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HİSSE SENEDİ FİYAT HAREKETLERİNİN TAHMİN EDİLMESİNDE MARKOV ZİNCİRLERİNİN KULLANILMASI: İMKB 10 BANKACILIK ENDEKSİ İŞLETMELERİ ÜZERİNE AMPİRİK BİR ÇALIŞMA / THE USE OF MARKOV CHAİNS FOR THE PREDİCTİON OF STOCK PRİCE MOVEMENTS: AN EMPİRİCAL STUDY ON THE ISE 10 BANKİNG INDEX FİRMS

Year 2014, Volume: 9 Issue: 35, 6158 - 6198, 01.06.2014

Kenan Ilarslan

https://doi.org/10.19168/jyu.32384
Cited By: 12

Abstract

“The aim of this study, is to predict the future stock price movements by Markov chains. In this research, daily closing prices of 10 stocks under the ISE 10 Banking index were used during the 02.01.2012-31.12.2012. As a result of the analysis, the price movement of nine stocks were predicted successfully. Within this framework, we can say that the use of Markov chains is useful to predict the future stock price movements.”

Keywords

Stock Price Movements Prediction Markov Chains.

References

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  • Atmeh, M. A ve Dobbs, I. M. 2006. “Technical Analysis and the Stochastic Properties of the Jordanian Stock Market Index Return.” Studies in Economics and Finance, 23 (2): 119-140.
  • Aygören, Hakan. 2006. “ İmkb-30 Endeks Davranışının Monte Carlo Simülasyonu ile İncelenmesi.” Muhasebe ve Finansman Dergisi, 29: 197-205.
  • Ayodele, A., Ayo, C. K., Adebiyi, M. O. ve Otokiti, S. O. 2012. “Stock Price Prediction Using Neural Network with Hybridized Market Indicators.” Journal of Emerging Trends in Computing and Information Sciences, 3 (1): 1
  • Başoğlu, U., Ceylan, A. ve Parasız, İ. 2009. Finans: Teori, Kurum, Uygulama. Bursa: Ekin Basım Yayın Dağıtım.
  • Can, T. ve Öz, E. 2009. “ Saklı Markov Modelleri Kullanılarak Türkiye’de Dolar Kurundaki Değişimin Tahmin Edilmesi.” İstanbul Üniversitesi İşletme Fakültesi Dergisi. 38 (1): 1-23.
  • Charitou, A. ve Panagiotides, G. 1999. “Financial Analysis, Future Earnings and Cash Flows, and the Prediction of Stock Returns: Evidence for the UK.” Accounting and Business Research, 29 (4): 281-298.
  • Chin, L. ve Hong, L. W. 2008. “Can Financial Ratios Predict The Malaysian Stock Return?.” Integration & Dissemination, (2): 7-8.
  • Deaves, R., Miu, P. ve White, C. B. 2008. “Canadian Stock Market Multiples and Their Predictive Content.” International Review of Economics and Finance, 17: 457-466.
  • Doubleday, K. J. ve Esunge, J. N. 2011. “Application of Markov Chains to Stock Trends.” Journal of Mathematics and Statistics, 7 (2): 103-106.
  • Dutta, A., Bandopadhyay, G. ve Sengupta, S. 2012. “Prediction of Stock Performance in the Indian Stock Market Using Logistic Regression.” International Journal of Business and Information, 7 (1): 105-136.
  • Düzakın, Erkut. 2005. İşletme Yöneticileri için Excel ile Sayısal Karar Verme Teknikleri. İstanbul: Kare Yayınları.
  • Elleuch, J. ve Trabelsi, L. 2009. “Fundamental Analysis Strategy and the Prediction of Stock Returns.” International Research Journal of Finance and Economics, (30): 95-107.
  • Eom, C., Oh, G. ve Woo, S. J. 2008. “Relationship Between Efficiency and Predictability in Stock Price Change.” Physica A, 387: 5511-5517.
