Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/1994
Full metadata record
DC FieldValueLanguage
dc.contributor.authorGüdelek, Mehmet Uğur-
dc.contributor.authorBölük, S. Arda-
dc.contributor.authorÖzbayoğlu, Ahmet Murat-
dc.date.accessioned2019-07-10T14:42:44Z
dc.date.available2019-07-10T14:42:44Z
dc.date.issued2017
dc.identifier.citationGudelek, M. U., Boluk, S. A., & Ozbayoglu, A. M. (2017, November). A deep learning based stock trading model with 2-D CNN trend detection. In 2017 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1-8). IEEE.en_US
dc.identifier.isbn978-1-5386-2726-6
dc.identifier.urihttps://ieeexplore.ieee.org/document/8285188-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/1994-
dc.descriptionIEEE Symposium Series on Computational Intelligence (IEEE SSCI) (2017 : Honolulu, HI)
dc.description.abstractThe success of convolutional neural networks in the field of computer vision has attracted the attention of many researchers from other fields. One of the research areas in which neural networks is actively used is financial forecasting. In this paper, we propose a novel method for predicting stock price movements using CNN. To avoid the high volatility of the market and to maximize the profit, ETFs are used as primary financial assets. We extract commonly used trend indicators and momentum indicators from financial time series data and use these as our features. Adopting a sliding window approach, we generate our images by taking snapshots that are bounded by the window over a daily period. We then perform daily predictions, namely, regression for predicting the ETF prices and classification for predicting the movement of the prices on the next day, which can be modified to estimate weekly or monthly trends. To increase the number of images, we use numerous ETFs. Finally, we evaluate our method by performing paper trading and calculating the final capital. We also compare our method's performance to commonly used classical trading strategies. Our results indicate that we can predict the next day's prices with 72% accuracy and end up with 5:1 of our initial capital, taking realistic values of transaction costs into account.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectfinanceen_US
dc.subjectforecastingen_US
dc.subjecttechnical indicatorsen_US
dc.titleA Deep Learning based Stock Trading Model with 2-D CNN Trend Detectionen_US
dc.typeConference Objecten_US
dc.departmentFaculties, Faculty of Engineering, Department of Computer Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.identifier.startpage74
dc.identifier.endpage81
dc.relation.tubitakinfo:eu-repo/grantAgreement/TÜBİTAK/EEEAG/215E248en_US
dc.authorid0000-0001-7998-5735-
dc.identifier.wosWOS:000428251400012en_US
dc.identifier.scopus2-s2.0-85046136407en_US
dc.institutionauthorÖzbayoğlu, Ahmet Murat-
dc.identifier.doi10.1109/SSCI.2017.8285188-
dc.authorwosidH-2328-2011-
dc.authorscopusid6505999525-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairetypeConference Object-
item.cerifentitytypePublications-
item.languageiso639-1en-
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
Show simple item record



CORE Recommender

SCOPUSTM   
Citations

21
checked on Mar 23, 2024

WEB OF SCIENCETM
Citations

53
checked on Jan 20, 2024

Page view(s)

156
checked on Mar 25, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.