A Robust Regression-Based Stock Exchange Forecasting and Determination of Correlation between Stock Markets
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Khan, Umair | - |
dc.contributor.author | Aadil, Farhan | - |
dc.contributor.author | Ghazanfar, Mustansar Ali | - |
dc.contributor.author | Khan, Salabat | - |
dc.contributor.author | Metawa, Noura | - |
dc.contributor.author | Muhammad, Khan | - |
dc.contributor.author | Mehmood, Irfan | - |
dc.contributor.author | Nam, Yunyoung | - |
dc.date.accessioned | 2021-08-11T11:43:39Z | - |
dc.date.available | 2021-08-11T11:43:39Z | - |
dc.date.issued | 2018-10 | - |
dc.identifier.issn | 2071-1050 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/5597 | - |
dc.description.abstract | Knowledge-based decision support systems for financial management are an important part of investment plans. Investors are avoiding investing in traditional investment areas such as banks due to low return on investment. The stock exchange is one of the major areas for investment presently. Various non-linear and complex factors affect the stock exchange. A robust stock exchange forecasting system remains an important need. From this line of research, we evaluate the performance of a regression-based model to check the robustness over large datasets. We also evaluate the effect of top stock exchange markets on each other. We evaluate our proposed model on the top 4 stock exchanges-New York, London, NASDAQ and Karachi stock exchange. We also evaluate our model on the top 3 companies-Apple, Microsoft, and Google. A huge (Big Data) historical data is gathered from Yahoo finance consisting of 20 years. Such huge data creates a Big Data problem. The performance of our system is evaluated on a 1-step, 6-step, and 12-step forecast. The experiments show that the proposed system produces excellent results. The results are presented in terms of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | MDPI Open Access Publishing | - |
dc.title | A Robust Regression-Based Stock Exchange Forecasting and Determination of Correlation between Stock Markets | - |
dc.type | Article | - |
dc.publisher.location | 스위스 | - |
dc.identifier.doi | 10.3390/su10103702 | - |
dc.identifier.scopusid | 2-s2.0-85054953198 | - |
dc.identifier.wosid | 000448559400344 | - |
dc.identifier.bibliographicCitation | Sustainability, v.10, no.10 | - |
dc.citation.title | Sustainability | - |
dc.citation.volume | 10 | - |
dc.citation.number | 10 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | ssci | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Science & Technology - Other Topics | - |
dc.relation.journalResearchArea | Environmental Sciences & Ecology | - |
dc.relation.journalWebOfScienceCategory | Green & Sustainable Science & Technology | - |
dc.relation.journalWebOfScienceCategory | Environmental Sciences | - |
dc.relation.journalWebOfScienceCategory | Environmental Studies | - |
dc.subject.keywordPlus | NEURAL-NETWORK | - |
dc.subject.keywordPlus | BAYESIAN-ANALYSIS | - |
dc.subject.keywordPlus | SELECTION MODEL | - |
dc.subject.keywordPlus | DECISION TREE | - |
dc.subject.keywordPlus | PREDICTION | - |
dc.subject.keywordPlus | PERFORMANCE | - |
dc.subject.keywordPlus | SYSTEM | - |
dc.subject.keywordPlus | INDEXES | - |
dc.subject.keywordPlus | ERROR | - |
dc.subject.keywordPlus | GA | - |
dc.subject.keywordAuthor | financial management | - |
dc.subject.keywordAuthor | stock exchange prediction | - |
dc.subject.keywordAuthor | regression | - |
dc.subject.keywordAuthor | forecasting | - |
dc.subject.keywordAuthor | correlation | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(31538) 22, Soonchunhyang-ro, Asan-si, Chungcheongnam-do, Republic of Korea+82-41-530-1114
COPYRIGHT 2021 by SOONCHUNHYANG UNIVERSITY ALL RIGHTS RESERVED.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.