  • Ergeç, Funda. 1996. “ Markov Analizi ile Hisse Senedi Fiyatının Tahmin Edilmesi.” İstanbul Üniversitesi İşletme Fakültesi Dergisi. 25 (2): 123-151.
  • Ferson, Wayne. 2007. Market Efficiency and Forecasting. S. Satchell, (Ed.), Foreasting Expected Returns in the Financial Markets içinde (1-16). Oxford: Elsevier.
  • Ford, J. L., Pok, W. C. ve Poshakwale, S. 2012. “The Return Predictability and Market Efficiency of the KLSE CI Stock Index Futures Market.” Journal of Emerging Market Finance, 11 (1): 37-60.
  • Gupta, C. B. 1992. Contemporary Management. New Delhi: Ashish Publishing.
  • Gupta, M. P. ve Khanna, R. B. 2009. Quantitative Techniques for Decision Making. New Delhi: PHI Learning.
  • Hillier, F. S. ve Lieberman, G. J. 2001. Introduction to Operations Research. New York: McGraw-Hill Publishing.
  • Hsu, Chih Ming. 2011. “A Hybrid Procedure with Feature Selection for Resolving Stock/Futures Price Forecasting Problems.” Neural Computing and Applications, 1-21.
  • Idolor, Eseoghene Joseph. 2010. “Security Prices as Markov Processes.” International Research Journal of Finance and Economics, 59: 62-76.
  • Jarrett, J. E. ve Schilling, J. 2008. “Daily Variation and Predicting Stock Market Returns for the Frakfurter Börse (Stock Market).” Journal of Business Economics and Management, 9 (3): 189-198.
  • Jasemi, M. ve Kimiagari, A. 2012. “An Investigation of Model Selection Criteria for Technical Analysis of Moving Average.” Journal of Industrial Engineering International, 8 (5): 1-9.
  • Jiang, X ve Lee, B. S. 2012. “Do Decomposed Financial Ratios Predict Stock Returns and Fundamentals Better?” The Financial Review, (47): 531-564.
  • Kara, Y., Boyacıoğlu, M. A. ve Baykan, Ö. K. 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.
  • Khan, Z. H., Alin, T. S. ve Hussain, M. A. 2011. “Price Prediction of Share Market Using Artificial Neural Network (ANN).” International Journal of Computer Applications, 22 (2): 42-47.
  • Kheradyar, S. ve Ibrahim, I. 2011. “Financial Ratios as Empiricial Predictors of Stock Return.” International Proceedings of Economics Development and Research, 10: 318-322.
  • Konuralp, Gürel. 2005. Sermaye Piyasaları: Analizler, Kuramlar ve Portföy Yönetimi. İstanbul: Alfa Basım Yayım Dağıtım.
  • Lange, Kenneth. 2010. Applied Probability. New York: Springer Publishing.
  • Leung, M. T., Daouk, H. ve Chen, A. S. 2000. “Forecasting Stock Indices: A Comparison of Classification and Level Estimation Models.” International Journal of Forecasting, 16: 173-190.
  • Levy, H. ve Post, T. 2005. Investments. Essex: Pearson Education.
  • Lewellen, Jonathan. 2004. “Predicting Returns with Financial Ratios.” Journal of Financial Economics, 74: 209-235.
  • Liu, Tie. 2010. “Application of Markov Chains to Analyze and Predict the Time Series.” Modern Applied Science, 4 (5): 162-166.
  • Metghalchi, M., Chang, Y. H. ve Du, J. 2011. “Technical Trading Rules for NASDAQ Composite Index.” International Research Journal of Finance and Economics, 73: 109-121.
  • Olaniyi, S. A. S., Adewole, K. S. ve Jimoh, R. G. 2011. “Stock Trend Prediction Using Regression Analysis- A Data Mining Approach.” Journal of Systems and Software, 1 (4): 154-157.
