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Türkiye’ de İşlem Gören Firmaların Sukuk Verileri ile Karşılaştırmalı Zaman Serisi Analizi

Year 2021, Volume: 25 Issue: 3, 1232 - 1248, 28.09.2021
https://doi.org/10.53487/ataunisosbil.644418

Abstract

İslami finans düzeni
içerisinde sukuk son zamanlarda hızlı bir gelişme gösteren yapı olarak
karşımıza çıkmaktadır. Müslüman ülkelerde ortaya çıkan ve tüm dünyada giderek
yaygınlaşan sukuk, bu alanda çalışmak isteyen finans uzmanlarına birçok yeni
çalışma alanı sunmaktadır. Genel olarak sukuk, İslamiyet’in ilk zamanlarında,
ticari ilişkilerin düzenlenmesi adına bir takım alışveriş usullerini kapsayan
finansal bir kavramdır. Ülkemizde sukuk ihracı yapan işletmelerden 5 tanesi
çalışmamızda ele alınmıştır. Çalışmada işlem gören bu şirketlerin fiyat
verileri kullanılarak, gelecekte olması düşünülen fiyat değerleri tahmin
edilmiştir. Bu tahmin kapsamında şirketler arası korelasyonlardan yola çıkarak
en iyi ilişkiye sahip 3 şirket belirlenerek, gelecekte olması muhtemel fiyat
değerleri tahmin edilmiştir. Çalışmada Bulanık Mantık yöntemi kullanılmıştır.
Tahminler istatistiksel başarı kriterleri olan hata kareleri ortalaması (HKO)
ve verimlilik katsayısı(VK) yardımıyla test edilmiştir. Bulanık Mantık
yöntemlerinin sukuk verilerinde kullanılması ile ilk olma özelliği taşıyan bu
çalışmada, Bulanık Mantık yönteminin finans problemleri için uygunluğu bir kez
daha kanıtlanmıştır.

