IDEAS home Printed from https://ideas.repec.org/a/wly/jforec/v40y2021i7p1325-1341.html
   My bibliography  Save this article

Human resources and corporate failure prediction modeling: Evidence from Belgium

Author

Listed:
  • Xavier Brédart
  • Eric Séverin
  • David Veganzones

Abstract

This paper analyzes the prediction performance of human resources (HR) variables in corporate failure modeling. We define corporate failure as a two‐phase process from financial distress to bankruptcy, so that we can determine the prediction power of HR variables along a firm's phase in the financial deterioration process. We demonstrate the use of HR variables and their application to a two‐phase corporate failure model, providing first evidence for the predictive power of HR variables. The experimental results, based on real‐world datasets from Belgium, show that HR variables used in conjugation with accounting‐based information improve the accuracy of prediction modeling. However, the predictive power of HR variables varies in different phases of corporate failure with better prediction accuracy during the initial symptoms of corporate failure (i.e., financial distress). Findings show that our proposed model predicted financial distress with 84.1%, whereas the accuracy decreased to 83.3% when predicting bankruptcy. Besides, they also show that, on average, the inclusion of HR variables improves the global accuracy of the prediction models of 3.8% and allows to decrease Type I error of 5%.

Suggested Citation

  • Xavier Brédart & Eric Séverin & David Veganzones, 2021. "Human resources and corporate failure prediction modeling: Evidence from Belgium," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(7), pages 1325-1341, November.
  • Handle: RePEc:wly:jforec:v:40:y:2021:i:7:p:1325-1341
    DOI: 10.1002/for.2770
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/for.2770
    Download Restriction: no

    File URL: https://libkey.io/10.1002/for.2770?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
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Umar Farooq & Muhammad Ali Jibran Qamar, 2019. "Predicting multistage financial distress: Reflections on sampling, feature and model selection criteria," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(7), pages 632-648, November.
    3. Yang, Z. R. & Platt, Marjorie B. & Platt, Harlan D., 1999. "Probabilistic Neural Networks in Bankruptcy Prediction," Journal of Business Research, Elsevier, vol. 44(2), pages 67-74, February.
    4. S. Balcaen & H. Ooghe, 2004. "Alternative methodologies in studies on business failure: do they produce better results than the classical statistical methods?," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/249, Ghent University, Faculty of Economics and Business Administration.
    5. Becchetti, Leonardo & Sierra, Jaime, 2003. "Bankruptcy risk and productive efficiency in manufacturing firms," Journal of Banking & Finance, Elsevier, vol. 27(11), pages 2099-2120, November.
    6. Patrick Aubert & Bruno Crépon & Philippe Zamora, 2009. "Le rendement apparent de la formation continue dans les entreprises : effets sur la productivité et les salaires," Économie et Prévision, Programme National Persée, vol. 187(1), pages 25-46.
    7. Bruce E. Hansen, 2000. "Sample Splitting and Threshold Estimation," Econometrica, Econometric Society, vol. 68(3), pages 575-604, May.
    8. Andreas Rauch & Serge A. Rijsdijk, 2013. "The Effects of General and Specific Human Capital on Long–Term Growth and Failure of Newly Founded Businesses," Entrepreneurship Theory and Practice, , vol. 37(4), pages 923-941, July.
    9. Balcaen, Sofie & Ooghe, Hubert, 2006. "35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems," The British Accounting Review, Elsevier, vol. 38(1), pages 63-93.
    10. 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.
    11. Micha, Bernard, 1984. "Analysis of business failures in France," Journal of Banking & Finance, Elsevier, vol. 8(2), pages 281-291, June.
    12. 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.
    13. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    14. 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.
    15. Rajeswararao S. Chaganti & Vijay Mahajan & Subhash Sharma, 1985. "Corporate Board Size, Composition And Corporate Failures In Retailing Industry[1]," Journal of Management Studies, Wiley Blackwell, vol. 22(4), pages 400-417, July.
    16. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 123-127.
    17. du Jardin, Philippe, 2015. "Bankruptcy prediction using terminal failure processes," European Journal of Operational Research, Elsevier, vol. 242(1), pages 286-303.
    18. Xiaobo Tang & Shixuan Li & Mingliang Tan & Wenxuan Shi, 2020. "Incorporating textual and management factors into financial distress prediction: A comparative study of machine learning methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 769-787, August.
    19. Donald C. Hambrick & Richard A. D'Aveni, 1992. "Top Team Deterioration as Part of the Downward Spiral of Large Corporate Bankruptcies," Management Science, INFORMS, vol. 38(10), pages 1445-1466, October.
    20. Christian Lohmann & Thorsten Ohliger, 2019. "Using accounting‐based information on young firms to predict bankruptcy," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(8), pages 803-819, December.
    21. Sofie Balcaen & Sophie Manigart & Hubert Ooghe, 2011. "From distress to exit: determinants of the time to exit," Journal of Evolutionary Economics, Springer, vol. 21(3), pages 407-446, August.
    22. Bester, Helmut & Milliou, Chrysovalantou & Petrakis, Emmanuel, 2012. "Wage bargaining, productivity growth and long-run industry structure," Labour Economics, Elsevier, vol. 19(6), pages 923-930.
    23. Addessi, William, 2014. "The productivity effect of permanent and temporary labor contracts in the Italian manufacturing sector," Economic Modelling, Elsevier, vol. 36(C), pages 666-672.
    24. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    25. Ciampi, Francesco, 2015. "Corporate governance characteristics and default prediction modeling for small enterprises. An empirical analysis of Italian firms," Journal of Business Research, Elsevier, vol. 68(5), pages 1012-1025.
    26. 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.
    27. 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.
    28. Tian, Shaonan & Yu, Yan & Guo, Hui, 2015. "Variable selection and corporate bankruptcy forecasts," Journal of Banking & Finance, Elsevier, vol. 52(C), pages 89-100.
    29. Bendickson, Joshua S. & Chandler, Timothy D., 2019. "Operational performance: The mediator between human capital developmental programs and financial performance," Journal of Business Research, Elsevier, vol. 94(C), pages 162-171.
    30. Gérard Ballot & Fathi Fakhfakh & Erol Taymaz, 2006. "Who Benefits from Training and R&D, the Firm or the Workers?," British Journal of Industrial Relations, London School of Economics, vol. 44(3), pages 473-495, September.
    31. Catherine M. Daily & Dan R. Dalton, 1994. "Corporate governance and the bankrupt firm: An empirical assessment," Strategic Management Journal, Wiley Blackwell, vol. 15(8), pages 643-654, October.
    32. William F. Messier, Jr. & James V. Hansen, 1988. "Inducing Rules for Expert System Development: An Example Using Default and Bankruptcy Data," Management Science, INFORMS, vol. 34(12), pages 1403-1415, December.
    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. Lenka Papíková & Mário Papík, 2022. "Effects of classification, feature selection, and resampling methods on bankruptcy prediction of small and medium‐sized enterprises," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(4), pages 254-281, October.
    2. Gérard Desmaison, 2021. "IMPACT DE LA CRISE SANITAIRE SUR LES ENTREPRENEURS AYANT SUBI UNE LIQUIDATION JUDICIAIRE Etude d'impact 2020 de l'association 60 000 REBONDS," Post-Print hal-03418337, HAL.
    3. Veganzones, David & Séverin, Eric & Chlibi, Souhir, 2023. "Influence of earnings management on forecasting corporate failure," International Journal of Forecasting, Elsevier, vol. 39(1), pages 123-143.

