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The classification of the firms traded in ?stanbul stock exchange by using support vector machines

機(jī)譯：使用支持向量機(jī)在伊斯坦布爾證券交易所交易的公司分類

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摘要

Bu ?al??mada, ?stanbul Menkul K?ymetler Borsas? 100 (IMKB-100) i?inde g?da, tekstil ve ?imento sekt?rlerinde faaliyet g?steren 42 ?irket ele al?nm??t?r. Bu ?irketler finansal oranlara ba?l? olarak ü? s?n?fa ayr?lmak istenmektedir. ?irketlere ili?kin 10 adet finansal oran?n 2006-2011 y?llar? aras?ndaki ortalama de?erleri ele al?nm??t?r. Bu oranlara ba?l? olarak kümeleme analizinden elde edilen s?n?flar belirlenmi?tir. Bu oranlar ve kümeleme analizi sonu?lar? bu makalenin veri kümesini olu?turmaktad?r. ??renme algoritmas?n?n ve s?n?fland?rman?n ba?ar?m?n? test etmek i?in tek ??kar?ml? ?apraz- do?rulama y?ntemi kullan?lm??t?r. Destek Vekt?r Makineleri (DVM) yakla??m? ile yap?lan s?n?fland?rma ?al??mas? %95,23 oran?nda do?ru s?n?fland?rmay? 12 destek vekt?rü ile yapm??t?r. Ayr?ca giri? duyarl?l?k analizi yap?larak bu 10 orandan en etkin olan 4 oran belirlenmi?tir. Bu oranlar en etkisizden en etkili olan fakt?re do?ru modelden s?ra ile ??kar?larak, bu d?rt fakt?rden hangilerinin al?nmas? ile en etkili DVM modeli elde edilebilece?i ara?t?r?lm??t?r. En iyi modelin ilk 3 fakt?re ba?l? olan model oldu?u belirlenmi?tir. Bu yeni modelde s?n?fland?rma ba?ar? oran? %97,61 ve destek vekt?r say?s? 12 olarak kalm??t?r.udIn this study, 42 companies operating in food, textile and cement sectors within ?stanbul 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.
機(jī)譯：在這項(xiàng)研究中，討論了伊斯坦布爾證券交易所100（IMKB-100）內(nèi)食品，紡織和水泥行業(yè)的42家公司。希望根據(jù)財(cái)務(wù)費(fèi)率將這些公司分為三類?？紤]了2006-2011年之間10個(gè)財(cái)務(wù)比率的平均值?；谶@些比率，確定從聚類分析獲得的類別。這些比率和聚類分析結(jié)果構(gòu)成了本文的數(shù)據(jù)集。單推理交叉驗(yàn)證方法用于測(cè)試學(xué)習(xí)算法和分類的性能。使用支持向量機(jī)（DVM）方法進(jìn)行的分類研究使用12個(gè)支持向量以95.23％的比率進(jìn)行了正確的分類。此外，通過(guò)分析輸入靈敏度，從這10個(gè)比率中確定了4個(gè)最有效的比率。研究表明，通過(guò)將這些比率從最無(wú)效的因素移到最有效的因素，可以通過(guò)獲得最有效的DVM模型獲得這四個(gè)因素中的哪一個(gè)。確定最佳模型是取決于前三個(gè)因素的模型。在這個(gè)新模型中，分類成功率保持在97.61％，支持向量的數(shù)量保持在12。 ud在這項(xiàng)研究中，處理了伊斯坦布爾證券交易所100（ISE-100）中食品，紡織和水泥行業(yè)的42家公司。目的是根據(jù)財(cái)務(wù)比率將這些公司分為三類。這些公司在2006年至2011年之間的10個(gè)財(cái)務(wù)比率的平均值已得到處理。基于這些比率，從聚類分析中得出類別。這些比率和聚類分析的結(jié)果是本文的數(shù)據(jù)集。為了測(cè)試學(xué)習(xí)算法的性能，使用分類留一法交叉驗(yàn)證方法。支持向量機(jī)方法進(jìn)行的分類研究在12種支持向量的幫助下進(jìn)行了95.23％的正確分類。此外，已經(jīng)進(jìn)行了輸入靈敏度分析，并從這10個(gè)中確定了4個(gè)最有效的比率，從影響力較小的一個(gè)比率中逐個(gè)將這些比率從模型中刪除，以便研究最有效的Support Vector Machine采用哪個(gè)比率獲得模型。可以看出，通過(guò)使用前三個(gè)比率可以獲得最佳模型。該模型的分類成功率為97.61％，支持向量的數(shù)量為12。

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  • 作者

    Karagül Kenan;

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  • 年度 2014
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