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Long short-term memory neural networks for predicting corporate credit ratings
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 neura...
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| Format: | Thesis |
| Language: | Eng |
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Department of Statistical Sciences
2024
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| _version_ | 1867613763024191488 |
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| access_status_str | Open Access |
| author | Chandoo, Ali Aonali |
| author2 | Nyirenda, Juwa Chiza |
| author_browse | Chandoo, Ali Aonali Nyirenda, Juwa Chiza |
| author_facet | Nyirenda, Juwa Chiza Chandoo, Ali Aonali |
| author_sort | Chandoo, Ali Aonali |
| collection | Thesis |
| description | 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. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/40400 |
| institution | University of Cape Town (South Africa) |
| language | Eng |
| last_indexed | 2026-06-10T12:41:18.763Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Department of Statistical Sciences |
| publisherStr | Department of Statistical Sciences |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/40400 Long short-term memory neural networks for predicting corporate credit ratings Chandoo, Ali Aonali Nyirenda, Juwa Chiza Statistical Sciences 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. 2024-07-05T13:05:53Z 2024-07-05T13:05:53Z 2024 2024-07-02T14:02:45Z Thesis / Dissertation Masters MSc http://hdl.handle.net/11427/40400 Eng application/pdf Department of Statistical Sciences Faculty of Science |
| spellingShingle | Statistical Sciences Chandoo, Ali Aonali Long short-term memory neural networks for predicting corporate credit ratings |
| thesis_degree_str | Master's |
| title | Long short-term memory neural networks for predicting corporate credit ratings |
| title_full | Long short-term memory neural networks for predicting corporate credit ratings |
| title_fullStr | Long short-term memory neural networks for predicting corporate credit ratings |
| title_full_unstemmed | Long short-term memory neural networks for predicting corporate credit ratings |
| title_short | Long short-term memory neural networks for predicting corporate credit ratings |
| title_sort | long short term memory neural networks for predicting corporate credit ratings |
| topic | Statistical Sciences |
| url | http://hdl.handle.net/11427/40400 |
| work_keys_str_mv | AT chandooaliaonali longshorttermmemoryneuralnetworksforpredictingcorporatecreditratings |