IDEAS home Printed from https://ideas.repec.org/a/spr/reaccs/v10y2005i1d10.1007_s11142-004-6341-9.html
   My bibliography  Save this article

Have Financial Statements Become Less Informative? Evidence from the Ability of Financial Ratios to Predict Bankruptcy

Author

Listed:
  • William H. Beaver

    (Stanford University)

  • Maureen F. McNichols

    (Stanford University)

  • Jung-Wu Rhie

    (Stanford University)

Abstract

Using a hazard model, we examine secular changes in the ability of financial statement data to predict bankruptcy from 1962 to 2002. We identify three trends in financial reporting that could influence predictive ability with respect to bankruptcy: FASB standards, the perceived increase in discretionary financial reporting behavior, and the increase in unrecognized assets and obligations. A parsimonious three-variable model provides significant explanatory power throughout the time period, with only a slight deterioration in predictive power from the first to the second time period. The striking feature of the results is the robustness of the predictive models over a forty-year period.

Suggested Citation

  • William H. Beaver & Maureen F. McNichols & Jung-Wu Rhie, 2005. "Have Financial Statements Become Less Informative? Evidence from the Ability of Financial Ratios to Predict Bankruptcy," Review of Accounting Studies, Springer, vol. 10(1), pages 93-122, March.
    • Handle: RePEc:spr:reaccs:v:10:y:2005:i:1:d:10.1007_s11142-004-6341-9
    DOI: 10.1007/s11142-004-6341-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11142-004-6341-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11142-004-6341-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 4, pages 123-127.
    2. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 4, pages 71-111.
    3. Zhen Deng & Baruch Lev & Francis Narin, 1999. "Science and Technology as Predictors of Stock Performance," Financial Analysts Journal, Taylor & Francis Journals, vol. 55(3), pages 20-32, May.
    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. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 18(1), pages 109-131.
    6. Wayne R. Landsman & Edward L. Maydew, 2002. "Has the Information Content of Quarterly Earnings Announcements Declined in the Past Three Decades?," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 40(3), pages 797-808, June.
    7. Francis, J & Schipper, K, 1999. "Have financial statements lost their relevance?," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 37(2), pages 319-352.
    8. Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-124, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. William H. Beaver & Maria Correia & Maureen F. McNichols, 2012. "Do differences in financial reporting attributes impair the predictive ability of financial ratios for bankruptcy?," Review of Accounting Studies, Springer, vol. 17(4), pages 969-1010, December.
    2. Maria Correia & Scott Richardson & İrem Tuna, 2012. "Value investing in credit markets," Review of Accounting Studies, Springer, vol. 17(3), pages 572-609, September.
    3. Correia, Maria, 2025. "Accounting and corporate failure: the evolving role of accounting information in bankruptcy prediction," LSE Research Online Documents on Economics 128340, London School of Economics and Political Science, LSE Library.
    4. Juha-Pekka Kallunki & Elina Pyykkö, 2013. "Do defaulting CEOs and directors increase the likelihood of financial distress of the firm?," Review of Accounting Studies, Springer, vol. 18(1), pages 228-260, March.
    5. Alnoor Bhimani & Mohamed Azzim Gulamhussen & Samuel Rocha Lopes, 2014. "Owner liability and financial reporting information as predictors of firm default in bank loans," Review of Accounting Studies, Springer, vol. 19(2), pages 769-804, June.
    6. Ming Xu & Chu Zhang, 2009. "Bankruptcy prediction: the case of Japanese listed companies," Review of Accounting Studies, Springer, vol. 14(4), pages 534-558, December.

    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. William H. Beaver & Maria Correia & Maureen F. McNichols, 2012. "Do differences in financial reporting attributes impair the predictive ability of financial ratios for bankruptcy?," Review of Accounting Studies, Springer, vol. 17(4), pages 969-1010, December.
    2. 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).
    3. 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.
    4. 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.
    5. Ahsan Habib & Mabel D' Costa & Hedy Jiaying Huang & Md. Borhan Uddin Bhuiyan & Li Sun, 2020. "Determinants and consequences of financial distress: review of the empirical literature," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 60(S1), pages 1023-1075, April.
    6. 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.
    7. Thao Tran & Ngoc Hong Nguyen & Binh Thien Le & Nam Thanh Vu & Duc Hong Vo, 2023. "Examining financial distress of the Vietnamese listed firms using accounting-based models," PLOS ONE, Public Library of Science, vol. 18(5), pages 1-17, May.
    8. Hamid Waqas & Rohani Md-Rus, 2018. "Predicting financial distress: Applicability of O-score model for Pakistani firms," Business and Economic Horizons (BEH), Prague Development Center, vol. 14(2), pages 389-401, April.
    9. Ken Li, 2024. "Liquidity ratios and corporate failures," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 64(1), pages 1111-1134, March.
    10. 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.
    11. Christian Lohmann & Steffen Möllenhoff & Thorsten Ohliger, 2023. "Nonlinear relationships in bankruptcy prediction and their effect on the profitability of bankruptcy prediction models," Journal of Business Economics, Springer, vol. 93(9), pages 1661-1690, November.
    12. Ilyes Abid & Farid Mkaouar & Olfa Kaabia, 2018. "Dynamic analysis of the forecasting bankruptcy under presence of unobserved heterogeneity," Annals of Operations Research, Springer, vol. 262(2), pages 241-256, March.
    13. Kumar, Rahul & Deb, Soumya Guha & Mukherjee, Shubhadeep, 2020. "Do words reveal the latent truth? Identifying communication patterns of corporate losers," Journal of Behavioral and Experimental Finance, Elsevier, vol. 26(C).
    14. Arvind Shrivastava & Kuldeep Kumar & Nitin Kumar, 2018. "Business Distress Prediction Using Bayesian Logistic Model for Indian Firms," Risks, MDPI, vol. 6(4), pages 1-15, October.
    15. Amin Jan & Maran Marimuthu & Muhammad Kashif Shad & Haseeb ur-Rehman & Muhammad Zahid & Ahmad Ali Jan, 2019. "Bankruptcy profile of the Islamic and conventional banks in Malaysia: a post-crisis period analysis," Economic Change and Restructuring, Springer, vol. 52(1), pages 67-87, February.
    16. Aytekin Ertan & Yun Lee & Regina Wittenberg-Moerman, 2025. "Unexpected defaults: the role of information opacity," Review of Accounting Studies, Springer, vol. 30(1), pages 899-949, March.
    17. 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.
    18. Henri Arno & Klaas Mulier & Joke Baeck & Thomas Demeester, 2022. "Next-Year Bankruptcy Prediction from Textual Data: Benchmark and Baselines," Papers 2208.11334, arXiv.org.
    19. 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..
    20. Zhang, Xuan & Ouyang, Ruolan & Liu, Ding & Xu, Liao, 2020. "Determinants of corporate default risk in China: The role of financial constraints," Economic Modelling, Elsevier, vol. 92(C), pages 87-98.

    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:spr:reaccs:v:10:y:2005:i:1:d:10.1007_s11142-004-6341-9. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.