TY - GEN
T1 - Neutrosophic Soft Set for Forecasting Indonesian Bond Yields
AU - Aini, Qonita Qurratu
AU - Mukhlash, Imam
AU - Fahim, Kistosil
AU - Jasmir,
AU - Fatimah, Fatia
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Fuzzy logic
KW - Multi-attribute forecasting
KW - Neutrosophic soft set
KW - Yield
UR - http://www.scopus.com/inward/record.url?scp=85203190647&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-67192-0_77
DO - 10.1007/978-3-031-67192-0_77
M3 - Conference contribution
AN - SCOPUS:85203190647
SN - 9783031671913
T3 - Lecture Notes in Networks and Systems
SP - 690
EP - 698
BT - Intelligent and Fuzzy Systems - Intelligent Industrial Informatics and Efficient Networks Proceedings of the INFUS 2024 Conference
A2 - Kahraman, Cengiz
A2 - Cevik Onar, Sezi
A2 - Cebi, Selcuk
A2 - Oztaysi, Basar
A2 - Ucal Sari, Irem
A2 - Tolga, A. Cagrı
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Intelligent and Fuzzy Systems, INFUS 2024
Y2 - 16 July 2024 through 18 July 2024
ER -