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https://hdl.handle.net/20.500.11851/848
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Sezer, Ömer Berat | - |
dc.contributor.author | Özbayoğlu, Ahmet Murat | - |
dc.date.accessioned | 2019-03-25T12:15:17Z | |
dc.date.available | 2019-03-25T12:15:17Z | |
dc.date.issued | 2018-09 | |
dc.identifier.citation | Sezer, O. B., & Ozbayoglu, A. M. (2018). Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach. Applied Soft Computing, 70, 525-538. | en_US |
dc.identifier.uri | https://doi.org/10.1016/j.asoc.2018.04.024 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.11851/848 | - |
dc.description.abstract | Computational intelligence techniques for financial trading systems have always been quite popular. In the last decade, deep learning models start getting more attention, especially within the image processing community. In this study, we propose a novel algorithmic trading model CNN-TA using a 2-D convolutional neural network based on image processing properties. In order to convert financial time series into 2-D images, 15 different technical indicators each with different parameter selections are utilized. Each indicator instance generates data for a 15 day period. As a result, 15 x 15 sized 2-D images are constructed. Each image is then labeled as Buy, Sell or Hold depending on the hills and valleys of the original time series. The results indicate that when compared with the Buy & Hold Strategy and other common trading systems over a long out-of-sample period, the trained model provides better results for stocks and ETFs. (C) 2018 Elsevier B.V. All rights reserved. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Elsevier Ltd | en_US |
dc.relation.ispartof | Applied Soft Computing Journal | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Financial forecasting | en_US |
dc.subject | Convolutional neural networks | en_US |
dc.subject | Stock market | en_US |
dc.subject | Deep learning | en_US |
dc.subject | Technical analysis | en_US |
dc.subject | Algorithmic trading | en_US |
dc.title | Algorithmic Financial Trading With Deep Convolutional Neural Networks: Time Series To Image Conversion Approach | en_US |
dc.type | Article | en_US |
dc.department | Faculties, Faculty of Engineering, Department of Computer Engineering | en_US |
dc.department | Fakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü | tr_TR |
dc.identifier.volume | 70 | en_US |
dc.identifier.startpage | 525 | en_US |
dc.identifier.endpage | 538 | en_US |
dc.relation.tubitak | [215E248] | |
dc.authorid | 0000-0001-7998-5735 | - |
dc.identifier.wos | WOS:000443296000034 | en_US |
dc.identifier.scopus | 2-s2.0-85048331794 | en_US |
dc.institutionauthor | Özbayoğlu, Ahmet Murat | - |
dc.institutionauthor | Sezer, Ömer Berat | - |
dc.identifier.doi | 10.1016/j.asoc.2018.04.024 | - |
dc.identifier.doi | 10.1016/j.asoc.2018.04.024 | - |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
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 |
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