References

  • Accounting and auditing organization for islamic financial institutions (AAOIFI), Accounting, Auditing and Governance Standards, 2010.
  • Ahmad, Nu. Ve Muda, M.(2013). Exchange rate pass-through estimates for sukuk issuing countries, Procedia Economics and Finance, ss. 134-139.
  • Altunkaynak, A. (2010). A predictive model for well loss using fuzzy logic approach, Hydrological Processes, 24(17), 2400-2404. doi:10.1002/hyp.7642.
  • Arundina, T., Omar , M.A. ve Kartiwi, M. (2015). The predictive accuracy of sukuk ratings; multinominal logistic and neural network inferences, Pacific-Basin Finance Journal 34, ss.273-292.
  • Cheng, H. C., Cheng, W. C. ve Wang W. W. (2008). Multi-attribute fuzzy time series method based on fuzzy clustering, Expert Systems with Applications 34 , ss. 1235-1242.
  • Chu, H. H., Chen, T. L., Cheng, C. H. ve Huang,C. C. (2009). Fuzzy dual-factor time-series for stock index forecasting, Expert Systems with Applications 36, ss. 165-171.
  • Hassan, M. R., Ramamohanarao, K., Kamruzzaman, J., Rahman, M.ve Hossain M. M. (2013). AHMM-bazed adaptive fuzzy inference system for stock market forecasting, Neurocomputing 104, ss.10-25.
  • Hazine Müsteşarlığı, Hazine Finansman Programı:2015 Yılı Gelişmeleri Ve 2016 Yılı Öngörüleri, https://www.hazine.gov.tr/tr-TR/Duyuru-Listesi-Sayfasi.(05.10.2017)
  • Huarng, K. ve Yu, H. K. (2005). A type 2 fuzzy time series model for stock indeex forecasting, Physica A 353, 19 March, ss. 445-462.
  • IIFM, Sukuk Report 1 st Edition, Bahreyn, 2010.
  • Jang, J. S. R. (1993). Anfis - adaptive-network-based fuzzy inference system, Ieee Transactions on Systems Man and Cybernetics, 23(3), 665-685.
  • Kim, K. ve Han, I. (2000). Genetic algorithms approachto feature discretization in artifical neural networks for the prediction of stock price index, Expert Systems with Applications 19, ss. 125-132.
  • Kim, K. (2003). Financial time series forecasting using support vector machines, Neurocomputing 55, 13 March, ss. 307-319.
  • Kimoto, T., Asakawa, K., Yoda, M. ve Takeoka, M. (1990). Stock market prediction system with moduar neural networks, IJCNN International Joint Conference on San Diego, CA, USA, ss. 1-6.
  • Kim, M. J., Han, I. ve Lee, C. (2004). Hybrid knowledge integration using the fuzzy genetic algorithm: prediction of the Korea Stock Price Index, Intellıgent Systems in Accounting, Finance and Management 12, Published online in Wiley Inter Science, ss. 43-60.
  • Liu, H.T., ve Wei, M.L. (2010). An improved fuzzy forecasting method for seasonal time series, Expert Systems with Applications 37, ss.6310- 6318.
  • Klein, P. O. ve Weill L. (2016). Why do companies issue sukuk?, Review of Financial Economics 31, ss.26-33.
  • Majhi, B. ve Anish C. M. (2015). Multiobjective optimization based adaptive models with fuzzy decision making for stock market forecasting, Neurocomputing 167, ss.502-511.
  • Mamdani, E. H. (1974). Application of fuzzy algorithms for control of simple dynamic plant. Electrical Engineers, Proceedings of the Institution of, 121(12), 1585-1588. doi:10.1049/piee.1974.0328.
  • Nagano, M. (2016). Who issues sukuk and when?: An analysis of the determinants of islamic bond issuance, Rewiew of Financial Economics 31, ss. 45-55.
  • Nan, G., Zhou, S., Kou, J. ve Lı, M. (2012). Heuristic biavariate forecasting model of multi- attribute fuzzy time series based on fuzzy clustering, International Journal of Information Technology & Decision Making, Vol. 11, No. 1, ss. 167- 195.
  • Shuaa Capital, Analysis of the Sukuk Market, 2007.
  • Smaouia, H. ve Nechi, S. (2017). Does sukuk market development spur economic growth?, Research in International Business and Finance 41, ss. 136-47.
  • Sun, B., Guo, H., Karimi, H. R., Ge, Y. ve Xiong, S (2015). Prediction of stock index futures prices based on fuzzy sets and multivariate fuzzy time series, Neurocomputing 151, ss.1528-1536.
  • Takagi, T., & Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics, SMC-15(1), 116-132.
  • Yiğiter Ş.Y., Sari S.S., Başakın E.E. (2017).Hisse senedi kapanış fiyatlarının yapay sinir ağları ve bulanık mantık çıkarım sistemleri ile tahmin edilmesi, kahramanmaraş sütçü imam üniversitesi iktisadi ve idari bilimler fakültesi dergisi, vol.7, pp.1-22
  • Yu, T. H. K. ve Huarng K. H. (2010). A neural network-based fuzzy time series model to improve forecasting, Expert Systems with Applications 37, ss. 3366-3372.
  • Wang, C. C. (2011). A comparison study between fuzzy time series model and ARIMA model for forecasting Taiwan Export, Expert Systems with Application 38, ss9296-9304.
  • Wei, L. Y. (2016). A Hybrid ANFIS model based on empirical mode decomposition for stock time series forecasting, Applied Soft Computing 42, ss. 368-376.
  • Wong, H. L., Tu, Y. H. ve Wang C. C. (2010). Application of fuzzy time series models for forecasting the amount of Taiwan Export, Expert Systems with Applications 37, ss.1465-1470.
  • Zarandi, M. H. F., Hadavandi, E. ve Turksen, I. B. (2012). A hybrid fuzzy intelligent agent-based system for stock price prediction, International Journal of Intelligent Systems, Volume 27, Issue 11, November, ss. 947-969.
  • Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353. doi:http://dx.doi.org/10.1016/S0019-9958(65)90241-X
Year 2021, Volume: 25 Issue: 3, 1232 - 1248, 28.09.2021
https://doi.org/10.53487/ataunisosbil.644418

