IDEAS home Printed from https://ideas.repec.org/a/fan/macoma/vhtml10.3280-maco2024-001009.html
   My bibliography  Save this article

The relevance of cash flow information in predicting corporate bankruptcy in Italian private companies

Author

Listed:
  • Simone Poli
  • Marco Gatti

Abstract

This study investigates the relevance of cash flow information in predicting corpo-rate bankruptcy in Italian private companies. The results indicate that while the cash flow-based model exhibits a high predictive capacity, it is less effective than the accrual-based model. In addition, cash flow-based ratios do not improve the predictive capacity of the accrual-based model. From a theoretical perspective, this study enriches existing literature on the relevance of cash flow information in predicting corporate bankruptcy by extending the investigation to the Italian con-text, which has not yet been sufficiently studied. From a practical standpoint, it provides Italian companies with new bankruptcy prediction models and offers pre-liminary suggestions regarding the relevance that should be attributed to cash flow information within the organizational, administrative, and accounting structures that they must establish to promptly detect crises and undertake appropriate initia-tives in a timely manner to comply with the requirements of the new legislation on business crises.

Suggested Citation

  • Simone Poli & Marco Gatti, 2024. "The relevance of cash flow information in predicting corporate bankruptcy in Italian private companies," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2024(1), pages 179-202.
    • Handle: RePEc:fan:macoma:v:html10.3280/maco2024-001009
    as

