{"id":{"repo_id":"cape-town","oai_identifier":"oai:open.uct.ac.za:11427/40400"},"canonical_url":"https://search.dev.ndltd.org/etd/cape-town/oai:open.uct.ac.za:11427/40400","repository":{"repo_id":"cape-town","name":"University of Cape Town","base_url":"https://open.uct.ac.za/oai/request"},"display":{"title":"Long short-term memory neural networks for predicting corporate credit ratings","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Chandoo, Ali Aonali"],"institution":"Department of Statistical Sciences","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Nyirenda, Juwa Chiza"],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024","date_published":"2024","updated_at":"2026-07-22T22:23:35Z","subjects":["Statistical Sciences"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/11427/40400","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Nyirenda, Juwa Chiza"]},{"key":"dc:creator","label":"Author","values":["Chandoo, Ali Aonali"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2024-07-05T13:05:53Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2024-07-05T13:05:53Z"]},{"key":"dc:date.issued","label":"Date","values":["2024"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["Department of Statistical Sciences"]},{"key":"dc:type","label":"Dc Type","values":["Thesis / Dissertation"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Masters","MSc"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Statistical Sciences"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/11427/40400"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:title","label":"Title","values":["Long short-term memory neural networks for predicting corporate credit ratings"]}]}],"canonical_facts":{"dc:contributor.advisor":["Nyirenda, Juwa Chiza"],"dc:creator":["Chandoo, Ali Aonali"],"dc:date.accessioned":["2024-07-05T13:05:53Z"],"dc:date.available":["2024-07-05T13:05:53Z"],"dc:date.issued":["2024"],"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.uri":["http://hdl.handle.net/11427/40400"],"dc:publisher.department":["Department of Statistical Sciences"],"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","MSc"]},"updated_at":"2026-07-22T22:23:35Z"}