Corporate financial distress prediction with multiperiod annual report data: A fusion deep neural network model

Chongren Wang; Pimei Gong; Jiawang Li; Zhiyi Wang

doi:10.1371/journal.pone.0333064

Peer Review History

Original SubmissionJanuary 27, 2025
24 Feb 2025 Decision Letter - Jiwei Tian, Editor -->PONE-D-25-01170-->-->Corporate financial distress prediction with multiperiod annual report data: A fusion deep neural network model-->-->PLOS ONE Dear Dr. Gong, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Please submit your revised manuscript by Apr 10 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. 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To do this, go to ‘Update my Information’ (in the upper left-hand corner of the main menu), and click on the Fetch/Validate link next to the ORCID field. This will take you to the ORCID site and allow you to create a new iD or authenticate a pre-existing iD in Editorial Manager. [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions -->Comments to the Author 1. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. --> Reviewer #1: Yes Reviewer #2: Partly ******** -->2. Has the statistical analysis been performed appropriately and rigorously? --> Reviewer #1: Yes Reviewer #2: Yes ****** -->3. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.--> Reviewer #1: Yes Reviewer #2: Yes ****** -->4. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.--> Reviewer #1: Yes Reviewer #2: Yes ****** -->5. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)--> Reviewer #1: The paper proposes a Fusion Deep Neural Network model based on Multiple annual Reports Text data and Financial data (MTF-FDNN). This model is capable of extracting long-text features from multi-period corporate annual reports as well as features from multi-period financial indicators. To further improve the manuscript, the following suggestions are given: 1、In the paper, some figures in the manuscript are a little blurry, please improve the clarity. 2、The formatting specifications throughout the manuscript need to be carefully checked and revised. For example, formulas need to be centered. 3、Since there are some papers in this topic, the contributions of the manuscript should be better summarized and listed. 4、While the introduction sets the context, a more explicit literature review section could better situate the study within the broader research landscape, such as Financial distress prediction with annual reports-based deep textual feature extraction: A hybrid approach, EVADE Targeted Adversarial False Data Injection Attacks for State Estimation in Smart Grid, and so on. These references could provide valuable insights into your research. 5、Add a section on the limitations of the work and future work in this paper. Reviewer #2: The introduction section 1. The introduction could be strengthened by incorporating more recent literature to better contextualize the study. For example, the sentence: “Previous studies have shown that predictive models that combine textual and financial data can significantly increase the accuracy of financial distress predictions.” lacks citations for the referenced studies. Similarly, the authors state: “In the early stages of financial distress prediction research, scholars domestically and internationally relied primarily on financial indicators from a single fiscal year, applying machine learning or deep learning techniques for prediction. In recent years, text mining has become a hot topic in academia.” Again, no supporting references are provided. Including relevant citations would enhance the credibility of these claims. 2. A clearer transition from the problem statement to the proposed solution would improve the overall flow of the introduction. 3. The choice of Longformer, Bi-LSTM, and TextCNN requires stronger theoretical justification. Why is Longformer better than other transformers? Why were Bi-LSTM and TextCNN combined instead of alternative architectures (e.g., hierarchical attention networks)? Providing a rationale for these choices would strengthen the methodological foundation of the study. 4. Adding a brief description of the structure of the article at the end of the introduction would enhance its clarity and help guide the reader through the following sections. The related works section 5. Some of the references cited in the manuscript are relatively old (e.g., Beaver, 1966). Additionally, the manuscript does not contain many sources, and most references are older than five years. For example, you state “Hajek P. et al. [15] applied XGBoost and AdaBoost algorithms for classification analysis of samples that integrate textual metrics. García V. et al. [16] further improved the classification accuracy of models by combining ensemble strategies such as bagging and random subspace methods including more recent studies would provide a more current context. ..”, the mentioned authors, as well as others, have more recent studies in this subject area. Including newer literature would help provide a more up-to-date context. 6. The organization of related studies into subsections is appreciated, as it enhances the clarity of the article. However, the discussion of individual studies is too brief. The authors introduce the problems each study addresses but do not provide an in-depth discussion of the methodologies or results. A more detailed analysis of prior work, including quantitative performance metrics (e.g., accuracy rates of previous models), would improve the quality of this section. Furthermore, while the limitations of prior studies are mentioned, the research gaps they leave unaddressed are not explicitly stated. Identifying these gaps more clearly would help to better understand the described studies within The related works section. The experiments and results section 7. The CSMAR dataset is used, but there is no justification for why this dataset is representative. More information about the dataset, such as the number of companies, the time span covered, and other relevant characteristics, should be provided. Additionally, there is no discussion of cross-validation or alternative training/test splits to ensure the model avoids overfitting. The authors should explain why they chose a 60:20:20 split and whether they experimented with other ratios (e.g., 70:20:10). 