Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

REVIEWER’S REPORT

Title: Decoding Financial Futures: The Power of Numerical Indicators in Predicting Bankruptcy

 

The research examines the behaviour of numerical indicators in predicting the 12 future bankruptcy of companies, by using statistical analysis models. Following the PRISMA standard, ten primary studies were included in this manuscript. The issues discussed in the article are undoubtedly important and interesting. The paper is well-written and easy to follow. The existing literature is well-cited. The obtained results are important for making valid business and financial decisions in all kinds of companies.

 

Bankruptcy prediction is always a current topic, it never loses its importance. Models for predicting financial (economic) distress are a particularly important tool for business. The authors emphasize the importance of profitability ratios, liquidity ratios, leverage ratios, and cash flow ratios in forecasting financial distress are emphasized in this manuscript. As part of that, they must ensure the cash flow indicators have an excellent predictive ability of the financial state of the companies by categorizing the healthy from the unhealthy ones with considerable accuracy. if it is taken into account that profit is a calculation category, and that cash flows are another flow of movement in the company, this is a key point for correctly identifying indicators of possible bankruptcy.

 

The author describes the problem being researched well and clearly. Pregled metoda predstavljen u Table 1. A summary of review studies is very useful for financial analysts when choosing a method for predicting bankruptcy. The author explains clearly in a logical sequence this problem. Furthermore, at the end of the conclusion, the author emphasizes the key messages. A summary of review studies is very useful for financial analysts when choosing a method for predicting bankruptcy. Furthermore, the authors point out very important limitations when it comes to the application of the bankruptcy model because economic and other circumstances (regulatory conditions, market situation) and the time when the model is applied should be taken into account.

 

The research resulted in several important conclusions:

- Most scholars used logistic regression to predict corporate bankruptcy.

- A small number of studies used variables consisting of cash flow ratio information to predict bankruptcy or risk.

- The most precise financial indicators used in bankruptcy prediction models are highlighted.

- The importance of profitability ratios, liquidity ratios, leverage ratios, and cash flow

ratios in forecasting financial distress are particularly highlighted according to their usability.

 

It is necessary to explain in one paragraph the importance of neural networks for bankruptcy detection, which would make the work even richer.

 

Shetty S, Musa M, Brédart X. Bankruptcy Prediction Using Machine Learning Techniques. Journal of Risk and Financial Management. 2022; 15(1):35. https://doi.org/10.3390/jrfm15010035

 

The conclusion should be extended in the sense of giving an explanation of the research problem as an announcement for the conclusions that follow.

 

I recommend citation of the following references, to draw attention to the financial sustainability of the company.

 

Srebro B, Mavrenski B, Bogojević Arsić V, Knežević S, Milašinović M, Travica J. Bankruptcy Risk Prediction in Ensuring the Sustainable Operation of Agriculture Companies. Sustainability. 2021; 13(14):7712. https://doi.org/10.3390/su13147712

Valaskova, K.; Durana, P.; Adamko, P.; Jaros, J. Financial Compass for Slovak Enterprises: Modeling Economic Stability of Agricultural Entities. J. Risk Financial Manag. 2020, 13, 92. https://doi.org/10.3390/jrfm13050092

 

Please, pay attention to the technical instructions, especially the references.

 

 

Comments for author File: Comments.pdf

Author Response

Response to Reviewer 1 Comments

Dear Reviewer,

Thank you for your thorough review of our manuscript. We greatly appreciate your valuable comments and suggestions, which have helped us, improve the clarity, rigor, and overall quality of the paper. Below, we address each of your comments in detail.

Thank you once again for taking the time to review this manuscript.

Yours sincerely,

The authors

                       

Comments 1: It is necessary to explain in one paragraph the importance of neural networks for bankruptcy detection, which would make the work even richer.