  • Ou, P. ve Wang, H. 2009. “Prediction of Stock Market Index Movement by Ten Data Mining Techniques.” Modern Applied Science, 12 (3): 28-42.
  • Öz, E. ve Erpolat, S. 2011. “An Application of Multivariate Markov Chain Model on the Changes in Exchange Rates: Turkey Case.” European Journal of Social Sciences, 18 (4): 542-552.
  • Özdemir, A. Y. ve Gümüşoğlu, Ş. 2007. “İşletmelerin Tahminleme Sorunlarının Çözümlenmesinde Markov Zincirleri Analizinin Uygulanması.” Dokuz Eylül Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 9 (1): 337-359.
  • Qian, B. ve Rasheed, K. 2007. “Stock Market Prediction with Multiple Classifiers.” Applied Intelligence, 26 (1): 25-33. Sagi, D. J. B., Blanco, P. F., Hidalgo, J. I. ve Domingo, F. J. S. 2012. “A Parallel Evolutionary Algorithm for Technical Market Indicators Optimization.” Natural Computing, September: 1-13.
  • Sapena, O., Botti, V. ve Argente, E. 2003. “Application of Neural Networks to Stock Prediction in “Pool” Companies.” Applied Artificial Intelligence, 17: 661-673.
  • Schmidt, B. Anatoly. 2005. Quantitative Finance for Physicists: An Introduction. California: Elsevier Academic Press. Sehgal, S. ve Gupta, M. 2007. “Tests of Technical Analysis in India.” The Journal of Business Perspective, 11 (3): 11
  • Seng, D. ve Hancock, J. R. 2012. “Fundamental Analysis and the Prediction of Earnings.” International Journal of Business and Management, 7 (3): 32-46.
  • Stewart, J. William. 2009. Probability, Markov Chains, Queues and Simulation: The Mathematical Basis of Performance Modeling. New Jersey: Princeton University Press.
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  • Tektaş, A. ve Karataş, A. 2004. “Yapay Sinir Ağları ve Finans Alanına Uygulanması: Hisse Senedi Fiyat Tahminlemesi.” Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 3 (4): 337-349.
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  • Yu, H., Nartea, G. V., Gan, C. ve Yao, L. J. 2013. “Predictive Ability and Profitability of Simple Technical Trading Rules: Recent Evidence from Southeast Asian Stock Markets.” International Review of Economics and Finance, 25: 356-371. EK A . Akbank Hisse Senedine Ait Kapanış Fiyatları ve Geçiş Durumları Tarih Fiyat Durum Tarih Fiyat Durum Tarih Fiyat Durum Tarih Fiyat Durum Tarih Fiyat Durum 2011 6,02 03.2012 7,02 -1 06.2012 6,36 1 09.2012 6,96 -1 12.2012 8,84 1 002012 6,04 1 03.2012 6,96 -1 06.2012 6,34 -1 09.2012 6,98 1 12.2012 8,76 -1 002012 6,26 1 03.2012 6,90 -1 06.2012 6,16 -1 09.2012 7,16 1 12.2012 8,70 -1 002012 6,02 -1 03.2012 6,98 1 06.2012 6,30 1 09.2012 7,06 -1 12.2012 8,68 -1 002012 5,92 -1 03.2012 7,00 1 06.2012 6,44 1 09.2012 7,18 1 12.2012 8,76 1 002012 5,76 -1 004.2012 7,06 1 06.2012 6,40 -1 09.2012 7,06 -1 12.2012 8,78 1 002012 5,66 -1 004.2012 7,10 1 06.2012 6,62 1 09.2012 7,10 1 12.2012 8,84 1 02012 5,84 1 004.2012 6,90 -1 007.2012 6,52 -1 09.2012 7,10 0 12.2012 8,84 02012 5,96 1 004.2012 6,90 0 007.2012 6,68 1 010.2012 7,20 1 12.2012 8,88 1 02012 6,10 1 004.2012 6,90 0 007.2012 6,76 1 010.2012 7,50 1 12.2012 8,80 -1 02012 5,96 -1 004.2012 6,86 -1 007.2012 6,58 -1 010.2012 7,38 -1 001.2013 8,96 1 02012 6,22 1 04.2012 6,80 -1 007.2012 6,72 1 010.2012 7,54 1 02012 6,38 1 04.2012 6,86 1 007.2012 6,70 -1 010.2012 7,72 1 02012 6,50 1 04.2012 6,86 