TY  - JOUR
T1  - Forecasting of Turkish Sovereign Sukuk Prices Using Artificial Neural Network Model
TT  - Türkiye’de Hazine Sukuk Fiyatlarının Yapay Sinir Ağı Modeli ile Tahmini
AU  - Çetin, Dilşad Tülgen
AU  - Metlek, Sedat
PY  - 2021
DA  - December
JF  - Acta Infologica
JO  - ACIN
PB  - İstanbul Üniversitesi
WT  - DergiPark
SN  - 2602-3563
SP  - 241
EP  - 254
VL  - 5
IS  - 2
LA  - en
AB  - 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.
KW  - Sukuk
KW  - Price Forecasting
KW  - Artificial Neural Network
KW  - Geopolitical Risk
KW  - Dollar Index
KW  - Volatility Index
N2  - 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.
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UR  - https://dergipark.org.tr/tr/pub/acin/issue//907990
L1  - https://dergipark.org.tr/tr/download/article-file/1678299
ER  -