IDEAS home Printed from https://ideas.repec.org/a/col/000129/018340.html
   My bibliography  Save this article

Financial ratios as a powerful instrument to predict insolvency; a study using boosting algorithms in Colombian firms

Author

Listed:
  • Diego Andrés Correa-Mejía
  • Mauricio Lopera-Castaño

Abstract

This study is motivated by the importance of accurately predicting insolvency before it happens. The paper aims to develop an insolvency prediction model for Colombian firms with one, two and three years of anticipation through financial ratios, keeping sample structures and taking into account insolvency-related regulation. This research contributes to the literature because unlike many studies, it takes legislation into account, explains the different types of financial ratios, and uses boosting algorithms without biasing the sample. Data from 11,812 Colombian companies covering the period 2012-2016 was used. The results show accuracy above 70% for insolvency predic-tion with one, two and three years of anticipation.

Suggested Citation

  • Diego Andrés Correa-Mejía & Mauricio Lopera-Castaño, 2020. "Financial ratios as a powerful instrument to predict insolvency; a study using boosting algorithms in Colombian firms," Estudios Gerenciales, Universidad Icesi, vol. 36(155), pages 229-238, June.
    • Handle: RePEc:col:000129:018340
    DOI: 10.18046/j.estger.2020.155.3588
    as

