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İMKB ENDEKS ÖNGÖRÜSÜ İÇİN İLERİ BESLEMELİ AĞ MİMARİSİNE SAHİP YAPAY SİNİR AĞI MODELLEMESİ

Year 2010, Issue: 5, 0 - , 14.05.2015

Abstract

Bu çalısmada 1997-2000 y ılları arasında Istanbul Menkul Kıymetler Borsasında gerçeklesen borsa endeks
değerinin tahminine ait bir uygulama yer almaktadır. Ayrıca borsada gerçeklesen fiyatların tahmininde
kullanılan nöral ağ sistemleri ve algoritmalarına ait matematiksel y öntemlerden de kısaca bahsedilmistir.
Pazarın yönü tahmin edilirken onüç değiskenli bir Nöral Ağ Sistemi kurulmus ve sistemin Hatayı Geriye
Yayma Algoritması ile değerlendirilmesi yapılmıstır. Tüm hesaplamalar ĐMKB Nöral Ağ Simulatörü adı
verilen bir programla yapılmıstır.

References

  • ADORF, H.M. (1989), Connectionism and Neural Networks: Knowledge
  • Based Systems in Astronomy A.Heck ve F. Murtagh (Ed), Berlin: Springer-Verlag.
  • BAKLAVACI, S. (1984), Analysis of Learning Algorithms in Neural Networks.
  • Msc Theses ITU Institute of Science and Technology, Đstanbul.
  • BLUM, A.(1992), Neural Networks in C++. New York: John Wiley and Sons
  • Inc.
  • BÖKESOY, A. (1994), Interdependency Between IMKB and NYSE. (MBA
  • Theses) Bilkent University Department of Management, Ankara.
  • CAO Q., LEGGIO K.B., SCHNIEDERJANS M. J. (2005), “A Comparison
  • Between Fama and French’s Model and Artifi cial Neural Networks in Predicting the
  • Chinese Stock Market”. Computers&Operations Research, Vol 32, 2499-2512
  • CHENG, W., MCCLAIN, B.W. and C. KELLY (1997), Artificial Neural
  • Networks Make Their Mark As A Powerful Tool For Đnvestors, Review Of
  • Business, 4–9.
  • CİVELEK, Ö. (1999), “Dairesel Plakların Nöro Fuzzy Tekniği ile Analizi”
  • Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi Cilt 1
  • Sayı 2.
  • DENNIS, S. ve DEVIN, Mc. A. (1997), Introduction to Neural Networks and
  • Brainwave Simulator. The University of Queensland School of Physhology.Internet
  • Adresi; http://www.itee.uq.edu.au/~cogs2010/cmc/chapters/IntroBrainWave, Erisim
  • Tarihi: 05.05.2008.
  • DUTTA, S. and SHEKHAR, S. (1988), Bond-Rating: A Non-Conservative
  • Application of Neural Networks. Proceedings of the IEEE International Conference
  • on Neural Networks 2,443–450.
  • FREEMAN, J. ve SAKAPURA, D. (1989), Neural Networks Algorithms
  • Applications and Programming Techniques. Sydney: Addision Wesley Publishing.
  • GENÇAY, R. (1998), “The Predictability of Security Returns with Simple
  • Technical Trading”, Journal of Empirical Finance, Vol 5, Issue 4, 347-359.
  • GILES C.L., LAWRENCE S., TSOI A.C. (1997), "Rule Inference For
  • Financial Prediction Using Recurrent Neural Networks," Proceedings of the
  • IEEE/IAFE Conf. on Computational Intelligence for Financial Engineering, 253,
  • IEEE.
  • GÖKÇAY, M. M. (1993), Yapay Sinir Ağları ve Uygulamaları (Yayınlanmamıs
  • Yüksek Lisans Tezi). Đstanbul Üniversitesi, Đstanbul.
  • HAMĐD S. A., IQBAL Z. (2004), “Using Neural Networks for Forecasting
  • Volatility of S&P500 Index Future Prices”, Journal of Business Research, Vol 57,
  • Issue 10, 1116-1125.
  • JAGOTA, A. (1998), Neural Computing Surveys, NCS Journal Volume 1
  • Internet Adresi; http://www.icsi.berkeley.edu/~jagota/NCS/, Erisim Tarihi:
  • 08.2009.
