Please use this identifier to cite or link to this item: https://hdl.handle.net/11499/39251
Title: The classification of the firms traded in istanbul stock exchange by using support vector machines
Other Titles: İstanbul Menkul Kıymetler Borsası’nda işlem gören firmaların destek vektör makineleri kullanılarak sınıflandırılması
Authors: Karagul, K
Keywords: ISE-100; Support vector machines; Financial rates; Classification;
Sensitivity analysis
Publisher: PAMUKKALE UNIV
Abstract: In this study, 42 companies operating in food, textile and cement sectors within Istanbul Stock Exchange 100 (ISE-100) have been handled. The aim is to classify these companies into three groups according to financial ratios. The average values of 10 financial ratios of these companies between the years 2006-2011 have been handled. Based on these ratios, classes are derived from cluster analysis. These ratios and the results of the cluster analysis are the data set of this article. In order to test the performance of the learning algorithm and classification leave-one-out cross-validation method is used. The classification study conducted by Support Vector Machines approach has performed 95.23% correct classification with the help of 12 support vectors. Moreover, input sensitivity analysis has been conducted and 4 most efficient ratios have been determined out of these 10. These ratios are removed from the model one by one starting from the less influential one in order to investigate by which ratios the most effective Support Vector Machine model is obtained. It is seen that the best model is obtained by using the first 3 ratios. The classification success for this model is 97.61% and the number of support vector is 12.
URI: https://hdl.handle.net/11499/39251
https://doi.org/10.5505/pajes.2014.63835
ISSN: 1300-7009
Appears in Collections:Honaz Meslek Yüksekokulu Koleksiyonu
TR Dizin İndeksli Yayınlar Koleksiyonu / TR Dizin Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Files in This Item:
File SizeFormat 
ae2e93cf-d273-4756-bf4c-0d325b6b92f2.pdf725.01 kBAdobe PDFView/Open
Show full item record



CORE Recommender

Page view(s)

96
checked on Aug 24, 2024

Download(s)

138
checked on Aug 24, 2024

Google ScholarTM

Check




Altmetric


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