IDEAS home Printed from https://ideas.repec.org/a/taf/acctbr/v29y1999i3p211-216.html
   My bibliography  Save this article

A neural network approach to the prediction of going concern status

Author

Listed:
  • Hian Koh
  • Sen Tan

Abstract

The assessment of a firm's going concern status is not an easy task. To assist auditors, going concern prediction models based on statistical methods such as multiple discriminant analysis and logit/probit analysis have been explored with some success. This study attempts to look at a different and more recent approach—neural networks. In particular, a neural network model of the feedforward, backpropagation type was constructed to predict a firm's going concern status from six financial ratios, using a data set containing 165 non-going concerns and 165 matched going concerns. On an evenly distributed hold-out sample, the trained network model correctly predicted all 30 test cases. The results suggest that neural networks can be a promising avenue of research and application in the going concern area.

Suggested Citation

  • Hian Koh & Sen Tan, 1999. "A neural network approach to the prediction of going concern status," Accounting and Business Research, Taylor & Francis Journals, vol. 29(3), pages 211-216.
    • Handle: RePEc:taf:acctbr:v:29:y:1999:i:3:p:211-216
    DOI: 10.1080/00014788.1999.9729581
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00014788.1999.9729581
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00014788.1999.9729581?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. Pamela K. Coats & L. Franklin Fant, 1993. "Recognizing Financial Distress Patterns Using a Neural Network Tool," Financial Management, Financial Management Association, vol. 22(3), Fall.
    2. Krishnagopal Menon & Kenneth B. Schwartz, 1987. "An empirical investigation of audit qualification decisions in the presence of going concern uncertainties," Contemporary Accounting Research, John Wiley & Sons, vol. 3(2), pages 302-315, March.
    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. Marius Hasslinger & Michael Olbrich & David Rapp, 2017. "Concerned about Going Concern: When do Entities in Liquidation have to be Considered a Non-Going Concern According to IFRS?," FINANCIAL REPORTING, FrancoAngeli Editore, vol. 2017(1), pages 31-61.
    2. Sabek Amine, 2023. "Unveiling the diverse efficacy of artificial neural networks and logistic regression: A comparative analysis in predicting financial distress," Croatian Review of Economic, Business and Social Statistics, Sciendo, vol. 9(1), pages 16-32, July.
    3. Llano Monelos Pablo De & Piñeiro Sánchez Carlos & Rodríguez López Manuel, 2014. "DEA as a business failure prediction tool. Application to the case of galician SMEs," Contaduría y Administración, Accounting and Management, vol. 59(2), pages 65-96, abril-jun.
    4. Ruey-Ching Hwang & K. F. Cheng & Jack C. Lee, 2007. "A semiparametric method for predicting bankruptcy," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(5), pages 317-342.
    5. Lin, Chin-Shien & Khan, Haider A. & Chang, Ruei-Yuan & Wang, Ying-Chieh, 2008. "A new approach to modeling early warning systems for currency crises: Can a machine-learning fuzzy expert system predict the currency crises effectively?," Journal of International Money and Finance, Elsevier, vol. 27(7), pages 1098-1121, November.
    6. Chen Jo-Hui & Diaz John Francis T., 2021. "Application of grey relational analysis and artificial neural networks on currency exchange-traded notes (ETNs)," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 25(2), pages 1-17, April.
    7. Nirosh Karuppu, 2009. "Evidence on Auditors Use of Business Continuity Models as an Analytical Procedure," Accounting & Taxation, The Institute for Business and Finance Research, vol. 1(1), pages 63-74.
    8. Pablo de Llano Monelos & Manuel Rodríguez López & Carlos Piñeiro Sánchez, 2013. "Bankruptcy Prediction Models in Galician companies. Application of Parametric Methodologies and Artificial Intelligence," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(1), pages 117-136.
    9. Yusuf Ali Al-Hroot, 2015. "The Influence Of Sample Size And Selection Of Financial Ratios In Bankruptcy Model Accuracy," Economic Review: Journal of Economics and Business, University of Tuzla, Faculty of Economics, vol. 13(1), pages 7-19, May.
    10. Nicoleta Bărbuță-Mișu & Mara Madaleno, 2020. "Assessment of Bankruptcy Risk of Large Companies: European Countries Evolution Analysis," JRFM, MDPI, vol. 13(3), pages 1-28, March.
    11. Moll, Jodie & Yigitbasioglu, Ogan, 2019. "The role of internet-related technologies in shaping the work of accountants: New directions for accounting research," The British Accounting Review, Elsevier, vol. 51(6).
    12. 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.
    13. Robert G. Biscontri, 2012. "A Radial Basis Function Approach To Earnings Forecast," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 19(1), pages 1-18, January.
    14. Yusuf Ali Al-Hroot, 2016. "A Comparison of Jordanian Bankruptcy Models: Multilayer Perceptron Neural Network and Discriminant Analysis," International Business Research, Canadian Center of Science and Education, vol. 9(12), pages 121-130, December.
    15. Laskai András, 2019. "AI foundations of the international business planning and the AI consciousness model," International Journal of Science and Business, IJSAB International, vol. 3(1), pages 17-28.
    16. Stuart, Iris & Shin, Yong-Chul & Cram, Donald P. & Karan, Vijay, 2013. "Review of choice-based, matched, and other stratified sample studies in auditing research," Journal of Accounting Literature, Elsevier, vol. 32(1), pages 88-113.
    17. Mark T. Leung & An-Sing Chen, 2005. "Performance evaluation of neural network architectures: the case of predicting foreign exchange correlations," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(6), pages 403-420.
    18. Thomas E. McKee, 2003. "Rough sets bankruptcy prediction models versus auditor signalling rates," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(8), pages 569-586.
    19. Amani, Farzaneh A. & Fadlalla, Adam M., 2017. "Data mining applications in accounting: A review of the literature and organizing framework," International Journal of Accounting Information Systems, Elsevier, vol. 24(C), pages 32-58.

