Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.11851/2010
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Sezer, Ömer Berat | - |
dc.contributor.author | Özbayoğlu, Ahmet Murat | - |
dc.contributor.author | Doğdu, Erdoğan. | - |
dc.date.accessioned | 2019-07-10T14:42:45Z | |
dc.date.available | 2019-07-10T14:42:45Z | |
dc.date.issued | 2017-04-13 | |
dc.identifier.citation | Sezer, O. B., Ozbayoglu, A. M., & Dogdu, E. (2017, April). An artificial neural network-based stock trading system using technical analysis and big data framework. In Proceedings of the SouthEast Conference (pp. 223-226). ACM. | en_US |
dc.identifier.isbn | 978-145035024-2 | |
dc.identifier.uri | https://dl.acm.org/citation.cfm?doid=3077286.3077294 | - |
dc.identifier.uri | https://arxiv.org/pdf/1712.09592.pdf | - |
dc.identifier.uri | https://hdl.handle.net/20.500.11851/2010 | - |
dc.description | ACM SouthEast Regional Conference (2017 : Kennesaw; United States) | |
dc.description.abstract | In this paper, a neural network-based stock price prediction and trading system using technical analysis indicators is presented. The model developed first converts the financial time series data into a series of buy-sell-hold trigger signals using the most commonly preferred technical analysis indicators. Then, a Multilayer Perceptron (MLP) artificial neural network (ANN) model is trained in the learning stage on the daily stock prices between 1997 and 2007 for all of the Dow30 stocks. Apache Spark big data framework is used in the training stage. The trained model is then tested with data from 2007 to 2017. The results indicate that by choosing the most appropriate technical indicators, the neural net- work model can achieve comparable results against the Buy and Hold strategy in most of the cases. Furthermore, fine tuning the technical indicators and/or optimization strategy can enhance the overall trading performance. Copyright 2017 ACM. | en_US |
dc.description.sponsorship | Assoc. for Computing Machinery (ACM) | |
dc.language.iso | en | en_US |
dc.publisher | Association for Computing Machinery, Inc. | en_US |
dc.relation.ispartof | Proceedings of the SouthEast Conference, ACMSE 2017 | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | Algorithmic trading | en_US |
dc.subject | Artificial neural network | en_US |
dc.subject | Multi layer perceptron | en_US |
dc.subject | Stock market | en_US |
dc.subject | Technical analysis | en_US |
dc.title | An Artificial Neural Network-Based Stock Trading System Using Technical Analysis and Big Data Framework | en_US |
dc.type | Conference Object | 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.startpage | 223 | |
dc.identifier.endpage | 226 | |
dc.authorid | 0000-0001-7998-5735 | - |
dc.identifier.scopus | 2-s2.0-85021417788 | en_US |
dc.institutionauthor | Özbayoğlu, Ahmet Murat | - |
dc.identifier.doi | 10.1145/3077286.3077294 | - |
dc.authorwosid | H-2328-2011 | - |
dc.authorscopusid | 6505999525 | - |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
item.openairetype | Conference Object | - |
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 |
CORE Recommender
SCOPUSTM
Citations
22
checked on Dec 21, 2024
Page view(s)
134
checked on Dec 23, 2024
Google ScholarTM
Check
Altmetric
Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.