IDEAS home Printed from https://ideas.repec.org/a/taf/raaexx/v21y2014i3p328-347.html
   My bibliography  Save this article

A delisting prediction model based on nonfinancial information

Author

Listed:
  • In Tae Hwang
  • Sun Min Kang
  • Shun Ji Jin

Abstract

The purpose of this study is to develop a model for predicting firm delistings based on nonfinancial information. The delisting model using nonfinancial information is more meaningful in that it can provide diverse stakeholders with earlier warning signals for predicting delistings. Nonfinancial information is generally disclosed to the public in a timely manner because it requires no procedure involving the settlement of accounts and audits. The results suggest that stakeholders should pay close attention to various qualitative factors that are not expressed in financial to predict delistings as early as possible and thus to minimize social losses from delistings.

Suggested Citation

  • In Tae Hwang & Sun Min Kang & Shun Ji Jin, 2014. "A delisting prediction model based on nonfinancial information," Asia-Pacific Journal of Accounting & Economics, Taylor & Francis Journals, vol. 21(3), pages 328-347, September.
  • Handle: RePEc:taf:raaexx:v:21:y:2014:i:3:p:328-347
    DOI: 10.1080/16081625.2014.882322
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/16081625.2014.882322
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/16081625.2014.882322?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Platt, Harlan D. & Platt, Marjorie B., 1991. "A note on the use of industry-relative ratios in bankruptcy prediction," Journal of Banking & Finance, Elsevier, vol. 15(6), pages 1183-1194, December.
    2. Dimitras, A. I. & Slowinski, R. & Susmaga, R. & Zopounidis, C., 1999. "Business failure prediction using rough sets," European Journal of Operational Research, Elsevier, vol. 114(2), pages 263-280, April.
    3. Altman, Edward I. & Marco, Giancarlo & Varetto, Franco, 1994. "Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)," Journal of Banking & Finance, Elsevier, vol. 18(3), pages 505-529, May.
    4. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    5. Nan Hu & Fangjun Wang & Peng Wang & Lee Yao & Junrui Zhang, 2012. "The impact of ultimate ownerships on audit fees: evidence from Chinese listed companies," Asia-Pacific Journal of Accounting & Economics, Taylor & Francis Journals, vol. 19(3), pages 352-373.
    6. Chen, Gongmeng & Firth, Michael & Gao, Daniel N. & Rui, Oliver M., 2006. "Ownership structure, corporate governance, and fraud: Evidence from China," Journal of Corporate Finance, Elsevier, vol. 12(3), pages 424-448, June.
    7. Collins, Robert A. & Green, Richard D., 1982. "Statistical methods for bankruptcy forecasting," Journal of Economics and Business, Elsevier, vol. 34(4), pages 349-354.
    8. Ken Y. Chen & Randal J. Elder & Yung-Ming Hsieh, 2011. "Corporate Governance, Growth Opportunities, and Earnings Restatements: Effects of a Corporate Governance Code," Asia-Pacific Journal of Accounting & Economics, Taylor & Francis Journals, vol. 18(2), pages 169-200.
    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. Shun-Ji Jin & In Tae Hwang & Sun Min Kang, 2018. "Improving Sustainability through a Dual Audit System," Sustainability, MDPI, vol. 10(1), pages 1-15, January.
    2. In Tae Hwang & Kang Sung Hur & Sun Min Kang, 2018. "Does the IFRS Effect Continue? An International Comparison," Sustainability, MDPI, vol. 10(12), pages 1-20, December.
    3. Ahmad, Abd Halim, 2019. "What factors discriminate reorganized and delisted distressed firms: Evidence from Malaysia," Finance Research Letters, Elsevier, vol. 29(C), pages 50-56.