8. The comparison between traditional machine learning models and MTF-FDNN is clear, but the claims of superiority would be more robust with statistical significance testing (e.g., p-values). Adding such analyses would strengthen the validity of the results. Conclusion and future works sections 9. The conclusion could be more reflective, discussing the broader impact and potential applications of the proposed model. 10. A deeper comparison between the proposed MTF-FDNN model and previous financial distress prediction models mentioned in the Related works section is missing. While the article presents results, it does not explicitly compare them to previous state-of-the-art models in financial distress prediction. The authors should clarify why MTF-FDNN is superior to BERT-based models, CNN-only models, or other hybrid architectures. 11. A discussion of computational efficiency and trade-offs between accuracy and interpretability would be beneficial. Addressing these aspects would provide a more balanced assessment of the model's practical implications. 12. There is no discussion including the limitations and implications of the research. Overall, the structure of the research article is non-standard. ****** -->6. PLOS authors have the option to publish the peer review history of their article (what does this mean? ). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy .--> Reviewer #1: No Reviewer #2: No ******** [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/ . PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org . Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0333064.r001
Revision 1
3 Apr 2025 Author Response Dear Editors: This is a resubmitted manuscript by Chongren Wang, Pimei Gong, Jiawang Li and Zhiyi Wang, entitled “Corporate financial distress prediction with multiperiod annual report data: A fusion deep neural network model” with Manuscript [PONE-D-25-01170]. We have carefully revised this paper according to the editorial decision letter one by one and explains how the paper has been changed point-to-point. See Section 1 for a detailed description. The authors have read and approved this version of the article, and due care has been taken to ensure the integrity of the work. No part of this paper has been published or submitted elsewhere. No conflict of interest exits in the submission of this manuscript. This paper elaborately details a fused deep neural network model based on multiperiod annual report text and financial indicator data to predict corporate financial distress. The main contributions of this study are as follows: (1) Development of a feature fusion model to extract semantic features from long texts. This study applies multiperiod financial report texts to the field of financial distress prediction and designs a novel neural network architecture tailored to the characteristics of multiperiod financial report texts. This approach enhances the prediction performance of financial distress models that incorporate long texts from multiperiod financial reports. First, the pretrained Longformer model is utilized to compute word vectors for the long texts in financial reports. Subsequently, the global features of the text were extracted via bidirectional long short-term memory (Bi-LSTM), and the local features were extracted via a Text Convolutional Neural Network (TextCNN). A feature-level fusion method is then used to construct multiperiod semantic features of long texts, offering a new perspective for extracting semantics from multiperiod financial report texts. (2) A dynamic financial distress prediction model is constructed on the basis of multiperiod annual report data. A dynamic financial distress prediction model is constructed on the basis of multimodal data from multiperiod annual report texts and financial indicators. First, multiple fully connected neural networks are used to process multiperiod financial indicator data. Next, a fusion model is designed to handle multiperiod annual report texts by extracting long texts from the Management Discussion and Analysis (MD&A) sections of multiple years. The feature fusion model is then used to compute the global and local semantic features of these long texts, and the results are integrated as multiperiod text semantic features. Finally, a neural network combines the extracted multiperiod textual semantics and multiperiod financial features for predictive computation. We believe the paper may be of particular interest to the readers of your journal. Correspondence should be addressed to Prof. Gong at the following address, phone, and email address: School of Management Science and Engineering Shandong University of Finance and Economics, Erhuandong Road, Lixia District, Jinan 250014, P.R. China. Tel: (+86) 19861818113 E-mail: m1926017049@gmail.com Thanks very much for your attention to my paper. We are looking forward to hearing from you soon. Sincerely yours, Pimei Gong Detailed Response to Reviewers Manuscript ID: [PONE-D-25-01170] Paper Title: Corporate financial distress prediction with multiperiod annual report data: A fusion deep neural network model Dear Editors and Reviewers, Thanks for your letter and for the reviewers’ constructive comments concerning our manuscript entitled “Corporate financial distress prediction with multiperiod annual report data: A fusion deep neural network model” (Manuscript [PONE-D-25-01170]). These comments are all valuable and very helpful for improving our paper, as well as the important guiding significance to our research. We have modified our paper according to these comments carefully. We have used the "Track Changes" function in Microsoft Word to revise the manuscript without making any unmarked modifications. Please see purple colored parts for the revisions. We deeply hope that our revised paper could meet your requirements. Section 1: a) and b) a. The entire comments made by Reviewers Reviewer #1: The paper proposes a Fusion Deep Neural Network model based on Multiple annual Reports Text data and Financial data (MTF-FDNN). This model is capable of extracting long-text features from multi-period corporate annual reports as well as features from multi-period financial indicators. To further improve the manuscript, the following suggestions are given: 1、In the paper, some figures in the manuscript are a little blurry, please improve the clarity. 