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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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Formato: | Online Artículo Texto |
Lenguaje: | English |
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PeerJ Inc.
2023
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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 |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-10280455 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
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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