Shetty S, Musa M, Brédart X. Bankruptcy Prediction Using Machine Learning Techniques. Journal of Risk and Financial Management. 2022; 15(1):35. https://doi.org/10.3390/jrfm15010035

 

Response 1: Thank you for pointing this out. We modified conclusions as follows:

 

Discussion

The present review focused on bankruptcy prediction using traditional statistical analysis methodologies, purposely avoiding machine learning techniques. By focusing on such approaches as regression analysis, discriminant analysis, and other standard statistical methods, the present study attempts to clearly present their effectiveness in predicting bankruptcy. At the same time, the importance of machine learning cannot be overlooked, in this area. Machine learning techniques, including extreme gradient boosting (XGBoost), support vector machine (SVM), and a deep neural network, have gained a place in the literature for capturing highly multivariate nature through non-linear relationships in financial data that leads to high precision especially for volatile environments [43]. For example, neural networks do not depend on predefined formulas or any assumptions related to data distributions like the traditional statistical methods do, but rather learn from data based on recognition of patterns to model the relationships among financial variables that are complex and non-linear. Such flexibility places artificial neural networks in a position to handle larger and more intricate datasets, a basic requirement for predicting financial performance in dynamic and uncertain environments, characterized by incomplete information. Adapting to new data, exposed neural networks continuously enhance their level of accuracy in predictions. Consequently, ANNs, under these circumstances, can be ideal tools in developing early-warning systems to cater for the high risks associated with bankruptcies for firms as well as financial intermediaries [43,44]

 

 (Please see the changes in our updated manuscript, Discussion, page 10-11 )

 

Comments 2: The conclusion should be extended in the sense of giving an explanation of the research problem as an announcement for the conclusions that follow. I recommend citation of the following references, to draw attention to the financial sustainability of the company.

Srebro B, Mavrenski B, Bogojević Arsić V, Knežević S, Milašinović M, Travica J. Bankruptcy Risk Prediction in Ensuring the Sustainable Operation of Agriculture Companies. Sustainability. 2021; 13(14):7712. https://doi.org/10.3390/su13147712

Valaskova, K.; Durana, P.; Adamko, P.; Jaros, J. Financial Compass for Slovak Enterprises: Modeling Economic Stability of Agricultural Entities. J. Risk Financial Manag. 2020, 13, 92. https://doi.org/10.3390/jrfm13050092

 

Response 2: Thank you for pointing this out. We modified conclusions as follows:

Conclusions

The research problem centers on understanding the effectiveness of traditional statistical analysis methods in predicting business bankruptcy, particularly in a landscape where economic conditions are ever-changing. The results of the present systematic review highlight:

1) The ability of numerators, through simple statistical analysis models, to predict the bankruptcy of businesses and companies.

2) Statistical analysis indicates that cash flow ratios are a dependable tool for forecasting financial distress.

3) Models are built with indicators from a specific economy; it is impossible to consider them stable and unchanging, as changes in a country's economic conditions can potentially impact their predictive accuracy.

Situating the models of bankruptcy prediction in a domain such that firms will be forecast to the period of exact time they risk facing bankruptcy is not the issue; instead, the issue is to state the likelihood that bankruptcy will eventually occur [48]. At this stage, these statistical models are used as an alarm system that gives companies critical signals requiring, in turn, actions to be taken that could include enhancement of the earnings aspect or even making their financial status more firm. This implies that by identifying impending financial distress, firms are in a position to introduce rectification measures that will prevent them from ultimately becoming insolvent. It is also of great importance to other stakeholders—for example, lenders and investors. For the lenders, estimating companies' chances of facing bankruptcy reduces credit risks and enables better management decisions on extending or handling credit based on informed judgments. Potential investors use these indicators in gauging a company’s financial health and insolvency risk profile to guide their investment decisions, avoid high-risk ventures, and make rational choices. Thus, models for bankruptcy prediction do not bring benefits only to the company itself but also to other stakeholders making their decisions with view to long-term interests associated with the financial condition of the company [49]. Bankruptcy prediction models play a very crucial role as strategies for sustainability in an organization. This helps firms to take an early advance notice of risks that might lead to their bankruptcy and take necessary precautions in building their financial base to avert the situation [48-50].

