Araştırma Makalesi
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BIST katılım endeksi için bir Markov zinciri analizi

Yıl 2019, Cilt: 21 Sayı: 1, 1 - 8, 15.03.2019
https://doi.org/10.25092/baunfbed.433310

Öz

Bu çalışma bir Markov zinciri (MZ) modeli ile Borsa İstanbul (BIST)’da yer alan Katılım Endekslerinin (KATLM, KAT50) hareketlerini tahmin etmeyi amaçlar. KAT50 Endeksi 9 Temmuz 2014 tarihinden bu yana işlem görmeye başladığından dolayı, literatürde bu endeks üzerinde yapılmış yeterince çalışma yoktur. Bu çalışmada ilk olarak, KATLM endeksinin 520 günlük (01.07.2014-29.07.2016), KAT50 endeksinin ise 514 günlük (09.07.2014-29.07.2016) kapanış değerleri göz önüne alınarak bir MZ modeli oluşturuldu. Bu modelde endekslerin artış, azalış ve sabit kalma durumları dikkate alındı. Endekslerin gelecekteki değerlerine ilişkin bir MZ analizi yapmak için geçiş olasılıkları matrisi oluşturuldu. Bu matristen yararlanılarak, kararlı durum analizi yapıldı ve KATLM-KAT30 endekslerinin gelecekteki hareketleri başarılı bir şekilde öngörüldü. Bu çalışmanın sonuçlarının, bireysel ve kurumsal yatırımcıların küresel ekonomilerdeki yatırım kararları için çok yararlı olduğu sonucuna varılabilir.

Kaynakça

  • Ertuğrul, Ö.F. and Tağluk, M.E., Forecasting financial indicators by generalized behavioral learning method, Soft Computing, 1-14, (2017).
  • Eraker, B., Mcmc analysis of diffusion models with application to finance, Journal of Business & Economic Statistics, 19(2), 177-191, (2001).
  • Maskawa, J.-I., Multivariate Markov chain modeling for stock markets, Physica A: Statistical Mechanics and its Applications, 324(1), 317-322, (2003).
  • Lu, S.-L. and Lee, K.-J., Measurement and comparison of credit risk by a Markov chain: An empirical investigation of bank loans in Taiwan, International Research Journal of Finance and Economics, 30, 108-131, (2009).
  • Wang, Y.-F., Cheng S. and Hsu, M.-H., Incorporating the Markov chain concept into fuzzy stochastic prediction of stock indexes, Applied Soft Computing, 10(2), 613-617, (2010).
  • Öz, E. and Erpolat, S., Çok değişkenli Markov zinciri modeli ve bir uygulama, İktisadi ve İdari Bilimler Dergisi, 29(2), 577-590, (2010).
  • Vasanthi, S., Subha M. and Nambi, S.T., An empirical study on stock index trend prediction using Markov chain analysis, Journal of Banking Financial Services and Insurance Research, 1(1), 72-91, (2011).
  • Ortobelli, L.S., Angelelli E. and Bianchi A., Financial applications of bivariate Markov processes, Mathematical Problems in Engineering, 2011, Article ID 347604, (2011).
  • Svoboda, M. and Lukas, L., Application of Markov chain analysis to trend prediction of stock indices, Proceedings of 30th International Conference Mathematical Methods in Economics. Karviná: Silesian University, School of Business Administration, 848-853, (2012).
  • Yavuz, M., Sakarya Ş. and Özdemir, N., Yapay sinir ağları ile risk-getiri tahmini ve portföy analizi, Niğde Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 8(4), 87-107, (2015).
  • Sakarya, S., Yavuz, M. Karaoglan A.D. and Özdemir, N., Stock market index prediction with neural network during financial crises: A review on Bist-100, Financial Risk and Management Reviews, 1(2), 53-67, (2015).
  • İlarslan, K., Hisse senedi fiyat hareketlerinin tahmin edilmesinde Markov zincirlerinin kullanılması: İmkb 10 bankacılık endeksi işletmeleri üzerine ampirik bir çalışma, Journal of Yaşar University, 9(35), 6158-6198, (2014).
  • Duran, A. and Caginalp, G., Data mining for overreaction in financial markets, IASTED International Conference on Software Engineering and Applications, Phoenix, Arizona, 28-35, (2005).
  • Akkaya, G.C., Demireli E. and Yakut, Ü.H., İşletmelerde finansal başarısızlık tahminlemesi: Yapay sinir ağları modeli ile İmkb üzerine bir uygulama, Eskişehir Osmangazi Üniversitesi Sosyal Bilimler Dergisi, 10(2), 187-216, (2009).
  • Serfozo, R., Basics of Applied Stochastic Processes, Springer Science & Business Media, 2009.
  • Ross, S.M., Introduction to Probability Models, Academic press, 2014.
  • Soloviev, V., Saptsin V. and Chabanenko, D., Markov chains application to the financial-economic time series prediction, arXiv preprint arXiv:1111.5254, (2011).
  • Idolor, E.J., Security prices as Markov processes, International Research Journal of Finance and Economics, 59(1), 62-76, (2010).
  • Cheng, C.-J., Chiu, S. Cheng C.-B. and Wu, J.-Y., Customer lifetime value prediction by a Markov chain based data mining model: Application to an auto repair and maintenance company in Taiwan, Scientia Iranica, 19, 3, 849-855, (2012).
  • Sattar, A.M., Ertuğrul, Ö.F. Gharabaghi, B. McBean E. and Cao, J., Extreme learning machine model for water network management, Neural Computing and Applications, 1-13, (2017).