0 07.2012 6,76 1 010.2012 7,88 1 02012 6,60 1 04.2012 6,82 -1 07.2012 6,74 -1 010.2012 7,82 -1 02012 6,74 1 04.2012 6,82 0 07.2012 6,60 -1 10.2012 7,86 1 02012 6,64 -1 04.2012 6,84 1 07.2012 6,66 1 10.2012 8,16 1 02012 6,52 -1 04.2012 6,74 -1 07.2012 6,72 1 10.2012 8,16 02012 6,50 -1 04.2012 6,64 -1 07.2012 6,70 -1 10.2012 8,20 1 02012 6,94 1 04.2012 6,66 1 07.2012 6,68 -1 10.2012 8,28 1 02012 6,90 -1 04.2012 6,56 -1 07.2012 6,62 -1 10.2012 8,38 1 02012 7,00 1 04.2012 6,72 1 07.2012 6,52 -1 10.2012 8,26 -1 02012 6,68 -1 04.2012 6,76 1 07.2012 6,38 -1 10.2012 8,34 1 002012 7,08 1 04.2012 6,70 -1 07.2012 6,44 1 10.2012 8,40 1 002012 7,30 1 04.2012 6,52 -1 07.2012 6,50 1 10.2012 8,32 -1 002012 7,24 -1 005.2012 6,50 -1 07.2012 6,72 1 10.2012 8,32 002012 7,26 1 005.2012 6,60 1 07.2012 6,78 1 10.2012 8,44 1 002012 7,22 -1 005.2012 6,54 -1 07.2012 6,82 1 10.2012 8,64 1 002012 7,30 1 005.2012 6,52 -1 07.2012 6,76 -1 011.2012 8,68 1 002012 7,04 -1 005.2012 6,50 -1 008.2012 6,78 1 011.2012 8,54 -1 02012 6,82 -1 005.2012 6,40 -1 008.2012 6,68 -1 011.2012 8,78 1 02012 6,98 1 05.2012 6,44 1 008.2012 6,82 1 011.2012 8,58 -1 02012 6,88 -1 05.2012 6,38 -1 008.2012 6,84 1 011.2012 8,48 -1 02012 6,88 0 05.2012 6,30 -1 008.2012 6,78 -1 011.2012 8,72 1 02012 7,02 1 05.2012 6,26 -1 008.2012 6,76 -1 011.2012 8,56 -1 02012 7,10 1 05.2012 6,40 1 008.2012 6,72 -1 11.2012 8,32 -1 02012 7,10 0 05.2012 6,18 -1 08.2012 6,78 1 11.2012 8,42 1 02012 7,08 -1 05.2012 6,22 1 08.2012 6,80 1 11.2012 8,36 -1 02012 6,98 -1 05.2012 6,10 -1 08.2012 6,72 -1 11.2012 8,40 1 02012 6,70 -1 05.2012 6,08 -1 08.2012 6,82 1 11.2012 8,28 -1 02012 6,78 1 05.2012 5,84 -1 08.2012 6,94 1 11.2012 8,14 -1 02012 6,62 -1 05.2012 5,80 -1 08.2012 7,10 1 11.2012 8,02 -1 02012 6,82 1 05.2012 5,44 -1 08.2012 7,16 1 11.2012 7,90 -1 02012 7,02 1 05.2012 5,32 -1 08.2012 7,10 -1 11.2012 7,86 -1 002012 7,02 0 05.2012 5,50 1 08.2012 7,08 -1 11.2012 8,04 1 002012 7,06 1 05.2012 5,42 -1 08.2012 7,08 0 11.2012 8,16 1 002012 6,86 -1 05.2012 5,70 1 08.2012 7,10 1 11.2012 8,22 1 002012 6,80 -1 006.2012 5,80 1 08.2012 7,10 0 11.2012 8,30 1 002012 6,82 1 006.2012 5,70 -1 08.2012 7,18 1 11.2012 8,44 1 002012 6,86 1 006.2012 5,56 -1 009.2012 7,14 -1 11.2012 8,38 -1 002012 6,86 0 006.2012 5,68 1 009.2012 7,10 -1 012.2012 8,50 1 02012 6,84 -1 006.2012 5,80 1 009.2012 7,20 1 012.2012 8,70 1 02012 6,78 -1 006.2012 5,98 1 009.2012 7,24 1 012.2012 8,74 1 02012 6,92 1 06.2012 6,04 1 009.2012 7,22 -1 012.2012 8,70 -1 02012 7,08 1 06.2012 6,12 1 09.2012 7,24 1 012.2012 8,72 1 02012 7,38 1 06.2012 6,06 -1 09.2012 7,22 -1 12.2012 8,78 1 02012 7,42 1 06.2012 6,08 1 09.2012 7,24 1 12.2012 8,78 02012 7,40 -1 06.2012 6,12 1 09.2012 7,14 -1 12.2012 8,80 1 02012 7,40 0 06.2012 6,04 -1 09.2012 7,22 1 12.2012 8,84 1 02012 7,40 0 06.2012 6,18 1 09.2012 7,18 -1 12.2012 8,70 -1 02012 7,26 -1 06.2012 6,20 1 09.2012 7,04 -1 12.2012 8,80 1 EK B . Analiz Kapsamında İncelenen Hisse Senetlerine Ait Geçiş Olasılıkları Matrisi ve Tahmin Sonuçları. Akbank Albaraka Türk Tarih Fiyat Durum Tarih Fiyat Durum 2012 8,80 -1 2012 1,70 -1 1 -1 1 47,62% 7,14% 45,24% 58,82% 11,76% 29,41% -1 50,46% 5,50% 44,04% 1 -1 1 49,61% 3,88% 46,51% 75,00% 6,25% 18,75% -1 49,53% 8,41% 42,06%

