FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4387404394> ?p ?o ?g. }

• W4387404394 endingPage "21" @default.
• W4387404394 startingPage "1" @default.
• W4387404394 abstract "ABSTRACTEven a slight increase in accuracy when predicting the direction of stock movements can have a significant impact on the rate of returns. However, determining the most suitable variables, methods, and parameters to predict price changes is extremely challenging due to the multitude of variables influencing these changes. This paper presents an innovative prediction framework that combines ensemble learning and feature selection algorithms to effectively capture daily stock movements. The study focuses on predicting the change between the opening and closing prices of the subsequent day and employs a daily sliding window cross-validation methodology. The framework comprises fourteen variable groups encompassing a range of financial and operational indicators. Experimental findings indicate that a competitive performance was achieved for stocks within the Borsa Istanbul 30 index. Light Gradient Boosting Machines and Shapley Additive Explanations emerges as the optimal model combination and exhibits superior performance compared to a buy-and-hold strategy.KEYWORDS: Stock market predictionfeature selectionensemble learningmachine learningforecastingemerging markets Disclosure statementNo potential conflict of interest was reported by the author(s).Author contributionMahmut Sami Sivri constructed the idea for research, planned the methodology to reach the conclusion, took responsibility in execution of the experiments, data management and reporting, logical interpretation, presentation of the results, literature review and construction of the whole of the manuscript.Alp Ustundag organised and supervised the course of the project and taking the responsibility, reviewed the article before submission and provided personnel, environmental and financial support and tools and instruments that are vital for the project.Additional informationFundingThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors." @default.
• W4387404394 created "2023-10-07" @default.
• W4387404394 creator A5011327617 @default.
• W4387404394 creator A5085571812 @default.
• W4387404394 date "2023-10-05" @default.
• W4387404394 modified "2023-10-07" @default.
• W4387404394 title "An adaptive and enhanced framework for daily stock market prediction using feature selection and ensemble learning algorithms" @default.
• W4387404394 cites W1965805826 @default.
• W4387404394 cites W1980836123 @default.
• W4387404394 cites W1986145156 @default.
• W4387404394 cites W1988195734 @default.
• W4387404394 cites W2011368107 @default.
• W4387404394 cites W2017537474 @default.
• W4387404394 cites W2022122945 @default.
• W4387404394 cites W2025053102 @default.
• W4387404394 cites W2066456070 @default.
• W4387404394 cites W2066795664 @default.
• W4387404394 cites W2080107088 @default.
• W4387404394 cites W2115754070 @default.
• W4387404394 cites W2135046866 @default.
• W4387404394 cites W2143426320 @default.
• W4387404394 cites W2189869213 @default.
• W4387404394 cites W2317515691 @default.
• W4387404394 cites W2408246687 @default.
• W4387404394 cites W2519118318 @default.
• W4387404394 cites W2542516223 @default.
• W4387404394 cites W2554780437 @default.
• W4387404394 cites W2560070550 @default.
• W4387404394 cites W2582394828 @default.
• W4387404394 cites W2588651064 @default.
• W4387404394 cites W2772397068 @default.
• W4387404394 cites W2833425706 @default.
• W4387404394 cites W2911964244 @default.
• W4387404394 cites W2924620446 @default.
• W4387404394 cites W2970016095 @default.
• W4387404394 cites W2973407367 @default.
• W4387404394 cites W2993491036 @default.
• W4387404394 cites W3014601769 @default.
• W4387404394 cites W3102476541 @default.
• W4387404394 cites W3184795752 @default.
• W4387404394 cites W3193962426 @default.
• W4387404394 cites W3214797884 @default.
• W4387404394 cites W333233685 @default.
• W4387404394 cites W4220696253 @default.
• W4387404394 cites W4290599852 @default.
• W4387404394 cites W4308458519 @default.
• W4387404394 cites W4312755239 @default.
• W4387404394 doi "https://doi.org/10.1080/2573234x.2023.2263522" @default.
• W4387404394 hasPublicationYear "2023" @default.
• W4387404394 type Work @default.
• W4387404394 citedByCount "0" @default.
• W4387404394 crossrefType "journal-article" @default.
• W4387404394 hasAuthorship W4387404394A5011327617 @default.
• W4387404394 hasAuthorship W4387404394A5085571812 @default.
• W4387404394 hasConcept C111472728 @default.
• W4387404394 hasConcept C119857082 @default.
• W4387404394 hasConcept C127413603 @default.
• W4387404394 hasConcept C138885662 @default.
• W4387404394 hasConcept C148483581 @default.
• W4387404394 hasConcept C149782125 @default.
• W4387404394 hasConcept C151730666 @default.
• W4387404394 hasConcept C154945302 @default.
• W4387404394 hasConcept C162324750 @default.
• W4387404394 hasConcept C169258074 @default.
• W4387404394 hasConcept C204036174 @default.
• W4387404394 hasConcept C2780299701 @default.
• W4387404394 hasConcept C2780565519 @default.
• W4387404394 hasConcept C2780762169 @default.
• W4387404394 hasConcept C41008148 @default.
• W4387404394 hasConcept C46686674 @default.
• W4387404394 hasConcept C70153297 @default.
• W4387404394 hasConcept C78519656 @default.
• W4387404394 hasConcept C86803240 @default.
• W4387404394 hasConceptScore W4387404394C111472728 @default.
• W4387404394 hasConceptScore W4387404394C119857082 @default.
• W4387404394 hasConceptScore W4387404394C127413603 @default.
• W4387404394 hasConceptScore W4387404394C138885662 @default.
• W4387404394 hasConceptScore W4387404394C148483581 @default.
• W4387404394 hasConceptScore W4387404394C149782125 @default.
• W4387404394 hasConceptScore W4387404394C151730666 @default.
• W4387404394 hasConceptScore W4387404394C154945302 @default.
• W4387404394 hasConceptScore W4387404394C162324750 @default.
• W4387404394 hasConceptScore W4387404394C169258074 @default.
• W4387404394 hasConceptScore W4387404394C204036174 @default.
• W4387404394 hasConceptScore W4387404394C2780299701 @default.
• W4387404394 hasConceptScore W4387404394C2780565519 @default.
• W4387404394 hasConceptScore W4387404394C2780762169 @default.
• W4387404394 hasConceptScore W4387404394C41008148 @default.
• W4387404394 hasConceptScore W4387404394C46686674 @default.
• W4387404394 hasConceptScore W4387404394C70153297 @default.
• W4387404394 hasConceptScore W4387404394C78519656 @default.
• W4387404394 hasConceptScore W4387404394C86803240 @default.
• W4387404394 hasLocation W43874043941 @default.
• W4387404394 hasOpenAccess W4387404394 @default.
• W4387404394 hasPrimaryLocation W43874043941 @default.
• W4387404394 hasRelatedWork W1985505753 @default.
• W4387404394 hasRelatedWork W2766514146 @default.
• W4387404394 hasRelatedWork W2885516856 @default.

• next