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Türkiye’de Hazine Sukuk Fiyatlarının Yapay Sinir Ağı Modeli ile Tahmini

Year 2021, Volume: 5 Issue: 2, 241 - 254, 30.12.2021

Abstract

Son yıllarda yapay sinir ağları, finansal zaman serilerinin tahmini, finansal başarısızlığın öngörülmesi ve derecelendirme notlarının sınıflandırılması gibi birçok alanda başarıyla uygulanmaktadır. Bununla birlikte, İslami sermaye piyasalarının en yaygın ürünü olarak nitelendirilen sukuk fiyatlarının tahmininde hemen hemen hiç uygulanmamıştır. Sukuk yeni bir finansal varlık olduğu için bu alanda yeterli çalışma bulunmamaktadır.. Bu nedenle çalışmada, Türkiye’deki hazine sukuk fiyatlarının yapay sinir ağı modeli ile tahmin edilmesi ve sukuk fiyatlarının tahminindeki belirleyicilerin ortaya konulması amaçlanmaktadır. Bu amaç doğrultusunda, Türkiye Hazine ve Maliye Bakanlığı tarafından ihraç edilen dolar bazlı uluslararası hazine sukuk fiyat verileri kullanılarak çok katmanlı geri beslemeli yapay sinir ağı modeli oluşturulmuştur. Dolar endeksi, volatilite endeksi, jeopolitik risk endeksi, Standard and Poor’s MENA sukuk endeksi ve Eurobond fiyatları geliştirilen modelin giriş değişkenlerini, hazine sukuk fiyatı ise modelin çıkışını oluşturmuştur. Sonuç olarak, hazine sukuk kapanış fiyatları tasarlanan model ile %99,98 başarı oranıyla doğru tahmin edilmiştir. Sukuk fiyatlarının yüksek başarıyla tahmini, sukuk yatırımcılarının risk algılamasının azaltılmasını ve kârlılığının artırılmasını sağlamada etkin bir rol oynayacaktır. Çalışmanın bulguları, yapay sinir ağı modelinin sukuk fiyatlarını tahmin etmede etkin bir model olduğunu kanıtlaması ve dolar endeksi, volatilite endeksi, jeopolitik risk endeksi, Standard and Poor’s MENA sukuk endeksi ve Eurobond fiyatlarının, sukuk fiyatlarını tahmin etmede belirleyici olduğunu ortaya koyması bakımından önem taşımaktadır.