    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. 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.
    2. Hyeongjun Kim & Hoon Cho & Doojin Ryu, 2020. "Corporate Default Predictions Using Machine Learning: Literature Review," Sustainability, MDPI, vol. 12(16), pages 1-11, August.
    3. Hyeongjun Kim & Hoon Cho & Doojin Ryu, 2022. "Corporate Bankruptcy Prediction Using Machine Learning Methodologies with a Focus on Sequential Data," Computational Economics, Springer;Society for Computational Economics, vol. 59(3), pages 1231-1249, March.
    4. Francesco Ciampi & Valentina Cillo & Fabio Fiano, 2020. "Combining Kohonen maps and prior payment behavior for small enterprise default prediction," Small Business Economics, Springer, vol. 54(4), pages 1007-1039, April.
    5. Eric Séverin & David Veganzones, 2021. "Can earnings management information improve bankruptcy prediction models?," Annals of Operations Research, Springer, vol. 306(1), pages 247-272, November.
    6. 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).
    7. 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.
    8. David Veganzones, 2022. "Corporate failure prediction using threshold‐based models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 956-979, August.
    9. Veganzones, David & Séverin, Eric & Chlibi, Souhir, 2023. "Influence of earnings management on forecasting corporate failure," International Journal of Forecasting, Elsevier, vol. 39(1), pages 123-143.
    10. Bai, Qing & Tian, Shaonan, 2020. "Innovate or die: Corporate innovation and bankruptcy forecasts," Journal of Empirical Finance, Elsevier, vol. 59(C), pages 88-108.
    11. 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.
    12. Dong, Manh Cuong & Tian, Shaonan & Chen, Cathy W.S., 2018. "Predicting failure risk using financial ratios: Quantile hazard model approach," The North American Journal of Economics and Finance, Elsevier, vol. 44(C), pages 204-220.
    13. 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.
    14. Gila Burde, 2018. "Improved Methods for Predicting the Financial Vulnerability of Nonprofit Organizations," Administrative Sciences, MDPI, vol. 8(1), pages 1-8, February.
    15. 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.
    16. Christian Lohmann & Thorsten Ohliger, 2020. "Bankruptcy prediction and the discriminatory power of annual reports: empirical evidence from financially distressed German companies," Journal of Business Economics, Springer, vol. 90(1), pages 137-172, February.
    17. Tian, Shaonan & Yu, Yan, 2017. "Financial ratios and bankruptcy predictions: An international evidence," International Review of Economics & Finance, Elsevier, vol. 51(C), pages 510-526.
    18. 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.
    19. Balcaen, Sofie & Ooghe, Hubert, 2006. "35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems," The British Accounting Review, Elsevier, vol. 38(1), pages 63-93.
    20. 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.

    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:wly:jforec:v:40:y:2021:i:7:p:1325-1341. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/2966 .

    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.