Hisse Senedi Fiyat Hareketlerinin Tahmin Edilmesinde Markov Zincirlerinin Kullanılması: İMKB 10 Bankacılık Endeksi İşletmeleri Üzerine Ampirik Bir Çalışma

Year 2014, Volume: 9 Issue: 35, 6158 - 6198, 01.06.2014

Kenan Ilarslan

https://doi.org/10.19168/jyu.32384
Cited By: 12

Abstract

Bu çalışmanın amacı Markov zincirleri ile gelecekteki hisse senedi fiyat hareketlerinin tahmin edilmesidir. Araştırmada İMKB 10 Bankacılık endeksine dahil on adet hisse senedinin 02.01.2012-31.12.2012 dönemine ait günlük kapanış fiyatları kullanılmıştır. Yapılan analiz sonucunda on adet hisse senedinden dokuzunun fiyat hareketi başarılı bir şekilde tahmin edilmiştir. Bu çerçevede gelecekteki hisse senedi fiyat hareketlerinin tahmininde Markov zincirleri yönteminin kullanılmasının başarılı olduğunu söyleyebiliriz.

Keywords

Hisse Senedi Fiyat Hareketleri Tahmin Markov Zincirleri

References

  • Alpan, F., Tevfik, G. ve Tevfik, A. T. 2000. Excel ile Finans . İstanbul: Literatür Yayınları.
  • Atmeh, M. A ve Dobbs, I. M. 2006. “Technical Analysis and the Stochastic Properties of the Jordanian Stock Market Index Return.” Studies in Economics and Finance, 23 (2): 119-140.
  • Aygören, Hakan. 2006. “ İmkb-30 Endeks Davranışının Monte Carlo Simülasyonu ile İncelenmesi.” Muhasebe ve Finansman Dergisi, 29: 197-205.
  • Ayodele, A., Ayo, C. K., Adebiyi, M. O. ve Otokiti, S. O. 2012. “Stock Price Prediction Using Neural Network with Hybridized Market Indicators.” Journal of Emerging Trends in Computing and Information Sciences, 3 (1): 1
  • Başoğlu, U., Ceylan, A. ve Parasız, İ. 2009. Finans: Teori, Kurum, Uygulama. Bursa: Ekin Basım Yayın Dağıtım.
  • Can, T. ve Öz, E. 2009. “ Saklı Markov Modelleri Kullanılarak Türkiye’de Dolar Kurundaki Değişimin Tahmin Edilmesi.” İstanbul Üniversitesi İşletme Fakültesi Dergisi. 38 (1): 1-23.
  • Charitou, A. ve Panagiotides, G. 1999. “Financial Analysis, Future Earnings and Cash Flows, and the Prediction of Stock Returns: Evidence for the UK.” Accounting and Business Research, 29 (4): 281-298.
  • Chin, L. ve Hong, L. W. 2008. “Can Financial Ratios Predict The Malaysian Stock Return?.” Integration & Dissemination, (2): 7-8.
  • Deaves, R., Miu, P. ve White, C. B. 2008. “Canadian Stock Market Multiples and Their Predictive Content.” International Review of Economics and Finance, 17: 457-466.
  • Doubleday, K. J. ve Esunge, J. N. 2011. “Application of Markov Chains to Stock Trends.” Journal of Mathematics and Statistics, 7 (2): 103-106.
  • Dutta, A., Bandopadhyay, G. ve Sengupta, S. 2012. “Prediction of Stock Performance in the Indian Stock Market Using Logistic Regression.” International Journal of Business and Information, 7 (1): 105-136.
  • Düzakın, Erkut. 2005. İşletme Yöneticileri için Excel ile Sayısal Karar Verme Teknikleri. İstanbul: Kare Yayınları.