In conclusion, this study underscores the important role numerical indicators play in company bankruptcy forecasting via statistical analysis models. Adhering to the PRISMA standard, our systematic review brings out the key findings from ten primary studies that were included. Importantly, it was shown in the results that even simple statistical methods can effectively predict financial distress, providing stakeholders with valuable insights into the health of organizations. The practical implications of our findings are substantial, as stakeholders, including investors, managers, and policymakers, can leverage these insights to enhance their decision-making processes. Stakeholders can ensure the appraisal of financial stability by focusing on the dependability of cash flow indicators. This also helps them come up with measures to avert crises. Equally, the theoretical implications contribute to the existing body of knowledge on the subject by stressing the role played by quantitative indicators in financial forecasting. Future researchers could further explore advanced analytical techniques and combine qualitative factors for richer predictive models in appraising corporate financial health.

 

(Please see the changes in our updated manuscript, Conclusions, page 11,12 )

 

 

Comments 3: Please, pay attention to the technical instructions, especially the references.

 

Response 3: Modified please see the updated version of our manuscript [References pages 12-14 ].

Reviewer 2 Report

Comments and Suggestions for Authors

In this paper, the authors explore decoding financial futures, the power of numerical indicators in predicting bankruptcy. The gap in the literature or unanswered question that motivated the current paper is clearly defined and provides the rationale for the paper.

It is suggested that the author(s) make certain additions in order to improve the quality of the work:

-          Complete the conclusion. Conclusion can be further elaborated by incorporating the practical and theoretical implications aspects mentioned in this paper.

-          The paper would have better conclusions by using some of the visualization tools (eg VOSviewer).

-          Are the references>

Reference 31. Sfakianakis E. Bankruptcy prediction model for listed companies in Greece. Investment Management and Financial Innovations [Internet]. 2021 May 27 [cited 2023 Dec 18];18(2):166–80. Available from: https://www.businessperspectives.org/index.php/journals/investment-management-and-financial-innovations/issue-381/bankruptcy-prediction-model-for-listed-com-panies-in-greece   

Reference 22. Sfakianakis E. Bankruptcy prediction model for listed companies in Greece. Investment Management and Financial Innovations. 2021;18(2):166-180.

Duplicate, is it the same reference?

 

Reference 31. Sfakianakis E. Bankruptcy prediction model for listed companies in Greece. Investment Management and Financial Innovations [Internet]. 2021 May 27 [cited 2023 Dec 18];18(2):166–80. Available from: https://www.businessperspectives.org/in-dex.php/journals/investment-management-and-financial-innovations/issue-381/bankruptcy-prediction-model-for-listed-com-panies-in-greece

not cited in the text of the paper.

 

Author Response

Response to Reviewer 2 Comments

Dear Reviewer,

Thank you for your thorough review of our manuscript. We greatly appreciate your valuable comments and suggestions, which have helped us, improve the clarity, rigor, and overall quality of the paper. Below, we address each of your comments in detail.

Thank you once again for taking the time to review this manuscript.

 

Yours sincerely,

The authors

                       

Comments 1: Complete the conclusion. Conclusion can be further elaborated by incorporating the practical and theoretical implications aspects mentioned in this paper.

 

 

Response 1: Thank you for pointing this out. We modified conclusions as follows:

 

Conclusions

The research problem centers on understanding the effectiveness of traditional statistical analysis methods in predicting business bankruptcy, particularly in a landscape where economic conditions are ever-changing. The results of the present systematic review highlight:

1) The ability of numerators, through simple statistical analysis models, to predict the bankruptcy of businesses and companies.

