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Year 2015, Volume: 8 Issue: 4, 87 - 107, 16.10.2015

MEHMET Yavuz , ŞAKİR Sakarya , NECATİ Özdemir

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

Keywords

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References

  • 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.
  • Armano, G., et al. (2005). A hybrid genetic-neural architecture for stock indexes forecasting.
  • Information Sciences, 170(1): 3-33. 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.
  • 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.
  • 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
  • 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.
  • 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.
  • Freitas, F. D., et al. (2009). Prediction-based portfolio optimization model using neural networks. Neurocomputing, 72(10): 2155-2170.
  • 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.
  • Özçam, M. (1997). Varlık fiyatlama modelleri aracılığıyla dinamik portföy yönetimi, Ankara:
  • 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.
  • Öztemel, E. (2012). Yapay sinir ağları, Papatya Yayıncılık, İstanbul.
  • Sakarya, Ş., et al. (2015). Stock market index prediction with neural network during financial crises: A review on Bist-100. Financial Risk and Management Reviews, 1(2):53-67
  • Steiner, M. and H.-G. Wittkemper (1997). Portfolio optimization with a neural network implementation of the coherent market hypothesis. European Journal of Operational Research, 100(1): 27-40.
  • Tektaş, A. and A. Karataş (2004). Yapay sinir ağlari ve finans alanina uygulanmasi: Hisse senedi fiyat tahminlemesi. Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 18(3-4).
  • Thawornwong, S. and D. Enke (2004). The adaptive selection of financial and economic variables for use with artificial neural networks. Neurocomputing, 56: 205-232.
  • Ticknor J.L. (2013). A Bayesian regularized artiŞcial neural network for stock market forecasting.
  • Expert Systems with Applications, 40(14): 5501-5506.
  • Vellido, A., et al. (1999). Neural networks in business: a survey of applications (1992–1998).
  • Expert Systems with Applications, 17(1): 51-70. Wang, J.-Z., et al. (2011). Forecasting stock indices with back propagation neural network. Expert
  • Systems with Applications, 38(11): 14346-14355.
  • Yalcin, U., et al. (2013). Optimization of cutting parameters in face milling with neural networks and Taguchi based on cutting force, surface roughness and temperatures. International
  • Journal of Production Research, 51(11): 3404-3414.
  • Yao J., Li, Y. et al. (2000). Option price forecasting using neural networks. The International
  • Journal of Management Science, 28(4): 455-466. Borsa İstanbul (BIST). Hisse Senetleri Piyasası / Gelişen İşletmeler Piyasası / Serbest İşlem Platformu Verileri. [online]. (24.01.2013). http://www.imkb.gov.tr/Data/StocksData.aspx EKLER

YAPAY SİNİR AĞLARI İLE RİSK-GETİRİ TAHMİNİ VE PORTFÖY ANALİZİ

Year 2015, Volume: 8 Issue: 4, 87 - 107, 16.10.2015

MEHMET Yavuz , ŞAKİR Sakarya , NECATİ Özdemir

Abstract

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.