    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. Fayçal Mraihi, 2016. "Distressed Company Prediction Using Logistic Regression: Tunisian’s Case," Quarterly Journal of Business Studies, Research Academy of Social Sciences, vol. 2(1), pages 34-54.
    2. 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.
    3. Fernando Zambrano Farias & María del Carmen Valls Martínez & Pedro Antonio Martín-Cervantes, 2021. "Explanatory Factors of Business Failure: Literature Review and Global Trends," Sustainability, MDPI, vol. 13(18), pages 1-26, September.
    4. Fayçal Mraihi & Inane Kanzari & Mohamed Tahar Rajhi, 2015. "Development of a Prediction Model of Failure in Tunisian Companies: Comparison between Logistic Regression and Support Vector Machines," International Journal of Empirical Finance, Research Academy of Social Sciences, vol. 4(3), pages 184-205.
    5. 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.
    6. Beynon, Malcolm J. & Peel, Michael J., 2001. "Variable precision rough set theory and data discretisation: an application to corporate failure prediction," Omega, Elsevier, vol. 29(6), pages 561-576, December.
    7. du Jardin, Philippe & Séverin, Eric, 2011. "Predicting corporate bankruptcy using a self-organizing map: An empirical study to improve the forecasting horizon of a financial failure model," MPRA Paper 44262, University Library of Munich, Germany.
    8. Catherine Refait, 2004. "La prévision de la faillite fondée sur l’analyse financière de l’entreprise : un état des lieux," Économie et Prévision, Programme National Persée, vol. 162(1), pages 129-147.
    9. García-Gallego, Ana & Mures-Quintana, María-Jesús, 2013. "La muestra de empresas en los modelos de predicción del fracaso: influencia en los resultados de clasificación || The Sample of Firms in Business Failure Prediction Models: Influence on Classification," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 15(1), pages 133-150, June.
    10. Sunti Tirapat & Aekkachai Nittayagasetwat, 1999. "An Investigation of Thai Listed Firms' Financial Distress Using Macro and Micro Variables," Multinational Finance Journal, Multinational Finance Journal, vol. 3(2), pages 103-125, June.
    11. Evangelos C. Charalambakis, 2015. "On the Prediction of Corporate Financial Distress in the Light of the Financial Crisis: Empirical Evidence from Greek Listed Firms," International Journal of the Economics of Business, Taylor & Francis Journals, vol. 22(3), pages 407-428, November.
    12. du Jardin, Philippe, 2012. "The influence of variable selection methods on the accuracy of bankruptcy prediction models," MPRA Paper 44383, University Library of Munich, Germany.
    13. Sami Ben Jabeur & Youssef Fahmi, 2014. "Les modèles de prévision de la défaillance des entreprises françaises : une approche comparative," Working Papers 2014-317, Department of Research, Ipag Business School.
    14. Harlan D. Platt & Marjorie B. Platt, 2008. "Financial Distress Comparison Across Three Global Regions," JRFM, MDPI, vol. 1(1), pages 1-34, December.
    15. Zhichao Luo & Pingyu Hsu & Ni Xu, 2020. "SME Default Prediction Framework with the Effective Use of External Public Credit Data," Sustainability, MDPI, vol. 12(18), pages 1-18, September.
    16. Yu Zhao & Huaming Du & Qing Li & Fuzhen Zhuang & Ji Liu & Gang Kou, 2022. "A Comprehensive Survey on Enterprise Financial Risk Analysis from Big Data Perspective," Papers 2211.14997, arXiv.org, revised May 2023.
    17. Michael Doumpos & Constantin Zopounidis, 1999. "A Multicriteria Discrimination Method for the Prediction of Financial Distress: The Case of Greece," Multinational Finance Journal, Multinational Finance Journal, vol. 3(2), pages 71-101, June.
    18. Şaban Çelik, 2013. "Micro Credit Risk Metrics: A Comprehensive Review," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 20(4), pages 233-272, October.
    19. Christian Lohmann & Thorsten Ohliger, 2017. "Nonlinear Relationships and Their Effect on the Bankruptcy Prediction," Schmalenbach Business Review, Springer;Schmalenbach-Gesellschaft, vol. 18(3), pages 261-287, August.
    20. Luca Ianni & Gianluca Marullo & Stefania Migliori & Francesco De Luca, 2021. "I modelli predittivi della crisi e dell?insolvenza aziendale. Una systematic review," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2021(2), pages 127-146.

    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:taf:raaexx:v:21:y:2014:i:3:p:328-347. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/raae20 .

    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.