References

  • AAOIFI. (2015). Sharia’a Standards (No:17 Investment Sukuk). Manama, Bahrain: Accounting and Auditing Organization for Islamic Financial Institutions.
  • Alam, N., Hassan, M. K., & Haque, M. A. (2013). Are Islamic bonds different from conventional bonds? International evidence from capital market tests. Borsa Istanbul Review, 13, 22–29. https://doi.org/10.1016/j.bir.2013.10.006
  • Ariff, M., Chazi, A., Safari, M., & Zarei, A. (2017). Significant difference in the yields of sukuk bonds versus conventional bonds. Journal of Emerging Market Finance, 16(2), 115–135. https://doi.org/10.1177/0972652717712352
  • Arundina, T., Azmi Omar, M., & Kartiwi, M. (2015). The predictive accuracy of sukuk ratings; multinomial logistic and neural network ınferences. Pacific Basin Finance Journal, 34(34), 273–292. https://doi.org/10.1016/j.pacfin.2015.03.002
  • Arundina, T., Kartiwi, M., & Omar, M. A. (2016). Artificial intelligence for Islamic sukuk rating predictions. In Artificial Intelligence in Financial Markets (pp. 211–241). Palgrave Macmillan UK. https://doi.org/10.1057/978-1-137-48880-0_8
  • Aslam, F., Mughal, K. S., Ali, A., & Mohmand, Y. T. (2020). Forecasting Islamic securities index using artificial neural networks: performance evaluation of technical indicators. Journal of Economic and Administrative Sciences, ahead-of-p(ahead-of-print). https://doi.org/10.1108/jeas-04-2020-0038
  • Caldara, D., & Iacoviello, M. (2018). Measuring geopolitical risk. International Finance Discussion Paper, 2018(1222), 1–66. https://doi.org/10.17016/ifdp.2018.1222
  • Çetin, D. T. (2021). İslami Finans Sisteminde Sukuk (1st Edit). Ankara, Turkey: Gazi Kitabevi.
  • Çetin, D. T. (2020). İslami Finans Sisteminde Sukuk: Türkiye’de Sukuk Fiyatlarının Yapay Sinir Ağları Modeli ile Tahmini [Sukuk in Islamic financial system: Forecasting sukuk prices in Turkey with artificial neural network model]. (Doctoral dissertation, Burdur Mehmet Akif Ersoy University).
  • Çetin, D. T. (2019). Türkiye’de jeopolitik risk ve İslami hisse senedi endeksi (katılım 30) arasındaki nedensellik ilişkisi: Ampirik bir analiz [The causality relation between geopolitical risk and Islamic stock index (participation 30) in Turkey: An empirical analysis]. In S. Erdoğan, A. Gedikli, & D. Ç. Yıldırım (Eds.), ISEFE, International Congress of Islamic Economy, Finance and Ethics (pp. 108–119). Umuttepe Yayınları.
  • Dhamija, A. K., & Bhalla, V. K. (2010). Financial time series forecasting: Comparison of neural networks and ARCH models. International Research Journal of Finance and Economics, 49, 194–212. https://www.academia.edu/download/33817126/irjfe_49_15.pdf
  • Godlewski, C. J., Turk-Ariss, R., & Weill, L. (2013). Sukuk vs. conventional bonds: A stock market perspective. Journal of Comparative Economics, 41, 745–761. https://doi.org/10.1016/j.jce.2013.02.006
  • Hatipoğlu, M., & Sekmen, T. (2018). Seçilmiş bazı risk faktörlerinin İslami borsalar üzerindeki etkileri [The impacts of some selected risk factors on Islamic stock markets]. MANAS Sosyal Araştırmalar Dergisi, 7(4), 1694–7215.
  • Hila, N. Z., Muhamad Safiih, L., & Mohamed, N. A. (2019). Empirical study of sukuk investment forecasting using artificial neural network base algorithm. International Journal of Innovations in Engineering and Technology, 13(3), 124–127. https://doi.org/10.21172/ijiet.133.19
  • Hossain, A., Zaman, F., Nasser, M., & Islam, M. M. (2009). Comparison of GARCH, neural network and support vector machine in financial time series prediction. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5909 LNCS, 597–602. https://doi.org/10.1007/978-3-642-11164-8_97
  • IFSB. (2020). Islamic Financial Services Industry Stability Report 2020. Kuala Lumpur, Malaysi. https://www.ifsb.org/sec03.php
  • Ismail, N., & Arundina, T. (2019). Sukuk rating prediction: The case of corporate sukuk in Indonesia. Pertanika Journal of Social Sciences and Humanities, 27(S2), 63–77. https://core.ac.uk/download/pdf/286784180.pdf#page=77
  • Livingstone, D. J. (2008). Artificial neural networks: methods and applications. Totowa, NJ, USA: Humana Press.
  • Mengi, D. F., & Metlek, S. (2020). Türkiye’nin Akdeniz bölgesine ait rüzgâr ekserjisinin çok katmanlı yapay sinir ağı ile modellenmesi [Modeling belongs to Turkey's Mediterranean coast wind of exergy multilayer neural network]. International Journal of Engineering and Innovative Research, 2(2), 102–120. http://dergipark.gov.tr/ijeir
  • Metlek, S., & Kayaalp, K. (2020). Derin öğrenme ve destek vektör makineleri ile görüntüden cinsiyet tahmini [Image gender prediction with deep learning and support vector machines]. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 8(3), 2208–2228. https://doi.org/https://doi.org/10.29130/dubited.707316
  • Mohd Saad, N., Haniff, M. N., & Ali, N. (2019). Corporate governance mechanisms with conventional bonds and Sukuk’ yield spreads. Pacific Basin Finance Journal. https://doi.org/10.1016/j.pacfin.2019.02.001
  • Naifar, N. (2016). Do global risk factors and macroeconomic conditions affect global Islamic index dynamics? A quantile regression approach. Quarterly Review of Economics and Finance, 61, 29–39. https://doi.org/10.1016/j.qref.2015.10.004
  • Özbayoglu, A. M., Gudelek, M. U., & Sezer, Ö. B. (2020). Deep learning for financial applications : A survey. ArXiv:2002.05786v1. http://arxiv.org/abs/2002.05786
  • Qian, X. (2017). Financial series prediction: Comparison between precision of time series models and machine learning methods. In arXiv. arXiv.
  • Raei, F., & Cakir, S. (2007). Sukuk vs. Eurobonds: Is there a difference in Value-At-Risk? In IMF Working Papers (WP/07/237). https://doi.org/10.5089/9781451868012.001
  • Siddiqui, T. A., & Abdullah, Y. (2017). Testing for predictive ability of conventional and shariah ındices of selected Gulf countries and economic regions using neural network modelling. Journal of Islamic Economics, Banking and Finance, 13(1), 171–186. https://doi.org/10.12816/0051161
  • Singh, R., & Srivastava, S. (2017). Stock prediction using deep learning. Multimedia Tools and Applications, 76, 18569-18584. https://doi.org/10.1007/s11042-016-4159-7
  • Tealab, A., Hefny, H., & Badr, A. (2017). Forecasting of nonlinear time series using ANN. Future Computing and Informatics Journal. https://doi.org/10.1016/j.fcij.2017.05.001
  • TKBB. (2020). Katılım bankaları sukuk ihraçları (2010-2019) [Sukuk issues of participation banks (2010-2019)]. https://tkbb.org.tr/Documents/Yonetmelikler/Katilim-Bankalari-2019.pdf
  • Wardani, L., Viverita, V., Husodo, Z. A., & Sunaryo, S. (2020). Contingent claim approach for pricing of sovereign sukuk for R&D financing in Indonesia. Emerging Markets Finance and Trade, 56(2), 338–350. https://doi.org/10.1080/1540496X.2019.1658067
  • Xu, Z., Zhang, J., Wang, J., & Xu, Z. (2020a). Prediction research of financial time series based on deep learning. Soft Computing, 24(11), 8295–8312. https://doi.org/10.1007/s00500-020-04788-w
  • Yakut, E., Elmas, B., & Selahattin, Y. (2014). Yapay sinir ağlari ve destek vektör makineleri yöntemleriyle borsa endeksi tahmini [Predicting stock-exchange index using methods of neural networks and support vector machines]. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 19(1), 139–157.
  • Yiğiter, Ş. Y., Sarı, S. S., Karabulut, T., & Başakın, E. E. (2018). Kira sertifikası fiyat değerlerinin makine öğrenmesi metodu ile tahmini [Estimation of lease certificate price evaluation through machine learning method]. International Journal of Islamic Economics and Finance Studies, 4(3), 74–82. https://doi.org/10.25272/ijisef.412760
  • Zhang, Y., Chu, G., & Shen, D. (2021). The role of investor attention in predicting stock prices: The long short-term memory networks perspective. Finance Research Letters, 38, 101484, 1–12. https://doi.org/10.1016/j.frl.2020.101484

