| Title |
Predicting company credit rating using artificial intelligence techniques from publicly available financial data / |
| Authors |
Širmenis, Juozas ; Kavaliauskas, Mindaugas ; Lagzdinytė-Budnikė, Ingrida |
| Full Text |
|
| Is Part of |
CEUR workshop proceedings: IVUS 2024: Information society and university studies 2024: proceedings of the 29th international conference on information society and university studies (IVUS 2023) Kaunas, Lithuania, May 17, 2024 / edited by: I. Veitaitė, A. Lopata, T. Krilavičius, M. Woźniak.. Aachen : CEUR-WS. 2024, vol. 3885, p. 49-58.. ISSN 1613-0073 |
| Keywords [eng] |
credit rating ; linear regression ; huber regression ; artificial neural network ; random forest ; financial statements |
| Abstract [eng] |
The study focuses on predicting credit rating using statistical methods (Linear and Huber Regressions) and machine learning techniques (Artificial Neural Network and Random Forest) while using publicly available financial data with additionally calculated features. The results show that machine learning techniques outperformed statistical methods significantly. The best results were obtained using the ANN model: MSE reached 0.063, MAE – 0.1858, R² - 0.9065, and RMSE – 0.251. The notable performance improvement across all models was noticed when incorporating additionally derived financial ratios, notwithstanding their derivation from metrics already included in the analysis. |
| Published |
Aachen : CEUR-WS |
| Type |
Conference paper |
| Language |
English |
| Publication date |
2024 |
