Research on the financial early warning models based on ensemble learning algorithms: Introducing MD&A and stock forum comments textual indicators.
This study analyzes 284 publicly listed companies first designated as ST or *ST between 2015 and 2023. It utilizes two types of textual indicators: Management's Discussion and Analysis (MD&A) and stock forum comments. PCA and MLP are employed for dimensionality reduction. The study compares...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0323737 |
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| _version_ | 1849762407401914368 |
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| author | Zhiheng Zhang Zhenji Zhu Yongjun Hua |
| author_facet | Zhiheng Zhang Zhenji Zhu Yongjun Hua |
| author_sort | Zhiheng Zhang |
| collection | DOAJ |
| description | This study analyzes 284 publicly listed companies first designated as ST or *ST between 2015 and 2023. It utilizes two types of textual indicators: Management's Discussion and Analysis (MD&A) and stock forum comments. PCA and MLP are employed for dimensionality reduction. The study compares the recognition performance of single-class models with ensemble learning models while also examining the impact of various base learners and meta-learners on the performance of the ensemble learning model. The findings show that using the two types of textual indicators significantly enhanced the model's accuracy in recognition. The single-class and ensemble learning models demonstrated average improvements of 1.24% and 1.75%, respectively. Notably, stock forum comments outperformed MD&A text. Additionally, the MLP proved more effective in feature processing than PCA. The D-M-BSA-FT model achieved an accuracy of 88.89%. Ensemble learning models outperform single classification models. After introducing textual features, the ensemble learning model achieved an average recognition accuracy of 85.31%, compared to 82.09% for the single classification model. Therefore, the financial warning model developed in this study provides valuable insights for enhancing the accuracy of financial warning identification. |
| format | Article |
| id | doaj-art-5affb2b56d4d40bf9d44eb752d0db0cc |
| institution | DOAJ |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-5affb2b56d4d40bf9d44eb752d0db0cc2025-08-20T03:05:44ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01205e032373710.1371/journal.pone.0323737Research on the financial early warning models based on ensemble learning algorithms: Introducing MD&A and stock forum comments textual indicators.Zhiheng ZhangZhenji ZhuYongjun HuaThis study analyzes 284 publicly listed companies first designated as ST or *ST between 2015 and 2023. It utilizes two types of textual indicators: Management's Discussion and Analysis (MD&A) and stock forum comments. PCA and MLP are employed for dimensionality reduction. The study compares the recognition performance of single-class models with ensemble learning models while also examining the impact of various base learners and meta-learners on the performance of the ensemble learning model. The findings show that using the two types of textual indicators significantly enhanced the model's accuracy in recognition. The single-class and ensemble learning models demonstrated average improvements of 1.24% and 1.75%, respectively. Notably, stock forum comments outperformed MD&A text. Additionally, the MLP proved more effective in feature processing than PCA. The D-M-BSA-FT model achieved an accuracy of 88.89%. Ensemble learning models outperform single classification models. After introducing textual features, the ensemble learning model achieved an average recognition accuracy of 85.31%, compared to 82.09% for the single classification model. Therefore, the financial warning model developed in this study provides valuable insights for enhancing the accuracy of financial warning identification.https://doi.org/10.1371/journal.pone.0323737 |
| spellingShingle | Zhiheng Zhang Zhenji Zhu Yongjun Hua Research on the financial early warning models based on ensemble learning algorithms: Introducing MD&A and stock forum comments textual indicators. PLoS ONE |
| title | Research on the financial early warning models based on ensemble learning algorithms: Introducing MD&A and stock forum comments textual indicators. |
| title_full | Research on the financial early warning models based on ensemble learning algorithms: Introducing MD&A and stock forum comments textual indicators. |
| title_fullStr | Research on the financial early warning models based on ensemble learning algorithms: Introducing MD&A and stock forum comments textual indicators. |
| title_full_unstemmed | Research on the financial early warning models based on ensemble learning algorithms: Introducing MD&A and stock forum comments textual indicators. |
| title_short | Research on the financial early warning models based on ensemble learning algorithms: Introducing MD&A and stock forum comments textual indicators. |
| title_sort | research on the financial early warning models based on ensemble learning algorithms introducing md amp a and stock forum comments textual indicators |
| url | https://doi.org/10.1371/journal.pone.0323737 |
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