IDEAS home Printed from https://ideas.repec.org/a/pab/rmcpee/v24y2018i1p54-88.html
   My bibliography  Save this article

Selection of Variables in Small Business Failure Analysis: Mean Selection vs. Median Selection || Selección de variables en el análisis de fracaso de empresas pequeñas: selección de medias frente a selección de medianas

Author

Listed:
  • Tascón, María T.

    (Departamento de Dirección y Economía de la Empresa. Universidad de León (España))

  • Castaño, Francisco J.

    (Departamento de Dirección y Economía de la Empresa. Universidad de León (España))

Abstract

This paper focuses on one of the most determinant processes in business failure assessment: Variable selection. After a preselection of variables based on previous empirical literature, we perform a statistical variable selection on a sample of small firms using both mean and median differences. As the resulting variables differ in each test, we have performed a varied group of business failure assessment methods (linear discriminant analysis, quadratic discriminant analysis, logistic discriminant analysis, k-th nearest-neighbor discriminant analysis, logit, and probit) to identify the implications of using one test or the other. Our results show that the nature of the sample determines not only the statistical variable selection test, but the most appropriate methods to assess business failure, which constitutes our main contribution. Additionally, we contribute new evidence on the addition of qualitative information (payment incidents), with previous evidence for SMEs being scarce. || Este trabajo se ocupa de uno de los procesos más determinantes en la evaluación del fracaso empresarial: la selección de variables. Tras una preselección de variables basada en los resultados empíricos de la literatura previa, llevamos a cabo una selección estadística de variables sobre una muestra de empresas pequeñas, utilizando tanto diferencias en medias como diferencias en medianas. Como las variables resultantes difieren con el test, hemos utilizado un variado grupo de métodos de evaluación de fracaso empresarial (LDA, QDA, LogDA, KNNDA, logit y probit) con el fin de identificar las implicaciones de usar uno u otro test. Nuestros resultados muestran que la naturaleza de la muestra determina no solo el test de selección estadística de variables, sino también los métodos más apropiados para evaluar el fracaso empresarial, lo que constituye nuestra principal contribución. Además, el trabajo proporciona nueva evidencia sobre la adición de información cualitativa (incidencias de pago), siendo escasa la evidencia previa para pymes.

Suggested Citation

  • Tascón, María T. & Castaño, Francisco J., 2017. "Selection of Variables in Small Business Failure Analysis: Mean Selection vs. Median Selection || Selección de variables en el análisis de fracaso de empresas pequeñas: selección de medias frente a se," 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. 24(1), pages 54-88, Diciembre.
  • Handle: RePEc:pab:rmcpee:v:24:y:2018:i:1:p:54-88
    as

    Download full text from publisher

    File URL: https://www.upo.es/revistas/index.php/RevMetCuant/article/view/2880
    Download Restriction: no