Forecasting of Turkish Sovereign Sukuk Prices Using Artificial Neural Network Model

Year 2021, Volume: 5 Issue: 2, 241 - 254, 30.12.2021

Abstract

Recently, artificial neural networks have been successfully applied in many areas such as forecasting financial time series, predicting financial failure, and classification of ratings. However, it has hardly been applied in forecasting sukuk prices, which is considered the most common Islamic capital market instrument. Since sukuk is a new financial asset, there are not enough studies in this area. Therefore, this study aims to forecast the Turkish sovereign sukuk prices using with artificial neural network model and to reveal the determinants in the forecasting of sukuk prices. For this purpose, a multi-layer feed forward artificial neural network model is designed using dollar-based international sovereign sukuk price data issued by the Turkish Ministry of Treasury and Finance. The dollar index, volatility index, geopolitical risk index, Standard and Poor’s Middle East and North Africa sukuk index, and Eurobond prices constituted as input variables of the designed model and the sovereign sukuk prices formed the output. As a result, the sovereign sukuk prices were forecasted accurately at the success rate of 99.98%. The accurate forecasting of sukuk prices will play a critical role in reducing the risk perception of sukuk investors and increasing their profitability. The findings of the study are important in terms of proving that the artificial neural network model is an effective model for forecasting the sukuk prices and revealing that the dollar index, volatility index, geopolitical risk index, Standard and Poor’s MENA sukuk index, and Eurobond prices are determinants in forecasting sukuk prices.

