IDEAS home Printed from https://ideas.repec.org/a/nwe/eajour/y2025i2p445-461.html
   My bibliography  Save this article

Predictability of Predictors of Corporate Failure Using Forward Logistic Regression: Evidence from Bangladesh

Author

Listed:
  • Md. Azim

    (University of Dhaka Ringgold standard institution, Bangladesh)

  • Md. Jamil Sharif

    (University of Dhaka Ringgold standard institution, Bangladesh)

Abstract

The purpose of this study is to find out the financial variables that predominantly influence the prediction of corporate failure. The study is based on forward logistic regression. The variables of the Altman Model have been utilized to test the predictability of predictors of corporate failure because this model is widely employed in this context. A total of 217 firm years of data from 2007 to 2019 have been utilized for this study. The findings of the study show that among the variables considered in this study, the ratio of Earnings before Interest & Taxes to Total Assets has the most predictive capability to predict corporate failure. Besides, the probability of failure can also be better predicted collectively by Net Working Capital Ratio, Equity-to-Liability Ratio, & Asset Turnover Ratio along with the Earnings before Interest and Taxes to Total Assets Ratio. An important finding indicates that the Retained Earnings to Total Assets Ratio is not an effective predictor when it comes to forecasting corporate failure. Through the application of Forward Logistic Regression, one can identify the most influential variables for predicting corporate failure. The decision makers can utilize the findings to identify the factors that possess the highest capability in forecasting corporate failure, thereby enabling them to take the necessary preventive measures.

Suggested Citation

  • Md. Azim & Md. Jamil Sharif, 2025. "Predictability of Predictors of Corporate Failure Using Forward Logistic Regression: Evidence from Bangladesh," Economic Alternatives, University of National and World Economy, Sofia, Bulgaria, issue 2, pages 445-461, June.
    • Handle: RePEc:nwe:eajour:y:2025:i:2:p:445-461
    as

