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BULANIK MANTIK VE YAPAY SİNİR AĞLARI İLE BORSA ENDEKS TAHMİNİ: GELİŞMİŞ VE GELİŞMEKTE OLAN ÜLKELER ÖRNEĞİ

Year 2018, Volume: 11 Issue: 1, 99 - 124, 21.07.2018
https://doi.org/10.17218/hititsosbil.390490

Abstract

Tahminlerin
finansal piyasalara uygulanmasına ilişkin kullanılan yöntemlerden bulanık
mantık ve yapay sinir ağları üzerine son yıllarda artan bilimsel çalışmalar
vardır.
Buradan hareketle çalışmada, gelişmiş ve
gelişmekte olan ülkelerde ele alınan modellerin tahmin performansını test etmek
için sekiz borsa endeksinin verileri kullanılarak iki etkin modelin tahminde gösterdikleri performansları
karşılaştırılmaya çalışılmıştır. Zaman serisi değerleri model oluşturma
aşamasında %60 eğitim, %40 test olarak iki gruba ayrılmıştır. Sonuç olarak
modellerin istatistiksel ve finansal performanslarını gösteren bazı
kanıtlar elde edilerek birçok çalışmada belirtilen hisse senedi getirilerini tahmin etmek için çeşitli yapay
zeka modellerini başarıyla uygulamanın umut verici sonuçlar verdiği gerçeğine
ulaşılmıştır.

