IDEAS home Printed from https://ideas.repec.org/a/gam/jjrfmx/v12y2019i2p55-d219945.html
   My bibliography  Save this article

Do Traditional Financial Distress Prediction Models Predict the Early Warning Signs of Financial Distress?

Author

Listed:
  • Sumaira Ashraf

    (Management Department, University of Évora, Largo dos Colegiais, nº 2, 7000-803 Évora, Portugal
    CEFAGE Research Center, University of Évora, 7000-812 Évora, Portugal)

  • Elisabete G. S. Félix

    (Management Department, University of Évora, Largo dos Colegiais, nº 2, 7000-803 Évora, Portugal
    CEFAGE Research Center, University of Évora, 7000-812 Évora, Portugal)

  • Zélia Serrasqueiro

    (CEFAGE Research Center, University of Évora, 7000-812 Évora, Portugal
    Department of Management and Economics, University of Beira Interior, Estrada do Sineiro, 6200-209 Covilhã, Portugal)

Abstract

Purpose: This study aims to compare the prediction accuracy of traditional distress prediction models for the firms which are at an early and advanced stage of distress in an emerging market, Pakistan, during 2001–2015. Design/methodology/approach: The methodology involves constructing model scores for financially distressed and stable firms and then comparing the prediction accuracy of the models with the original position. In addition to the testing for the whole sample period, comparison of the accuracy of the distress prediction models before, during, and after the financial crisis was also done. Findings: The results indicate that the three-variable probit model has the highest overall prediction accuracy for our sample, while the Z-score model more accurately predicts insolvency for both types of firms, i.e., those that are at an early stage as well as those that are at an advanced stage of financial distress. Furthermore, the study concludes that the predictive ability of all the traditional financial distress prediction models declines during the period of the financial crisis. Originality/value: An important contribution is the widening of the definition of financially distressed firms to consider the early warning signs related to failure in dividend/bonus declaration, quotation of face value, annual general meeting, and listing fee. Further, the results suggest that there is a need to develop a model by identifying variables which will have a higher impact on the financial distress of firms operating in both developed and developing markets.

