Predicting the Direction of Movement for Stock Price Index Using Machine Learning Methods

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dc.authorid0000-0003-4842-2635
dc.authorscopusid11539603200
dc.authorwosidTufekci, Pinar/ABA-5121-2020
dc.contributor.authorTüfekci, Pınar
dc.date.accessioned2022-05-11T14:15:49Z
dc.date.available2022-05-11T14:15:49Z
dc.date.issued2016
dc.departmentFakülteler, Çorlu Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description2nd International Afro-European Conference for Industrial Advancement (AECIA) -- SEP 09-11, 2015 -- Engn Sch Digital Sci, AllianSTIC Lab, Villejuif, FRANCE
dc.description.abstractStock price prediction with high accuracy may offer significant opportunities for the investors who make decisions on making profit or having high gains over the stocks in stock markets. The aim of this study is to predict the movement directions (UP/DOWN) of the Istanbul Stock Exchange National 100 (ISE National 100) Index accurately for short-term futures by using three machine learning methods, which are Logistic Regression (LR), Support Vector Machines (SVMs), and Multilayer Perceptron (MLP). Two datasets used in this study are composed of sessional and daily points of data over a 5-year period from November 2007 to November 2012. During the prediction of the movement directions, the following factors were taken into account; data of macroeconomic indicators, gold prices, oil prices, foreign exchange prices, stock price indexes in various countries, and the data of ISE National 100 index for past sessions and prior days, which are used as input variables in the datasets. In connection with that, the most effective features of these input variables were determined by using some feature selection methods. As a result, the movement directions of ISE National 100 were predicted with higher accuracies by using reduced datasets than original datasets and the best performances were found by LR classifier.
dc.description.sponsorshipTech Univ Ostrava, Dept Comp Sci, Univ Sfax, Res Grp Intelligent Machines
dc.identifier.doi10.1007/978-3-319-29504-6_45
dc.identifier.endpage492
dc.identifier.isbn978-3-319-29504-6
dc.identifier.isbn978-3-319-29503-9
dc.identifier.issn2194-5357
dc.identifier.issn2194-5365
dc.identifier.scopus2-s2.0-84958279489
dc.identifier.scopusqualityN/A
dc.identifier.startpage477
dc.identifier.urihttps://doi.org/10.1007/978-3-319-29504-6_45
dc.identifier.urihttps://hdl.handle.net/20.500.11776/6084
dc.identifier.volume427
dc.identifier.wosWOS:000371912400045
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorTüfekci, Pınar
dc.language.isoen
dc.publisherSpringer-Verlag Berlin
dc.relation.ispartofProceedings of the Second International Afro-European Conference For Industrial Advancement (Aecia 2015)
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectData mining
dc.subjectPrediction of stock market prices
dc.subjectIstanbul stock exchange National 100 Index
dc.subjectSupport Vector Machines
dc.titlePredicting the Direction of Movement for Stock Price Index Using Machine Learning Methods
dc.typeConference Object

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