References

  • RODRIGUEZ, J. V. Perez (2005), ‘’STAR and ANN models: Forecasting performance on the Spanish “Ibex-35” stock index’’, Journal of Empirical Finance Volume 12, Issue 3, June, 490-509.
  • DUALIBE, C., VERLEYSEN, M. ve JESPERS, P.G. (2003), ‘’Design of Analog Fuzzy Logic Controlers in CMOS Technologie Implementation, Test and Application’’, Kluwer Academic Publishers, 227 s,USA.
  • SCHIERHOLT, Karsten & DAĞLI, Cihan H. (1996) ‘’Stock Market Prediction Using Different Neural Network Classification Architectures’’, Proceedings of the IEEE/IAFE Conference on Computational Intelligence for Financial Engineering, Institute of Electrical and Electronics Engineers (IEEE), Jan, 72-78.
  • ŞEN, Z. (2004), Mühendislikte Bulanık Mantık İle Modelleme Prensipleri.” Su Vakfı Yayınları. 191 s, Türkiye.
  • ABDELMOUEZ, Ghada, HASHEM Sherif R., ATIYA, Amir F. & El-GMALA Mohamed A. (2007), ‘’Neural Network vs. Linear Models for Stock Market Sectors Forecasting’’, Proceedings of International Joint Conference on Neural Networks, Orlando, Florida, USA, August 12- 17, 1-5.
  • TAKAGI, T., & SUGENO, M. (1985), ‘’Fuzzy identification of systems and its application to modeling and control’’, IEEE Trans. Syst. Man Cybern., 15, 116–132.
  • THAWORNWONG, Suraphan , ENKE, David (2003), ‘’Forecasting Stock Returns with Artificial Neural Networks’’, Neural Networks in Business Forecasting,47- 79.
  • YANG, K., WI, M. & LIN, J. (2012), ‘’The Application of Fuzzy Neural Network in Stock Price Forecasting Basad on Genetic Algorithm Discovering Fuzzy Rule’’, 8th International Conference on Natural Computation, ss. 470-474.
  • YARAR, Aalpaslan (2010), Susurluk Havzası Yağış Akış Verilerinin Modellenmesi, Selçuk Üniversitesi, Fen Bilimleri Enstitüsü, İnşaat Mühendisliği Anabilim Dalı, Doktora Tezi.
  • ZADEH, L. A. (1965). Fuzzy Sets. Information and Control, 8: 338-353.
  • LEE, Kyungjoo, YOO Sehwan & JIN Jongdae J. (2007), ‘’Neural Network Model vs. SARIMA Model In Forecasting Korean Stock Price Index (KOSPI)’’, Issues in Information Systems, Volume VIII, No. 2, 372-378.
  • ZHORA, D. V. (2005), ‘’Data Preprocessing for Stock Market Forecasting using RandomSubspace Classifier Network’’, Proceedings of International Joint Conference on Neural Networks, Montreal, Canada, July 31 – August 4, 2549-2554.
  • ZORIN, A. & BORISOV, A. (2012). Modelling Riga Stock Exchange İndex Using Neural Networks.
  • EGELİ, B., ÖZTURAN M. & BERTAN B. (2003). Stock Market Prediction Using Artificial Neural Networks.
  • ALTAN Şenol, (2008), ‘’Döviz Kuru Öngörü Performansı için Alternatif Bir Yaklaşım: Yapay Sinir Ağı’’, Gazi Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 10 / 2, 141-160.ALTAY, Erdinç & SATMAN M. Hakan (2005), ‘’Stock Market Forecasting: Artifical Neural Network and Linear Regression Comparison in an Emerging Market’’, Journal of Financial Management and Analysis, 18(2), 18-33.
  • ATIYA, A., TALAAT, N. & SHAHEEN, S. (1997), ‘’An Efficient Stock Market Forecasting Model Using Neural Networks’’, Proceedings of International Conference on Neural Networks, Vol. 4, Houston, 9-12 June, 2112-2115.
  • AVCI, Emin (2007), ‘’Forecasting Daily and Sessional Returns of the ISE-100 İndex with Neural Network Models’’, Doğuş Üniversitesi Dergisi, 8 (2), 128-142.
  • BENGOECHEA, A. Glaria, URETA, C. Ordonez, SAAVEDRA, M. Marchant & MEDİNA, N. Opazo (1996), ‘’Stock Market indeces in Santiago de Chile: forecasting using neural networks’’, Neural Networks, IEEE International Conference on, Volume: 4.
  • BOYACIOĞLU, Melek A., AVCI, Derya (2010). ‘’An Adaptive Network-Based Fuzzy Inference System (ANFIS) for the prediction of stock market return: The case of the Istanbul Stock Exchang’’, Expert Systems with Applications 37 :7908– 7912.