A Markov chain analysis for BIST participation index

Yıl 2019, Cilt: 21 Sayı: 1, 1 - 8, 15.03.2019
https://doi.org/10.25092/baunfbed.433310

Öz

This study addresses the trend estimation of the participation indices (PARTI) in the Istanbul Stock Exchange (BIST) using Markov chain (MC) theory. PARTI can be regarded as the Participation 50 Index (KAT50) and the Participation 30 Index (KATLM). Since KAT50 has only been calculated since 9th July 2014, there are only a few studies on this index. Therefore, in this study, we examine the PARTI indices. Firstly, we have employed MC method using 520 daily closing values of KATLM, between 1st July 2014 and 29th July 2016. For the KAT50 index, we used 514 daily closing values between 9th July 2014 and 29th July 2016, considering the states of these indices as increasing, decreasing or remaining stable. In order to perform a Markov chain analysis relating to prediction of the future situation, a transition probability matrix was created. Using this matrix, a steady-state analysis of the chain was performed and the future trends of KAT50-KATLM were forecasted successfully. It can be concluded that the results of this study are very helpful for individual and institutional investors’ investment decisions within global economies.

Kaynakça

  • Ertuğrul, Ö.F. and Tağluk, M.E., Forecasting financial indicators by generalized behavioral learning method, Soft Computing, 1-14, (2017).
  • Eraker, B., Mcmc analysis of diffusion models with application to finance, Journal of Business & Economic Statistics, 19(2), 177-191, (2001).
  • Maskawa, J.-I., Multivariate Markov chain modeling for stock markets, Physica A: Statistical Mechanics and its Applications, 324(1), 317-322, (2003).
  • Lu, S.-L. and Lee, K.-J., Measurement and comparison of credit risk by a Markov chain: An empirical investigation of bank loans in Taiwan, International Research Journal of Finance and Economics, 30, 108-131, (2009).
  • Wang, Y.-F., Cheng S. and Hsu, M.-H., Incorporating the Markov chain concept into fuzzy stochastic prediction of stock indexes, Applied Soft Computing, 10(2), 613-617, (2010).
  • Öz, E. and Erpolat, S., Çok değişkenli Markov zinciri modeli ve bir uygulama, İktisadi ve İdari Bilimler Dergisi, 29(2), 577-590, (2010).
  • Vasanthi, S., Subha M. and Nambi, S.T., An empirical study on stock index trend prediction using Markov chain analysis, Journal of Banking Financial Services and Insurance Research, 1(1), 72-91, (2011).
  • Ortobelli, L.S., Angelelli E. and Bianchi A., Financial applications of bivariate Markov processes, Mathematical Problems in Engineering, 2011, Article ID 347604, (2011).
  • Svoboda, M. and Lukas, L., Application of Markov chain analysis to trend prediction of stock indices, Proceedings of 30th International Conference Mathematical Methods in Economics. Karviná: Silesian University, School of Business Administration, 848-853, (2012).
  • Yavuz, M., Sakarya Ş. and Özdemir, N., Yapay sinir ağları ile risk-getiri tahmini ve portföy analizi, Niğde Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 8(4), 87-107, (2015).
  • Sakarya, S., Yavuz, M. Karaoglan A.D. and Özdemir, N., Stock market index prediction with neural network during financial crises: A review on Bist-100, Financial Risk and Management Reviews, 1(2), 53-67, (2015).