  • Elleuch, J. ve Trabelsi, L. 2009. “Fundamental Analysis Strategy and the Prediction of Stock Returns.” International Research Journal of Finance and Economics, (30): 95-107.
  • Eom, C., Oh, G. ve Woo, S. J. 2008. “Relationship Between Efficiency and Predictability in Stock Price Change.” Physica A, 387: 5511-5517.
  • Ergeç, Funda. 1996. “ Markov Analizi ile Hisse Senedi Fiyatının Tahmin Edilmesi.” İstanbul Üniversitesi İşletme Fakültesi Dergisi. 25 (2): 123-151.
  • Ferson, Wayne. 2007. Market Efficiency and Forecasting. S. Satchell, (Ed.), Foreasting Expected Returns in the Financial Markets içinde (1-16). Oxford: Elsevier.
  • Ford, J. L., Pok, W. C. ve Poshakwale, S. 2012. “The Return Predictability and Market Efficiency of the KLSE CI Stock Index Futures Market.” Journal of Emerging Market Finance, 11 (1): 37-60.
  • Gupta, C. B. 1992. Contemporary Management. New Delhi: Ashish Publishing.
  • Gupta, M. P. ve Khanna, R. B. 2009. Quantitative Techniques for Decision Making. New Delhi: PHI Learning.
  • Hillier, F. S. ve Lieberman, G. J. 2001. Introduction to Operations Research. New York: McGraw-Hill Publishing.
  • Hsu, Chih Ming. 2011. “A Hybrid Procedure with Feature Selection for Resolving Stock/Futures Price Forecasting Problems.” Neural Computing and Applications, 1-21.
  • Idolor, Eseoghene Joseph. 2010. “Security Prices as Markov Processes.” International Research Journal of Finance and Economics, 59: 62-76.
  • Jarrett, J. E. ve Schilling, J. 2008. “Daily Variation and Predicting Stock Market Returns for the Frakfurter Börse (Stock Market).” Journal of Business Economics and Management, 9 (3): 189-198.
  • Jasemi, M. ve Kimiagari, A. 2012. “An Investigation of Model Selection Criteria for Technical Analysis of Moving Average.” Journal of Industrial Engineering International, 8 (5): 1-9.
  • Jiang, X ve Lee, B. S. 2012. “Do Decomposed Financial Ratios Predict Stock Returns and Fundamentals Better?” The Financial Review, (47): 531-564.
  • Kara, Y., Boyacıoğlu, M. A. ve Baykan, Ö. K. 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.
  • Khan, Z. H., Alin, T. S. ve Hussain, M. A. 2011. “Price Prediction of Share Market Using Artificial Neural Network (ANN).” International Journal of Computer Applications, 22 (2): 42-47.
  • Kheradyar, S. ve Ibrahim, I. 2011. “Financial Ratios as Empiricial Predictors of Stock Return.” International Proceedings of Economics Development and Research, 10: 318-322.
  • Konuralp, Gürel. 2005. Sermaye Piyasaları: Analizler, Kuramlar ve Portföy Yönetimi. İstanbul: Alfa Basım Yayım Dağıtım.
  • Lange, Kenneth. 2010. Applied Probability. New York: Springer Publishing.
  • Leung, M. T., Daouk, H. ve Chen, A. S. 2000. “Forecasting Stock Indices: A Comparison of Classification and Level Estimation Models.” International Journal of Forecasting, 16: 173-190.