2、The formatting specifications throughout the manuscript need to be carefully checked and revised. For example, formulas need to be centered. 3、Since there are some papers in this topic, the contributions of the manuscript should be better summarized and listed. 4、While the introduction sets the context, a more explicit literature review section could better situate the study within the broader research landscape, such as Financial distress prediction with annual reports-based deep textual feature extraction: A hybrid approach, EVADE Targeted Adversarial False Data Injection Attacks for State Estimation in Smart Grid, and so on. These references could provide valuable insights into your research. 5、Add a section on the limitations of the work and future work in this paper. Reviewer #2 The introduction section 1. The introduction could be strengthened by incorporating more recent literature to better contextualize the study. For example, the sentence: “Previous studies have shown that predictive models that combine textual and financial data can significantly increase the accuracy of financial distress predictions.” lacks citations for the referenced studies. Similarly, the authors state: “In the early stages of financial distress prediction research, scholars domestically and internationally relied primarily on financial indicators from a single fiscal year, applying machine learning or deep learning techniques for prediction. In recent years, text mining has become a hot topic in academia.” Again, no supporting references are provided. Including relevant citations would enhance the credibility of these claims. 2. A clearer transition from the problem statement to the proposed solution would improve the overall flow of the introduction. 3. The choice of Longformer, Bi-LSTM, and TextCNN requires stronger theoretical justification. Why is Longformer better than other transformers? Why were Bi-LSTM and TextCNN combined instead of alternative architectures (e.g., hierarchical attention networks)? Providing a rationale for these choices would strengthen the methodological foundation of the study. 4. Adding a brief description of the structure of the article at the end of the introduction would enhance its clarity and help guide the reader through the following sections. The related works section 5. Some of the references cited in the manuscript are relatively old (e.g., Beaver, 1966). Additionally, the manuscript does not contain many sources, and most references are older than five years. For example, you state “Hajek P. et al. [15] applied XGBoost and AdaBoost algorithms for classification analysis of samples that integrate textual metrics. García V. et al. [16] further improved the classification accuracy of models by combining ensemble strategies such as bagging and random subspace methods including more recent studies would provide a more current context.”, the mentioned authors, as well as others, have more recent studies in this subject area. Including newer literature would help provide a more up-to-date context. 6. The organization of related studies into subsections is appreciated, as it enhances the clarity of the article. However, the discussion of individual studies is too brief. The authors introduce the problems each study addresses but do not provide an in-depth discussion of the methodologies or results. A more detailed analysis of prior work, including quantitative performance metrics (e.g., accuracy rates of previous models), would improve the quality of this section. Furthermore, while the limitations of prior studies are mentioned, the research gaps they leave unaddressed are not explicitly stated. Identifying these gaps more clearly would help to better understand the described studies within The related works section. The experiments and results section 7. The CSMAR dataset is used, but there is no justification for why this dataset is representative. More information about the dataset, such as the number of companies, the time span covered, and other relevant characteristics, should be provided. Additionally, there is no discussion of cross-validation or alternative training/test splits to ensure the model avoids overfitting. The authors should explain why they chose a 60:20:20 split and whether they experimented with other ratios (e.g., 70:20:10). 8. The comparison between traditional machine learning models and MTF-FDNN is clear, but the claims of superiority would be more robust with statistical significance testing (e.g., p-values). Adding such analyses would strengthen the validity of the results. Conclusion and future works sections 9. The conclusion could be more reflective, discussing the broader impact and potential applications of the proposed model. 10. A deeper comparison between the proposed MTF-FDNN model and previous financial distress prediction models mentioned in the Related works section is missing. While the article presents results, it does not explicitly compare them to previous state-of-the-art models in financial distress prediction. The authors should clarify why MTF-FDNN is superior to BERT-based models, CNN-only models, or other hybrid architectures. 11. A discussion of computational efficiency and trade-offs between accuracy and interpretability would be beneficial. Addressing these aspects would provide a more balanced assessment of the model's practical implications. 