2) Statistical analysis indicates that cash flow ratios are a dependable tool for forecasting financial distress.

3) Models are built with indicators from a specific economy; it is impossible to consider them stable and unchanging, as changes in a country's economic conditions can potentially impact their predictive accuracy.

Situating the models of bankruptcy prediction in a domain such that firms will be forecast to the period of exact time they risk facing bankruptcy is not the issue; instead, the issue is to state the likelihood that bankruptcy will eventually occur [48]. At this stage, these statistical models are used as an alarm system that gives companies critical signals requiring, in turn, actions to be taken that could include enhancement of the earnings aspect or even making their financial status more firm. This implies that by identifying impending financial distress, firms are in a position to introduce rectification measures that will prevent them from ultimately becoming insolvent. It is also of great importance to other stakeholders—for example, lenders and investors. For the lenders, estimating companies' chances of facing bankruptcy reduces credit risks and enables better management decisions on extending or handling credit based on informed judgments. Potential investors use these indicators in gauging a company’s financial health and insolvency risk profile to guide their investment decisions, avoid high-risk ventures, and make rational choices. Thus, models for bankruptcy prediction do not bring benefits only to the company itself but also to other stakeholders making their decisions with view to long-term interests associated with the financial condition of the company [49]. Bankruptcy prediction models play a very crucial role as strategies for sustainability in an organization. This helps firms to take an early advance notice of risks that might lead to their bankruptcy and take necessary precautions in building their financial base to avert the situation [48-50].

In conclusion, this study underscores the important role numerical indicators play in company bankruptcy forecasting via statistical analysis models. Adhering to the PRISMA standard, our systematic review brings out the key findings from ten primary studies that were included. Importantly, it was shown in the results that even simple statistical methods can effectively predict financial distress, providing stakeholders with valuable insights into the health of organizations. The practical implications of our findings are substantial, as stakeholders, including investors, managers, and policymakers, can leverage these insights to enhance their decision-making processes. Stakeholders can ensure the appraisal of financial stability by focusing on the dependability of cash flow indicators. This also helps them come up with measures to avert crises. Equally, the theoretical implications contribute to the existing body of knowledge on the subject by stressing the role played by quantitative indicators in financial forecasting. Future researchers could further explore advanced analytical techniques and combine qualitative factors for richer predictive models in appraising corporate financial health.

 (Please see the changes in our updated manuscript, Conlusions, page 11, 12 )

 

 

Comments 2: The paper would have better conclusions by using some of the visualization tools (eg VOSviewer).

 

Response 2: Thank you for your valuable feedback. We appreciate your suggestion to incorporate visualization tools like VOSviewer. While we have made revisions to the conclusion section to enhance clarity, we believe that the current conclusion part effectively convey our findings. Nonetheless, we will consider your suggestion for future work. Thank you again for your insights.

 

Comments 3: -          Are the references:

Reference 31. Sfakianakis E. Bankruptcy prediction model for listed companies in Greece. Investment Management and Financial Innovations [Internet]. 2021 May 27 [cited 2023 Dec 18];18(2):166–80. Available from: https://www.businessperspectives.org/index.php/journals/investment-management-and-financial-innovations/issue-381/bankruptcy-prediction-model-for-listed-com-panies-in-greece  

Reference 22. Sfakianakis E. Bankruptcy prediction model for listed companies in Greece. Investment Management and Financial Innovations. 2021;18(2):166-180.

Duplicate, is it the same reference?

 

Reference 31. Sfakianakis E. Bankruptcy prediction model for listed companies in Greece. Investment Management and Financial Innovations [Internet]. 2021 May 27 [cited 2023 Dec 18];18(2):166–80. Available from: https://www.businessperspectives.org/in-dex.php/journals/investment-management-and-financial-innovations/issue-381/bankruptcy-prediction-model-for-listed-com-panies-in-greece

not cited in the text of the paper.