  • CHUNG, C. M., CHEONG, W. C. & CHUNG L. C. (2000), ‘’Financial Time Series Forecasting by Neural Network Using Conjugate Gradient Learning Algorithm andMultiple Linear Regression Weight Initialization’’, Computing in Economics and Finance 2000, Society for Computational Economics, no 61.
  • DEMİRPENÇE, H. K. (2005). ‘’Akımlarının Yapay Sinir Ağları ile Tahmin’’. Antalya Yöresinin İnşaat Mühendisliği Sorunları Kongresi, Antalya.
  • ADEBIYI, A.A.& AYO, C.K. (2011), ‘’Fuzzy-neural model with hybrid market indicators for stock forecasting’’, Int. J. Electronic Finance, Vol. 5, No. 3.
  • GURESEN, Erkam, KAYAKUTLU, Gulgun & DAİM, Tuğrul U. (2011), ‘’Using artificial neural network models in stock market index prediction’’, Expert Systems with Applications Volume 38, Issue 8, August, Pages 10389-10397.
  • HILL, Tim, MARQUEZ, Leorey, CONNOR, M. O’ Connor & REMUS William (1993). Artificial Neural Network Models for Forecasting and Decision Making. International Journal of Forecasting, 10, 5-15.
  • JANDAGHI, Gholamreza, TEHRANI, Reza, HOSSINPOUR, Davoud, GHOLIPOUR, Rahmatollah & SHADKAM, Seyer Amir Shahidi (2010), ‘’Application of Fuzzy- neural networks in multi-ahead forecast of stock price’’, African Journal of Business Management Vol. 4(6), pp. 903-914, June.
  • KARA, Yakup, BOYACIOĞLU, Acar, M. & BAYKAN, Ömer Kaan (2011), ’’ Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange’’, Expert Systems with Applications, 38, 5311–5319.
  • KARAATLI, Meltem, GÜNGÖR, İbrahim, DEMİR, Yusuf & KALAYCI Şeref. (2005), ‘’Hisse Senedi Fiyat Hareketlerinin Yapay Sinir Ağları Yöntemi ile Tahmin Edilmesi’’, Yönetim ve Ekonomi Araştırmaları Dergisi, Sayı:3, 22-48.
  • KILIÇ, Süleyman B., PAKSOY Semih & GENÇ, Tolga (2014), ‘’Forecasting the Direction of BIST 100 Returns with Artificial Neural Network Models’’, International Journal of Latest Trends in Finance & Economic Sciences, Vol‐4 No. 3 September, 2014, 759-765.
  • KIM, K., OH K. J. & HAN, I. (2000). Neural Network Forecasting of Stock Price Index to Integrate Change- Point Detection with Genetic Algorithms. KUTLU, Birgül & BADUR, Bertan (2009), ‘’Yapay Sinir Ağları ile Borsa Endeksi Tahmini’’, Yönetim Yıl:20, Sayı: 63, Haziran, 25-40.
  • LI Rong J. & XIONG Zhi B. (2005), ‘’Forecasting Stock Market with Fuzzy Neural Networks’’, Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, 18-21 August, 3475-3479.
  • LIU, Qiong, LU, Xin, REN F. & KUROİWA S. (2004), ‘’Automatic Estimation of Stock Market Forecasting and Generating the Corresponding Natural Language Expression’’, Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC’04), 1-5.
  • MAMDANI, E. H. (1974), ‘2Application of fuzzy algorithms for simple dynamic plant’’, Proc. IEEE, 121(12), 1585–1588.
  • MACIELL, L., GOMIDE, F. & ROSANGELA B. (2012), ‘’Evolving Fuzzy Modeling for Stock Market Forecasting’’, IPMU 2012, Part IV, CCIS 300, pp. 20–29.
  • PAVLIDIS, N. G., PLAGIANAKOS, V. P., TASOULIS, D.K. & VRAHATIS M. N. (2003), ‘’Financial Forecasting through Unsupervised Clustering and Neural Networks’’, Operational Research, May, Volume 6, Issue 2, 103–127.
  • PHUA, P. K. H., ZHU, X. & KOH, C. H. (2003), ‘’Forecasting Stock Index Increments Using Neural Networks with Trust Region Methods’’, Neural Networks, 2003. Proceedings of the International Joint Conference on, Volume: 1, 260-265.
  • RAST, M. (1999), ‘’Forecasting with Fuzzy Neural Networks: A Case Study in Stock Market Crash Situations. Fuzzy Information Processing Society’’, NAFIPS. 18th International Conference of the North American, 418-420.
Year 2018, Volume: 11 Issue: 1, 99 - 124, 21.07.2018
https://doi.org/10.17218/hititsosbil.390490