    Download full text from publisher

    File URL: https://doi.org/10.18046/j.estger.2020.155.3588
    Download Restriction: no
    File URL: https://libkey.io/10.18046/j.estger.2020.155.3588?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. Raffaella Calabrese & Silvia Angela Osmetti, 2013. "Modelling small and medium enterprise loan defaults as rare events: the generalized extreme value regression model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(6), pages 1172-1188, June.
    2. De Mooij, Ruud & Hebous, Shafik, 2018. "Curbing corporate debt bias: Do limitations to interest deductibility work?," Journal of Banking & Finance, Elsevier, vol. 96(C), pages 368-378.
    3. Loredana Cultrera & Xavier Brédart, 2016. "Bankruptcy prediction: the case of Belgian SMEs," Review of Accounting and Finance, Emerald Group Publishing Limited, vol. 15(1), pages 101-119, February.
    4. Hernandez Tinoco, Mario & Wilson, Nick, 2013. "Financial distress and bankruptcy prediction among listed companies using accounting, market and macroeconomic variables," International Review of Financial Analysis, Elsevier, vol. 30(C), pages 394-419.
    5. Ng, Anthony C. & Rezaee, Zabihollah, 2015. "Business sustainability performance and cost of equity capital," Journal of Corporate Finance, Elsevier, vol. 34(C), pages 128-149.
    6. Stewart Jones & David Johnstone & Roy Wilson, 2017. "Predicting Corporate Bankruptcy: An Evaluation of Alternative Statistical Frameworks," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 44(1-2), pages 3-34, January.
    7. Nishihara, Michi & Shibata, Takashi, 2016. "Asset sale, debt restructuring, and liquidation," Journal of Economic Dynamics and Control, Elsevier, vol. 67(C), pages 73-92.
    8. Mu, Congming & Wang, Anxing & Yang, Jinqiang, 2017. "Optimal capital structure with moral hazard," International Review of Economics & Finance, Elsevier, vol. 48(C), pages 326-338.
    9. Darush Yazdanfar & Peter Öhman, 2015. "Debt financing and firm performance: an empirical study based on Swedish data," Journal of Risk Finance, Emerald Group Publishing, vol. 16(1), pages 102-118, January.
    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. Gintare Giriūniene & Lukas Giriūnas & Mangirdas Morkunas & Laura Brucaite, 2019. "A Comparison on Leading Methodologies for Bankruptcy Prediction: The Case of the Construction Sector in Lithuania," Economies, MDPI, vol. 7(3), pages 1-20, August.
    2. Alexandra Horobet & Stefania Cristina Curea & Alexandra Smedoiu Popoviciu & Cosmin-Alin Botoroga & Lucian Belascu & Dan Gabriel Dumitrescu, 2021. "Solvency Risk and Corporate Performance: A Case Study on European Retailers," JRFM, MDPI, vol. 14(11), pages 1-34, November.
    3. Sumaira Ashraf & Elisabete G. S. Félix & Zélia Serrasqueiro, 2019. "Do Traditional Financial Distress Prediction Models Predict the Early Warning Signs of Financial Distress?," JRFM, MDPI, vol. 12(2), pages 1-17, April.
    4. Ahmed, Shamima & Alshater, Muneer M. & Ammari, Anis El & Hammami, Helmi, 2022. "Artificial intelligence and machine learning in finance: A bibliometric review," Research in International Business and Finance, Elsevier, vol. 61(C).
    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. Muñoz-Izquierdo, Nora & Segovia-Vargas, María Jesús & Camacho-Miñano, María-del-Mar & Pascual-Ezama, David, 2019. "Explaining the causes of business failure using audit report disclosures," Journal of Business Research, Elsevier, vol. 98(C), pages 403-414.
    7. Katarina Valaskova & Tomas Kliestik & Lucia Svabova & Peter Adamko, 2018. "Financial Risk Measurement and Prediction Modelling for Sustainable Development of Business Entities Using Regression Analysis," Sustainability, MDPI, vol. 10(7), pages 1-15, June.
    8. Magali Costa & Inês Lisboa & Ana Gameiro, 2022. "Is the Financial Report Quality Important in the Default Prediction? SME Portuguese Construction Sector Evidence," Risks, MDPI, vol. 10(5), pages 1-24, May.
    9. 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.
    10. Modina, Michele & Pietrovito, Filomena & Gallucci, Carmen & Formisano, Vincenzo, 2023. "Predicting SMEs’ default risk: Evidence from bank-firm relationship data," The Quarterly Review of Economics and Finance, Elsevier, vol. 89(C), pages 254-268.
    11. Bae, John & Kim, Sang-Joon & Oh, Hannah, 2017. "Taming polysemous signals: The role of marketing intensity on the relationship between financial leverage and firm performance," Review of Financial Economics, Elsevier, vol. 33(C), pages 29-40.
    12. Mario Vaupel & David Bendig & Denise Fischer-Kreer & Malte Brettel, 2023. "The Role of Share Repurchases for Firms’ Social and Environmental Sustainability," Journal of Business Ethics, Springer, vol. 183(2), pages 401-428, March.
    13. Zhang, Dongyang, 2023. "Does green finance really inhibit extreme hypocritical ESG risk? A greenwashing perspective exploration," Energy Economics, Elsevier, vol. 121(C).
    14. Hui-Ching Hsieh & Viona Claresta & Thi Minh Ngoc Bui, 2020. "Green Building, Cost of Equity Capital and Corporate Governance: Evidence from US Real Estate Investment Trusts," Sustainability, MDPI, vol. 12(9), pages 1-21, May.
    15. 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.
    16. Rosmah Nizam & Zulkefly Abdul Karim & Tamat Sarmidi & Aisyah Abdul Rahman, 2020. "Financial Inclusion and Firms Growth in Manufacturing Sector: A Threshold Regression Analysis in Selected Asean Countries," Economies, MDPI, vol. 8(4), pages 1-12, October.
    17. Campa, Domenico & Camacho-Miñano, María-del-Mar, 2015. "The impact of SME’s pre-bankruptcy financial distress on earnings management tools," International Review of Financial Analysis, Elsevier, vol. 42(C), pages 222-234.
    18. 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.
    19. 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.
    20. Jaroslaw Kaczmarek & Sergio Luis Nanez Alonso & Andrzej Sokolowski & Kamil Fijorek & Sabina Denkowska, 2021. "Financial threat profiles of industrial enterprises in Poland," Oeconomia Copernicana, Institute of Economic Research, vol. 12(2), pages 463-498, June.

    More about this item

    Keywords

    insolvency prediction; bankruptcy; financial analysis; financial ratios; boosting algorithm;
    All these keywords.

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G38 - Financial Economics - - Corporate Finance and Governance - - - Government Policy and Regulation
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

    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:col:000129:018340. 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: Coordinador ICESI (email available below). General contact details of provider: https://edirc.repec.org/data/fciceco.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.