 

 

Response 3: Thank you for your well noticed comment. We have carefully reviewed and updated the references throughout the manuscript. Modifications were made both in the text and in the reference list. Please see the updated version of our manuscript [References pages 12-14].

Reviewer 3 Report

Comments and Suggestions for Authors

Please see attached report.

Comments for author File: Comments.pdf

Author Response

Response to Reviewer 3 Comments

 

Dear Reviewer,

Thank you for your thorough review of our manuscript. We greatly appreciate your valuable comments and suggestions, which have helped us, improve the clarity, rigor, and overall quality of the paper. Below, we address each of your comments in detail.

Thank you once again for taking the time to review this manuscript.

 

Yours sincerely,

The authors

                       

 

Title and Abstract:

Comment 1. The title is misleading, as it gives the impression that the paper is about financial derivatives (futures), which is not the case. I suggest refining the title to something more accurate, such as “The Power of Numerical Indicators in Predicting Bankruptcy: A Systematic Review.” This title better aligns with the content of your manuscript.

 

Response 1. The title modified as proposed: The Power of Numerical Indicators in Predicting Bankruptcy: A Systematic Review

Please see the updated version of our manuscript (Title page 1)

 

Comment 2. Consequently, the abstract needs to be revised to accurately reflect the scope of your work. You did not conduct any empirical testing of models related to bankruptcy prediction, as currently implied. Be more direct, indicating that the paper is a systematic review rather than an empirical study.

 

Response 2. Thank you for pointing this out. We modified abstract as follows:

 

This paper systematically reviews the behavior of numerical indicators in predicting future bankruptcy of companies through statistical analysis models. Following the PRISMA standard, ten primary studies were included in the review. The obtained results underline 1) the ability of numerical indicators, through simple statistical analysis models, to forecast the bankruptcy of businesses and companies and 2) the reliability of cash flows in predicting financial distress through statistical analysis and that 3) models are built with indicators from a specific economy; it is impossible to consider them stable and unchanging, as changes in a country's economic conditions can potentially impact their predictive accuracy.

Please see the updated version of our manuscript (Abstract page 1)

 

Introduction:

Comment 3. In the introduction, you mention that the best economic analysis is conducted using numerical indicators. I recommend citing relevant work on this domain. Additionally, the statement “through these forecasting models, potential business crises can be avoided” should be reformulated. Consider referring to Shahrour et al. (2021) and broadening the statement to something more comprehensive, such as: “Improving the accuracy of default models is crucial for their role as early warning systems for corporations regarding their financial health, ultimately helping to prevent default.”

 

Response 3. Thank you for pointing this out. We modified conclusions as follows:

The most well-known method of economic analysis takes place with the use of numerical indicators [1].

Moreover, improving the accuracy of default models is crucial for their role as early warning systems for corporations regarding their financial health, ultimately helping to prevent default [7].

Please see the updated version of our manuscript Introduction,  page 1

 

Comment 4. On the next page, you discuss financial difficulty and bankruptcy, but you fail to define bankruptcy, which is a significant oversight given the paper's focus. I suggest to define default risk clearly. The same work of Shahrour et al. (2021) can be helpful. Moreover, while your discussion revolves around numerical indicators, you do not provide specific examples of what these indicators are or how they relate to default risk.

 

Response 4. Thank you for pointing this out. We modified introduction as follows: Please see the updated version of our manuscript page 1

 

Cash flow ratios (cash to sales, cash to assets and cash to equity) present a dynamic view of the company's financial performance by capturing the statement changes, while the traditional ratios (current ratio, quick ratio, debt-to-Equity Ratio) provide a static view of the financial performance by measuring a single point in time [5]. Bankruptcy is the legal status of a company that is unable to repay its debts, leading to court proceedings for the settlement of obligations, often resulting in liquidation or reorganization. Default risk, on the other hand, refers to the likelihood that a company will be unable to meet its debt obligations as they become due. It is a key indicator of financial distress and can serve as a precursor to bankruptcy [6,7]

 

 

Comment 5. Given that your paper is a systematic review rather than an empirical study, the research question presented at the end of the introduction is misleading. It should be replaced with research questions that align with the PRISMA framework.

