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Journals / Ömer Halisdemir Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi / 2015 / Cilt: 8 - Sayı: 4

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YAPAY SİNİR AĞLARI İLE RİSK-GETİRİ TAHMİNİ VE PORTFÖY ANALİZİ

MEHMET YAVUZ
ŞAKİR SAKARYA
NECATİ ÖZDEMİR
Pages
87–107
Publication Date
2015-10-16
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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

📌 3 Key Takeaways

🎯
Objective & Hypothesis: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.
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Key Findings & Data:According to test results, the best results on the basis of return and risk were obtained in portfolios which generated from equity.
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Conclusion & Impact: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
🔬Design:Modeling & Simulation
📍Scope:2010 Verileri
⚙️Variables:Yapay Sinir Ağları · Risk-Getiri Tahmini · Minimum Risk

Abstract

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

Özet

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 binde 5’in altında olduğu gözlenmiştir. Uygulamanın optimizasyon kısmında, maksimum getiriye sahip portföyün getirisi olan %7.5916 değeri için YSA 0.0567 hata oranı ile %7.1590 değerini bulmuştur.

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Cite This Article

YAVUZ, M., SAKARYA, Ş., ÖZDEMİR, N. (2015). -. Ömer Halisdemir Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, Cilt: 8 - Sayı: 4, 87–107. https://ajindex.com/articles/155648

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References (12)

  1. Aghababaeyan, R. et al. (2011). Forecasting the Tehran Stock Market by artificial neural network. International Journal of Advanced Computer Science and Applications, Special Issue on Artificial Intelligence. Akcan A. ve Kartal C. (2011). İMKB sigorta endeksini oluşturan şirketlerin hisse senedi fiyatlarının yapay sinir ağları ile tahmini. Muhasebe ve Finasnman Dergisi, 27-40.
  2. Armano, G., et al. (2005). A hybrid genetic-neural architecture for stock indexes forecasting. Information Sciences, 170(1): 3-33.
  3. Boyacioglu M.A. ve Avcı D. (2010). An adaptive network-based fuzzy inference system (ANFIS) for the prediction of stock market return: The case of the Istanbul Stock Exchange. Expert Systems with Applications, 37(12): 7908-7912.
  4. Chang, P.-C., et al. (2003). A neural network with a case based dynamic window for stock trading prediction. Expert Systems with Applications, 36(3): 6889-6898.
  5. Chen, A.-S., et al. (2003). Application of neural networks to an emerging financial market: forecasting and trading the Taiwan Stock Index. Computers & Operations Research, 30(6): 901-9
  6. Chun, S.-H. and S. H. Kim (2004). Data mining for financial prediction and trading: application to single and multiple markets. Expert Systems with Applications, 26(2): 131-139.
  7. Enke, D. and S. Thawornwong (2005). The use of data mining and neural networks for forecasting stock market returns. Expert Systems with Applications, 29(4): 927-940. Fernández, A. and S. Gómez (2007). Portfolio selection using neural networks. Computers & Operations Research, 34(4): 1177-1191.
  8. Freitas, F. D., et al. (2009). Prediction-based portfolio optimization model using neural networks. Neurocomputing, 72(10): 2155-2170.
  9. Kara, Y., et al. (2011). Predicting direction of stock price index movement using artiŞcial neural networks and support vector machines: The sample of the Istanbul Stock Exchange. Expert Systems with Applications, 38(5), 5311-5319.
  10. Özçam, M. (1997). Varlık fiyatlama modelleri aracılığıyla dinamik portföy yönetimi, Ankara:
  11. Sermaye Piyasası Kurulu. Karaoglan, A. D. (2011). An integrated neural network structure for recognizing autocorrelated and trending processes. Mathematical and Computational Applications, 16(2): 514. Kutlu, B. and B. Badur (2009). Yapay sinir ağları ile borsa endeksi tahmini. Yönetim, 20 (63): 25 Oh, K. J., et al. (2006). An early warning system for detection of financial crisis using financial market volatility. Expert Systems, 23(2): 83-98.
  12. Öztemel, E. (2012). Yapay sinir