IDEAS home Printed from https://ideas.repec.org/p/bdr/borrec/1084.html

Indicadores de alerta temprana para el sector corporativo privado colombiano

Author

Listed:
  • María Fernanda Meneses-González

    (Banco de la República de Colombia)

  • Javier Eliecer Pirateque-Niño

    (Banco de la República de Colombia)

  • Santiago David Segovia-Baquero

    (Banco de la República de Colombia)

Abstract

Este documento valida la utilidad de algunas variables financieras en la identificación temprana de acumulación de vulnerabilidades para el sector corporativo privado en Colombia. Para esto, se estudia la evolución de varios indicadores para firmas que han entrado en distress financiero. Adicionalmente, se valida la capacidad predictiva de los indicadores in-sample y out-of-sample. Los resultados sugieren que la razón de endeudamiento y una medida de debt-to-cashflow son las que mejor información proveen. Asimismo, se encuentra que la desagregación de las firmas por sector económico y el uso de medidas conjuntas aumentan la capacidad de identificación de situaciones de vulnerabilidad en este sector. **** ABSTRACT: This paper assesses the usefulness of some financial variables in predicting episodes of vulnerability for the private corporate sector in Colombia. We analyse the evolution of several indicators for firms that have experienced episodes of distress. Additionally, we validate the predictive power of our indicators by using in-sample and out-of-sample tests. The results suggest that the ratio of financial obligations to assets, as well as a measure of debt-to-cashflow provide better information. Likewise, we find that classifying the firms by economic sector and the use of several variables at the same time improve the ability to detect changes in the financial health of firms.

Suggested Citation

  • María Fernanda Meneses-González & Javier Eliecer Pirateque-Niño & Santiago David Segovia-Baquero, 2019. "Indicadores de alerta temprana para el sector corporativo privado colombiano," Borradores de Economia 1084, Banco de la Republica de Colombia.
    • Handle: RePEc:bdr:borrec:1084
    DOI: 10.32468/be.1084
    as