  • KELLEY, M. (1998), “Cells&SCI Back to Basics Part I”, Parapl egia News
  • Articles, PN Magazine, NewYork.
  • KRYZANOWSKI, L., GALLER, M. and D. WRIGHT (1993), Using
  • Artifi cial Neural Network to Pick Stocks. Financial Analysts Journal, 21–27.
  • LASKE, O. (1992), “A Conversation With Marvin Minsky”, AI Magazine
  • Vol 13 No:13.
  • LEIGH, W., PURVIS, R., RAGUSA, J.M. (2002a), Forecasting the NYSE
  • Composite Index With Technical Analysis, Pattern Recognizer, Neural Network,
  • and Genetic Algorithm: A Case Study In Romantic Decision Support, Decision
  • Support Systems, Vol 32, Issue 4, 361-377.
  • LEIGHT, W., PAZ, M., PURVIS, R. (2002b), An Analysis of a Hybrid
  • Neural Network and Pattern Recognition Technique for Predi cting Short Term
  • Increases In The NYSE Composite Index, Omega, Vol 30, Issue 2, 69-76.
  • LIN, F.C. and LIN, M. (1993), Analysis of Financial Data Using Neural
  • Nets. AI Expert ,36–41.
  • MALLARIS M., SALCHENBERGER, L. (1996), Using Neural Networks to
  • Forecast the S&P100 Implied Volatility, Neurocomputing, Vol 10, Issue 2, 83-195.
  • MOODY, J. (1995), Economic Forecasting: Challenges and Neural Network
  • Solutions” Computer Science Department Graduate Instıtute Portand. Internet Erisimi,
  • http://neural.cse.ogi.edu; cd pub/neural/papers, Erisim Tarihi: 04.03.2009.
  • ORR, G. (1994), “Computation In The Brain ” Neural Networks Willamette
  • University Salem Oregon Lecture Not es: No:1, Internet Erisimi;
  • http://www.willamette.edu/~gorr/classes/cs449/intro.html, Erisim Tarihi: 16.03.2009.
  • ÖREN, T. (1990), Advances in Artifi cial Intelligence in Software
  • Engineering. Vol I, New York: JAI Press Inc.
  • ÖZDEMĐR, S. (1996), Hisse Senedi Fiyatlarını Etkileyen Değiskenlerin
  • İstatiksel Analizi” (Yayınlanmamıs Yüks ek Lisans Tezi) Marmara Üniversitesi, Đstanbul.
  • ÖZSOY, Đ., FIRAT, M. (2004), “Kirissiz Dösemeli Betonarme Bir Binada
  • Olusan Yatay Deplasmanın Yapay Sinir Ağları ile Tahmini”, DEÜ Mühendislik Fakültesi
  • Fen ve Mühendislik Dergisi, Cilt 6, Sayı 1 ,51-63.
  • RAO, V. VE RAO, H. V. (1993), C++ in Neural Networks and Fuzzy Logic,
  • New York: MIS Press.
  • SIMPSON, P. K. (1991), Neural Network Paradigm, AGARD, 179,2 (1-33).
  • SUNGUR, M. (1995), Hesaplama Yönüyl e Yapay Sinir Ağları: Öğrenme ve
  • Eniyileme” Mühendis Gözüyle Yapay Sinir Ağları. ODTU-TUBITAK Elekt. Mühendisleri
  • Odası IEEE Türkiye Subesi Bilg. Kolu Yayını.
  • TRIPPI, R. R. (1996), Neural Networks in Finance and Investing. Irwin
  • Professional Publishing.
  • TRIPPI, R. and DESIENO, D. Trading Equity Index Futures With a Neural
  • Network, Journal of Portfolio Management, 27–33.
  • ULUSOY, T. (2001), Yapay Sinir Ağları Kullanılarak Đstanbul Menkul Kıymetler
  • Borsası Đndeks Öngörüsü. Yayınlanmamıs Yüksek Lisans Tezi. Baskent Üniversitesi,
  • Sosyal Bilimler Enstitüsü.
  • WALCZAK, S. (2001), An empirical analysis of data requirements for
  • financial forecasting with neural networks. Journal of Management Information
  • Systems 17 4, 203–222
  • YAZICI, C. A., ÖĞÜS, E., ANKARALI, S., CANAN, S., ANKARALI, H.,
  • ve ZEKĐ AKKUS (2007), “Yapay Sinir Ağlarına Genel Bakıs”, (der.), Türkiye Klinikleri
  • J. Med Sci 27:65-71.
Year 2010, Issue: 5, 0 - , 14.05.2015