12. There is no discussion including the limitations and implications of the research. b. The Point-to-Point responses to the Reviewers. Reply to Editors We are grateful to you for providing us with the opportunity to strengthen the paper. The detailed explanations can be seen below. Reviewer #1: Comment 1: Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. Response: We have revised the manuscript accordingly to ensure compliance with the journal’s guidelines, including formatting, file naming, and citation styles. Comment 2: Please note that PLOS ONE has specific guidelines on code sharing for submissions in which author-generated code underpins the findings in the manuscript. In these cases, we expect all author-generated code to be made available without restrictions upon publication of the work. Response: Additional explanation on code sharing was provided in the data availability section, and necessary code is available on GitHub (https://github.com/1926017049/MTF-FDNN-model). Comment 3: Please state what role the funders took in the study. If the funders had no role, please state: "The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript." Response: The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript and declaration has been added to the funding section. Comment 4: PLOS requires an ORCID iD for the corresponding author in Editorial Manager on papers submitted after December 6th, 2016. Please ensure that you have an ORCID iD and that it is validated in Editorial Manager. To do this, go to ‘Update my Information’ (in the upper left-hand corner of the main menu), and click on the Fetch/Validate link next to the ORCID field. This will take you to the ORCID site and allow you to create a new iD or authenticate a pre-existing iD in Editorial Manager. Response: We have ensured that the corresponding author has a registered and validated ORCID iD in Editorial Manager. Thanks again for your kind reminder. We deeply hope that our revised paper could meet your requirements. Thank you very much again for your kind help. Reply to Reviewer #1 We are grateful to you for identifying the weaknesses in our paper and providing us with the opportunity to strengthen the paper. The detailed explanations can be seen below. Reviewer #1: The paper proposes a Fusion Deep Neural Network model based on Multiple annual Reports Text data and Financial data (MTF-FDNN). This model is capable of extracting long-text features from multi-period corporate annual reports as well as features from multi-period financial indicators. Comment 1: In the paper, some figures in the manuscript are a little blurry, please improve the clarity. Response: We have addressed the issue of blurry figures in the manuscript by replacing the affected images with high-resolution versions. Additionally, all figures were carefully checked and adjusted for sharpness to ensure consistency throughout the document. Comment 2: The formatting specifications throughout the manuscript need to be carefully checked and revised. For example, formulas need to be centered. Response: We have thoroughly revised the manuscript formatting according to the journal's guidelines, with specific attention to the issues you raised: Formula Alignment: All formulas have been centered and consistently numbered. Comment 3: Since there are some papers in this topic, the contributions of the manuscript should be better summarized and listed. Response: In the introduction section, we have clearly summarized the main contributions of our manuscript. Specifically, our contributions include: (1) Development of a feature fusion model to extract semantic features from long texts. This study applies multiperiod financial report texts to the field of financial distress prediction and designs a novel neural network architecture tailored to the characteristics of multiperiod financial report texts. This approach enhances the prediction performance of financial distress models that incorporate long texts from multiperiod financial reports. First, the pretrained Longformer model is utilized to compute word vectors for the long texts in financial reports. Subsequently, the global features of the text were extracted via bidirectional long short-term memory (Bi-LSTM), and the local features were extracted via a Text Convolutional Neural Network (TextCNN). A feature-level fusion method is then used to construct multiperiod semantic features of long texts, offering a new perspective for extracting semantics from multiperiod financial report texts. (2) A dynamic financial distress prediction model is constructed on the basis of multiperiod annual report data. A dynamic financial distress prediction model is constructed on the basis of multimodal data from multiperiod annual report texts and financial indicators. First, multiple fully connected neural networks are used to process multiperiod financial indicator data. Next, a fusion model is designed to handle multiperiod annual report texts by extracting long texts from the Management Discussion and Analysis (MD&A) sections of multiple years. The feature fusion model is then used to compute the global and local semantic features of these long texts, and the results are integrated as multiperiod text semantic features. Finally, a neural network combines the extracted multiperiod textual semantics and multiperiod financial features for predictive computation. To further clarify our contributions, we have refined the wording in the introduction to ensure that readers can better understand the novelty and significance of our work. We hope this revision addresses the reviewer's concern. Thank you for your insightful suggestion. Comment 4: While the introduction sets the context, a more explicit literature review section could better situate the study within the broader research landscape, such as financial distress prediction with annual reports-based deep textual feature extr Attachments Attachment Submitted filename: Response to Reviewers.docx https://doi.org/10.1371/journal.pone.0333064.r002