 

Response 5. Thank you for your insightful comment. We agree that the question "Using statistical analysis methods, can the numerical indicators predict companies' future bankruptcy?" does not fully align with the PRISMA framework. To better align with the PRISMA framework, the question should focus on reviewing and synthesizing existing literature. We modified the research question as:

What numerical indicators and statistical analysis methods have been used in the literature to predict companies' future bankruptcy?

Please see the updated version of our manuscript Introduction, page 2

 

Comment 6. Additionally, the customary final paragraph in the introduction that outlines the sections of the paper is missing. The introduction should build a strong case for the paper, clearly state the research question, highlight your contribution to the field, and emphasize the novelty and practical relevance of your findings. It’s crucial to outline the main findings and their significance to the industry. Currently, the introduction does not effectively explain the rationale behind your study or the importance of the connections you are making.

 

Response 6. Thank you for pointing this out. We modified the introduction as follows: Please see the updated version of our manuscript Introduction, page 2

 

The increasing complexity of the global business environment has made corporate bankruptcy a critical concern for stakeholders, from investors to company executives. Accurate prediction of bankruptcy is not only essential for risk management but also for strategic planning, product development, and market positioning. By fully understanding the behavior of indicators, stakeholders are now equipped with a powerful tool that enhances their decision-making ability. This knowledge empowers members of organizations, enabling them to make informed choices about product development, marketing strategies, and investments. Consequently, this review serves as a cornerstone, closing the divide between theoretical understanding and real-world implementation within the field of business strategy.

The scope of this review includes studies that have applied numerical indicators and statistical models to predict bankruptcy across various industries and geographies. This paper follows the PRISMA framework, ensuring a rigorous and structured analysis of the available literature. The findings of this review are particularly relevant for business leaders, investors, and policymakers seeking to improve risk management practices and anticipate financial distress in companies. In addition, the review provides a foundation for future research, encouraging the development of more sophisticated models and approaches for predicting bankruptcy in an increasingly complex economic landscape. The research question is posed as follows:

What numerical indicators and statistical analysis methods have been used in the literature to predict companies' future bankruptcy?

 

Methodology:

Comment 7. In the first paragraph of the methodology section, you should cite and provide examples of numerical indicators, including both traditional and cash flow indicators, to enhance clarity.

 

Response 7. Thank you for your suggestion regarding the first paragraph of the methodology section. To address this, we have added more information in the introduction part (page 1) to enhance clarity on the types of numerical indicators considered in the review. More specific:

 

Cash flow ratios (cash to sales, cash to assets and cash to equity) present a dynamic view of the company's financial performance by capturing the statement changes, while the traditional ratios (current ratio, quick ratio, debt-to-Equity Ratio) provide a static view of the financial performance by measuring a single point in time [5].

In the results section, we believe that the detailed table we have provided offers comprehensive insights into the various numerical indicators used in the literature. This table summarizes the key indicators and their application across multiple studies, giving readers sufficient details without overloading the methodology section with repetitive information. By presenting the indicators clearly in the results, we ensure that the discussion remains focused and easy to follow.

 

Results:

Comment 8. In the results section, lines 98-99, you mention that larger corporations are more prone to default risk. This contradicts the default risk literature in top-tier journals. Additionally, given your discussion on default risk and the importance of size, it is surprising that you do not reference the seminal work of Altman (1968).

 

 

Response 8. Thank you for pointing this out. We modified discussion part page 9 as follows: Please see the updated version of our manuscript:

 

At this point, it is essential to reference the seminal work of Altman [30], who developed the Z-Score model, one of the most influential and widely used tools for bankruptcy prediction. Specifically, Altman demonstrated the importance of factors like company size, profitability, and leverage in assessing bankruptcy.