Suggested Citation

  • Sumaira Ashraf & Elisabete G. S. Félix & Zélia Serrasqueiro, 2019. "Do Traditional Financial Distress Prediction Models Predict the Early Warning Signs of Financial Distress?," JRFM, MDPI, vol. 12(2), pages 1-17, April.
  • Handle: RePEc:gam:jjrfmx:v:12:y:2019:i:2:p:55-:d:219945
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1911-8074/12/2/55/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1911-8074/12/2/55/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tykvová, Tereza & Borell, Mariela, 2012. "Do private equity owners increase risk of financial distress and bankruptcy?," Journal of Corporate Finance, Elsevier, vol. 18(1), pages 138-150.
    2. John Y. Campbell & Jens Hilscher & Jan Szilagyi, 2008. "In Search of Distress Risk," Journal of Finance, American Finance Association, vol. 63(6), pages 2899-2939, December.
    3. Wen-Ying Cheng & Ender Su & Sheng-Jung Li, 2006. "A Financial Distress Pre-Warning Study by Fuzzy Regression Model of TSE-Listed Companies," Asian Academy of Management Journal of Accounting and Finance (AAMJAF), Penerbit Universiti Sains Malaysia, vol. 2(2), pages 75-93.
    4. 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.
    5. Scott, James, 1981. "The probability of bankruptcy: A comparison of empirical predictions and theoretical models," Journal of Banking & Finance, Elsevier, vol. 5(3), pages 317-344, September.
    6. Wu, Y. & Gaunt, C. & Gray, S., 2010. "A comparison of alternative bankruptcy prediction models," Journal of Contemporary Accounting and Economics, Elsevier, vol. 6(1), pages 34-45.
    7. Richard Whitaker, 1999. "The early stages of financial distress," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 23(2), pages 123-132, June.
    8. Antonio Trujillo-Ponce & Reyes Samaniego-Medina & Clara Cardone-Riportella, 2014. "Examining what best explains corporate credit risk: accounting-based versus market-based models," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 15(2), pages 253-276, April.
    9. Blum, M, 1974. "Failing Company Discriminant-Analysis," Journal of Accounting Research, Wiley Blackwell, vol. 12(1), pages 1-25.
    10. Bonfim, Diana, 2009. "Credit risk drivers: Evaluating the contribution of firm level information and of macroeconomic dynamics," Journal of Banking & Finance, Elsevier, vol. 33(2), pages 281-299, February.
    11. Deakin, Eb, 1972. "Discriminant Analysis Of Predictors Of Business Failure," Journal of Accounting Research, Wiley Blackwell, vol. 10(1), pages 167-179.
    12. Wanke, Peter & Barros, Carlos P. & Faria, João R., 2015. "Financial distress drivers in Brazilian banks: A dynamic slacks approach," European Journal of Operational Research, Elsevier, vol. 240(1), pages 258-268.
    13. Ijaz, Muhammad Shahzad & Hunjra, Ahmed Imran & Hameed, Zahid & Maqbool, Adnan & Azam, Rauf i, 2013. "Assessing the Financial Failure Using Z-Score and Current Ratio: A Case of Sugar Sector Listed Companies of Karachi Stock Exchange," MPRA Paper 60787, University Library of Munich, Germany.
    14. Korol, Tomasz, 2013. "Early warning models against bankruptcy risk for Central European and Latin American enterprises," Economic Modelling, Elsevier, vol. 31(C), pages 22-30.
    15. 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.
    16. 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.
    17. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    18. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    19. Zmijewski, Me, 1984. "Methodological Issues Related To The Estimation Of Financial Distress Prediction Models," Journal of Accounting Research, Wiley Blackwell, vol. 22, pages 59-82.
    20. Rüdiger Fahlenbrach & Robert Prilmeier & René M. Stulz, 2012. "This Time Is the Same: Using Bank Performance in 1998 to Explain Bank Performance during the Recent Financial Crisis," Journal of Finance, American Finance Association, vol. 67(6), pages 2139-2185, December.
    21. Chen, G M & Merville, L J, 1999. "An Analysis of the Underreported Magnitude of the Total Indirect Costs of Financial Distress," Review of Quantitative Finance and Accounting, Springer, vol. 13(3), pages 277-293, November.
    22. Julio Pindado & Luis Rodrigues, 2005. "Determinants of Financial Distress Costs," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 19(4), pages 343-359, December.
    23. Evangelos C. Charalambakis & Ian Garrett, 2016. "On the prediction of financial distress in developed and emerging markets: Does the choice of accounting and market information matter? A comparison of UK and Indian Firms," Review of Quantitative Finance and Accounting, Springer, vol. 47(1), pages 1-28, July.
    24. Jones, Stewart & Johnstone, David & Wilson, Roy, 2015. "An empirical evaluation of the performance of binary classifiers in the prediction of credit ratings changes," Journal of Banking & Finance, Elsevier, vol. 56(C), pages 72-85.
    25. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 123-127.
    26. Duchin, Ran & Ozbas, Oguzhan & Sensoy, Berk A., 2010. "Costly external finance, corporate investment, and the subprime mortgage credit crisis," Journal of Financial Economics, Elsevier, vol. 97(3), pages 418-435, September.
    27. Wruck, Karen Hopper, 1990. "Financial distress, reorganization, and organizational efficiency," Journal of Financial Economics, Elsevier, vol. 27(2), pages 419-444, October.
    28. David A. Hensher & Stewart Jones & William H. Greene, 2007. "An Error Component Logit Analysis of Corporate Bankruptcy and Insolvency Risk in Australia," The Economic Record, The Economic Society of Australia, vol. 83(260), pages 86-103, March.