1 Aug 2025 Decision Letter - Arne Johannssen, Editor -->PONE-D-25-01170R1-->-->Corporate financial distress prediction with multiperiod annual report data: A fusion deep neural network model-->-->PLOS ONE Dear Dr. Gong, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. ============================== Please carefully address the comments raised by the reviewers. ============================== Please submit your revised manuscript by Sep 15 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. Please include the following items when submitting your revised manuscript:--> A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'. An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'. If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter. If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols . We look forward to receiving your revised manuscript. Kind regards, Arne Johannssen Academic Editor PLOS ONE Journal Requirements: If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise. [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions -->Comments to the Author 1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.--> Reviewer #1: (No Response) Reviewer #2: All comments have been addressed ******** -->2. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. --> Reviewer #1: (No Response) Reviewer #2: Yes ****** -->3. Has the statistical analysis been performed appropriately and rigorously? --> Reviewer #1: (No Response) Reviewer #2: Yes ****** -->4. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.--> Reviewer #1: (No Response) Reviewer #2: Yes ****** -->5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.--> Reviewer #1: (No Response) Reviewer #2: Yes ****** -->6. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)--> Reviewer #1: Please revise carefully based on the comments raised. Although experimental results are provided, the paper would benefit from comparative analysis with current state-of-the-art approaches. More in depth comparison and analysis should be given in the manuscript. Reviewer #2: The authors deal with an interesting topic. The topic is well work out at the required level in term of content and of formal aspect. ****** -->7. PLOS authors have the option to publish the peer review history of their article (what does this mean? ). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy .--> Reviewer #1: No Reviewer #2: No ******** [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/ . PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org . Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0333064.r003
Revision 2
12 Aug 2025 Author Response Section 1: a) and b) a. The entire comments made by Reviewers Reviewer #1: Please revise carefully based on the comments raised. Although experimental results are provided, the paper would benefit from comparative analysis with current state-of-the-art approaches. More in depth comparison and analysis should be given in the manuscript. Reviewer #2: The authors deal with an interesting topic. The topic is well work out at the required level in term of content and of formal aspect. b. The Point-to-Point responses to the Reviewers. Reply to Reviewer #1 We are grateful to you for providing us with the opportunity to strengthen the paper. The detailed explanations can be seen below. Reviewer #1: Please revise carefully based on the comments raised. Although experimental results are provided, the paper would benefit from comparative analysis with current state-of-the-art approaches. More in depth comparison and analysis should be given in the manuscript. Response: Thank you for your valuable comments. We have added a new section, 4.4 "Parameter Sensitivity Analysis," in the manuscript, where we conduct a detailed analysis of several key parameters to further enrich the experimental results and discussion. Please refer to the main text for details. We hope these additions better demonstrate the advantages and performance of our method. Attachments Attachment Submitted filename: Response_to_Reviewers_auresp_2.docx https://doi.org/10.1371/journal.pone.0333064.r004
9 Sep 2025 Decision Letter - Arne Johannssen, Editor Corporate financial distress prediction with multiperiod annual report data: A fusion deep neural network model PONE-D-25-01170R2 Dear Dr. Gong, We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication. An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. For questions related to billing, please contact billing support . If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. Kind regards, Arne Johannssen Academic Editor PLOS ONE Additional Editor Comments (optional): Reviewer #2: Reviewers' comments: Reviewer's Responses to Questions -->Comments to the Author 1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.--> Reviewer #2: All comments have been addressed ******** -->2. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. --> Reviewer #2: Yes ****** -->3. Has the statistical analysis been performed appropriately and rigorously? --> Reviewer #2: Yes ****** -->4. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.--> Reviewer #2: Yes ****** -->5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.--> Reviewer #2: Yes ****** -->6. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)--> Reviewer #2: I have reviewed the revised manuscript and the authors' responses to the comments from the second review. I am pleased to see that all the questions and concerns raised have been thoroughly addressed in their revisions. ****** -->7. PLOS authors have the option to publish the peer review history of their article (what does this mean? ). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy .--> Reviewer #2: No ******** https://doi.org/10.1371/journal.pone.0333064.r005
Formally Accepted
Acceptance Letter - Arne Johannssen, Editor PONE-D-25-01170R2 PLOS ONE Dear Dr. Gong, I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team. At this stage, our production department will prepare your paper for publication. This includes ensuring the following: * All references, tables, and figures are properly cited * All relevant supporting information is included in the manuscript submission, * There are no issues that prevent the paper from being properly typeset You will receive further instructions from the production team, including instructions on how to review your proof when it is ready. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few days to review your paper and let you know the next and final steps. Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. You will receive an invoice from PLOS for your publication fee after your manuscript has reached the completed accept phase. If you receive an email requesting payment before acceptance or for any other service, this may be a phishing scheme. Learn how to identify phishing emails and protect your accounts at https://explore.plos.org/phishing. If we can help with anything else, please email us at customercare@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Profesor Arne Johannssen Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0333064.r006

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