Abstract

References

  • ADORF, H.M. (1989), Connectionism and Neural Networks: Knowledge
  • Based Systems in Astronomy A.Heck ve F. Murtagh (Ed), Berlin: Springer-Verlag.
  • BAKLAVACI, S. (1984), Analysis of Learning Algorithms in Neural Networks.
  • Msc Theses ITU Institute of Science and Technology, Đstanbul.
  • BLUM, A.(1992), Neural Networks in C++. New York: John Wiley and Sons
  • Inc.
  • BÖKESOY, A. (1994), Interdependency Between IMKB and NYSE. (MBA
  • Theses) Bilkent University Department of Management, Ankara.
  • CAO Q., LEGGIO K.B., SCHNIEDERJANS M. J. (2005), “A Comparison
  • Between Fama and French’s Model and Artifi cial Neural Networks in Predicting the
  • Chinese Stock Market”. Computers&Operations Research, Vol 32, 2499-2512
  • CHENG, W., MCCLAIN, B.W. and C. KELLY (1997), Artificial Neural
  • Networks Make Their Mark As A Powerful Tool For Đnvestors, Review Of
  • Business, 4–9.
  • CİVELEK, Ö. (1999), “Dairesel Plakların Nöro Fuzzy Tekniği ile Analizi”
  • Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi Cilt 1
  • Sayı 2.
  • DENNIS, S. ve DEVIN, Mc. A. (1997), Introduction to Neural Networks and
  • Brainwave Simulator. The University of Queensland School of Physhology.Internet
  • Adresi; http://www.itee.uq.edu.au/~cogs2010/cmc/chapters/IntroBrainWave, Erisim
  • Tarihi: 05.05.2008.
  • DUTTA, S. and SHEKHAR, S. (1988), Bond-Rating: A Non-Conservative
  • Application of Neural Networks. Proceedings of the IEEE International Conference
  • on Neural Networks 2,443–450.
  • FREEMAN, J. ve SAKAPURA, D. (1989), Neural Networks Algorithms
  • Applications and Programming Techniques. Sydney: Addision Wesley Publishing.
  • GENÇAY, R. (1998), “The Predictability of Security Returns with Simple
  • Technical Trading”, Journal of Empirical Finance, Vol 5, Issue 4, 347-359.
  • GILES C.L., LAWRENCE S., TSOI A.C. (1997), "Rule Inference For
  • Financial Prediction Using Recurrent Neural Networks," Proceedings of the
  • IEEE/IAFE Conf. on Computational Intelligence for Financial Engineering, 253,
  • IEEE.
  • GÖKÇAY, M. M. (1993), Yapay Sinir Ağları ve Uygulamaları (Yayınlanmamıs
  • Yüksek Lisans Tezi). Đstanbul Üniversitesi, Đstanbul.
  • HAMĐD S. A., IQBAL Z. (2004), “Using Neural Networks for Forecasting
  • Volatility of S&P500 Index Future Prices”, Journal of Business Research, Vol 57,
  • Issue 10, 1116-1125.
  • JAGOTA, A. (1998), Neural Computing Surveys, NCS Journal Volume 1
  • Internet Adresi; http://www.icsi.berkeley.edu/~jagota/NCS/, Erisim Tarihi:
  • 08.2009.
  • KELLEY, M. (1998), “Cells&SCI Back to Basics Part I”, Parapl egia News
  • Articles, PN Magazine, NewYork.
  • KRYZANOWSKI, L., GALLER, M. and D. WRIGHT (1993), Using
  • Artifi cial Neural Network to Pick Stocks. Financial Analysts Journal, 21–27.
  • LASKE, O. (1992), “A Conversation With Marvin Minsky”, AI Magazine
  • Vol 13 No:13.
  • LEIGH, W., PURVIS, R., RAGUSA, J.M. (2002a), Forecasting the NYSE
  • Composite Index With Technical Analysis, Pattern Recognizer, Neural Network,
  • and Genetic Algorithm: A Case Study In Romantic Decision Support, Decision