  • İlarslan, K., Hisse senedi fiyat hareketlerinin tahmin edilmesinde Markov zincirlerinin kullanılması: İmkb 10 bankacılık endeksi işletmeleri üzerine ampirik bir çalışma, Journal of Yaşar University, 9(35), 6158-6198, (2014).
  • Duran, A. and Caginalp, G., Data mining for overreaction in financial markets, IASTED International Conference on Software Engineering and Applications, Phoenix, Arizona, 28-35, (2005).
  • Akkaya, G.C., Demireli E. and Yakut, Ü.H., İşletmelerde finansal başarısızlık tahminlemesi: Yapay sinir ağları modeli ile İmkb üzerine bir uygulama, Eskişehir Osmangazi Üniversitesi Sosyal Bilimler Dergisi, 10(2), 187-216, (2009).
  • Serfozo, R., Basics of Applied Stochastic Processes, Springer Science & Business Media, 2009.
  • Ross, S.M., Introduction to Probability Models, Academic press, 2014.
  • Soloviev, V., Saptsin V. and Chabanenko, D., Markov chains application to the financial-economic time series prediction, arXiv preprint arXiv:1111.5254, (2011).
  • Idolor, E.J., Security prices as Markov processes, International Research Journal of Finance and Economics, 59(1), 62-76, (2010).
  • Cheng, C.-J., Chiu, S. Cheng C.-B. and Wu, J.-Y., Customer lifetime value prediction by a Markov chain based data mining model: Application to an auto repair and maintenance company in Taiwan, Scientia Iranica, 19, 3, 849-855, (2012).
  • Sattar, A.M., Ertuğrul, Ö.F. Gharabaghi, B. McBean E. and Cao, J., Extreme learning machine model for water network management, Neural Computing and Applications, 1-13, (2017).
Toplam 20 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Araştırma Makalesi
Yazarlar

Mehmet Yavuz

Yayımlanma Tarihi 15 Mart 2019
Gönderilme Tarihi 24 Mart 2018
Yayımlandığı Sayı Yıl 2019 Cilt: 21 Sayı: 1

Kaynak Göster

APA Yavuz, M. (2019). A Markov chain analysis for BIST participation index. Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 21(1), 1-8. https://doi.org/10.25092/baunfbed.433310
AMA Yavuz M. A Markov chain analysis for BIST participation index. BAUN Fen. Bil. Enst. Dergisi. Mart 2019;21(1):1-8. doi:10.25092/baunfbed.433310
Chicago Yavuz, Mehmet. “A Markov Chain Analysis for BIST Participation Index”. Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi 21, sy. 1 (Mart 2019): 1-8. https://doi.org/10.25092/baunfbed.433310.
EndNote Yavuz M (01 Mart 2019) A Markov chain analysis for BIST participation index. Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi 21 1 1–8.
IEEE M. Yavuz, “A Markov chain analysis for BIST participation index”, BAUN Fen. Bil. Enst. Dergisi, c. 21, sy. 1, ss. 1–8, 2019, doi: 10.25092/baunfbed.433310.
ISNAD Yavuz, Mehmet. “A Markov Chain Analysis for BIST Participation Index”. Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi 21/1 (Mart 2019), 1-8. https://doi.org/10.25092/baunfbed.433310.
JAMA Yavuz M. A Markov chain analysis for BIST participation index. BAUN Fen. Bil. Enst. Dergisi. 2019;21:1–8.
MLA Yavuz, Mehmet. “A Markov Chain Analysis for BIST Participation Index”. Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi, c. 21, sy. 1, 2019, ss. 1-8, doi:10.25092/baunfbed.433310.
Vancouver Yavuz M. A Markov chain analysis for BIST participation index. BAUN Fen. Bil. Enst. Dergisi. 2019;21(1):1-8.