    Download full text from publisher

    File URL: http://www.francoangeli.it/riviste/Scheda_Rivista.aspx?IDArticolo=75458&Tipo=ArticoloPDF
    Download Restriction: Single articles can be downloaded buying download credits, for info: https://www.francoangeli.it/DownloadCredit
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Giuseppe Arcuri & Nadine Levratto, 2020. "Early stage SME bankruptcy: does the local banking market matter?," Small Business Economics, Springer, vol. 54(2), pages 421-436, February.
    2. Allen Edwards, 1948. "Note on the “correction for continuity” in testing the significance of the difference between correlated proportions," Psychometrika, Springer;The Psychometric Society, vol. 13(3), pages 185-187, September.
    3. Jairaj Gupta & Mariachiara Barzotto & Amir Khorasgani, 2018. "Does size matter in predicting SMEs failure?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 23(4), pages 571-605, October.
    4. repec:eme:mfppss:03074350110767114 is not listed on IDEAS
    5. Almamy, Jeehan & Aston, John & Ngwa, Leonard N., 2016. "An evaluation of Altman's Z-score using cash flow ratio to predict corporate failure amid the recent financial crisis: Evidence from the UK," Journal of Corporate Finance, Elsevier, vol. 36(C), pages 278-285.
    6. Andreas Charitou & Evi Neophytou & Chris Charalambous, 2004. "Predicting corporate failure: empirical evidence for the UK," European Accounting Review, Taylor & Francis Journals, vol. 13(3), pages 465-497.
    7. Quinn McNemar, 1947. "Note on the sampling error of the difference between correlated proportions or percentages," Psychometrika, Springer;The Psychometric Society, vol. 12(2), pages 153-157, June.
    8. Pompe, Paul P.M. & Bilderbeek, Jan, 2005. "The prediction of bankruptcy of small- and medium-sized industrial firms," Journal of Business Venturing, Elsevier, vol. 20(6), pages 847-868, November.
    9. Balcaen, Sofie & Ooghe, Hubert, 2006. "35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems," The British Accounting Review, Elsevier, vol. 38(1), pages 63-93.
    10. 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.
    11. Edward I. Altman & Gabriele Sabato, 2013. "MODELING CREDIT RISK FOR SMEs: EVIDENCE FROM THE US MARKET," World Scientific Book Chapters, in: Oliviero Roggi & Edward I Altman (ed.), Managing and Measuring Risk Emerging Global Standards and Regulations After the Financial Crisis, chapter 9, pages 251-279, World Scientific Publishing Co. Pte. Ltd..
    12. Francesco Ciampi, 2017. "The Need for Specific Modelling of Small Enterprise Default Prediction: Empirical Evidence from Italian Small Manufacturing Firms," International Journal of Business and Management, Canadian Center of Science and Education, vol. 12(12), pages 251-251, November.
    13. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    14. N. Dewaelheyns & C. Van Hulle, 2004. "The Impact of Business Groups on Bankruptcy Prediction Modeling," Review of Business and Economic Literature, KU Leuven, Faculty of Economics and Business (FEB), Review of Business and Economic Literature, vol. 0(4), pages 623-645.
    15. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 123-127.
    16. Amendola, Alessandra & Restaino, Marialuisa & Sensini, Luca, 2015. "An analysis of the determinants of financial distress in Italy: A competing risks approach," International Review of Economics & Finance, Elsevier, vol. 37(C), pages 33-41.
    17. Mario Situm, 2023. "Factors affecting micro and small business distress in Austria," International Journal of Entrepreneurial Venturing, Inderscience Enterprises Ltd, vol. 15(1), pages 19-62.
    18. Amrizah Kamaluddin & Norhafizah Ishak & Nor Farizal Mohammed, 2019. "Financial Distress Prediction Through Cash Flow Ratios Analysis," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 10(3), pages 63-76, May.
    19. Anna Maria Arcari, 2018. "Preventing crises and managing turnaround processes in SMEs The role of economic measurement tools," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2018(3), pages 131-155.
    20. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    21. Ciampi, Francesco, 2015. "Corporate governance characteristics and default prediction modeling for small enterprises. An empirical analysis of Italian firms," Journal of Business Research, Elsevier, vol. 68(5), pages 1012-1025.
    22. 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.
    23. repec:eme:mfppss:v:39:y:2013:i:7:p:667-676 is not listed on IDEAS
    24. du Jardin, Philippe, 2009. "Bankruptcy prediction models: How to choose the most relevant variables?," MPRA Paper 44380, University Library of Munich, Germany.
    25. Jairaj Gupta & Nicholas Wilson & Andros Gregoriou & Jerome Healy, 2014. "The value of operating cash flow in modelling credit risk for SMEs," Applied Financial Economics, Taylor & Francis Journals, vol. 24(9), pages 649-660, May.
    26. Francesco Ciampi & Valentina Cillo & Fabio Fiano, 2020. "Combining Kohonen maps and prior payment behavior for small enterprise default prediction," Small Business Economics, Springer, vol. 54(4), pages 1007-1039, April.
