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 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.

Grantor

Department of Statistical Sciences

Year dc:date.issued

2024

Author dc:creator

• Chandoo, Ali Aonali

Advisor dc:contributor.advisor

• Nyirenda, Juwa Chiza

• Statistical Sciences

Handle dc:identifier.uri

http://hdl.handle.net/11427/40400

OAI identifier oai:identifier

oai:open.uct.ac.za:11427/40400