  • Levy, H. ve Post, T. 2005. Investments. Essex: Pearson Education.
  • Lewellen, Jonathan. 2004. “Predicting Returns with Financial Ratios.” Journal of Financial Economics, 74: 209-235.
  • Liu, Tie. 2010. “Application of Markov Chains to Analyze and Predict the Time Series.” Modern Applied Science, 4 (5): 162-166.
  • Metghalchi, M., Chang, Y. H. ve Du, J. 2011. “Technical Trading Rules for NASDAQ Composite Index.” International Research Journal of Finance and Economics, 73: 109-121.
  • Olaniyi, S. A. S., Adewole, K. S. ve Jimoh, R. G. 2011. “Stock Trend Prediction Using Regression Analysis- A Data Mining Approach.” Journal of Systems and Software, 1 (4): 154-157.
  • Ou, P. ve Wang, H. 2009. “Prediction of Stock Market Index Movement by Ten Data Mining Techniques.” Modern Applied Science, 12 (3): 28-42.
  • Öz, E. ve Erpolat, S. 2011. “An Application of Multivariate Markov Chain Model on the Changes in Exchange Rates: Turkey Case.” European Journal of Social Sciences, 18 (4): 542-552.
  • Özdemir, A. Y. ve Gümüşoğlu, Ş. 2007. “İşletmelerin Tahminleme Sorunlarının Çözümlenmesinde Markov Zincirleri Analizinin Uygulanması.” Dokuz Eylül Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 9 (1): 337-359.
  • Qian, B. ve Rasheed, K. 2007. “Stock Market Prediction with Multiple Classifiers.” Applied Intelligence, 26 (1): 25-33. Sagi, D. J. B., Blanco, P. F., Hidalgo, J. I. ve Domingo, F. J. S. 2012. “A Parallel Evolutionary Algorithm for Technical Market Indicators Optimization.” Natural Computing, September: 1-13.
  • Sapena, O., Botti, V. ve Argente, E. 2003. “Application of Neural Networks to Stock Prediction in “Pool” Companies.” Applied Artificial Intelligence, 17: 661-673.
  • Schmidt, B. Anatoly. 2005. Quantitative Finance for Physicists: An Introduction. California: Elsevier Academic Press. Sehgal, S. ve Gupta, M. 2007. “Tests of Technical Analysis in India.” The Journal of Business Perspective, 11 (3): 11
  • Seng, D. ve Hancock, J. R. 2012. “Fundamental Analysis and the Prediction of Earnings.” International Journal of Business and Management, 7 (3): 32-46.
  • Stewart, J. William. 2009. Probability, Markov Chains, Queues and Simulation: The Mathematical Basis of Performance Modeling. New Jersey: Princeton University Press.
  • Taha, A. Hamdy. 2007. Operations Research: An Introduction. New Jersey: Pearson Prentice Hall.
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  • Tektaş, A. ve Karataş, A. 2004. “Yapay Sinir Ağları ve Finans Alanına Uygulanması: Hisse Senedi Fiyat Tahminlemesi.” Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 3 (4): 337-349.
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There are 54 citations in total.

