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Application of deep learning in recognition of accrued earnings management

We choose the sample data in Chinese capital market to compare the measurement effect of earnings management with Deep Belief Network, Deep Convolution Generative Adversarial Network, Generalized Regression Neural Network and modified Jones model by performance. We find that Deep Belief Network has...

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Detalles Bibliográficos
Autores principales: Li, Jia, Sun, Zhoutianyang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9976311/
https://www.ncbi.nlm.nih.gov/pubmed/36873477
http://dx.doi.org/10.1016/j.heliyon.2023.e13664
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author Li, Jia
Sun, Zhoutianyang
author_facet Li, Jia
Sun, Zhoutianyang
author_sort Li, Jia
collection PubMed
description We choose the sample data in Chinese capital market to compare the measurement effect of earnings management with Deep Belief Network, Deep Convolution Generative Adversarial Network, Generalized Regression Neural Network and modified Jones model by performance. We find that Deep Belief Network has the best effect, while Deep Convolution Generative Adversarial Network has no significant advantage, and the measurement effect of Generalized Regression Neural Network and modified Jones model have little difference. This paper provides empirical evidence that neural networks based on deep learning technology and other artificial intelligence technologies can be widely applied to measure earnings management in the future.
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spelling pubmed-99763112023-03-02 Application of deep learning in recognition of accrued earnings management Li, Jia Sun, Zhoutianyang Heliyon Research Article We choose the sample data in Chinese capital market to compare the measurement effect of earnings management with Deep Belief Network, Deep Convolution Generative Adversarial Network, Generalized Regression Neural Network and modified Jones model by performance. We find that Deep Belief Network has the best effect, while Deep Convolution Generative Adversarial Network has no significant advantage, and the measurement effect of Generalized Regression Neural Network and modified Jones model have little difference. This paper provides empirical evidence that neural networks based on deep learning technology and other artificial intelligence technologies can be widely applied to measure earnings management in the future. Elsevier 2023-02-13 /pmc/articles/PMC9976311/ /pubmed/36873477 http://dx.doi.org/10.1016/j.heliyon.2023.e13664 Text en © 2023 The Authors. Published by Elsevier Ltd. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research Article
Li, Jia
Sun, Zhoutianyang
Application of deep learning in recognition of accrued earnings management
title Application of deep learning in recognition of accrued earnings management
title_full Application of deep learning in recognition of accrued earnings management
title_fullStr Application of deep learning in recognition of accrued earnings management
title_full_unstemmed Application of deep learning in recognition of accrued earnings management
title_short Application of deep learning in recognition of accrued earnings management
title_sort application of deep learning in recognition of accrued earnings management
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9976311/
https://www.ncbi.nlm.nih.gov/pubmed/36873477
http://dx.doi.org/10.1016/j.heliyon.2023.e13664
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