Forecasting Sovereign Credit Ratings Using Differential Evolution and Logic Aggregation in IBA Framework
Апстракт
The sovereign credit rating is considered as a quantified assessment of country's economic and political stability. Due to its importance and increasing amount of available information, the sovereign credit rating is considered as a hot topic in the last few years. However, the models that predict the credit ratings used by the several big credit rating agencies are unavailable, and can therefore be considered as the black boxes. In this paper, we are tackling this problem of predicting sovereign credit ratings by proposing a hybrid model based on interpolative Boolean algebra (IBA) and differential evolution (DE). Namely, we aim to obtain a logical/pseudo-logical function in IBA framework using DE metaheuristic that could underline connections of chosen macroeconomic indicators and sovereign credit ratings. Such functions are easy to interpret and able to make a subtle fuzzy gradation among countries. Country's economic indicators together with credit ratings from 2000 to 2016 are use...d for the model training. Acquired model is further tested on the data for 2017 and 2018.
Кључне речи:
Sovereign credit rating / Prediction / Interpolative Boolean algebra / Differential evolutionИзвор:
Lecture Notes in Networks and Systems, 2022, 308, 506-513Издавач:
- Springer Science and Business Media Deutschland GmbH
Институција/група
Fakultet organizacionih naukaTY - CONF AU - Jelinek, Srđan AU - Milošević, Pavle AU - Rakićević, Aleksandar AU - Petrović, Bratislav PY - 2022 UR - https://rfos.fon.bg.ac.rs/handle/123456789/2419 AB - The sovereign credit rating is considered as a quantified assessment of country's economic and political stability. Due to its importance and increasing amount of available information, the sovereign credit rating is considered as a hot topic in the last few years. However, the models that predict the credit ratings used by the several big credit rating agencies are unavailable, and can therefore be considered as the black boxes. In this paper, we are tackling this problem of predicting sovereign credit ratings by proposing a hybrid model based on interpolative Boolean algebra (IBA) and differential evolution (DE). Namely, we aim to obtain a logical/pseudo-logical function in IBA framework using DE metaheuristic that could underline connections of chosen macroeconomic indicators and sovereign credit ratings. Such functions are easy to interpret and able to make a subtle fuzzy gradation among countries. Country's economic indicators together with credit ratings from 2000 to 2016 are used for the model training. Acquired model is further tested on the data for 2017 and 2018. PB - Springer Science and Business Media Deutschland GmbH C3 - Lecture Notes in Networks and Systems T1 - Forecasting Sovereign Credit Ratings Using Differential Evolution and Logic Aggregation in IBA Framework EP - 513 SP - 506 VL - 308 DO - 10.1007/978-3-030-85577-2_60 UR - conv_3683 ER -
@conference{ author = "Jelinek, Srđan and Milošević, Pavle and Rakićević, Aleksandar and Petrović, Bratislav", year = "2022", abstract = "The sovereign credit rating is considered as a quantified assessment of country's economic and political stability. Due to its importance and increasing amount of available information, the sovereign credit rating is considered as a hot topic in the last few years. However, the models that predict the credit ratings used by the several big credit rating agencies are unavailable, and can therefore be considered as the black boxes. In this paper, we are tackling this problem of predicting sovereign credit ratings by proposing a hybrid model based on interpolative Boolean algebra (IBA) and differential evolution (DE). Namely, we aim to obtain a logical/pseudo-logical function in IBA framework using DE metaheuristic that could underline connections of chosen macroeconomic indicators and sovereign credit ratings. Such functions are easy to interpret and able to make a subtle fuzzy gradation among countries. Country's economic indicators together with credit ratings from 2000 to 2016 are used for the model training. Acquired model is further tested on the data for 2017 and 2018.", publisher = "Springer Science and Business Media Deutschland GmbH", journal = "Lecture Notes in Networks and Systems", title = "Forecasting Sovereign Credit Ratings Using Differential Evolution and Logic Aggregation in IBA Framework", pages = "513-506", volume = "308", doi = "10.1007/978-3-030-85577-2_60", url = "conv_3683" }
Jelinek, S., Milošević, P., Rakićević, A.,& Petrović, B.. (2022). Forecasting Sovereign Credit Ratings Using Differential Evolution and Logic Aggregation in IBA Framework. in Lecture Notes in Networks and Systems Springer Science and Business Media Deutschland GmbH., 308, 506-513. https://doi.org/10.1007/978-3-030-85577-2_60 conv_3683
Jelinek S, Milošević P, Rakićević A, Petrović B. Forecasting Sovereign Credit Ratings Using Differential Evolution and Logic Aggregation in IBA Framework. in Lecture Notes in Networks and Systems. 2022;308:506-513. doi:10.1007/978-3-030-85577-2_60 conv_3683 .
Jelinek, Srđan, Milošević, Pavle, Rakićević, Aleksandar, Petrović, Bratislav, "Forecasting Sovereign Credit Ratings Using Differential Evolution and Logic Aggregation in IBA Framework" in Lecture Notes in Networks and Systems, 308 (2022):506-513, https://doi.org/10.1007/978-3-030-85577-2_60 ., conv_3683 .