References

  • AAOIFI. (2015). Sharia’a Standards (No:17 Investment Sukuk). Manama, Bahrain: Accounting and Auditing Organization for Islamic Financial Institutions.
  • Alam, N., Hassan, M. K., & Haque, M. A. (2013). Are Islamic bonds different from conventional bonds? International evidence from capital market tests. Borsa Istanbul Review, 13, 22–29. https://doi.org/10.1016/j.bir.2013.10.006
  • Ariff, M., Chazi, A., Safari, M., & Zarei, A. (2017). Significant difference in the yields of sukuk bonds versus conventional bonds. Journal of Emerging Market Finance, 16(2), 115–135. https://doi.org/10.1177/0972652717712352
  • Arundina, T., Azmi Omar, M., & Kartiwi, M. (2015). The predictive accuracy of sukuk ratings; multinomial logistic and neural network ınferences. Pacific Basin Finance Journal, 34(34), 273–292. https://doi.org/10.1016/j.pacfin.2015.03.002
  • Arundina, T., Kartiwi, M., & Omar, M. A. (2016). Artificial intelligence for Islamic sukuk rating predictions. In Artificial Intelligence in Financial Markets (pp. 211–241). Palgrave Macmillan UK. https://doi.org/10.1057/978-1-137-48880-0_8
  • Aslam, F., Mughal, K. S., Ali, A., & Mohmand, Y. T. (2020). Forecasting Islamic securities index using artificial neural networks: performance evaluation of technical indicators. Journal of Economic and Administrative Sciences, ahead-of-p(ahead-of-print). https://doi.org/10.1108/jeas-04-2020-0038
  • Caldara, D., & Iacoviello, M. (2018). Measuring geopolitical risk. International Finance Discussion Paper, 2018(1222), 1–66. https://doi.org/10.17016/ifdp.2018.1222
  • Çetin, D. T. (2021). İslami Finans Sisteminde Sukuk (1st Edit). Ankara, Turkey: Gazi Kitabevi.
  • Çetin, D. T. (2020). İslami Finans Sisteminde Sukuk: Türkiye’de Sukuk Fiyatlarının Yapay Sinir Ağları Modeli ile Tahmini [Sukuk in Islamic financial system: Forecasting sukuk prices in Turkey with artificial neural network model]. (Doctoral dissertation, Burdur Mehmet Akif Ersoy University).
  • Çetin, D. T. (2019). Türkiye’de jeopolitik risk ve İslami hisse senedi endeksi (katılım 30) arasındaki nedensellik ilişkisi: Ampirik bir analiz [The causality relation between geopolitical risk and Islamic stock index (participation 30) in Turkey: An empirical analysis]. In S. Erdoğan, A. Gedikli, & D. Ç. Yıldırım (Eds.), ISEFE, International Congress of Islamic Economy, Finance and Ethics (pp. 108–119). Umuttepe Yayınları.
  • Dhamija, A. K., & Bhalla, V. K. (2010). Financial time series forecasting: Comparison of neural networks and ARCH models. International Research Journal of Finance and Economics, 49, 194–212. https://www.academia.edu/download/33817126/irjfe_49_15.pdf
  • Godlewski, C. J., Turk-Ariss, R., & Weill, L. (2013). Sukuk vs. conventional bonds: A stock market perspective. Journal of Comparative Economics, 41, 745–761. https://doi.org/10.1016/j.jce.2013.02.006
  • Hatipoğlu, M., & Sekmen, T. (2018). Seçilmiş bazı risk faktörlerinin İslami borsalar üzerindeki etkileri [The impacts of some selected risk factors on Islamic stock markets]. MANAS Sosyal Araştırmalar Dergisi, 7(4), 1694–7215.
  • Hila, N. Z., Muhamad Safiih, L., & Mohamed, N. A. (2019). Empirical study of sukuk investment forecasting using artificial neural network base algorithm. International Journal of Innovations in Engineering and Technology, 13(3), 124–127. https://doi.org/10.21172/ijiet.133.19
  • Hossain, A., Zaman, F., Nasser, M., & Islam, M. M. (2009). Comparison of GARCH, neural network and support vector machine in financial time series prediction. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5909 LNCS, 597–602. https://doi.org/10.1007/978-3-642-11164-8_97
  • IFSB. (2020). Islamic Financial Services Industry Stability Report 2020. Kuala Lumpur, Malaysi. https://www.ifsb.org/sec03.php
  • Ismail, N., & Arundina, T. (2019). Sukuk rating prediction: The case of corporate sukuk in Indonesia. Pertanika Journal of Social Sciences and Humanities, 27(S2), 63–77. https://core.ac.uk/download/pdf/286784180.pdf#page=77
