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LR, C5.0, CART, DVM Yöntemlerini Kullanarak Hisse Senedi Getiri Siniflandirma Tahmini Yapilmasi ve Kullanilan Yöntemlerin Karsilastirilmasi: Türkiye’de BIST’de Bir Uygulama

Author

Listed:
  • Emre YAKUT

    (Osmaniye Korkut Ata Üniversitesi)

  • Eray GEMICI

    (Gaziantep Üniversitesi)

Abstract

Hisse senedi getiri siniflandirma tahmini her zaman icin yatirimcilarin ve analizcilerin ilgisini ceken bir arastirma alani olmustur. Bu calismada BIST 100 endeksinde islem gören kimya, kaucuk ve plastik ürünleri imalati sanayinde yer alan, faaliyetleri 2009- 2014 yillari arasinda süreklilik gösteren 18 sirketin hisse senedi getirilerinde etkili olan faktörler belirlenerek, hisse senedi getirileri tahmin edilmeye çalisilmistir. Söz konusu verilerin veri madenciligi yöntemlerinden olan LR analiz, C5.0 algoritmasi, CART algoritmasi ve DVM yöntemleri kullanilarak analiz islemleri gerceklestirilmis, hisse senedi getiri siniflandirma tahmininde anlamli ve faydali bilgileri ortaya cikarmak için karar agacina ait kurallar elde edilmistir. Yapilan analizler sonucunda LR analizi %75, C5.0 algoritmasi %88, CART algoritmasi %89,8 ve DVM analizi %75,9’luk dogru siniflandirma basarisi gerceklestirmistir. Pozitif ve negatif hisse senedi getiri siniflandirma tahminine etki eden en önemli degiskenlerin “piyasa/defter degeri degiskeni”, “TÜFE degiskeni” ve “brüt kar marji degiskeni” oldugu saptanmistir. Yatirimcilar ve analizciler için önerdigimiz modelin degiskenleri ile birlikte hisse senedi getiri tahmininde kullanilmasinin uygun olabilecegi gözlenmistir.

Suggested Citation

  • Emre YAKUT & Eray GEMICI, 2017. "LR, C5.0, CART, DVM Yöntemlerini Kullanarak Hisse Senedi Getiri Siniflandirma Tahmini Yapilmasi ve Kullanilan Yöntemlerin Karsilastirilmasi: Türkiye’de BIST’de Bir Uygulama," Ege Academic Review, Ege University Faculty of Economics and Administrative Sciences, vol. 17(4), pages 461-479.
    • Handle: RePEc:ege:journl:v:17:y:2017:i:4:p:461-479
    DOI: 10.21121/eab.2017431296
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