  • Support Systems, Vol 32, Issue 4, 361-377.
  • LEIGHT, W., PAZ, M., PURVIS, R. (2002b), An Analysis of a Hybrid
  • Neural Network and Pattern Recognition Technique for Predi cting Short Term
  • Increases In The NYSE Composite Index, Omega, Vol 30, Issue 2, 69-76.
  • LIN, F.C. and LIN, M. (1993), Analysis of Financial Data Using Neural
  • Nets. AI Expert ,36–41.
  • MALLARIS M., SALCHENBERGER, L. (1996), Using Neural Networks to
  • Forecast the S&P100 Implied Volatility, Neurocomputing, Vol 10, Issue 2, 83-195.
  • MOODY, J. (1995), Economic Forecasting: Challenges and Neural Network
  • Solutions” Computer Science Department Graduate Instıtute Portand. Internet Erisimi,
  • http://neural.cse.ogi.edu; cd pub/neural/papers, Erisim Tarihi: 04.03.2009.
  • ORR, G. (1994), “Computation In The Brain ” Neural Networks Willamette
  • University Salem Oregon Lecture Not es: No:1, Internet Erisimi;
  • http://www.willamette.edu/~gorr/classes/cs449/intro.html, Erisim Tarihi: 16.03.2009.
  • ÖREN, T. (1990), Advances in Artifi cial Intelligence in Software
  • Engineering. Vol I, New York: JAI Press Inc.
  • ÖZDEMĐR, S. (1996), Hisse Senedi Fiyatlarını Etkileyen Değiskenlerin
  • İstatiksel Analizi” (Yayınlanmamıs Yüks ek Lisans Tezi) Marmara Üniversitesi, Đstanbul.
  • ÖZSOY, Đ., FIRAT, M. (2004), “Kirissiz Dösemeli Betonarme Bir Binada
  • Olusan Yatay Deplasmanın Yapay Sinir Ağları ile Tahmini”, DEÜ Mühendislik Fakültesi
  • Fen ve Mühendislik Dergisi, Cilt 6, Sayı 1 ,51-63.
  • RAO, V. VE RAO, H. V. (1993), C++ in Neural Networks and Fuzzy Logic,
  • New York: MIS Press.
  • SIMPSON, P. K. (1991), Neural Network Paradigm, AGARD, 179,2 (1-33).
  • SUNGUR, M. (1995), Hesaplama Yönüyl e Yapay Sinir Ağları: Öğrenme ve
  • Eniyileme” Mühendis Gözüyle Yapay Sinir Ağları. ODTU-TUBITAK Elekt. Mühendisleri
  • Odası IEEE Türkiye Subesi Bilg. Kolu Yayını.
  • TRIPPI, R. R. (1996), Neural Networks in Finance and Investing. Irwin
  • Professional Publishing.
  • TRIPPI, R. and DESIENO, D. Trading Equity Index Futures With a Neural
  • Network, Journal of Portfolio Management, 27–33.
  • ULUSOY, T. (2001), Yapay Sinir Ağları Kullanılarak Đstanbul Menkul Kıymetler
  • Borsası Đndeks Öngörüsü. Yayınlanmamıs Yüksek Lisans Tezi. Baskent Üniversitesi,
  • Sosyal Bilimler Enstitüsü.
  • WALCZAK, S. (2001), An empirical analysis of data requirements for
  • financial forecasting with neural networks. Journal of Management Information
  • Systems 17 4, 203–222
  • YAZICI, C. A., ÖĞÜS, E., ANKARALI, S., CANAN, S., ANKARALI, H.,
  • ve ZEKĐ AKKUS (2007), “Yapay Sinir Ağlarına Genel Bakıs”, (der.), Türkiye Klinikleri
  • J. Med Sci 27:65-71.
There are 89 citations in total.

Details

Primary Language Turkish
Journal Section Articles
Authors

Tolga Ulusoy This is me

Publication Date May 14, 2015
Published in Issue Year 2010 Issue: 5

Cite

APA Ulusoy, T. (2015). İMKB ENDEKS ÖNGÖRÜSÜ İÇİN İLERİ BESLEMELİ AĞ MİMARİSİNE SAHİP YAPAY SİNİR AĞI MODELLEMESİ. Uluslararası İktisadi Ve İdari İncelemeler Dergisi(5). https://doi.org/10.18092/ijeas.90183

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