    29. Zong-Jun Wang & Xiao-Lan Deng, 2006. "Corporate Governance and Financial Distress: Evidence from Chinese Listed Companies," Chinese Economy, Taylor & Francis Journals, vol. 39(5), pages 5-27, October.
    30. Agarwal, Vineet & Taffler, Richard, 2008. "Comparing the performance of market-based and accounting-based bankruptcy prediction models," Journal of Banking & Finance, Elsevier, vol. 32(8), pages 1541-1551, August.
    31. Grice, John Stephen & Ingram, Robert W., 2001. "Tests of the generalizability of Altman's bankruptcy prediction model," Journal of Business Research, Elsevier, vol. 54(1), pages 53-61, October.
    32. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    33. Veronique Vermoesen & Marc Deloof & Eddy Laveren, 2013. "Long-term debt maturity and financing constraints of SMEs during the Global Financial Crisis," Small Business Economics, Springer, vol. 41(2), pages 433-448, August.
    34. Li, Yuanzhi & Zhong, Zhaodong (Ken), 2013. "Investing in Chapter 11 stocks: Trading, value, and performance," Journal of Financial Markets, Elsevier, vol. 16(1), pages 33-60.
    35. Stewart Jones & David Johnstone & Roy Wilson, 2017. "Predicting Corporate Bankruptcy: An Evaluation of Alternative Statistical Frameworks," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 44(1-2), pages 3-34, January.
    36. Sudheer Chava & Robert A. Jarrow, 2008. "Bankruptcy Prediction with Industry Effects," World Scientific Book Chapters, in: Financial Derivatives Pricing Selected Works of Robert Jarrow, chapter 21, pages 517-549, World Scientific Publishing Co. Pte. Ltd..
    37. 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.
    38. Wilcox, Jw, 1971. "Simple Theory Of Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 9(2), pages 389-345.
    39. Abdul RASHID & Qaiser ABBAS, 2011. "Predicting Bankruptcy in Pakistan," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania - AGER, vol. 0(9(562)), pages 103-128, September.
    40. Dietrich, Andreas & Wanzenried, Gabrielle, 2011. "Determinants of bank profitability before and during the crisis: Evidence from Switzerland," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 21(3), pages 307-327, July.
    41. 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.
    42. Mselmi, Nada & Lahiani, Amine & Hamza, Taher, 2017. "Financial distress prediction: The case of French small and medium-sized firms," International Review of Financial Analysis, Elsevier, vol. 50(C), pages 67-80.
    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. 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).
    2. 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 1-25, September.
    3. Farida Titik Kristanti, 2019. "Integrating Capital Structure, Financial and Non-Financial Performance: Distress Prediction of SMEs," GATR Journals afr175, Global Academy of Training and Research (GATR) Enterprise.
    4. 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.
    5. Rafael Becerra-Vicario & David Alaminos & Eva Aranda & Manuel A. Fernández-Gámez, 2020. "Deep Recurrent Convolutional Neural Network for Bankruptcy Prediction: A Case of the Restaurant Industry," Sustainability, MDPI, vol. 12(12), pages 1-15, June.
    6. Jessica Putri RARASSATI & Yosman BUSTAMAN, 2021. "Analysis Of Financial Distress Determinants And The Role Of Corporate Governance For Risk Mitigation On Listed Indonesian Manufacturing Companies: Covid-19 Pandemic," Business Excellence and Management, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 11(5), pages 182-195, October.
    7. Han He & Sicheng Li & Lin Hu & Nelson Duarte & Otilia Manta & Xiao-Guang Yue, 2019. "Risk Factor Identification of Sustainable Guarantee Network Based on Logistic Regression Algorithm," Sustainability, MDPI, vol. 11(13), pages 1-19, June.
    8. Serhiy Zabolotnyy & Mirosław Wasilewski, 2019. "The Concept of Financial Sustainability Measurement: A Case of Food Companies from Northern Europe," Sustainability, MDPI, vol. 11(18), pages 1-16, September.
    9. You-Shyang Chen & Chien-Ku Lin & Chih-Min Lo & Su-Fen Chen & Qi-Jun Liao, 2021. "Comparable Studies of Financial Bankruptcy Prediction Using Advanced Hybrid Intelligent Classification Models to Provide Early Warning in the Electronics Industry," Mathematics, MDPI, vol. 9(20), pages 1-26, October.
    10. Tijana Matejić & Snežana Knežević & Vesna Bogojević Arsić & Tijana Obradović & Stefan Milojević & Miljan Adamović & Aleksandra Mitrović & Marko Milašinović & Dragoljub Simonović & Goran Milošević & Ma, 2022. "Assessing the Impact of the COVID-19 Crisis on Hotel Industry Bankruptcy Risk through Novel Forecasting Models," Sustainability, MDPI, vol. 14(8), pages 1-44, April.
    11. Eltigani Mohamed Ali Ahmed, 2021. "Leadership and organizational distress: Review of literature," International Journal of Research in Business and Social Science (2147-4478), Center for the Strategic Studies in Business and Finance, vol. 10(6), pages 01-18, September.
    12. Andrzej Jaki & Wojciech Ćwięk, 2020. "Bankruptcy Prediction Models Based on Value Measures," JRFM, MDPI, vol. 14(1), pages 1-14, December.
    13. ElBannan, Mona A., 2021. "On the prediction of financial distress in emerging markets: What matters more? Empirical evidence from Arab spring countries," Emerging Markets Review, Elsevier, vol. 47(C).
    14. Ashraf, Sumaira & Félix, Elisabete G.S. & Serrasqueiro, Zélia, 2020. "Development and testing of an augmented distress prediction model: A comparative study on a developed and an emerging market," Journal of Multinational Financial Management, Elsevier, vol. 57.