Abstract

References

  • RODRIGUEZ, J. V. Perez (2005), ‘’STAR and ANN models: Forecasting performance on the Spanish “Ibex-35” stock index’’, Journal of Empirical Finance Volume 12, Issue 3, June, 490-509.
  • DUALIBE, C., VERLEYSEN, M. ve JESPERS, P.G. (2003), ‘’Design of Analog Fuzzy Logic Controlers in CMOS Technologie Implementation, Test and Application’’, Kluwer Academic Publishers, 227 s,USA.
  • SCHIERHOLT, Karsten & DAĞLI, Cihan H. (1996) ‘’Stock Market Prediction Using Different Neural Network Classification Architectures’’, Proceedings of the IEEE/IAFE Conference on Computational Intelligence for Financial Engineering, Institute of Electrical and Electronics Engineers (IEEE), Jan, 72-78.
  • ŞEN, Z. (2004), Mühendislikte Bulanık Mantık İle Modelleme Prensipleri.” Su Vakfı Yayınları. 191 s, Türkiye.
  • ABDELMOUEZ, Ghada, HASHEM Sherif R., ATIYA, Amir F. & El-GMALA Mohamed A. (2007), ‘’Neural Network vs. Linear Models for Stock Market Sectors Forecasting’’, Proceedings of International Joint Conference on Neural Networks, Orlando, Florida, USA, August 12- 17, 1-5.
  • TAKAGI, T., & SUGENO, M. (1985), ‘’Fuzzy identification of systems and its application to modeling and control’’, IEEE Trans. Syst. Man Cybern., 15, 116–132.
  • THAWORNWONG, Suraphan , ENKE, David (2003), ‘’Forecasting Stock Returns with Artificial Neural Networks’’, Neural Networks in Business Forecasting,47- 79.
  • YANG, K., WI, M. & LIN, J. (2012), ‘’The Application of Fuzzy Neural Network in Stock Price Forecasting Basad on Genetic Algorithm Discovering Fuzzy Rule’’, 8th International Conference on Natural Computation, ss. 470-474.
  • YARAR, Aalpaslan (2010), Susurluk Havzası Yağış Akış Verilerinin Modellenmesi, Selçuk Üniversitesi, Fen Bilimleri Enstitüsü, İnşaat Mühendisliği Anabilim Dalı, Doktora Tezi.
  • ZADEH, L. A. (1965). Fuzzy Sets. Information and Control, 8: 338-353.
  • LEE, Kyungjoo, YOO Sehwan & JIN Jongdae J. (2007), ‘’Neural Network Model vs. SARIMA Model In Forecasting Korean Stock Price Index (KOSPI)’’, Issues in Information Systems, Volume VIII, No. 2, 372-378.
  • ZHORA, D. V. (2005), ‘’Data Preprocessing for Stock Market Forecasting using RandomSubspace Classifier Network’’, Proceedings of International Joint Conference on Neural Networks, Montreal, Canada, July 31 – August 4, 2549-2554.
  • ZORIN, A. & BORISOV, A. (2012). Modelling Riga Stock Exchange İndex Using Neural Networks.
  • EGELİ, B., ÖZTURAN M. & BERTAN B. (2003). Stock Market Prediction Using Artificial Neural Networks.
  • ALTAN Şenol, (2008), ‘’Döviz Kuru Öngörü Performansı için Alternatif Bir Yaklaşım: Yapay Sinir Ağı’’, Gazi Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 10 / 2, 141-160.ALTAY, Erdinç & SATMAN M. Hakan (2005), ‘’Stock Market Forecasting: Artifical Neural Network and Linear Regression Comparison in an Emerging Market’’, Journal of Financial Management and Analysis, 18(2), 18-33.
  • ATIYA, A., TALAAT, N. & SHAHEEN, S. (1997), ‘’An Efficient Stock Market Forecasting Model Using Neural Networks’’, Proceedings of International Conference on Neural Networks, Vol. 4, Houston, 9-12 June, 2112-2115.
  • AVCI, Emin (2007), ‘’Forecasting Daily and Sessional Returns of the ISE-100 İndex with Neural Network Models’’, Doğuş Üniversitesi Dergisi, 8 (2), 128-142.
  • BENGOECHEA, A. Glaria, URETA, C. Ordonez, SAAVEDRA, M. Marchant & MEDİNA, N. Opazo (1996), ‘’Stock Market indeces in Santiago de Chile: forecasting using neural networks’’, Neural Networks, IEEE International Conference on, Volume: 4.
  • BOYACIOĞLU, Melek A., AVCI, Derya (2010). ‘’An Adaptive Network-Based Fuzzy Inference System (ANFIS) for the prediction of stock market return: The case of the Istanbul Stock Exchang’’, Expert Systems with Applications 37 :7908– 7912.
