Hybrid Modeling Approaches for Forecasting the Yield of Iranian Islamic Treasury Bonds

Subject Areas : Financial Engineering

Pegah Shahrokhi ¹ , Seyed Mohammad Hasheminejad ^{2 *} , Seyyedeh Atefeh Hosseini ³

1 - Department of Accounting and Finance ,Firuzkuh Branch, Islamic Azad University ,Firuzkuh,Iran
2 - Department of Business Management, Tehran Medical Science Branch ,Islamic Azad Univrsity,Tehran,Iran
3 - Department of Accounting, Firuzkuh Branch, Islamic Azad University, ,Firuzkuh ,Iran

Received: 2025-07-04 Accepted : 2025-08-15 Published : 2025-07-23

Keywords: Yield Estimation, Regression, Treasury Bonds, Multiple Linear Regression,

Abstract :

Forecasting financial variables, especially the returns of debt instruments, plays a vital role in economic decision-making and risk management. Although the forecasting literature in financial markets is extensive, few studies have focused on predicting the returns of Islamic Treasury Bonds with unconventional structures. Moreover, despite the importance of these bonds, very limited work has been done using machine learning in the debt market. This study aims to predict the returns of Islamic Treasury Bonds using three models: Multiple Linear Regression (MLR), Multilayer Perceptron Neural Network (MLP), and Radial Basis Function Neural Network (RBF). Monthly data from 2018 to 2023 were collected using Excel and Python. The training and evaluation of the models were carried out in MATLAB. Eleven influential variables were selected based on previous studies and expert opinions. The models' performance was evaluated using Root Mean Square Error (RMSE) and the coefficient of determination (R²). The findings indicate that the Multilayer Perceptron Neural Network model has higher accuracy in predicting the returns of Islamic Treasury Bonds compared to Multiple Linear Regression and Radial Basis Function models. These results suggest that neural network models can serve as more effective tools in financial and economic analyses, significantly enhancing forecasting accuracy.