Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/4031
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dc.contributor.authorSezer, Ömer Berat-
dc.contributor.authorÖzbayoğlu, Ahmet Murat-
dc.date.accessioned2021-01-25T11:28:54Z-
dc.date.available2021-01-25T11:28:54Z-
dc.date.issued2020
dc.identifier.citationSezer, O. B., and Ozbayoglu, A. M. (2019). Financial trading model with stock bar chart image time series with deep convolutional neural networks. arXiv preprint arXiv:1903.04610.en_US
dc.identifier.issn10798587
dc.identifier.urihttps://hdl.handle.net/20.500.11851/4031-
dc.identifier.urihttp://autosoftjournal.net/paperShow.php?paper=100000065-
dc.description.abstractEven though computational intelligence techniques have been extensively utilized in financial trading systems, almost all developed models use the time series data for price prediction or identifying buy-sell points. However, in this study we decided to use 2-D stock bar chart images directly without introducing any additional time series associated with the underlying stock. We propose a novel algorithmic trading model CNN-BI (Convolutional Neural Network with Bar Images) using a 2-D Convolutional Neural Network. We generated 2-D images of sliding windows of 30-day bar charts for Dow 30 stocks and trained a deep Convolutional Neural Network (CNN) model for our algorithmic trading model. We tested our model separately between 2007-2012 and 2012-2017 for representing different market conditions. The results indicate that the model was able to outperform Buy and Hold strategy, especially in trendless or bear markets. Since this is a preliminary study and probably one of the first attempts using such an unconventional approach, there is always potential for improvement. Overall, the results are promising and the model might be integrated as part of an ensemble trading model combined with different strategies.en_US
dc.language.isoenen_US
dc.publisherTech Science Pressen_US
dc.relation.ispartofIntelligent Automation and Soft Computingen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAlgorithmic Tradingen_US
dc.subjectComputational Intelligenceen_US
dc.subjectConvolutional Neural Networksen_US
dc.subjectDeep Learningen_US
dc.subjectFinancial Forecastingen_US
dc.subjectStock Marketen_US
dc.titleFinancial Trading Model with Stock Bar Chart Image Time Series with Deep Convolutional Neural Networksen_US
dc.typeArticleen_US
dc.departmentFaculties, Faculty of Engineering, Department of Computer Engineeringen_US
dc.departmentFaculties, Faculty of Engineering, Department of Artificial Intelligence Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.departmentFakülteler, Mühendislik Fakültesi, Yapay Zeka Mühendisliği Bölümütr_TR
dc.identifier.volume26
dc.identifier.issue2
dc.identifier.startpage323
dc.identifier.endpage334
dc.relation.tubitakinfo:eu-repo/grantAgreement/TÜBİTAK/EEEAG/215E248en_US
dc.authorid0000-0001-7998-5735-
dc.identifier.wosWOS:000589893300011en_US
dc.identifier.scopus2-s2.0-85090528444en_US
dc.institutionauthorÖzbayoğlu, Ahmet Murat-
dc.identifier.doi10.31209/2018.100000065-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ2-
item.openairetypeArticle-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.languageiso639-1en-
item.grantfulltextnone-
item.cerifentitytypePublications-
crisitem.author.dept02.1. Department of Artificial Intelligence Engineering-
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Yapay Zeka Mühendisliği Bölümü / Department of Artificial Intelligence Engineering
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