Keywords

Yapay Sinir Ağları Risk-Getiri Tahmini Minimum Risk Maksimum Getiri

References

  • 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.
  • Armano, G., et al. (2005). A hybrid genetic-neural architecture for stock indexes forecasting.
  • Information Sciences, 170(1): 3-33. 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.
  • 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.
  • 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
  • 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.
  • 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.
  • Freitas, F. D., et al. (2009). Prediction-based portfolio optimization model using neural networks. Neurocomputing, 72(10): 2155-2170.
  • 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.
  • Özçam, M. (1997). Varlık fiyatlama modelleri aracılığıyla dinamik portföy yönetimi, Ankara:
  • 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.
  • Öztemel, E. (2012). Yapay sinir ağları, Papatya Yayıncılık, İstanbul.
  • Sakarya, Ş., et al. (2015). Stock market index prediction with neural network during financial crises: A review on Bist-100. Financial Risk and Management Reviews, 1(2):53-67
  • Steiner, M. and H.-G. Wittkemper (1997). Portfolio optimization with a neural network implementation of the coherent market hypothesis. European Journal of Operational Research, 100(1): 27-40.
  • Tektaş, A. and A. Karataş (2004). Yapay sinir ağlari ve finans alanina uygulanmasi: Hisse senedi fiyat tahminlemesi. Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 18(3-4).
  • Thawornwong, S. and D. Enke (2004). The adaptive selection of financial and economic variables for use with artificial neural networks. Neurocomputing, 56: 205-232.
  • Ticknor J.L. (2013). A Bayesian regularized artiŞcial neural network for stock market forecasting.
  • Expert Systems with Applications, 40(14): 5501-5506.
  • Vellido, A., et al. (1999). Neural networks in business: a survey of applications (1992–1998).
  • Expert Systems with Applications, 17(1): 51-70. Wang, J.-Z., et al. (2011). Forecasting stock indices with back propagation neural network. Expert
  • Systems with Applications, 38(11): 14346-14355.
  • Yalcin, U., et al. (2013). Optimization of cutting parameters in face milling with neural networks and Taguchi based on cutting force, surface roughness and temperatures. International
  • Journal of Production Research, 51(11): 3404-3414.
  • Yao J., Li, Y. et al. (2000). Option price forecasting using neural networks. The International
  • Journal of Management Science, 28(4): 455-466. Borsa İstanbul (BIST). Hisse Senetleri Piyasası / Gelişen İşletmeler Piyasası / Serbest İşlem Platformu Verileri. [online]. (24.01.2013). http://www.imkb.gov.tr/Data/StocksData.aspx EKLER
There are 32 citations in total.

Details

Primary Language Turkish
Journal Section Articles
Authors

MEHMET Yavuz

ŞAKİR Sakarya

NECATİ Özdemir

Publication Date October 16, 2015
Published in Issue Year 2015 Volume: 8 Issue: 4

Cite

APA Yavuz, M., Sakarya, Ş., & Özdemir, N. (2015). YAPAY SİNİR AĞLARI İLE RİSK-GETİRİ TAHMİNİ VE PORTFÖY ANALİZİ. Niğde Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi, 8(4), 87-107.
AMA Yavuz M, Sakarya Ş, Özdemir N. YAPAY SİNİR AĞLARI İLE RİSK-GETİRİ TAHMİNİ VE PORTFÖY ANALİZİ. Niğde Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi. October 2015;8(4):87-107.
Chicago Yavuz, MEHMET, ŞAKİR Sakarya, and NECATİ Özdemir. “YAPAY SİNİR AĞLARI İLE RİSK-GETİRİ TAHMİNİ VE PORTFÖY ANALİZİ”. Niğde Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi 8, no. 4 (October 2015): 87-107.
EndNote Yavuz M, Sakarya Ş, Özdemir N (October 1, 2015) YAPAY SİNİR AĞLARI İLE RİSK-GETİRİ TAHMİNİ VE PORTFÖY ANALİZİ. Niğde Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi 8 4 87–107.
IEEE M. Yavuz, Ş. Sakarya, and N. Özdemir, “YAPAY SİNİR AĞLARI İLE RİSK-GETİRİ TAHMİNİ VE PORTFÖY ANALİZİ”, Niğde Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, vol. 8, no. 4, pp. 87–107, 2015.
ISNAD Yavuz, MEHMET et al. “YAPAY SİNİR AĞLARI İLE RİSK-GETİRİ TAHMİNİ VE PORTFÖY ANALİZİ”. Niğde Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi 8/4 (October 2015), 87-107.
JAMA Yavuz M, Sakarya Ş, Özdemir N. YAPAY SİNİR AĞLARI İLE RİSK-GETİRİ TAHMİNİ VE PORTFÖY ANALİZİ. Niğde Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi. 2015;8:87–107.
MLA Yavuz, MEHMET et al. “YAPAY SİNİR AĞLARI İLE RİSK-GETİRİ TAHMİNİ VE PORTFÖY ANALİZİ”. Niğde Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi, vol. 8, no. 4, 2015, pp. 87-107.
Vancouver Yavuz M, Sakarya Ş, Özdemir N. YAPAY SİNİR AĞLARI İLE RİSK-GETİRİ TAHMİNİ VE PORTFÖY ANALİZİ. Niğde Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi. 2015;8(4):87-107.
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