IDEAS home Printed from https://ideas.repec.org/p/bdr/borrec/1084.html
   My bibliography  Save this paper

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. Christensen, Ian & Li, Fuchun, 2014. "Predicting financial stress events: A signal extraction approach," Journal of Financial Stability, Elsevier, vol. 14(C), pages 54-65.
    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. 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.
    4. 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.
    5. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 123-127.
    6. 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.
    7. 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.
    8. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    9. 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.
    10. 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.
    11. Piotroski, JD, 2000. "Value investing: The use of historical financial statement information to separate winners from losers," Journal of Accounting Research, Wiley Blackwell, vol. 38, pages 1-41.
    12. 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.
    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. 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.
    2. 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).
    3. Khushbu Agrawal, 2015. "Default Prediction Using Piotroski’s F-score," Global Business Review, International Management Institute, vol. 16(5_suppl), pages 175-186, October.
    4. 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.
    5. 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.
    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. 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.
    8. Enrico Supino & Nicola Piras, 2022. "Le performance dei modelli di credit scoring in contesti di forte instabilit? macroeconomica: il ruolo delle Reti Neurali Artificiali," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2022(2), pages 41-61.
    9. E. Fedorova A. & M. Chukhlantseva A. & D. Chekrizov V. & ЕЛЕНА Федорова АНАТОЛЬЕВНА & МАРИЯ Чухланцева АЛЕКСАНДРОВНА & ДМИТРИЙ Чекризов ВАСИЛЬЕВИЧ, 2017. "Нормативные значения коэффициентов финансовой устойчивости: особенности видов экономической деятельности // Normative Values of Financial Stability Ratios: Industry-Specific Features," Управленческие науки // Management Science, ФГОБУВО Финансовый университет при Правительстве Российской Федерации // Financial University under The Government of Russian Federation, vol. 7(2), pages 44-55.
    10. Eling, Martin & Jia, Ruo, 2018. "Business failure, efficiency, and volatility: Evidence from the European insurance industry," International Review of Financial Analysis, Elsevier, vol. 59(C), pages 58-76.
    11. Le, Hong Hanh & Viviani, Jean-Laurent, 2018. "Predicting bank failure: An improvement by implementing a machine-learning approach to classical financial ratios," Research in International Business and Finance, Elsevier, vol. 44(C), pages 16-25.
    12. 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.
    13. Juraini Zainol Abidin & Nur Adiana Hiau Abdullah & Karren Lee-Hwei Khaw, 2020. "Predicting SMEs Failure: Logistic Regression vs Artificial Neural Network Models," Capital Markets Review, Malaysian Finance Association, vol. 28(2), pages 29-41.
    14. Geoffroy Enjolras & Philippe Madiès, 2020. "The role of bank analysts and scores in the prediction of financial distress: Evidence from French farms," Economics Bulletin, AccessEcon, vol. 40(4), pages 2978-2993.
    15. Nasim Nasirpour & Alireza Mazdaki & Esmail Enayati, 2016. "The Investigation and Comparison of the Performance of Heuristic Methods in the Prediction of the Type of Auditor’s Opinion in Firms Accepted in Tehran Stock Exchange," Asian Social Science, Canadian Center of Science and Education, vol. 12(6), pages 148-148, June.
    16. David Veganzones, 2022. "Corporate failure prediction using threshold‐based models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 956-979, August.
    17. Amin Jan & Maran Marimuthu & Muhammad Kashif Shad & Haseeb ur-Rehman & Muhammad Zahid & Ahmad Ali Jan, 2019. "Bankruptcy profile of the Islamic and conventional banks in Malaysia: a post-crisis period analysis," Economic Change and Restructuring, Springer, vol. 52(1), pages 67-87, February.
    18. Ivana Podhorska, 2016. "Success of prediction models in Slovak companies," GATR Journals gjbssr446, Global Academy of Training and Research (GATR) Enterprise.
    19. Juan García Lara & Beatriz Osma & Evi Neophytou, 2009. "Earnings quality in ex‐post failed firms," Accounting and Business Research, Taylor & Francis Journals, vol. 39(2), pages 119-138.
    20. Nandita Mishra & Shruti Ashok & Deepak Tandon, 2024. "Predicting Financial Distress in the Indian Banking Sector: A Comparative Study Between the Logistic Regression, LDA and ANN Models," Global Business Review, International Management Institute, vol. 25(6), pages 1540-1558, December.

    More about this item

    Keywords

    debt-to-cashflow; alerta temprana; endeudamiento; distress financiero; estabilidad financiera; debt-to-cashflow; early warning; indebtness; financial distress; financial stability.;
    All these 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.