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The design of early warning software systems for financial crises in high-tech businesses using fusion models in the context of sustainable economic growth

Enterprises are urged to continue implementing the sustainable development strategy in their business operations as “carbon neutrality” and “carbon peak” gradually become the current stage’s worldwide targets. High-tech businesses (HTE) need to be better equipped to manage financial risks and avoid...

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Autor principal: Guo, Houfang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280455/
https://www.ncbi.nlm.nih.gov/pubmed/37346723
http://dx.doi.org/10.7717/peerj-cs.1326
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author Guo, Houfang
author_facet Guo, Houfang
author_sort Guo, Houfang
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description Enterprises are urged to continue implementing the sustainable development strategy in their business operations as “carbon neutrality” and “carbon peak” gradually become the current stage’s worldwide targets. High-tech businesses (HTE) need to be better equipped to manage financial risks and avoid financial crises in the face of severe market competition. The most popular machine learning models—logistic regression, XGBoost, and BP neural networks—are chosen as the base models in this study. The three models are combined using the stacking method to train and forecast the fusion models while offering other researchers some basic model research ideas. The financial crisis early warning (FCEW) of HTE is built concurrently by contrasting the fusion of various quantitative basis models and the fusion procedures of voting and averaging. The outcomes demonstrate that the fusion model outperforms the single model in terms of performance, and the stacked fusion model has the best early warning impact. By comparing and comparing the effect of three fusion models on financial crisis warnings of high-tech enterprises, it makes up for the defect of low accuracy of traditional forecasting methods. It improves the sustainable development path of enterprises.
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spelling pubmed-102804552023-06-21 The design of early warning software systems for financial crises in high-tech businesses using fusion models in the context of sustainable economic growth Guo, Houfang PeerJ Comput Sci Data Mining and Machine Learning Enterprises are urged to continue implementing the sustainable development strategy in their business operations as “carbon neutrality” and “carbon peak” gradually become the current stage’s worldwide targets. High-tech businesses (HTE) need to be better equipped to manage financial risks and avoid financial crises in the face of severe market competition. The most popular machine learning models—logistic regression, XGBoost, and BP neural networks—are chosen as the base models in this study. The three models are combined using the stacking method to train and forecast the fusion models while offering other researchers some basic model research ideas. The financial crisis early warning (FCEW) of HTE is built concurrently by contrasting the fusion of various quantitative basis models and the fusion procedures of voting and averaging. The outcomes demonstrate that the fusion model outperforms the single model in terms of performance, and the stacked fusion model has the best early warning impact. By comparing and comparing the effect of three fusion models on financial crisis warnings of high-tech enterprises, it makes up for the defect of low accuracy of traditional forecasting methods. It improves the sustainable development path of enterprises. PeerJ Inc. 2023-04-21 /pmc/articles/PMC10280455/ /pubmed/37346723 http://dx.doi.org/10.7717/peerj-cs.1326 Text en © 2023 Guo https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Data Mining and Machine Learning
Guo, Houfang
The design of early warning software systems for financial crises in high-tech businesses using fusion models in the context of sustainable economic growth
title The design of early warning software systems for financial crises in high-tech businesses using fusion models in the context of sustainable economic growth
title_full The design of early warning software systems for financial crises in high-tech businesses using fusion models in the context of sustainable economic growth
title_fullStr The design of early warning software systems for financial crises in high-tech businesses using fusion models in the context of sustainable economic growth
title_full_unstemmed The design of early warning software systems for financial crises in high-tech businesses using fusion models in the context of sustainable economic growth
title_short The design of early warning software systems for financial crises in high-tech businesses using fusion models in the context of sustainable economic growth
title_sort design of early warning software systems for financial crises in high-tech businesses using fusion models in the context of sustainable economic growth
topic Data Mining and Machine Learning
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280455/
https://www.ncbi.nlm.nih.gov/pubmed/37346723
http://dx.doi.org/10.7717/peerj-cs.1326
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