Research on the financial early warning models based on ensemble learning algorithms: Introducing MD&A and stock forum comments textual indicators

Zhiheng Zhang, Zhenji Zhu and Yongjun Hua

PLOS ONE, 2025, vol. 20, issue 5, 1-26

Abstract: 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.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0323737

DOI: 10.1371/journal.pone.0323737

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