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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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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. |
format | Online Article Text |
id | pubmed-9976311 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT lijia applicationofdeeplearninginrecognitionofaccruedearningsmanagement AT sunzhoutianyang applicationofdeeplearninginrecognitionofaccruedearningsmanagement |