    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. Ashraf, Sumaira & Félix, Elisabete G.S. & Serrasqueiro, Zélia, 2020. "Development and testing of an augmented distress prediction model: A comparative study on a developed and an emerging market," Journal of Multinational Financial Management, Elsevier, vol. 57.
    2. 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.
    3. Chien-Min Kang & Ming-Chieh Wang & Lin Lin, 2022. "Financial Distress Prediction of Cooperative Financial Institutions—Evidence for Taiwan Credit Unions," IJFS, MDPI, vol. 10(2), pages 1-25, April.
    4. Duc Hong Vo & Binh Ninh Vo Pham & Chi Minh Ho & Michael McAleer, 2019. "Corporate Financial Distress of Industry Level Listings in Vietnam," JRFM, MDPI, vol. 12(4), pages 1-17, September.
    5. Mohammad Mahdi Mousavi & Jamal Ouenniche, 2018. "Multi-criteria ranking of corporate distress prediction models: empirical evaluation and methodological contributions," Annals of Operations Research, Springer, vol. 271(2), pages 853-886, December.
    6. 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.
    7. Evangelos C. Charalambakis & Ian Garrett, 2019. "On corporate financial distress prediction: What can we learn from private firms in a developing economy? Evidence from Greece," Review of Quantitative Finance and Accounting, Springer, vol. 52(2), pages 467-491, February.
    8. Nawaf Almaskati & Ron Bird & Yue Lu & Danny Leung, 2019. "The Role of Corporate Governance and Estimation Methods in Predicting Bankruptcy," Working Papers in Economics 19/16, University of Waikato.
    9. 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).
    10. 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.
    11. 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.
    12. Sanjay Sehgal & Ritesh Kumar Mishra & Ajay Jaisawal, 2021. "A search for macroeconomic determinants of corporate financial distress," Indian Economic Review, Springer, vol. 56(2), pages 435-461, December.
    13. Vo, D.H. & Pham, B.V.-N. & Pham, T.V.-T. & McAleer, M.J., 2019. "Corporate Financial Distress of Industry Level Listings in an Emerging Market," Econometric Institute Research Papers EI2019-15, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    14. Stewart Jones, 2017. "Corporate bankruptcy prediction: a high dimensional analysis," Review of Accounting Studies, Springer, vol. 22(3), pages 1366-1422, September.
    15. Alam, Nurul & Gao, Junbin & Jones, Stewart, 2021. "Corporate failure prediction: An evaluation of deep learning vs discrete hazard models," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 75(C).
    16. Bhanu Pratap Singh & Alok Kumar Mishra, 2016. "Re-estimation and comparisons of alternative accounting based bankruptcy prediction models for Indian companies," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 2(1), pages 1-28, December.
    17. Leila Bateni & Farshid Asghari, 2020. "Bankruptcy Prediction Using Logit and Genetic Algorithm Models: A Comparative Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 55(1), pages 335-348, January.
    18. 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.
    19. Paulo V. Carvalho & José D. Curto & Rodrigo Primor, 2022. "Macroeconomic determinants of credit risk: Evidence from the Eurozone," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 2054-2072, April.
    20. John Nkwoma Inekwe, 2016. "Financial Distress, Employees’ Welfare and Entrepreneurship Among SMEs," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 129(3), pages 1135-1153, December.

    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:gam:jjrfmx:v:12:y:2019:i:2:p:55-:d:219945. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: https://www.mdpi.com .

    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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.