Comparison of Stock 'Trading' Decision Support Systems Based on Object Recognition Algorithms on Candlestick Charts

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dc.authorscopusid57195108559en_US
dc.authorscopusid36503422100en_US
dc.authorscopusid36544118500en_US
dc.contributor.authorTemur, G.
dc.contributor.authorBirogul, S.
dc.contributor.authorKose, U.
dc.date.accessioned2024-08-23T16:07:34Z
dc.date.available2024-08-23T16:07:34Z
dc.date.issued2024en_US
dc.departmentDüzce Üniversitesien_US
dc.description.abstractThe fundamental purpose of every investor making investments in financial fields, is to make profit by buying an investment instrument at a low price and selling the same at a higher price. In this study, within the framework of the aforementioned standpoint, an effective 'Trading' decision support model was designed, which can be used for stock market analyses, parity analyses, index analyses, and for the stock analyses of other stock exchanges, briefly for all investment instruments for which candlestick charts are created. An innovative model design was achieved through a bilateral perspective with both financial and scientific aspects by designing these models that operated on a pattern detection basis. The study incorporated the use of 2D candlestick charts of the BIST stocks. The charts were labeled in two separate data sets. Initially, 10,000 pieces of data were labeled on 550 2D candlestick charts, which were trained with YoloV3 Data Group-1 (DG-1). Subsequently, the data set was increased to 20,000 pieces. Out of this set of 20,000 labeled data prepared, 10,000 labeled data were picked randomly. The newly-created set of 10,000 labeled data was named DG-2, which was trained with the YoloV3, YoloV4, Faster R-CNN, SDD algorithms. An assessment was made regarding the performance results obtained following the trainings implemented for these four chosen algorithms. For the aforementioned assessment, three different scenarios were created, and out of all these scenarios, the YoloV3 DG-2 algorithm, which was trained with an improved data set, was observed to be most successful one. As a result of the comparative test scenarios, the YoloV3 DG-2 model achieved a pattern recognition success of 98%. On the other hand, it was also observed to have achieved a prediction success of 100%, while bringing in a return by 89.94%, regarding the object class detected. In addition, no additional parameters were used in this observed gain success. Consequently, the YoloV3 DG-2, determined as the final model, could be implemented as a decision support model for all investment instruments for which a candlestick chart can be created. © 2013 IEEE.en_US
dc.identifier.doi10.1109/ACCESS.2024.3411991
dc.identifier.endpage83562en_US
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-85196060740en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage83551en_US
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2024.3411991
dc.identifier.urihttps://hdl.handle.net/20.500.12684/14734
dc.identifier.volume12en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofIEEE Accessen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectcandlestick charten_US
dc.subjectCNNen_US
dc.subjectDeep learningen_US
dc.subjectfinanceen_US
dc.subjectobject detectionen_US
dc.subjectobject recognitionen_US
dc.subjecttrend decisionen_US
dc.subjectCommerceen_US
dc.subjectDecision makingen_US
dc.subjectDecision support systemsen_US
dc.subjectDeep learningen_US
dc.subjectElectronic tradingen_US
dc.subjectFinancial marketsen_US
dc.subjectGraphic methodsen_US
dc.subjectObject detectionen_US
dc.subjectObject recognitionen_US
dc.subjectCandlestick charten_US
dc.subjectDeep learningen_US
dc.subjectLabelingsen_US
dc.subjectObjects detectionen_US
dc.subjectObjects recognitionen_US
dc.subjectPredictive modelsen_US
dc.subjectTrend decisionen_US
dc.subjectYOLOen_US
dc.subjectInvestmentsen_US
dc.titleComparison of Stock 'Trading' Decision Support Systems Based on Object Recognition Algorithms on Candlestick Chartsen_US
dc.typeArticleen_US

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