    File URL: https://www.upo.es/revistas/index.php/RevMetCuant/article/view/2880/2277
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Peel, MJ & Peel, DA & Pope, PF, 1986. "Predicting corporate failure-- Some results for the UK corporate sector," Omega, Elsevier, vol. 14(1), pages 5-12.
    2. 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.
    3. 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.
    4. Martin, Daniel, 1977. "Early warning of bank failure : A logit regression approach," Journal of Banking & Finance, Elsevier, vol. 1(3), pages 249-276, November.
    5. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    6. Jin, Justin Yiqiang & Kanagaretnam, Kiridaran & Lobo, Gerald J., 2011. "Ability of accounting and audit quality variables to predict bank failure during the financial crisis," Journal of Banking & Finance, Elsevier, vol. 35(11), pages 2811-2819, November.
    7. Eisenbeis, Robert A, 1977. "Pitfalls in the Application of Discriminant Analysis in Business, Finance, and Economics," Journal of Finance, American Finance Association, vol. 32(3), pages 875-900, June.
    8. Sueyoshi, Toshiyuki & Goto, Mika, 2009. "Can R&D expenditure avoid corporate bankruptcy? Comparison between Japanese machinery and electric equipment industries using DEA-discriminant analysis," European Journal of Operational Research, Elsevier, vol. 196(1), pages 289-311, July.
    9. Christopher F Baum, 2006. "An Introduction to Modern Econometrics using Stata," Stata Press books, StataCorp LP, number imeus, March.
    10. Ravi Kumar, P. & Ravi, V., 2007. "Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review," European Journal of Operational Research, Elsevier, vol. 180(1), pages 1-28, July.
    11. 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.
    12. Sueyoshi, Toshiyuki & Goto, Mika, 2009. "DEA-DA for bankruptcy-based performance assessment: Misclassification analysis of Japanese construction industry," European Journal of Operational Research, Elsevier, vol. 199(2), pages 576-594, December.
    13. Dambolena, Ismael G & Khoury, Sarkis J, 1980. "Ratio Stability and Corporate Failure," Journal of Finance, American Finance Association, vol. 35(4), pages 1017-1026, September.
    14. Collins, Robert A. & Green, Richard D., 1982. "Statistical methods for bankruptcy forecasting," Journal of Economics and Business, Elsevier, vol. 34(4), pages 349-354.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. Mohammad Mahdi Mousavi & Jamal Ouenniche & Kaoru Tone, 2023. "A dynamic performance evaluation of distress prediction models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 756-784, July.
    3. Dimitras, A. I. & Zanakis, S. H. & Zopounidis, C., 1996. "A survey of business failures with an emphasis on prediction methods and industrial applications," European Journal of Operational Research, Elsevier, vol. 90(3), pages 487-513, May.
    4. Sueyoshi, Toshiyuki & Goto, Mika, 2009. "Methodological comparison between DEA (data envelopment analysis) and DEA-DA (discriminant analysis) from the perspective of bankruptcy assessment," European Journal of Operational Research, Elsevier, vol. 199(2), pages 561-575, December.
    5. Sueyoshi, Toshiyuki & Goto, Mika, 2009. "DEA-DA for bankruptcy-based performance assessment: Misclassification analysis of Japanese construction industry," European Journal of Operational Research, Elsevier, vol. 199(2), pages 576-594, December.
    6. Premachandra, I.M. & Bhabra, Gurmeet Singh & Sueyoshi, Toshiyuki, 2009. "DEA as a tool for bankruptcy assessment: A comparative study with logistic regression technique," European Journal of Operational Research, Elsevier, vol. 193(2), pages 412-424, March.
    7. Douglas, Ella & Lont, David & Scott, Tom, 2014. "Finance company failure in New Zealand during 2006–2009: Predictable failures?," Journal of Contemporary Accounting and Economics, Elsevier, vol. 10(3), pages 277-295.
    8. 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.
    9. Premachandra, I.M. & Chen, Yao & Watson, John, 2011. "DEA as a tool for predicting corporate failure and success: A case of bankruptcy assessment," Omega, Elsevier, vol. 39(6), pages 620-626, December.
    10. du Jardin, Philippe, 2015. "Bankruptcy prediction using terminal failure processes," European Journal of Operational Research, Elsevier, vol. 242(1), pages 286-303.
    11. Zeineb Affes & Rania Hentati-Kaffel, 2016. "Predicting US banks bankruptcy: logit versus Canonical Discriminant analysis," Post-Print halshs-01281948, HAL.
    12. Sueyoshi, Toshiyuki & Goto, Mika, 2009. "Can R&D expenditure avoid corporate bankruptcy? Comparison between Japanese machinery and electric equipment industries using DEA-discriminant analysis," European Journal of Operational Research, Elsevier, vol. 196(1), pages 289-311, July.
    13. Zeineb Affes & Rania Hentati-Kaffel, 2016. "Predicting US banks bankruptcy: logit versus Canonical Discriminant analysis," Documents de travail du Centre d'Economie de la Sorbonne 16016, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    14. John W. Pacey & Toan M. Pham, 1990. "The Predictiveness of Bankruptcy Models: Methodological Problems and Evidence," Australian Journal of Management, Australian School of Business, vol. 15(2), pages 315-337, December.
    15. Koen W. de Bock, 2017. "The best of two worlds: Balancing model strength and comprehensibility in business failure prediction using spline-rule ensembles," Post-Print hal-01588059, HAL.
    16. De Bock, Koen W. & Coussement, Kristof & Lessmann, Stefan, 2020. "Cost-sensitive business failure prediction when misclassification costs are uncertain: A heterogeneous ensemble selection approach," European Journal of Operational Research, Elsevier, vol. 285(2), pages 612-630.
    17. Ioannis Tsolas, 2015. "Firm credit risk evaluation: a series two-stage DEA modeling framework," Annals of Operations Research, Springer, vol. 233(1), pages 483-500, October.
    18. 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.
    19. Abbas, Qaiser & Rashid, Abdul, 2011. "Modeling Bankruptcy Prediction for Non-Financial Firms: The Case of Pakistan," MPRA Paper 28161, University Library of Munich, Germany.
    20. du Jardin, Philippe & Séverin, Eric, 2012. "Forecasting financial failure using a Kohonen map: A comparative study to improve model stability over time," European Journal of Operational Research, Elsevier, vol. 221(2), pages 378-396.

    More about this item

    Keywords

    small business failure; variable selection; discriminant analysis; probit; logit; financial ratios; qualitative information; fracaso en pequeñas empresas; selección de variables; análisis discriminante; ratios financieros; información cualitativa;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • L25 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Firm Performance
    • M41 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Accounting

    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:pab:rmcpee:v:24:y:2018:i:1:p:54-88. 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: Publicación Digital - UPO (email available below). General contact details of provider: https://edirc.repec.org/data/dmupoes.html .

    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.