Long short-term memory neural networks for predicting corporate credit ratings
| dc.contributor.advisor | Nyirenda, Juwa Chiza | |
| dc.contributor.author | Chandoo, Ali Aonali | |
| dc.date.accessioned | 2024-07-05T13:05:53Z | |
| dc.date.available | 2024-07-05T13:05:53Z | |
| dc.date.issued | 2024 | |
| dc.date.updated | 2024-07-02T14:02:45Z | |
| dc.description.abstract | Credit ratings are an important tool when assessing financial instruments and investments. The existing literature shows that long short-term memory (LSTM) neural networks are the best neural network to predict credit ratings, while random forests have been shown to perform better than regular neural networks. As at the beginning of this study, no study had compared the performance of LSTM and random forests despite their reported superior performance. This study compares the performance of random forests and LSTM neural networks in predicting corporate credit ratings in the USA using Standard and Poor's data. The study finds that while LSTM neural networks pose serious competition, random forests have a slight edge over LSTM neural networks, showing that it is still worth using older and simpler techniques in predicting credit ratings. | |
| dc.identifier.apacitation | Chandoo, A. A. (2024). <i>Long short-term memory neural networks for predicting corporate credit ratings</i>. (). ,Faculty of Science ,Department of Statistical Sciences. Retrieved from http://hdl.handle.net/11427/40400 | en_ZA |
| dc.identifier.chicagocitation | Chandoo, Ali Aonali. <i>"Long short-term memory neural networks for predicting corporate credit ratings."</i> ., ,Faculty of Science ,Department of Statistical Sciences, 2024. http://hdl.handle.net/11427/40400 | en_ZA |
| dc.identifier.citation | Chandoo, A.A. 2024. Long short-term memory neural networks for predicting corporate credit ratings. . ,Faculty of Science ,Department of Statistical Sciences. http://hdl.handle.net/11427/40400 | en_ZA |
| dc.identifier.ris | TY - Thesis / Dissertation AU - Chandoo, Ali Aonali AB - Credit ratings are an important tool when assessing financial instruments and investments. The existing literature shows that long short-term memory (LSTM) neural networks are the best neural network to predict credit ratings, while random forests have been shown to perform better than regular neural networks. As at the beginning of this study, no study had compared the performance of LSTM and random forests despite their reported superior performance. This study compares the performance of random forests and LSTM neural networks in predicting corporate credit ratings in the USA using Standard and Poor's data. The study finds that while LSTM neural networks pose serious competition, random forests have a slight edge over LSTM neural networks, showing that it is still worth using older and simpler techniques in predicting credit ratings. DA - 2024 DB - OpenUCT DP - University of Cape Town KW - Statistical Sciences LK - https://open.uct.ac.za PY - 2024 T1 - Long short-term memory neural networks for predicting corporate credit ratings TI - Long short-term memory neural networks for predicting corporate credit ratings UR - http://hdl.handle.net/11427/40400 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/40400 | |
| dc.identifier.vancouvercitation | Chandoo AA. Long short-term memory neural networks for predicting corporate credit ratings. []. ,Faculty of Science ,Department of Statistical Sciences, 2024 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/40400 | en_ZA |
| dc.language.rfc3066 | Eng | |
| dc.publisher.department | Department of Statistical Sciences | |
| dc.publisher.faculty | Faculty of Science | |
| dc.subject | Statistical Sciences | |
| dc.title | Long short-term memory neural networks for predicting corporate credit ratings | |
| dc.type | Thesis / Dissertation | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationlevel | MSc |