IDEAS home Printed from https://ideas.repec.org/p/col/000122/010715.html
   My bibliography  Save this paper

Estimación del Riesgo de crédito para empresas del sector real en Colombia

Author

Listed:
  • Claudia Sepúlveda Rivillas
  • Walter Reina Gutiérrez
  • Juan Carlos Gutiérrez Betancur

Abstract

Resumen:El objetivo de la presente investigación es proponer un modelo probit para datos de panel desbalanceado con efectos aleatorios que permita estimar la probabilidad de quiebra de las empresas del sector real en Colombia, para inferir del riesgo de crédito, tomando información de empresas solventes y en estrés financiero, de las bases de datos de la Superintendencia de Sociedades y B.P.R, durante 2002-2008. Se partió del análisis fundamental, centrado en los indicadores de rentabilidad, apalancamiento, liquidez y solvencia, que propone Penman (2010). El aporte de esta investigación es el énfasis en los apalancamientos operativo y financiero y su efecto en la probabilidad de quiebra. Como principal hallazgo se resalta el efecto menos nocivo del apalancamiento operativo frente al impacto del apalancamiento financiero en épocas de crisis.Abstract:The objective of this research is to proposed a Probit Model for unbalanced panel data with random effects to estimate the probability of bankruptcy in the real sector firms in Colombia, to infer of credit risk of solvent and in financial distress firms, taking information from solvent companies and financial stress, the databases of the Superintendency of Companies, and BPR, since 2002-2008. Was based on the fundamental analysis, focusing on indicators of profitability, leverage, liquidity and solvency proposed by Penman (2010). The contribution of this research is the emphasis on operating and financial leverage and its effect on the probability of bankruptcy. Like main finding is highlights the less harmful effect of operating leverage in front the impact of financial leverage in times of crisis.

Suggested Citation

  • Claudia Sepúlveda Rivillas & Walter Reina Gutiérrez & Juan Carlos Gutiérrez Betancur, 2012. "Estimación del Riesgo de crédito para empresas del sector real en Colombia," Documentos de Trabajo de Valor Público 10715, Universidad EAFIT.
    • Handle: RePEc:col:000122:010715
    as

    Download full text from publisher

    File URL: http://repository.eafit.edu.co/bitstream/10784/729/1/2012_25_Juan_Carlos_Gutierrez.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Henry Laverde Rojas, 2008. "Análisis de vulnerabilidad empresarial y sus efectos sobre la vulnerabilidad bancaria en Colombia: una aplicación delenfoque de hoja de balances," Revista CIFE, Universidad Santo Tomás, December.
    2. 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.
    3. Ángela González Arbeláez, 2010. "Determinantes del riesgo del crédito comercial en Colombia," Vniversitas Económica, Universidad Javeriana - Bogotá, vol. 0(0), pages 1-69, February.
    4. 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.
    5. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    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. Matthew Smith & Francisco Alvarez, 2022. "Predicting Firm-Level Bankruptcy in the Spanish Economy Using Extreme Gradient Boosting," Computational Economics, Springer;Society for Computational Economics, vol. 59(1), pages 263-295, January.
    2. 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.
    3. Ruey-Ching Hwang & Jhao-Siang Siao & Huimin Chung & C. Chu, 2011. "Assessing bankruptcy prediction models via information content of technical inefficiency," Journal of Productivity Analysis, Springer, vol. 36(3), pages 263-273, December.
    4. Antonio Davila & George Foster & Xiaobin He & Carlos Shimizu, 2015. "The rise and fall of startups: Creation and destruction of revenue and jobs by young companies," Australian Journal of Management, Australian School of Business, vol. 40(1), pages 6-35, February.
    5. Giordani, Paolo & Jacobson, Tor & Schedvin, Erik von & Villani, Mattias, 2014. "Taking the Twists into Account: Predicting Firm Bankruptcy Risk with Splines of Financial Ratios," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 49(4), pages 1071-1099, August.
    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. Sanghoon Lee & Keunho Choi & Donghee Yoo, 2020. "Predicting the Insolvency of SMEs Using Technological Feasibility Assessment Information and Data Mining Techniques," Sustainability, MDPI, vol. 12(23), pages 1-17, November.
    8. van der Heijden, Hans, 2022. "Predicting industry sectors from financial statements: An illustration of machine learning in accounting research," The British Accounting Review, Elsevier, vol. 54(5).
    9. 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.
    10. Hunter, John & Isachenkova, Natalia, 2006. "Aggregate economy risk and company failure: An examination of UK quoted firms in the early 1990s," Journal of Policy Modeling, Elsevier, vol. 28(8), pages 911-919, November.
    11. 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.
    12. Ke Wang & Darrell Duffie, 2004. "Multi-Period Corporate Failure Prediction With Stochastic Covariates," Econometric Society 2004 Far Eastern Meetings 747, Econometric Society.
    13. Lauren Stagnol, 2015. "Designing a corporate bond index on solvency criteria," EconomiX Working Papers 2015-39, University of Paris Nanterre, EconomiX.
    14. Matúš Mihalovič, 2018. "Využitie skóringových modelov pri predikcii defaultu ekonomických subjektov v Slovenskej republike [Applicability of Scoring Models in Firms' Default Prediction. The Case of Slovakia]," Politická ekonomie, Prague University of Economics and Business, vol. 2018(6), pages 689-708.
    15. Dimitras, A. I. & Slowinski, R. & Susmaga, R. & Zopounidis, C., 1999. "Business failure prediction using rough sets," European Journal of Operational Research, Elsevier, vol. 114(2), pages 263-280, April.
    16. James R. Coakley & Carol E. Brown, 2000. "Artificial neural networks in accounting and finance: modeling issues," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 9(2), pages 119-144, June.
    17. Lin, Hsiou-Wei William & Lo, Huai-Chun & Wu, Ruei-Shian, 2016. "Modeling default prediction with earnings management," Pacific-Basin Finance Journal, Elsevier, vol. 40(PB), pages 306-322.
    18. Clifford S. Asness & Andrea Frazzini & Lasse Heje Pedersen, 2019. "Quality minus junk," Review of Accounting Studies, Springer, vol. 24(1), pages 34-112, March.
    19. Rahaman, Mohammad M. & Rau, P. Raghavendra & Zaman, Ashraf Al, 2020. "The effect of supply chain power on bank financing," Journal of Banking & Finance, Elsevier, vol. 114(C).
    20. Yu-Ling Lin & Ta-Cheng Chang & Su-Jing Yeh, 2012. "Default Risk and Equity Returns: Evidence from the Taiwan Equities Market," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 19(2), pages 181-204, May.

    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:col:000122:010715. 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: Valor Público EAFIT - Centro de estudios e incidencia (email available below). General contact details of provider: https://edirc.repec.org/data/cieafco.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.