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Bankruptcy prediction using a discrete-time duration model incorporating temporal and macroeconomic dependencies

Author

Listed:
  • Chae Woo Nam

    (National Pension Research Institute, Seoul, Korea)

  • Tong Suk Kim

    (Korea Advanced Institute of Science and Technology, Seoul, Korea)

  • Nam Jung Park

    (Stanford University, U.S.A.)

  • Hoe Kyung Lee

    (Korea Advanced Institute of Science and Technology, Seoul, Korea)

Abstract

The purpose of this paper is to build an alternative method of bankruptcy prediction that accounts for some deficiencies in previous approaches that resulted in poor out-of-sample performances. Most of the traditional approaches suffer from restrictive presumptions and structural limitations and fail to reflect the panel properties of financial statements and|or the common macroeconomic influence. Extending the work of Shumway (2001), we present a duration model with time-varying covariates and a baseline hazard function incorporating macroeconomic dependencies. Using the proposed model, we investigate how the hazard rates of listed companies in the Korea Stock Exchange (KSE) are affected by changes in the macroeconomic environment and by time-varying covariate vectors that show unique financial characteristics of each company. We also investigate out-of-sample forecasting performances of the suggested model and demonstrate improvements produced by allowing temporal and macroeconomic dependencies. Copyright © 2008 John Wiley & Sons, Ltd.

Suggested Citation

  • Chae Woo Nam & Tong Suk Kim & Nam Jung Park & Hoe Kyung Lee, 2008. "Bankruptcy prediction using a discrete-time duration model incorporating temporal and macroeconomic dependencies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(6), pages 493-506.
  • Handle: RePEc:jof:jforec:v:27:y:2008:i:6:p:493-506
    DOI: 10.1002/for.985
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    File URL: http://hdl.handle.net/10.1002/for.985
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    References listed on IDEAS

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    1. Giesecke, Kay, 2001. "Correlated default with incomplete information," SFB 373 Discussion Papers 2002,30, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
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    Cited by:

    1. repec:eee:riibaf:v:42:y:2017:i:c:p:1383-1393 is not listed on IDEAS
    2. Situm Mario, 2014. "Inability of Gearing-Ratio as Predictor for Early Warning Systems," Business Systems Research, De Gruyter Open, vol. 5(2), pages 23-45, September.
    3. Ferreira Filipe, Sara & Grammatikos, Theoharry & Michala, Dimitra, 2014. "Forecasting Distress in European SME Portfolios," MPRA Paper 53572, University Library of Munich, Germany.
    4. TOBBACK, Ellen & MOEYERSOMS, Julie & STANKOVA, Marija & MARTENS, David, 2016. "Bankruptcy prediction for SMEs using relational data," Working Papers 2016004, University of Antwerp, Faculty of Applied Economics.
    5. Liviu Tudor & Mădălina Ecaterina Popescu & Marin Andreica, 2015. "A Decision Support System to Predict Financial Distress. The Case Of Romania," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 170-179, December.
    6. El Kalak, Izidin & Hudson, Robert, 2016. "The effect of size on the failure probabilities of SMEs: An empirical study on the US market using discrete hazard model," International Review of Financial Analysis, Elsevier, vol. 43(C), pages 135-145.
    7. Denissa Satriavi, 2011. "Comparison Of Predicting Financial Distress Using Hazard Model Without And Incorporating Macroeconomic Variable As Baseline Hazard Rate," Working Papers in Business, Management and Finance 201105, Department of Management and Business, Padjadjaran University, revised Dec 2011.
    8. repec:enr:rpaper:0008 is not listed on IDEAS
    9. repec:spr:annopr:v:262:y:2018:i:2:d:10.1007_s10479-016-2143-2 is not listed on IDEAS
    10. Maria H. Kim & Graham Partington, 2015. "Dynamic forecasts of financial distress of Australian firms," Australian Journal of Management, Australian School of Business, vol. 40(1), pages 135-160, February.
    11. Hernandez Tinoco, Mario & Wilson, Nick, 2013. "Financial distress and bankruptcy prediction among listed companies using accounting, market and macroeconomic variables," International Review of Financial Analysis, Elsevier, vol. 30(C), pages 394-419.
    12. repec:luc:wpaper:13-2 is not listed on IDEAS
    13. Cole, Rebel A. & Wu, Qiongbing, 2009. "Is hazard or probit more accurate in predicting financial distress? Evidence from U.S. bank failures," MPRA Paper 24688, University Library of Munich, Germany, revised 01 Aug 2010.
    14. Mãdãlina Ecaterina POPESCU, 2015. "Proposal for a Decision Support System to Predict Financial Distress," REVISTA DE MANAGEMENT COMPARAT INTERNATIONAL/REVIEW OF INTERNATIONAL COMPARATIVE MANAGEMENT, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 16(1), pages 112-118, March.
    15. Hee-Koung Joeng & Ming-Hui Chen & Sangwook Kang, 2016. "Proportional exponentiated link transformed hazards (ELTH) models for discrete time survival data with application," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 22(1), pages 38-62, January.
    16. Dimitra Michala & Theoharry Grammatikos & Sara Ferreira Filipe, 2013. "Forecasting distress in European SME portfolios," LSF Research Working Paper Series 13-2, Luxembourg School of Finance, University of Luxembourg.
    17. Sanchez-Barrios, Luis Javier & Andreeva, Galina & Ansell, Jake, 2016. "“Time-to-profit scorecards for revolving credit”," European Journal of Operational Research, Elsevier, vol. 249(2), pages 397-406.
    18. Maghyereh, Aktham I. & Awartani, Basel, 2014. "Bank distress prediction: Empirical evidence from the Gulf Cooperation Council countries," Research in International Business and Finance, Elsevier, vol. 30(C), pages 126-147.
    19. repec:enr:rpaper:0001 is not listed on IDEAS
    20. Bruneau, C. & de Bandt, O. & El Amri, W., 2012. "Macroeconomic fluctuations and corporate financial fragility," Journal of Financial Stability, Elsevier, vol. 8(4), pages 219-235.
    21. Filipe, Sara Ferreira & Grammatikos, Theoharry & Michala, Dimitra, 2016. "Forecasting distress in European SME portfolios," Journal of Banking & Finance, Elsevier, vol. 64(C), pages 112-135.
    22. Jairaj Gupta & Andros Gregoriou & Jerome Healy, 2015. "Forecasting bankruptcy for SMEs using hazard function: To what extent does size matter?," Review of Quantitative Finance and Accounting, Springer, vol. 45(4), pages 845-869, November.

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