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...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-12-01
|
| Series: | Systems Science & Control Engineering |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/21642583.2024.2305411 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|---|
| ISSN: | 2164-2583 |