    Download full text from publisher

    File URL: https://doi.org/10.32468/be.1084
    Download Restriction: no
    File URL: https://libkey.io/10.32468/be.1084?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. 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.
    2. Juan Sebastián Lemus & Adriana María Corredor-Waldron & Javier Gutiérrez Rueda, 2012. "Fragility determinants of the private corporate sector in Colombia," Temas de Estabilidad Financiera 066, Banco de la Republica de Colombia.
    3. Piotroski, JD, 2000. "Value investing: The use of historical financial statement information to separate winners from losers," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 38, pages 1-41.
    4. Felipe Clavijo Ramírez & Jorge Luis Hurtado Guarín & Oscar Fernando Jaulín Méndez & Javier Pirateque Niño, 2016. "El requerimiento de capital contracíclico en Colombia," Borradores de Economia 963, Banco de la Republica de Colombia.
    5. Christensen, Ian & Li, Fuchun, 2014. "Predicting financial stress events: A signal extraction approach," Journal of Financial Stability, Elsevier, vol. 14(C), pages 54-65.
    6. Bruce A. Ramsay & Peter Sarlin, 2016. "Ending Over‐lending: Assessing Systemic Risk with Debt to Cash Flow," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 21(1), pages 36-57, January.
    7. 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.
    8. Ernani Teixeira Torres Filho & Norberto Montani Martins & Caroline Yukari Miaguti, 2017. "Minsky's Financial Fragility: An Empirical Analysis of Electricity Distribution Companies in Brazil (2007-15)," Economics Working Paper Archive wp_896, Levy Economics Institute.
    9. Beaver, William H. & Correia, Maria & McNichols, Maureen F., 2011. "Financial Statement Analysis and the Prediction of Financial Distress," Foundations and Trends(R) in Accounting, now publishers, vol. 5(2), pages 99-173, May.
    10. Mathias Drehmann & Kostas Tsatsaronis, 2014. "The credit-to-GDP gap and countercyclical capital buffers: questions and answers," BIS Quarterly Review, Bank for International Settlements, March.
    11. Michael D. Bordo & Michael J. Dueker & David C. Wheelock, 2002. "Aggregate Price Shocks and Financial Instability: A Historical Analysis," Economic Inquiry, Western Economic Association International, vol. 40(4), pages 521-538, October.
    12. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 4, pages 123-127.
    13. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 4, pages 71-111.
    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. Yasser Alhenawi & Martha Stilwell, 2017. "Value creation and the probability of success in merger and acquisition transactions," Review of Quantitative Finance and Accounting, Springer, vol. 49(4), pages 1041-1085, November.
    2. Elena Rond'os-Casas & Germ`a Coenders & Miquel Carreras-Sim'o & N'uria Arimany-Serrat, 2025. "The use of financial and sustainability ratios to map a sector. An approach using compositional data," Papers 2509.06468, arXiv.org.
    3. Ken Li, 2024. "Liquidity ratios and corporate failures," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 64(1), pages 1111-1134, March.
    4. Kumar, Rahul & Deb, Soumya Guha & Mukherjee, Shubhadeep, 2020. "Do words reveal the latent truth? Identifying communication patterns of corporate losers," Journal of Behavioral and Experimental Finance, Elsevier, vol. 26(C).
    5. Aytekin Ertan & Yun Lee & Regina Wittenberg-Moerman, 2025. "Unexpected defaults: the role of information opacity," Review of Accounting Studies, Springer, vol. 30(1), pages 899-949, March.
    6. 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.
    7. Khushbu Agrawal, 2015. "Default Prediction Using Piotroski’s F-score," Global Business Review, International Management Institute, vol. 16(5_suppl), pages 175-186, October.
    8. B Korcan Ak & Patricia M Dechow & Yuan Sun & Annika Yu Wang, 2013. "The use of financial ratio models to help investors predict and interpret significant corporate events," Australian Journal of Management, Australian School of Business, vol. 38(3), pages 553-598, December.
    9. deHaan, Ed & Li, Jiacui & Watts, Edward M., 2023. "Retail bond investors and credit ratings," Journal of Accounting and Economics, Elsevier, vol. 76(1).
    10. Sapkota, Niranjan, 2025. "The crypto collapse chronicles: Decoding cryptocurrency exchange defaults," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 99(C).
    11. 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.
    12. 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.
    13. 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).
    14. Pavol Durana & Lucia Michalkova & Andrej Privara & Josef Marousek & Milos Tumpach, 2021. "Does the life cycle affect earnings management and bankruptcy?," Oeconomia Copernicana, Institute of Economic Research, vol. 12(2), pages 425-461, June.
    15. Jagjeevan Kanoujiya & Shailesh Rastogi, 2024. "Nexus between efficiency and financial distress of listed firms in India: a comparative study using frontier techniques," OPSEARCH, Springer;Operational Research Society of India, vol. 61(2), pages 835-866, June.
    16. Ralph W. Adler, 1996. "Exploring the Seeds of Organisational Decline," Australian Accounting Review, CPA Australia, vol. 6(12), pages 24-36, September.
    17. 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.
    18. 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.
    19. Mahfuzur Rahman & Cheong Li Sa & Md. Abdul Kaium Masud, 2021. "Predicting Firms’ Financial Distress: An Empirical Analysis Using the F-Score Model," JRFM, MDPI, vol. 14(5), pages 1-16, May.
    20. J. F. Juliá-Igual & R. Cervelló-Royo & I. Berné-Lafuente, 2017. "Market value analysis of a Chinese e-commerce holding group: a multicriteria approach," Service Business, Springer;Pan-Pacific Business Association, vol. 11(3), pages 475-490, September.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation

    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:bdr:borrec:1084. 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: Clorith Angélica Bahos Olivera (email available below). General contact details of provider: https://edirc.repec.org/data/brcgvco.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.