  • Livingstone, D. J. (2008). Artificial neural networks: methods and applications. Totowa, NJ, USA: Humana Press.
  • Mengi, D. F., & Metlek, S. (2020). Türkiye’nin Akdeniz bölgesine ait rüzgâr ekserjisinin çok katmanlı yapay sinir ağı ile modellenmesi [Modeling belongs to Turkey's Mediterranean coast wind of exergy multilayer neural network]. International Journal of Engineering and Innovative Research, 2(2), 102–120. http://dergipark.gov.tr/ijeir
  • Metlek, S., & Kayaalp, K. (2020). Derin öğrenme ve destek vektör makineleri ile görüntüden cinsiyet tahmini [Image gender prediction with deep learning and support vector machines]. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 8(3), 2208–2228. https://doi.org/https://doi.org/10.29130/dubited.707316
  • Mohd Saad, N., Haniff, M. N., & Ali, N. (2019). Corporate governance mechanisms with conventional bonds and Sukuk’ yield spreads. Pacific Basin Finance Journal. https://doi.org/10.1016/j.pacfin.2019.02.001
  • Naifar, N. (2016). Do global risk factors and macroeconomic conditions affect global Islamic index dynamics? A quantile regression approach. Quarterly Review of Economics and Finance, 61, 29–39. https://doi.org/10.1016/j.qref.2015.10.004
  • Özbayoglu, A. M., Gudelek, M. U., & Sezer, Ö. B. (2020). Deep learning for financial applications : A survey. ArXiv:2002.05786v1. http://arxiv.org/abs/2002.05786
  • Qian, X. (2017). Financial series prediction: Comparison between precision of time series models and machine learning methods. In arXiv. arXiv.
  • Raei, F., & Cakir, S. (2007). Sukuk vs. Eurobonds: Is there a difference in Value-At-Risk? In IMF Working Papers (WP/07/237). https://doi.org/10.5089/9781451868012.001
  • Siddiqui, T. A., & Abdullah, Y. (2017). Testing for predictive ability of conventional and shariah ındices of selected Gulf countries and economic regions using neural network modelling. Journal of Islamic Economics, Banking and Finance, 13(1), 171–186. https://doi.org/10.12816/0051161
  • Singh, R., & Srivastava, S. (2017). Stock prediction using deep learning. Multimedia Tools and Applications, 76, 18569-18584. https://doi.org/10.1007/s11042-016-4159-7
  • Tealab, A., Hefny, H., & Badr, A. (2017). Forecasting of nonlinear time series using ANN. Future Computing and Informatics Journal. https://doi.org/10.1016/j.fcij.2017.05.001
  • TKBB. (2020). Katılım bankaları sukuk ihraçları (2010-2019) [Sukuk issues of participation banks (2010-2019)]. https://tkbb.org.tr/Documents/Yonetmelikler/Katilim-Bankalari-2019.pdf
  • Wardani, L., Viverita, V., Husodo, Z. A., & Sunaryo, S. (2020). Contingent claim approach for pricing of sovereign sukuk for R&D financing in Indonesia. Emerging Markets Finance and Trade, 56(2), 338–350. https://doi.org/10.1080/1540496X.2019.1658067
  • Xu, Z., Zhang, J., Wang, J., & Xu, Z. (2020a). Prediction research of financial time series based on deep learning. Soft Computing, 24(11), 8295–8312. https://doi.org/10.1007/s00500-020-04788-w
  • Yakut, E., Elmas, B., & Selahattin, Y. (2014). Yapay sinir ağlari ve destek vektör makineleri yöntemleriyle borsa endeksi tahmini [Predicting stock-exchange index using methods of neural networks and support vector machines]. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 19(1), 139–157.
  • Yiğiter, Ş. Y., Sarı, S. S., Karabulut, T., & Başakın, E. E. (2018). Kira sertifikası fiyat değerlerinin makine öğrenmesi metodu ile tahmini [Estimation of lease certificate price evaluation through machine learning method]. International Journal of Islamic Economics and Finance Studies, 4(3), 74–82. https://doi.org/10.25272/ijisef.412760
  • Zhang, Y., Chu, G., & Shen, D. (2021). The role of investor attention in predicting stock prices: The long short-term memory networks perspective. Finance Research Letters, 38, 101484, 1–12. https://doi.org/10.1016/j.frl.2020.101484
There are 34 citations in total.

