 是一种预测股市趋势的潜在技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信