 

Conclusion:

Comment 9. The conclusion needs to be rewritten to summarize the key findings, discuss their relevance, and propose future research directions. Since this is a systematic review, the conclusion should emphasize the implications of your findings for the field and suggest areas for further exploration.

 

Response 9. Thank you for pointing this out. We modified conclusions as follows: Please see the updated version of our manuscript (page 10)

 

Conclusions

The research problem centers on understanding the effectiveness of traditional statistical analysis methods in predicting business bankruptcy, particularly in a landscape where economic conditions are ever-changing. The results of the present systematic review highlight:

1) The ability of numerators, through simple statistical analysis models, to predict the bankruptcy of businesses and companies.

2) Statistical analysis indicates that cash flow ratios are a dependable tool for forecasting financial distress.

3) Models are built with indicators from a specific economy; it is impossible to consider them stable and unchanging, as changes in a country's economic conditions can potentially impact their predictive accuracy.

Situating the models of bankruptcy prediction in a domain such that firms will be forecast to the period of exact time they risk facing bankruptcy is not the issue; instead, the issue is to state the likelihood that bankruptcy will eventually occur [48]. At this stage, these statistical models are used as an alarm system that gives companies critical signals requiring, in turn, actions to be taken that could include enhancement of the earnings aspect or even making their financial status more firm. This implies that by identifying impending financial distress, firms are in a position to introduce rectification measures that will prevent them from ultimately becoming insolvent. It is also of great importance to other stakeholders—for example, lenders and investors. For the lenders, estimating companies' chances of facing bankruptcy reduces credit risks and enables better management decisions on extending or handling credit based on informed judgments. Potential investors use these indicators in gauging a company’s financial health and insolvency risk profile to guide their investment decisions, avoid high-risk ventures, and make rational choices. Thus, models for bankruptcy prediction do not bring benefits only to the company itself but also to other stakeholders making their decisions with view to long-term interests associated with the financial condition of the company [49]. Bankruptcy prediction models play a very crucial role as strategies for sustainability in an organization. This helps firms to take an early advance notice of risks that might lead to their bankruptcy and take necessary precautions in building their financial base to avert the situation [48-50].

In conclusion, this study underscores the important role numerical indicators play in company bankruptcy forecasting via statistical analysis models. Adhering to the PRISMA standard, our systematic review brings out the key findings from ten primary studies that were included. Importantly, it was shown in the results that even simple statistical methods can effectively predict financial distress, providing stakeholders with valuable insights into the health of organizations. The practical implications of our findings are substantial, as stakeholders, including investors, managers, and policymakers, can leverage these insights to enhance their decision-making processes. Stakeholders can ensure the appraisal of financial stability by focusing on the dependability of cash flow indicators. This also helps them come up with measures to avert crises. Equally, the theoretical implications contribute to the existing body of knowledge on the subject by stressing the role played by quantitative indicators in financial forecasting. Future researchers could further explore advanced analytical techniques and combine qualitative factors for richer predictive models in appraising corporate financial health.

 

 

Minor Comments:

Comment 10. On page 2, line 74, consider dropping the word “international.”

 

Response 10. Deleted and modified as follows:

Primary studies from the literature

Please see the updated version of our manuscript (Methodology, page 2)

 

Comment 11. Cited References:

Altman, E. I. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. The Journal of Finance, 23(4), 589–609.

Shahrour, M. H., Girerd-Potin, I., & Taramasco, O. (2021). Corporate social responsibility and firm default risk in the Eurozone: a market-based approach. Managerial Finance, 47(7), 975–997.

 

Response 11. Added.  References were cited both in the text and in the reference list.

 

 

Round 2

Reviewer 3 Report

Comments and Suggestions for Authors

Thank you for the revised manuscript, which I am glad to accept.