    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. Yu-Shan Chen & Ke-Chiun Chang, 2009. "Using neural network to analyze the influence of the patent performance upon the market value of the US pharmaceutical companies," Scientometrics, Springer;Akadémiai Kiadó, vol. 80(3), pages 637-655, September.
    2. Wu, Chloe Yu-Hsuan & Hsu, Hwa-Hsien & Haslam, Jim, 2016. "Audit committees, non-audit services, and auditor reporting decisions prior to failure," The British Accounting Review, Elsevier, vol. 48(2), pages 240-256.
    3. Philip Swicegood & Jeffrey A. Clark, 2001. "Off‐site monitoring systems for predicting bank underperformance: a comparison of neural networks, discriminant analysis, and professional human judgment," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 10(3), pages 169-186, September.
    4. Kattan, MW & Cooper, RB, 1998. "The predictive accuracy of computer-based classification decision techniques.A review and research directions," Omega, Elsevier, vol. 26(4), pages 467-482, August.
    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. 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.
    7. 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.
    8. Mary Jane Lenard & Pervaiz Alam & David Booth & Gregory Madey, 2001. "Decision‐making capabilities of a hybrid system applied to the auditor's going‐concern assessment," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 10(1), pages 1-23, March.
    9. Rogelio A. Mancisidor & Kjersti Aas, 2022. "Multimodal Generative Models for Bankruptcy Prediction Using Textual Data," Papers 2211.08405, arXiv.org, revised Feb 2024.
    10. Harlan L. Etheridge & Kathy H. Y. Hsu, 2015. "Minimizing the Costs of Using Models to Assess the Financial Health of Banks," International Journal of Business and Social Research, LAR Center Press, vol. 5(11), pages 9-18, November.
    11. Olson, Dennis & Zoubi, Taisier A., 2008. "Using accounting ratios to distinguish between Islamic and conventional banks in the GCC region," The International Journal of Accounting, Elsevier, vol. 43(1), pages 45-65, March.
    12. Hu, Yu-Chiang & Ansell, Jake, 2007. "Measuring retail company performance using credit scoring techniques," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1595-1606, December.
    13. Yu‐Feng Hsu & Wei‐Po Lee, 2020. "Evaluation of the going‐concern status for companies: An ensemble framework‐based model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(4), pages 687-706, July.
    14. Kolari, James & Glennon, Dennis & Shin, Hwan & Caputo, Michele, 2002. "Predicting large US commercial bank failures," Journal of Economics and Business, Elsevier, vol. 54(4), pages 361-387.
    15. Richard Oduro & Michael Amoh Aseidu, 2017. "A Model to Predict Corporate Failure in the Developing Economies: A Case of Listed Companies on the Ghana Stock Exchange," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 7(4), pages 1-5.
    16. Chrysovalantis Gaganis & Fotios Pasiouras & Charalambos Spathis & Constantin Zopounidis, 2007. "A comparison of nearest neighbours, discriminant and logit models for auditing decisions," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 15(1‐2), pages 23-40, January.
    17. Ann Gaeremynck & Marleen Willekens, 2003. "The endogenous relationship between audit-report type and business termination: evidence on private firms in a non-litigious environment," Accounting and Business Research, Taylor & Francis Journals, vol. 33(1), pages 65-79.
    18. Vladislav V. Afanasev & Yulia A. Tarasova, 2022. "Default Prediction for Housing and Utilities Management Firms Using Non-Financial Data," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 6, pages 91-110, December.
    19. Greta Falavigna, 2008. "Nouveaux instruments d’évaluation pour le risque financier d’entreprise," CERIS Working Paper 200801, CNR-IRCrES Research Institute on Sustainable Economic Growth - Torino (TO) ITALY - former Institute for Economic Research on Firms and Growth - Moncalieri (TO) ITALY.
    20. Peresetsky, A. A., 2011. "What factors drive the Russian banks license withdrawal," MPRA Paper 41507, University Library of Munich, Germany.

    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:acctbr:v:29:y:1999:i:3:p:211-216. 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/RABR20 .

    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.