Details

Primary Language English
Journal Section Research Article
Authors

Dilşad Tülgen Çetin 0000-0001-9321-6991

Sedat Metlek 0000-0002-0393-9908

Early Pub Date September 13, 2021
Publication Date December 30, 2021
Submission Date April 1, 2021
Published in Issue Year 2021 Volume: 5 Issue: 2

Cite

APA Çetin, D. T., & Metlek, S. (2021). Forecasting of Turkish Sovereign Sukuk Prices Using Artificial Neural Network Model. Acta Infologica, 5(2), 241-254.
AMA Çetin DT, Metlek S. Forecasting of Turkish Sovereign Sukuk Prices Using Artificial Neural Network Model. ACIN. December 2021;5(2):241-254.
Chicago Çetin, Dilşad Tülgen, and Sedat Metlek. “Forecasting of Turkish Sovereign Sukuk Prices Using Artificial Neural Network Model”. Acta Infologica 5, no. 2 (December 2021): 241-54.
EndNote Çetin DT, Metlek S (December 1, 2021) Forecasting of Turkish Sovereign Sukuk Prices Using Artificial Neural Network Model. Acta Infologica 5 2 241–254.
IEEE D. T. Çetin and S. Metlek, “Forecasting of Turkish Sovereign Sukuk Prices Using Artificial Neural Network Model”, ACIN, vol. 5, no. 2, pp. 241–254, 2021.
ISNAD Çetin, Dilşad Tülgen - Metlek, Sedat. “Forecasting of Turkish Sovereign Sukuk Prices Using Artificial Neural Network Model”. Acta Infologica 5/2 (December 2021), 241-254.
JAMA Çetin DT, Metlek S. Forecasting of Turkish Sovereign Sukuk Prices Using Artificial Neural Network Model. ACIN. 2021;5:241–254.
MLA Çetin, Dilşad Tülgen and Sedat Metlek. “Forecasting of Turkish Sovereign Sukuk Prices Using Artificial Neural Network Model”. Acta Infologica, vol. 5, no. 2, 2021, pp. 241-54.
Vancouver Çetin DT, Metlek S. Forecasting of Turkish Sovereign Sukuk Prices Using Artificial Neural Network Model. ACIN. 2021;5(2):241-54.