  • CHUNG, C. M., CHEONG, W. C. & CHUNG L. C. (2000), ‘’Financial Time Series Forecasting by Neural Network Using Conjugate Gradient Learning Algorithm andMultiple Linear Regression Weight Initialization’’, Computing in Economics and Finance 2000, Society for Computational Economics, no 61.
  • DEMİRPENÇE, H. K. (2005). ‘’Akımlarının Yapay Sinir Ağları ile Tahmin’’. Antalya Yöresinin İnşaat Mühendisliği Sorunları Kongresi, Antalya.
  • ADEBIYI, A.A.& AYO, C.K. (2011), ‘’Fuzzy-neural model with hybrid market indicators for stock forecasting’’, Int. J. Electronic Finance, Vol. 5, No. 3.
  • GURESEN, Erkam, KAYAKUTLU, Gulgun & DAİM, Tuğrul U. (2011), ‘’Using artificial neural network models in stock market index prediction’’, Expert Systems with Applications Volume 38, Issue 8, August, Pages 10389-10397.
  • HILL, Tim, MARQUEZ, Leorey, CONNOR, M. O’ Connor & REMUS William (1993). Artificial Neural Network Models for Forecasting and Decision Making. International Journal of Forecasting, 10, 5-15.
  • JANDAGHI, Gholamreza, TEHRANI, Reza, HOSSINPOUR, Davoud, GHOLIPOUR, Rahmatollah & SHADKAM, Seyer Amir Shahidi (2010), ‘’Application of Fuzzy- neural networks in multi-ahead forecast of stock price’’, African Journal of Business Management Vol. 4(6), pp. 903-914, June.
  • KARA, Yakup, BOYACIOĞLU, Acar, M. & BAYKAN, Ömer Kaan (2011), ’’ Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange’’, Expert Systems with Applications, 38, 5311–5319.
  • KARAATLI, Meltem, GÜNGÖR, İbrahim, DEMİR, Yusuf & KALAYCI Şeref. (2005), ‘’Hisse Senedi Fiyat Hareketlerinin Yapay Sinir Ağları Yöntemi ile Tahmin Edilmesi’’, Yönetim ve Ekonomi Araştırmaları Dergisi, Sayı:3, 22-48.
  • KILIÇ, Süleyman B., PAKSOY Semih & GENÇ, Tolga (2014), ‘’Forecasting the Direction of BIST 100 Returns with Artificial Neural Network Models’’, International Journal of Latest Trends in Finance & Economic Sciences, Vol‐4 No. 3 September, 2014, 759-765.
  • KIM, K., OH K. J. & HAN, I. (2000). Neural Network Forecasting of Stock Price Index to Integrate Change- Point Detection with Genetic Algorithms. KUTLU, Birgül & BADUR, Bertan (2009), ‘’Yapay Sinir Ağları ile Borsa Endeksi Tahmini’’, Yönetim Yıl:20, Sayı: 63, Haziran, 25-40.
  • LI Rong J. & XIONG Zhi B. (2005), ‘’Forecasting Stock Market with Fuzzy Neural Networks’’, Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, 18-21 August, 3475-3479.
  • LIU, Qiong, LU, Xin, REN F. & KUROİWA S. (2004), ‘’Automatic Estimation of Stock Market Forecasting and Generating the Corresponding Natural Language Expression’’, Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC’04), 1-5.
  • MAMDANI, E. H. (1974), ‘2Application of fuzzy algorithms for simple dynamic plant’’, Proc. IEEE, 121(12), 1585–1588.
  • MACIELL, L., GOMIDE, F. & ROSANGELA B. (2012), ‘’Evolving Fuzzy Modeling for Stock Market Forecasting’’, IPMU 2012, Part IV, CCIS 300, pp. 20–29.
  • PAVLIDIS, N. G., PLAGIANAKOS, V. P., TASOULIS, D.K. & VRAHATIS M. N. (2003), ‘’Financial Forecasting through Unsupervised Clustering and Neural Networks’’, Operational Research, May, Volume 6, Issue 2, 103–127.
  • PHUA, P. K. H., ZHU, X. & KOH, C. H. (2003), ‘’Forecasting Stock Index Increments Using Neural Networks with Trust Region Methods’’, Neural Networks, 2003. Proceedings of the International Joint Conference on, Volume: 1, 260-265.
  • RAST, M. (1999), ‘’Forecasting with Fuzzy Neural Networks: A Case Study in Stock Market Crash Situations. Fuzzy Information Processing Society’’, NAFIPS. 18th International Conference of the North American, 418-420.
There are 36 citations in total.