Abstract

References

  • Accounting and auditing organization for islamic financial institutions (AAOIFI), Accounting, Auditing and Governance Standards, 2010.
  • Ahmad, Nu. Ve Muda, M.(2013). Exchange rate pass-through estimates for sukuk issuing countries, Procedia Economics and Finance, ss. 134-139.
  • Altunkaynak, A. (2010). A predictive model for well loss using fuzzy logic approach, Hydrological Processes, 24(17), 2400-2404. doi:10.1002/hyp.7642.
  • Arundina, T., Omar , M.A. ve Kartiwi, M. (2015). The predictive accuracy of sukuk ratings; multinominal logistic and neural network inferences, Pacific-Basin Finance Journal 34, ss.273-292.
  • Cheng, H. C., Cheng, W. C. ve Wang W. W. (2008). Multi-attribute fuzzy time series method based on fuzzy clustering, Expert Systems with Applications 34 , ss. 1235-1242.
  • Chu, H. H., Chen, T. L., Cheng, C. H. ve Huang,C. C. (2009). Fuzzy dual-factor time-series for stock index forecasting, Expert Systems with Applications 36, ss. 165-171.
  • Hassan, M. R., Ramamohanarao, K., Kamruzzaman, J., Rahman, M.ve Hossain M. M. (2013). AHMM-bazed adaptive fuzzy inference system for stock market forecasting, Neurocomputing 104, ss.10-25.
  • Hazine Müsteşarlığı, Hazine Finansman Programı:2015 Yılı Gelişmeleri Ve 2016 Yılı Öngörüleri, https://www.hazine.gov.tr/tr-TR/Duyuru-Listesi-Sayfasi.(05.10.2017)
  • Huarng, K. ve Yu, H. K. (2005). A type 2 fuzzy time series model for stock indeex forecasting, Physica A 353, 19 March, ss. 445-462.
  • IIFM, Sukuk Report 1 st Edition, Bahreyn, 2010.
  • Jang, J. S. R. (1993). Anfis - adaptive-network-based fuzzy inference system, Ieee Transactions on Systems Man and Cybernetics, 23(3), 665-685.
  • Kim, K. ve Han, I. (2000). Genetic algorithms approachto feature discretization in artifical neural networks for the prediction of stock price index, Expert Systems with Applications 19, ss. 125-132.
  • Kim, K. (2003). Financial time series forecasting using support vector machines, Neurocomputing 55, 13 March, ss. 307-319.
  • Kimoto, T., Asakawa, K., Yoda, M. ve Takeoka, M. (1990). Stock market prediction system with moduar neural networks, IJCNN International Joint Conference on San Diego, CA, USA, ss. 1-6.
  • Kim, M. J., Han, I. ve Lee, C. (2004). Hybrid knowledge integration using the fuzzy genetic algorithm: prediction of the Korea Stock Price Index, Intellıgent Systems in Accounting, Finance and Management 12, Published online in Wiley Inter Science, ss. 43-60.
  • Liu, H.T., ve Wei, M.L. (2010). An improved fuzzy forecasting method for seasonal time series, Expert Systems with Applications 37, ss.6310- 6318.
  • Klein, P. O. ve Weill L. (2016). Why do companies issue sukuk?, Review of Financial Economics 31, ss.26-33.
  • Majhi, B. ve Anish C. M. (2015). Multiobjective optimization based adaptive models with fuzzy decision making for stock market forecasting, Neurocomputing 167, ss.502-511.
  • Mamdani, E. H. (1974). Application of fuzzy algorithms for control of simple dynamic plant. Electrical Engineers, Proceedings of the Institution of, 121(12), 1585-1588. doi:10.1049/piee.1974.0328.
  • Nagano, M. (2016). Who issues sukuk and when?: An analysis of the determinants of islamic bond issuance, Rewiew of Financial Economics 31, ss. 45-55.
  • Nan, G., Zhou, S., Kou, J. ve Lı, M. (2012). Heuristic biavariate forecasting model of multi- attribute fuzzy time series based on fuzzy clustering, International Journal of Information Technology & Decision Making, Vol. 11, No. 1, ss. 167- 195.
  • Shuaa Capital, Analysis of the Sukuk Market, 2007.
  • Smaouia, H. ve Nechi, S. (2017). Does sukuk market development spur economic growth?, Research in International Business and Finance 41, ss. 136-47.
  • Sun, B., Guo, H., Karimi, H. R., Ge, Y. ve Xiong, S (2015). Prediction of stock index futures prices based on fuzzy sets and multivariate fuzzy time series, Neurocomputing 151, ss.1528-1536.
  • Takagi, T., & Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics, SMC-15(1), 116-132.
  • Yiğiter Ş.Y., Sari S.S., Başakın E.E. (2017).Hisse senedi kapanış fiyatlarının yapay sinir ağları ve bulanık mantık çıkarım sistemleri ile tahmin edilmesi, kahramanmaraş sütçü imam üniversitesi iktisadi ve idari bilimler fakültesi dergisi, vol.7, pp.1-22
  • Yu, T. H. K. ve Huarng K. H. (2010). A neural network-based fuzzy time series model to improve forecasting, Expert Systems with Applications 37, ss. 3366-3372.
  • Wang, C. C. (2011). A comparison study between fuzzy time series model and ARIMA model for forecasting Taiwan Export, Expert Systems with Application 38, ss9296-9304.
  • Wei, L. Y. (2016). A Hybrid ANFIS model based on empirical mode decomposition for stock time series forecasting, Applied Soft Computing 42, ss. 368-376.
  • Wong, H. L., Tu, Y. H. ve Wang C. C. (2010). Application of fuzzy time series models for forecasting the amount of Taiwan Export, Expert Systems with Applications 37, ss.1465-1470.
  • Zarandi, M. H. F., Hadavandi, E. ve Turksen, I. B. (2012). A hybrid fuzzy intelligent agent-based system for stock price prediction, International Journal of Intelligent Systems, Volume 27, Issue 11, November, ss. 947-969.
  • Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353. doi:http://dx.doi.org/10.1016/S0019-9958(65)90241-X
There are 32 citations in total.

Details

Primary Language Turkish
Journal Section Makaleler
Authors

Şule Yüksel Yiğiter 0000-0003-3230-5784

Salim Sercan Sarı This is me 0000-0003-2607-5249

Eyyup Ensar Başakın 0000-0002-9045-5302

Publication Date September 28, 2021
Published in Issue Year 2021 Volume: 25 Issue: 3

Cite

APA Yiğiter, Ş. Y., Sarı, S. S., & Başakın, E. E. (2021). Türkiye’ de İşlem Gören Firmaların Sukuk Verileri ile Karşılaştırmalı Zaman Serisi Analizi. Atatürk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 25(3), 1232-1248. https://doi.org/10.53487/ataunisosbil.644418

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