IDEAS home Printed from https://ideas.repec.org/a/gam/jrisks/v7y2019i2p67-d239504.html
   My bibliography  Save this article

Credit Risk Assessment Model for Small and Micro-Enterprises: The Case of Lithuania

Author

Listed:
  • Rasa Kanapickiene

    (Department of Finance, Faculty of Economics and Business Administration, Vilnius University, 10222 Vilnius, Lithuania)

  • Renatas Spicas

    (Kaunas Region Credit Union, 44249 Kaunas, Lithuania)

Abstract

In this research, trade credit is analysed form a seller (supplier) perspective. Trade credit allows the supplier to increase sales and profits but creates the risk that the customer will not pay, and at the same time increases the risk of the supplier’s insolvency. If the supplier is a small or micro-enterprise (SMiE), it is usually an issue of human and technical resources. Therefore, when dealing with these issues, the supplier needs a high accuracy but simple and highly interpretable trade credit risk assessment model that allows for assessing the risk of insolvency of buyers (who are usually SMiE). The aim of the research is to create a statistical enterprise trade credit risk assessment (ETCRA) model for Lithuanian small and micro-enterprises (SMiE). In the empirical analysis, the financial and non-financial data of 734 small and micro-sized enterprises in the period of 2010–2012 were chosen as the samples. Based on the logistic regression, the ETCRA model was developed using financial and non-financial variables. In the ETCRA model, the enterprise’s financial performance is assessed from different perspectives: profitability, liquidity, solvency, and activity. Varied model variants have been created using (i) only financial ratios and (ii) financial ratios and non-financial variables. Moreover, the inclusion of non-financial variables in the model does not substantially improve the characteristics of the model. This means that the models that use only financial ratios can be used in practice, and the models that include non-financial variables can also be used. The designed models can be used by suppliers when making decisions of granting a trade credit for small or micro-enterprises.