Details

Primary Language Turkish
Journal Section Articles
Authors

Ali Özer This is me

Salim Sercan Sarı This is me

Eyyup Ensar Başakın

Publication Date July 21, 2018
Published in Issue Year 2018 Volume: 11 Issue: 1

Cite

APA Özer, A., Sarı, S. S., & Başakın, E. E. (2018). BULANIK MANTIK VE YAPAY SİNİR AĞLARI İLE BORSA ENDEKS TAHMİNİ: GELİŞMİŞ VE GELİŞMEKTE OLAN ÜLKELER ÖRNEĞİ. Hitit Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 11(1), 99-124. https://doi.org/10.17218/hititsosbil.390490
AMA Özer A, Sarı SS, Başakın EE. BULANIK MANTIK VE YAPAY SİNİR AĞLARI İLE BORSA ENDEKS TAHMİNİ: GELİŞMİŞ VE GELİŞMEKTE OLAN ÜLKELER ÖRNEĞİ. hititsosbil. June 2018;11(1):99-124. doi:10.17218/hititsosbil.390490
Chicago Özer, Ali, Salim Sercan Sarı, and Eyyup Ensar Başakın. “BULANIK MANTIK VE YAPAY SİNİR AĞLARI İLE BORSA ENDEKS TAHMİNİ: GELİŞMİŞ VE GELİŞMEKTE OLAN ÜLKELER ÖRNEĞİ”. Hitit Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 11, no. 1 (June 2018): 99-124. https://doi.org/10.17218/hititsosbil.390490.
EndNote Özer A, Sarı SS, Başakın EE (June 1, 2018) BULANIK MANTIK VE YAPAY SİNİR AĞLARI İLE BORSA ENDEKS TAHMİNİ: GELİŞMİŞ VE GELİŞMEKTE OLAN ÜLKELER ÖRNEĞİ. Hitit Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 11 1 99–124.
IEEE A. Özer, S. S. Sarı, and E. E. Başakın, “BULANIK MANTIK VE YAPAY SİNİR AĞLARI İLE BORSA ENDEKS TAHMİNİ: GELİŞMİŞ VE GELİŞMEKTE OLAN ÜLKELER ÖRNEĞİ”, hititsosbil, vol. 11, no. 1, pp. 99–124, 2018, doi: 10.17218/hititsosbil.390490.
ISNAD Özer, Ali et al. “BULANIK MANTIK VE YAPAY SİNİR AĞLARI İLE BORSA ENDEKS TAHMİNİ: GELİŞMİŞ VE GELİŞMEKTE OLAN ÜLKELER ÖRNEĞİ”. Hitit Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 11/1 (June 2018), 99-124. https://doi.org/10.17218/hititsosbil.390490.
JAMA Özer A, Sarı SS, Başakın EE. BULANIK MANTIK VE YAPAY SİNİR AĞLARI İLE BORSA ENDEKS TAHMİNİ: GELİŞMİŞ VE GELİŞMEKTE OLAN ÜLKELER ÖRNEĞİ. hititsosbil. 2018;11:99–124.
MLA Özer, Ali et al. “BULANIK MANTIK VE YAPAY SİNİR AĞLARI İLE BORSA ENDEKS TAHMİNİ: GELİŞMİŞ VE GELİŞMEKTE OLAN ÜLKELER ÖRNEĞİ”. Hitit Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, vol. 11, no. 1, 2018, pp. 99-124, doi:10.17218/hititsosbil.390490.
Vancouver Özer A, Sarı SS, Başakın EE. BULANIK MANTIK VE YAPAY SİNİR AĞLARI İLE BORSA ENDEKS TAHMİNİ: GELİŞMİŞ VE GELİŞMEKTE OLAN ÜLKELER ÖRNEĞİ. hititsosbil. 2018;11(1):99-124.