Neutrosophic Soft Set for Forecasting Indonesian Bond Yields

Bonds are tradable investment instruments that offer yields, representing the promised return on investment. Unlike fixed-interest bonds, bond yields typically fluctuate, so accurate yield predictions are crucial for investors. These fluctuations may include increase, decrease, and steady yield values, aligning well with the principles of the neutrosophic soft set. In this study, we apply the neutrosophic soft set theory to predict Indonesian bond yields in a multi-attribute time series framework. We consider closing yield, opening yield, and daily amplitude as predictor variables. We achieve shallow low prediction errors through experiments with varied training data ranges and n-order variations. We discover that the lowest values for Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE) are 0.0436, 0.6462%, and 0.0514, respectively. These errors are achieve when \(n = 13\) , with a two-year train data length. These results underscore the efficacy of the neutrosophic soft set in accurately predicting the closing yield of Indonesian bonds.