    Download full text from publisher

    File URL: https://www.unwe.bg/doi/eajournal/2025.2/EA.2025.2.08.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 4, pages 71-111.
    2. J.E. Boritz & D.B. Kennedy & Augusto de Miranda e Albuquerque, 1995. "Predicting Corporate Failure Using a Neural Network Approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 4(2), pages 95-111, June.
    3. Edward I. Altman, 2018. "Applications of Distress Prediction Models: What Have We Learned After 50 Years from the Z-Score Models?," IJFS, MDPI, vol. 6(3), pages 1-15, August.
    4. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    5. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 4, pages 123-127.
    6. Noora Alzayed & Rasol Eskandari & Hassan Yazdifar, 2023. "Bank failure prediction: corporate governance and financial indicators," Review of Quantitative Finance and Accounting, Springer, vol. 61(2), pages 601-631, August.
    7. A. N. K. Mizan & Md. Mahabbat Hossain, 2014. "Financial Soundness of Cement Industry of Bangladesh: An Empirical Investigation Using Z-score," American Journal of Trade and Policy, Asian Business Consortium, vol. 1(1), pages 16-22.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Barboza, Flavio & Altman, Edward, 2024. "Predicting financial distress in Latin American companies: A comparative analysis of logistic regression and random forest models," The North American Journal of Economics and Finance, Elsevier, vol. 72(C).
    2. Tomasz Korol, 2019. "Dynamic Bankruptcy Prediction Models for European Enterprises," JRFM, MDPI, vol. 12(4), pages 1-15, December.
    3. Fuentes González, Fabián & Webb, Janette & Sharmina, Maria & Hannon, Matthew & Braunholtz-Speight, Timothy & Pappas, Dimitrios, 2022. "Local energy businesses in the United Kingdom: Clusters and localism determinants based on financial ratios," Energy, Elsevier, vol. 239(PB).
    4. Sanghoon Lee & Keunho Choi & Donghee Yoo, 2023. "Building a core rule-based decision tree to explain the causes of insolvency in small and medium-sized enterprises more easily," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-16, December.
    5. Dewan Azmal Hossain & Saykat Chandra Roy & MD. Sajadul Huque Mim, 2020. "Current Scenario of Financial Soundness of Textile, Pharmaceutical and Chemical Industry of Bangladesh: An Emphasize on Altman’s Z-Score for Publicly Traded Manufacturing Companies," International Journal of Science and Business, IJSAB International, vol. 4(4), pages 16-34.
    6. Samuel Opoku & Kingsley Opoku Appiah & Prince Gyimah, 2024. "Can We Predict the Financial Distress of Banks in Sub-Saharan Africa?," SAGE Open, , vol. 14(3), pages 21582440241, August.
    7. Jackson, Richard H.G. & Wood, Anthony, 2013. "The performance of insolvency prediction and credit risk models in the UK: A comparative study," The British Accounting Review, Elsevier, vol. 45(3), pages 183-202.
    8. Antonio Davila & George Foster & Xiaobin He & Carlos Shimizu, 2015. "The rise and fall of startups: Creation and destruction of revenue and jobs by young companies," Australian Journal of Management, Australian School of Business, vol. 40(1), pages 6-35, February.
    9. Li, Chunyu & Lou, Chenxin & Luo, Dan & Xing, Kai, 2021. "Chinese corporate distress prediction using LASSO: The role of earnings management," International Review of Financial Analysis, Elsevier, vol. 76(C).
    10. Pavol Durana & Lucia Michalkova & Andrej Privara & Josef Marousek & Milos Tumpach, 2021. "Does the life cycle affect earnings management and bankruptcy?," Oeconomia Copernicana, Institute of Economic Research, vol. 12(2), pages 425-461, June.
    11. Simon Cornée, 2014. "Soft Information and Default Prediction in Cooperative and Social Banks," Journal of Entrepreneurial and Organizational Diversity, European Research Institute on Cooperative and Social Enterprises, vol. 3(1), pages 89-103, June.
    12. Zhou, Fanyin & Fu, Lijun & Li, Zhiyong & Xu, Jiawei, 2022. "The recurrence of financial distress: A survival analysis," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1100-1115.
    13. Modina, Michele & Pietrovito, Filomena & Gallucci, Carmen & Formisano, Vincenzo, 2023. "Predicting SMEs’ default risk: Evidence from bank-firm relationship data," The Quarterly Review of Economics and Finance, Elsevier, vol. 89(C), pages 254-268.
    14. Jie Sun & Jie Li & Hamido Fujita & Wenguo Ai, 2023. "Multiclass financial distress prediction based on one‐versus‐one decomposition integrated with improved decision‐directed acyclic graph," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(5), pages 1167-1186, August.
    15. Guido Max Mantovani & Gregory Gadzinski, 2022. "How to Rate the Financial Performance of Private Companies? A Tailored Integrated Rating Methodology Applied to North-Eastern Italian Districts," JRFM, MDPI, vol. 15(11), pages 1-18, October.
    16. Enrico Supino & Nicola Piras, 2022. "Le performance dei modelli di credit scoring in contesti di forte instabilit? macroeconomica: il ruolo delle Reti Neurali Artificiali," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2022(2), pages 41-61.
    17. Adriana Csikosova & Maria Janoskova & Katarina Culkova, 2020. "Application of Discriminant Analysis for Avoiding the Risk of Quarry Operation Failure," JRFM, MDPI, vol. 13(10), pages 1-14, September.
    18. Haoming Wang & Xiangdong Liu, 2021. "Undersampling bankruptcy prediction: Taiwan bankruptcy data," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-17, July.
    19. Trueck, Stefan & Rachev, Svetlozar T., 2008. "Rating Based Modeling of Credit Risk," Elsevier Monographs, Elsevier, edition 1, number 9780123736833.
    20. E. Fedorova A. & M. Chukhlantseva A. & D. Chekrizov V. & ЕЛЕНА Федорова АНАТОЛЬЕВНА & МАРИЯ Чухланцева АЛЕКСАНДРОВНА & ДМИТРИЙ Чекризов ВАСИЛЬЕВИЧ, 2017. "Нормативные значения коэффициентов финансовой устойчивости: особенности видов экономической деятельности // Normative Values of Financial Stability Ratios: Industry-Specific Features," Управленческие науки // Management Science, ФГОБУВО Финансовый университет при Правительстве Российской Федерации // Financial University under The Government of Russian Federation, vol. 7(2), pages 44-55.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nwe:eajour:y:2025:i:2:p:445-461. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Vanya Lazarova (email available below). General contact details of provider: https://edirc.repec.org/data/unweebg.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.