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YAPAY SİNİR AĞLARI İLE RİSK-GETİRİ TAHMİNİ VE PORTFÖY ANALİZİ

RISK-RETURN PREDICTION AND PORTFOLIO ANALYSIS WITH ARTIFICIAL NEURAL NETWORKS

Journal Name:

  • Niğde Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi

Publication Year:

  • 2015

Key Words:

Keywords (Original Language):

Author NameUniversity of AuthorFaculty of Author
Abstract (2. Language): 
In this study, using the monthly average returns of 140 stocks contained in ISE-Industrial Index for the year 2010, risk-return forecasting and portfolio optimization were aimed. For this purpose, using these stocks, equal-weighted portfolios were formed according to companies' active sizes, market capitalizations, trading volumes and equities. Meanwhile risks and returns of these portfolios were calculated. An artificial neural network was trained using the founded values and testing process was realized with this network was trained. According to test results, the best results on the basis of return and risk were obtained in portfolios which generated from equity. In addition, the error rate of ANN's return prediction was realized approximately 1 percent, the amount of error of risk estimate was observed as less than 0.5 percent.
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Abstract (Original Language): 
Bu çalışmada, BIST-Sınai Endeksi'nde yer alan 140 hisse senedinin 2010 yılına ait aylık ortalama getirileri kullanılarak risk-getiri tahmini ve portföy optimizasyonu amaçlanmıştır. Bu amaç doğrultusunda, belirtilen hisse senetleri ile aktif büyüklük, piyasa değeri, işlem hacmi ve özsermaye niceliklerine göre eşit ağırlıklı portföyler oluşturulmuş ve bu portföylerin risk-getirileri hesaplanmıştır. Bu değerler kullanılarak bir yapay sinir ağı (YSA) modeli eğitilmiş ve eğitilen bu ağ ile de test işlemi gerçekleştirilmiştir. Test sonucunda getiri ve risk bazında en iyi sonuç özsermayeye göre oluşturulan portföylerde elde edilmiştir. Ayrıca YSA ile getiri tahmininin %1 'in altında hata oranı ile gerçekleştiği, risk tahmininde ise hata miktarının %0.5'in altında olduğu gözlenmiştir.
FULL TEXT (PDF): 
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  • 4
87
107
  • Turkish

JEL Codes:

REFERENCES

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