Artificial Intelligence in Corporate Sustainability: Using LSTM and GRU for Going Concern Prediction

“Going concern” is a professional term in the domain of accounting and auditing. The issuance of appropriate audit opinions by certified public accountants (CPAs) and auditors is critical to companies as a going concern, as misjudgment and/or failure to identify the probability of bankruptcy can cau...

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Autores principales:	Der-Jang Chi, Chien-Chou Chu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	going concern prediction artificial intelligence (AI) corporate sustainability deep learning algorithm long short-term memory (LSTM) gated recurrent unit (GRU) Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/ddb8b03f4c9b4b7f802d2e1b6f25f4ac
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spelling	oai:doaj.org-article:ddb8b03f4c9b4b7f802d2e1b6f25f4ac2021-11-11T19:23:00ZArtificial Intelligence in Corporate Sustainability: Using LSTM and GRU for Going Concern Prediction10.3390/su1321116312071-1050https://doaj.org/article/ddb8b03f4c9b4b7f802d2e1b6f25f4ac2021-10-01T00:00:00Zhttps://www.mdpi.com/2071-1050/13/21/11631https://doaj.org/toc/2071-1050“Going concern” is a professional term in the domain of accounting and auditing. The issuance of appropriate audit opinions by certified public accountants (CPAs) and auditors is critical to companies as a going concern, as misjudgment and/or failure to identify the probability of bankruptcy can cause heavy losses to stakeholders and affect corporate sustainability. In the era of artificial intelligence (AI), deep learning algorithms are widely used by practitioners, and academic research is also gradually embarking on projects in various domains. However, the use of deep learning algorithms in the prediction of going concern remains limited. In contrast to those in the literature, this study uses long short-term memory (LSTM) and gated recurrent unit (GRU) for learning and training, in order to construct effective and highly accurate going-concern prediction models. The sample pool consists of the Taiwan Stock Exchange Corporation (TWSE) and the Taipei Exchange (TPEx) listed companies in 2004–2019, including 86 companies with going concern doubt and 172 companies without going concern doubt. In other words, 258 companies in total are sampled. There are 20 research variables, comprising 16 financial variables and 4 non-financial variables. The results are based on performance indicators such as accuracy, precision, recall/sensitivity, specificity, F1-scores, and Type I and Type II error rates, and both the LSTM and GRU models perform well. As far as accuracy is concerned, the LSTM model reports 96.15% accuracy while GRU shows 94.23% accuracy.Der-Jang ChiChien-Chou ChuMDPI AGarticlegoing concern predictionartificial intelligence (AI)corporate sustainabilitydeep learning algorithmlong short-term memory (LSTM)gated recurrent unit (GRU)Environmental effects of industries and plantsTD194-195Renewable energy sourcesTJ807-830Environmental sciencesGE1-350ENSustainability, Vol 13, Iss 11631, p 11631 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	going concern prediction artificial intelligence (AI) corporate sustainability deep learning algorithm long short-term memory (LSTM) gated recurrent unit (GRU) Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
spellingShingle	going concern prediction artificial intelligence (AI) corporate sustainability deep learning algorithm long short-term memory (LSTM) gated recurrent unit (GRU) Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Der-Jang Chi Chien-Chou Chu Artificial Intelligence in Corporate Sustainability: Using LSTM and GRU for Going Concern Prediction
description	“Going concern” is a professional term in the domain of accounting and auditing. The issuance of appropriate audit opinions by certified public accountants (CPAs) and auditors is critical to companies as a going concern, as misjudgment and/or failure to identify the probability of bankruptcy can cause heavy losses to stakeholders and affect corporate sustainability. In the era of artificial intelligence (AI), deep learning algorithms are widely used by practitioners, and academic research is also gradually embarking on projects in various domains. However, the use of deep learning algorithms in the prediction of going concern remains limited. In contrast to those in the literature, this study uses long short-term memory (LSTM) and gated recurrent unit (GRU) for learning and training, in order to construct effective and highly accurate going-concern prediction models. The sample pool consists of the Taiwan Stock Exchange Corporation (TWSE) and the Taipei Exchange (TPEx) listed companies in 2004–2019, including 86 companies with going concern doubt and 172 companies without going concern doubt. In other words, 258 companies in total are sampled. There are 20 research variables, comprising 16 financial variables and 4 non-financial variables. The results are based on performance indicators such as accuracy, precision, recall/sensitivity, specificity, F1-scores, and Type I and Type II error rates, and both the LSTM and GRU models perform well. As far as accuracy is concerned, the LSTM model reports 96.15% accuracy while GRU shows 94.23% accuracy.
format	article
author	Der-Jang Chi Chien-Chou Chu
author_facet	Der-Jang Chi Chien-Chou Chu
author_sort	Der-Jang Chi
title	Artificial Intelligence in Corporate Sustainability: Using LSTM and GRU for Going Concern Prediction
title_short	Artificial Intelligence in Corporate Sustainability: Using LSTM and GRU for Going Concern Prediction
title_full	Artificial Intelligence in Corporate Sustainability: Using LSTM and GRU for Going Concern Prediction
title_fullStr	Artificial Intelligence in Corporate Sustainability: Using LSTM and GRU for Going Concern Prediction
title_full_unstemmed	Artificial Intelligence in Corporate Sustainability: Using LSTM and GRU for Going Concern Prediction
title_sort	artificial intelligence in corporate sustainability: using lstm and gru for going concern prediction
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/ddb8b03f4c9b4b7f802d2e1b6f25f4ac
work_keys_str_mv	AT derjangchi artificialintelligenceincorporatesustainabilityusinglstmandgruforgoingconcernprediction AT chienchouchu artificialintelligenceincorporatesustainabilityusinglstmandgruforgoingconcernprediction
_version_	1718431557891391488

Artificial Intelligence in Corporate Sustainability: Using LSTM and GRU for Going Concern Prediction

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