Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/4132
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dc.contributor.authorÖzbayoğlu, Ahmet Murat-
dc.contributor.authorBahadır, İsmet-
dc.date.accessioned2021-02-04T05:48:10Z
dc.date.available2021-02-04T05:48:10Z
dc.date.issued2008
dc.identifier.citationOzbayoglu, A.M and I Bahadır. Comparison of Bayesian Estimation and Neural Network Model in Stock Market Trading. Intelligent Engineering Systems Through Artificial Neural Networks. ASME, pp. 581–586.en_US
dc.identifier.isbn0791802823
dc.identifier.isbn9780791802823
dc.identifier.urihttps://asmedigitalcollection.asme.org/ebooks/book/216/chapter-abstract/62748/Comparison-of-Bayesian-Estimation-and-Neural?redirectedFrom=fulltext-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/4132-
dc.description.abstractIn this study, a decision support system for stock market prediction is proposed. This model uses the historical data of 180K data points obtained from the 215 highest volume ETFs that are open for trade in NYSE. The data is analyzed with several different criteria such as next 1,2,3,4,5 days percent increase/decrease, percent moves with respect to 50/200 day Moving Averages, changes in RSI, MACD values, direction of movement within Bollinger Bands, etc. The next day prediction is made by statistical analysis on the data using a Bayesian Maximum Likelihood decision model and the best course of action (which ETF is most likely to increase its value) is identified. The training data for the model is the historical data of these ETFs between 1999 and 2006. With the trained network, 2007 data has been tested and the results are analyzed. In order to compare the performance of the model, a multilayer perceptron neural network is developed using the same training and testing data and the results are compared. For performance evaluation, both models analyze which ETF is most likely to create the best short term (1 day – 5 days) rate of return and perform buy/sell decisions accordingly. The results indicate that both models can be used in stock/ETF selection in short term stock market trading, however neural network model provided better results.en_US
dc.language.isoenen_US
dc.publisherASME Pressen_US
dc.relation.ispartofIntelligent Engineering Systems through Artificial Neural Networksen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.subjectNeural network modelsen_US
dc.subjectStock marketsen_US
dc.titleComparison of Bayesian Estimation and Neural Network Model in Stock Market Tradingen_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.volume18en_US
dc.identifier.startpage581en_US
dc.identifier.endpage586en_US
dc.authorid0000-0001-7998-5735-
dc.institutionauthorÖzbayoğlu, Ahmet Murat-
dc.identifier.doi10.1115/1.802823.paper73-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
item.openairetypeConference Object-
item.languageiso639-1en-
item.grantfulltextopen-
item.fulltextWith Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
crisitem.author.dept02.1. Department of Artificial Intelligence Engineering-
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
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