Suggested Citation

  • Rasa Kanapickiene & Renatas Spicas, 2019. "Credit Risk Assessment Model for Small and Micro-Enterprises: The Case of Lithuania," Risks, MDPI, vol. 7(2), pages 1-23, June.
    • Handle: RePEc:gam:jrisks:v:7:y:2019:i:2:p:67-:d:239504
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-9091/7/2/67/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-9091/7/2/67/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lin, Tsung-Te & Chou, Jian-Hsin, 2015. "Trade credit and bank loan: Evidence from Chinese firms," International Review of Economics & Finance, Elsevier, vol. 36(C), pages 17-29.
    2. Matthew D. Hill & Gary W. Kelly & Lorenzo A. Preve & Virginia Sarria-Allende, 2017. "Trade Credit or Financial Credit? An International Study of the Choice and Its Influences," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 53(10), pages 2318-2332, October.
    3. D. J. Hand & W. E. Henley, 1997. "Statistical Classification Methods in Consumer Credit Scoring: a Review," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 160(3), pages 523-541, September.
    4. Dang, Chongyu & (Frank) Li, Zhichuan & Yang, Chen, 2018. "Measuring firm size in empirical corporate finance," Journal of Banking & Finance, Elsevier, vol. 86(C), pages 159-176.
    5. Nur Adiana Hiau Abdullah & Muhammad M. Ma'aji & Karren Lee-Hwei Khaw, 2016. "The Value of Governance Variables in Predicting Financial Distress Among Small and Medium-Sized Enterprises in Malaysia," Asian Academy of Management Journal of Accounting and Finance (AAMJAF), Penerbit Universiti Sains Malaysia, vol. 12(Suppl. 1), pages 1-77–91.
    6. Frank Li & Tao Li & Dylan Minor, 2016. "CEO power, corporate social responsibility, and firm value: a test of agency theory," International Journal of Managerial Finance, Emerald Group Publishing Limited, vol. 12(5), pages 611-628, October.
    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. Fernandes, Guilherme Barreto & Artes, Rinaldo, 2016. "Spatial dependence in credit risk and its improvement in credit scoring," European Journal of Operational Research, Elsevier, vol. 249(2), pages 517-524.
    9. Yu-Chung Tsao, 2010. "Two-phase pricing and inventory management for deteriorating and fashion goods under trade credit," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 72(1), pages 107-127, August.
    10. Richard Pike & Nam Sang Cheng, 2001. "Credit Management: An Examination of Policy Choices, Practices and Late Payment in UK Companies," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 28(7&8), pages 1013-1042.
    11. Crone, Sven F. & Finlay, Steven, 2012. "Instance sampling in credit scoring: An empirical study of sample size and balancing," International Journal of Forecasting, Elsevier, vol. 28(1), pages 224-238.
    12. Sang Jun Cho & Chune Young Chung & Jason Young, 2019. "Study on the Relationship between CSR and Financial Performance," Sustainability, MDPI, vol. 11(2), pages 1-26, January.
    13. Lessmann, Stefan & Baesens, Bart & Seow, Hsin-Vonn & Thomas, Lyn C., 2015. "Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research," European Journal of Operational Research, Elsevier, vol. 247(1), pages 124-136.
    14. Richard Pike & Nam Sang Cheng, 2001. "Credit Management: An Examination of Policy Choices, Practices and Late Payment in UK Companies," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 28(7‐8), pages 1013-1042, September.
    15. Jun†Tae Han & Jae†Seok Choi & Myeon†Jung Kim & Jina Jeong, 2018. "Developing a Risk Group Predictive Model for Korean Students Falling into Bad Debt," Asian Economic Journal, East Asian Economic Association, vol. 32(1), pages 3-14, March.
    16. Fabbri, Daniela & Klapper, Leora F., 2016. "Bargaining power and trade credit," Journal of Corporate Finance, Elsevier, vol. 41(C), pages 66-80.
    17. Crook, Jonathan N. & Edelman, David B. & Thomas, Lyn C., 2007. "Recent developments in consumer credit risk assessment," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1447-1465, December.
    18. Verbraken, Thomas & Bravo, Cristián & Weber, Richard & Baesens, Bart, 2014. "Development and application of consumer credit scoring models using profit-based classification measures," European Journal of Operational Research, Elsevier, vol. 238(2), pages 505-513.
    19. Naoyuki Yoshino & Farhad Taghizadeh-Hesary, 2014. "Analytical framework on credit risks for financing small and medium-sized enterprises in Asia," Asia-Pacific Development Journal, United Nations Economic and Social Commission for Asia and the Pacific (ESCAP), vol. 21(2), pages 1-21, December.
    20. Shenoy, Jaideep & Williams, Ryan, 2017. "Trade credit and the joint effects of supplier and customer financial characteristics," Journal of Financial Intermediation, Elsevier, vol. 29(C), pages 68-80.
    21. 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.
    22. Edward I. Altman & Gabriele Sabato, 2013. "MODELING CREDIT RISK FOR SMEs: EVIDENCE FROM THE US MARKET," World Scientific Book Chapters, in: Oliviero Roggi & Edward I Altman (ed.), Managing and Measuring Risk Emerging Global Standards and Regulations After the Financial Crisis, chapter 9, pages 251-279, World Scientific Publishing Co. Pte. Ltd..
    23. You Zhu & Chi Xie & Bo Sun & Gang-Jin Wang & Xin-Guo Yan, 2016. "Predicting China’s SME Credit Risk in Supply Chain Financing by Logistic Regression, Artificial Neural Network and Hybrid Models," Sustainability, MDPI, vol. 8(5), pages 1-17, May.
    24. Tor Jacobson & Erik Schedvin, 2015. "Trade Credit and the Propagation of Corporate Failure: An Empirical Analysis," Econometrica, Econometric Society, vol. 83(4), pages 1315-1371, July.
    25. Gour Chandra Mahata & Sujit Kumar De, 2016. "An EOQ inventory system of ameliorating items for price dependent demand rate under retailer partial trade credit policy," OPSEARCH, Springer;Operational Research Society of India, vol. 53(4), pages 889-916, December.
    26. Teresa Herrador-Alcaide & Montserrat Hernández-Solís, 2019. "Empirical Study Regarding Non-Financial Disclosure for Social Conscious Consumption in the Spanish E-Credit Market," Sustainability, MDPI, vol. 11(3), pages 1-26, February.
    27. Wang, Kai & Zhao, Ruiqing & Peng, Jin, 2018. "Trade credit contracting under asymmetric credit default risk: Screening, checking or insurance," European Journal of Operational Research, Elsevier, vol. 266(2), pages 554-568.
    28. Tsuruta, Daisuke, 2015. "Bank loan availability and trade credit for small businesses during the financial crisis," The Quarterly Review of Economics and Finance, Elsevier, vol. 55(C), pages 40-52.
    29. Jacobson, Tor & von Schedvin, Erik, 2012. "Trade Credit and the Propagation of Corporate Failure: An Empirical Analysis," Working Paper Series 263, Sveriges Riksbank (Central Bank of Sweden).
    30. Silvia Cateni & Marco Vannucci & Marco Vannocci & Valentina Colla, 2013. "Variable Selection and Feature Extraction Through Artificial Intelligence Techniques," Chapters, in: Leandro Valim De Freitas & Ana Paula Barbosa Rodrigues De Freitas (ed.), Multivariate Analysis in Management, Engineering and the Sciences, IntechOpen.
    31. Godfred Adjapong Afrifa & Ernest Gyapong, 2017. "Net trade credit: what are the determinants?," International Journal of Managerial Finance, Emerald Group Publishing Limited, vol. 13(3), pages 246-266, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Teoh, Wenji, 2019. "The Market Risk on Domino's Pizza Incorporation's Peformance," MPRA Paper 97244, University Library of Munich, Germany, revised 15 Nov 2019.
    2. Peng Liu & Daxin Dong, 2020. "Impact of Economic Policy Uncertainty on Trade Credit Provision: The Role of Social Trust," Sustainability, MDPI, vol. 12(4), pages 1-24, February.
    3. Carmen Gallucci & Rosalia Santullli & Michele Modina & Vincenzo Formisano, 2023. "Financial ratios, corporate governance and bank-firm information: a Bayesian approach to predict SMEs’ default," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 27(3), pages 873-892, September.
    4. Rasa Kanapickiene & Greta Keliuotyte-Staniuleniene & Deimante Teresiene, 2021. "Disclosure of Non-Current Tangible Assets Information in Private Sector Entities Financial Statements: The Case of Lithuania," Economies, MDPI, vol. 9(2), pages 1-64, May.
    5. Maribel Paredes-Torres & Ana del Rocío Cando-Zumba & José Varela-Aldás, 2022. "Income Tax for Microenterprises in the COVID-19 Pandemic: A Case Study on Ecuador," Sustainability, MDPI, vol. 14(5), pages 1-20, February.
    6. Wenji, Teoh, 2019. "Market Risk on Domino's Pizza Incorporation's Performance," MPRA Paper 97319, University Library of Munich, Germany, revised 15 Nov 2019.

