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https://hdl.handle.net/20.500.11851/4031
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Sezer, Ömer Berat | - |
dc.contributor.author | Özbayoğlu, Ahmet Murat | - |
dc.date.accessioned | 2021-01-25T11:28:54Z | - |
dc.date.available | 2021-01-25T11:28:54Z | - |
dc.date.issued | 2020 | |
dc.identifier.citation | Sezer, 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.issn | 10798587 | |
dc.identifier.uri | https://hdl.handle.net/20.500.11851/4031 | - |
dc.identifier.uri | http://autosoftjournal.net/paperShow.php?paper=100000065 | - |
dc.description.abstract | Even 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.iso | en | en_US |
dc.publisher | Tech Science Press | en_US |
dc.relation.ispartof | Intelligent Automation and Soft Computing | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | Algorithmic Trading | en_US |
dc.subject | Computational Intelligence | en_US |
dc.subject | Convolutional Neural Networks | en_US |
dc.subject | Deep Learning | en_US |
dc.subject | Financial Forecasting | en_US |
dc.subject | Stock Market | en_US |
dc.title | Financial Trading Model With Stock Bar Chart Image Time Series With Deep Convolutional Neural Networks | en_US |
dc.type | Article | en_US |
dc.department | Faculties, Faculty of Engineering, Department of Computer Engineering | en_US |
dc.department | Faculties, Faculty of Engineering, Department of Artificial Intelligence Engineering | en_US |
dc.department | Fakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü | tr_TR |
dc.department | Fakülteler, Mühendislik Fakültesi, Yapay Zeka Mühendisliği Bölümü | tr_TR |
dc.identifier.volume | 26 | |
dc.identifier.issue | 2 | |
dc.identifier.startpage | 323 | |
dc.identifier.endpage | 334 | |
dc.relation.tubitak | info:eu-repo/grantAgreement/TÜBİTAK/EEEAG/215E248 | en_US |
dc.authorid | 0000-0001-7998-5735 | - |
dc.identifier.wos | WOS:000589893300011 | en_US |
dc.identifier.scopus | 2-s2.0-85090528444 | en_US |
dc.institutionauthor | Özbayoğlu, Ahmet Murat | - |
dc.identifier.doi | 10.31209/2018.100000065 | - |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.identifier.scopusquality | Q3 | - |
item.openairetype | Article | - |
item.languageiso639-1 | en | - |
item.grantfulltext | none | - |
item.fulltext | No Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.cerifentitytype | Publications | - |
crisitem.author.dept | 02.1. 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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