Details

Primary Language Turkish
Journal Section Articles
Authors

Kenan Ilarslan This is me

Publication Date June 1, 2014
Published in Issue Year 2014 Volume: 9 Issue: 35

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APA Ilarslan, K. (2014). Hisse Senedi Fiyat Hareketlerinin Tahmin Edilmesinde Markov Zincirlerinin Kullanılması: İMKB 10 Bankacılık Endeksi İşletmeleri Üzerine Ampirik Bir Çalışma. Yaşar Üniversitesi E-Dergisi, 9(35), 6158-6198. https://doi.org/10.19168/jyu.32384
AMA Ilarslan K. Hisse Senedi Fiyat Hareketlerinin Tahmin Edilmesinde Markov Zincirlerinin Kullanılması: İMKB 10 Bankacılık Endeksi İşletmeleri Üzerine Ampirik Bir Çalışma. Yaşar Üniversitesi E-Dergisi. June 2014;9(35):6158-6198. doi:10.19168/jyu.32384
Chicago Ilarslan, Kenan. “Hisse Senedi Fiyat Hareketlerinin Tahmin Edilmesinde Markov Zincirlerinin Kullanılması: İMKB 10 Bankacılık Endeksi İşletmeleri Üzerine Ampirik Bir Çalışma”. Yaşar Üniversitesi E-Dergisi 9, no. 35 (June 2014): 6158-98. https://doi.org/10.19168/jyu.32384.
EndNote Ilarslan K (June 1, 2014) Hisse Senedi Fiyat Hareketlerinin Tahmin Edilmesinde Markov Zincirlerinin Kullanılması: İMKB 10 Bankacılık Endeksi İşletmeleri Üzerine Ampirik Bir Çalışma. Yaşar Üniversitesi E-Dergisi 9 35 6158–6198.
IEEE K. Ilarslan, “Hisse Senedi Fiyat Hareketlerinin Tahmin Edilmesinde Markov Zincirlerinin Kullanılması: İMKB 10 Bankacılık Endeksi İşletmeleri Üzerine Ampirik Bir Çalışma”, Yaşar Üniversitesi E-Dergisi, vol. 9, no. 35, pp. 6158–6198, 2014, doi: 10.19168/jyu.32384.
ISNAD Ilarslan, Kenan. “Hisse Senedi Fiyat Hareketlerinin Tahmin Edilmesinde Markov Zincirlerinin Kullanılması: İMKB 10 Bankacılık Endeksi İşletmeleri Üzerine Ampirik Bir Çalışma”. Yaşar Üniversitesi E-Dergisi 9/35 (June 2014), 6158-6198. https://doi.org/10.19168/jyu.32384.
JAMA Ilarslan K. Hisse Senedi Fiyat Hareketlerinin Tahmin Edilmesinde Markov Zincirlerinin Kullanılması: İMKB 10 Bankacılık Endeksi İşletmeleri Üzerine Ampirik Bir Çalışma. Yaşar Üniversitesi E-Dergisi. 2014;9:6158–6198.
MLA Ilarslan, Kenan. “Hisse Senedi Fiyat Hareketlerinin Tahmin Edilmesinde Markov Zincirlerinin Kullanılması: İMKB 10 Bankacılık Endeksi İşletmeleri Üzerine Ampirik Bir Çalışma”. Yaşar Üniversitesi E-Dergisi, vol. 9, no. 35, 2014, pp. 6158-9, doi:10.19168/jyu.32384.
Vancouver Ilarslan K. Hisse Senedi Fiyat Hareketlerinin Tahmin Edilmesinde Markov Zincirlerinin Kullanılması: İMKB 10 Bankacılık Endeksi İşletmeleri Üzerine Ampirik Bir Çalışma. Yaşar Üniversitesi E-Dergisi. 2014;9(35):6158-9.

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