A hybrid approach of deep learning to forecast financial performance: from unsupervised to supervised
The financial performance of a listed company is a common concern for shareholders, creditors, employees, securities analysts, and the government. Measuring and forecasting financial performance informs stakeholders about a company's overall well-being. In this study, we propose a hybrid approa...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Systems Science & Control Engineering |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/21642583.2024.2305411 |
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| _version_ | 1846118852996366336 |
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| author | Jiadong Teng |
| author_facet | Jiadong Teng |
| author_sort | Jiadong Teng |
| collection | DOAJ |
| description | The financial performance of a listed company is a common concern for shareholders, creditors, employees, securities analysts, and the government. Measuring and forecasting financial performance informs stakeholders about a company's overall well-being. In this study, we propose a hybrid approach that combines grey relation analysis, Self-Organized Mapping (SOM) neural network, and convolutional neural network to assess the financial performance of listed companies. Grey relation analysis measures financial performance, SOM neural network clusters, and convolutional neural network forecasts. Compared to other models, the hybrid convolutional neural network model has a better predictive effect, accurately forecasting the financial status of listed companies. Findings also reveal that 70.93 percent of listed companies in agriculture, forestry, husbandry, and fisheries have a poor financial status. |
| format | Article |
| id | doaj-art-f1ada50b63ac40d6b8dcb92a0c67e986 |
| institution | Kabale University |
| issn | 2164-2583 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Systems Science & Control Engineering |
| spelling | doaj-art-f1ada50b63ac40d6b8dcb92a0c67e9862024-12-17T09:06:12ZengTaylor & Francis GroupSystems Science & Control Engineering2164-25832024-12-0112110.1080/21642583.2024.2305411A hybrid approach of deep learning to forecast financial performance: from unsupervised to supervisedJiadong Teng0College of Computer and Information Science, Southwest University, Chongqing, People’s Republic of ChinaThe financial performance of a listed company is a common concern for shareholders, creditors, employees, securities analysts, and the government. Measuring and forecasting financial performance informs stakeholders about a company's overall well-being. In this study, we propose a hybrid approach that combines grey relation analysis, Self-Organized Mapping (SOM) neural network, and convolutional neural network to assess the financial performance of listed companies. Grey relation analysis measures financial performance, SOM neural network clusters, and convolutional neural network forecasts. Compared to other models, the hybrid convolutional neural network model has a better predictive effect, accurately forecasting the financial status of listed companies. Findings also reveal that 70.93 percent of listed companies in agriculture, forestry, husbandry, and fisheries have a poor financial status.https://www.tandfonline.com/doi/10.1080/21642583.2024.2305411Grey relation analysisSOM neural networkconvolutional neural networkfinancial performancehybrid approach |
| spellingShingle | Jiadong Teng A hybrid approach of deep learning to forecast financial performance: from unsupervised to supervised Systems Science & Control Engineering Grey relation analysis SOM neural network convolutional neural network financial performance hybrid approach |
| title | A hybrid approach of deep learning to forecast financial performance: from unsupervised to supervised |
| title_full | A hybrid approach of deep learning to forecast financial performance: from unsupervised to supervised |
| title_fullStr | A hybrid approach of deep learning to forecast financial performance: from unsupervised to supervised |
| title_full_unstemmed | A hybrid approach of deep learning to forecast financial performance: from unsupervised to supervised |
| title_short | A hybrid approach of deep learning to forecast financial performance: from unsupervised to supervised |
| title_sort | hybrid approach of deep learning to forecast financial performance from unsupervised to supervised |
| topic | Grey relation analysis SOM neural network convolutional neural network financial performance hybrid approach |
| url | https://www.tandfonline.com/doi/10.1080/21642583.2024.2305411 |
| work_keys_str_mv | AT jiadongteng ahybridapproachofdeeplearningtoforecastfinancialperformancefromunsupervisedtosupervised AT jiadongteng hybridapproachofdeeplearningtoforecastfinancialperformancefromunsupervisedtosupervised |