    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. Francesco Ciampi & Valentina Cillo & Fabio Fiano, 2020. "Combining Kohonen maps and prior payment behavior for small enterprise default prediction," Small Business Economics, Springer, vol. 54(4), pages 1007-1039, April.
    2. Youssef Zizi & Mohamed Oudgou & Abdeslam El Moudden, 2020. "Determinants and Predictors of SMEs’ Financial Failure: A Logistic Regression Approach," Risks, MDPI, vol. 8(4), pages 1-21, October.
    3. Juraini Zainol Abidin & Nur Adiana Hiau Abdullah & Karren Lee-Hwei Khaw, 2020. "Predicting SMEs Failure: Logistic Regression vs Artificial Neural Network Models," Capital Markets Review, Malaysian Finance Association, vol. 28(2), pages 29-41.
    4. Ángel Beade & Manuel Rodríguez & José Santos, 2024. "Multiperiod Bankruptcy Prediction Models with Interpretable Single Models," Computational Economics, Springer;Society for Computational Economics, vol. 64(3), pages 1357-1390, September.
    5. David Veganzones, 2022. "Corporate failure prediction using threshold‐based models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 956-979, August.
    6. Keijo Kohv & Oliver Lukason, 2021. "What Best Predicts Corporate Bank Loan Defaults? An Analysis of Three Different Variable Domains," Risks, MDPI, vol. 9(2), pages 1-19, January.
    7. Elsayed, Mohamed & Elshandidy, Tamer, 2020. "Do narrative-related disclosures predict corporate failure? Evidence from UK non-financial publicly quoted firms," International Review of Financial Analysis, Elsevier, vol. 71(C).
    8. 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.
    9. Serrano-Cinca, Carlos & Gutiérrez-Nieto, Begoña & Bernate-Valbuena, Martha, 2019. "The use of accounting anomalies indicators to predict business failure," European Management Journal, Elsevier, vol. 37(3), pages 353-375.
    10. Mohammad Mahdi Mousavi & Jamal Ouenniche & Kaoru Tone, 2023. "A dynamic performance evaluation of distress prediction models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 756-784, July.
    11. Salwa Kessioui & Michalis Doumpos & Constantin Zopounidis, 2023. "A Bibliometric Overview of the State-of-the-Art in Bankruptcy Prediction Methods and Applications," World Scientific Book Chapters, in: Emilios Galariotis & Alexandros Garefalakis & Christos Lemonakis & Marios Menexiadis & Constantin Zo (ed.), Governance and Financial Performance Current Trends and Perspectives, chapter 6, pages 123-153, World Scientific Publishing Co. Pte. Ltd..
    12. Frieda Rikkers & Andre E. Thibeault, 2009. "A Structural form Default Prediction Model for SMEs, Evidence from the Dutch Market," Multinational Finance Journal, Multinational Finance Journal, vol. 13(3-4), pages 229-264, September.
    13. Apostolos G. Christopoulos & Ioannis G. Dokas & Iraklis Kollias & John Leventides, 2019. "An implementation of Soft Set Theory in the Variables Selection Process for Corporate Failure Prediction Models. Evidence from NASDAQ Listed Firms," Bulletin of Applied Economics, Risk Market Journals, vol. 6(1), pages 1-20.
    14. Jairaj Gupta & Nicholas Wilson & Andros Gregoriou & Jerome Healy, 2014. "The value of operating cash flow in modelling credit risk for SMEs," Applied Financial Economics, Taylor & Francis Journals, vol. 24(9), pages 649-660, May.
    15. Veganzones, David & Séverin, Eric & Chlibi, Souhir, 2023. "Influence of earnings management on forecasting corporate failure," International Journal of Forecasting, Elsevier, vol. 39(1), pages 123-143.
    16. Scalzer, Rodrigo S. & Rodrigues, Adriano & Macedo, Marcelo Álvaro da S. & Wanke, Peter, 2019. "Financial distress in electricity distributors from the perspective of Brazilian regulation," Energy Policy, Elsevier, vol. 125(C), pages 250-259.
    17. fernández, María t. Tascón & gutiérrez, Francisco J. Castaño, 2012. "Variables y Modelos Para La Identificación y Predicción Del Fracaso Empresarial: Revisión de La Investigación Empírica Reciente," Revista de Contabilidad - Spanish Accounting Review, Elsevier, vol. 15(1), pages 7-58.
    18. Alessandro Bitetto & Stefano Filomeni & Michele Modina, 2021. "Understanding corporate default using Random Forest: The role of accounting and market information," DEM Working Papers Series 205, University of Pavia, Department of Economics and Management.
    19. Youssef Zizi & Amine Jamali-Alaoui & Badreddine El Goumi & Mohamed Oudgou & Abdeslam El Moudden, 2021. "An Optimal Model of Financial Distress Prediction: A Comparative Study between Neural Networks and Logistic Regression," Risks, MDPI, vol. 9(11), pages 1-24, November.
    20. Xavier Brédart & Eric Séverin & David Veganzones, 2021. "Human resources and corporate failure prediction modeling: Evidence from Belgium," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(7), pages 1325-1341, November.

    More about this item

    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:fan:macoma:v:html10.3280/maco2024-001009. 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: Stefania Rosato (email available below). General contact details of provider: http://www.francoangeli.it/riviste/sommario.aspx?IDRivista=166 .

    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.