    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. Raffaella Calabrese & Galina Andreeva & Jake Ansell, 2019. "“Birds of a Feather” Fail Together: Exploring the Nature of Dependency in SME Defaults," Risk Analysis, John Wiley & Sons, vol. 39(1), pages 71-84, January.
    2. Huei-Wen Teng & Michael Lee, 2019. "Estimation Procedures of Using Five Alternative Machine Learning Methods for Predicting Credit Card Default," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 22(03), pages 1-27, September.
    3. Richard Chamboko & Jorge Miguel Bravo, 2020. "A Multi-State Approach to Modelling Intermediate Events and Multiple Mortgage Loan Outcomes," Risks, MDPI, vol. 8(2), pages 1-29, June.
    4. José Willer Prado & Valderí Castro Alcântara & Francisval Melo Carvalho & Kelly Carvalho Vieira & Luiz Kennedy Cruz Machado & Dany Flávio Tonelli, 2016. "Multivariate analysis of credit risk and bankruptcy research data: a bibliometric study involving different knowledge fields (1968–2014)," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(3), pages 1007-1029, March.
    5. Cathcart, Lara & Dufour, Alfonso & Rossi, Ludovico & Varotto, Simone, 2020. "The differential impact of leverage on the default risk of small and large firms," Journal of Corporate Finance, Elsevier, vol. 60(C).
    6. Masahiro Enomoto, 2018. "Effects of Corporate Governance on the Relationship between Accounting Quality and Trade Credit: Evidence from Japan," Discussion Paper Series DP2018-12, Research Institute for Economics & Business Administration, Kobe University, revised Dec 2023.
    7. 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.
    8. 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.
    9. Cao Son Tran & Dan Nicolau & Richi Nayak & Peter Verhoeven, 2021. "Modeling Credit Risk: A Category Theory Perspective," JRFM, MDPI, vol. 14(7), pages 1-21, July.
    10. David Veganzones, 2022. "Corporate failure prediction using threshold‐based models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 956-979, August.
    11. Sigrist, Fabio & Leuenberger, Nicola, 2023. "Machine learning for corporate default risk: Multi-period prediction, frailty correlation, loan portfolios, and tail probabilities," European Journal of Operational Research, Elsevier, vol. 305(3), pages 1390-1406.
    12. Belinda L. Del Gaudio & Gabriele Sampagnaro & Claudio Porzio & Vincenzo Verdoliva, 2022. "The signaling role of trade credit in bank lending decisions: Evidence from small and medium‐sized enterprises," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 49(1-2), pages 327-354, January.
    13. Dumitrescu, Elena & Hué, Sullivan & Hurlin, Christophe & Tokpavi, Sessi, 2022. "Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects," European Journal of Operational Research, Elsevier, vol. 297(3), pages 1178-1192.
    14. Hasan, Mostafa Monzur & Habib, Ahsan, 2019. "Social capital and trade credit," International Review of Financial Analysis, Elsevier, vol. 61(C), pages 158-174.
    15. Juan Laborda & Seyong Ryoo, 2021. "Feature Selection in a Credit Scoring Model," Mathematics, MDPI, vol. 9(7), pages 1-22, March.
    16. Norden, Lars & Udell, Gregory F. & Wang, Teng, 2020. "Do bank bailouts affect the provision of trade credit?11All errors are our own. The views expressed in this paper are solely those of the authors and should not be interpreted as reflecting the views ," Journal of Corporate Finance, Elsevier, vol. 60(C).
    17. Hussein A. Abdou & John Pointon, 2011. "Credit Scoring, Statistical Techniques And Evaluation Criteria: A Review Of The Literature," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 18(2-3), pages 59-88, April.
    18. Elena Ivona DUMITRESCU & Sullivan HUE & Christophe HURLIN & Sessi TOKPAVI, 2020. "Machine Learning or Econometrics for Credit Scoring: Let’s Get the Best of Both Worlds," LEO Working Papers / DR LEO 2839, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    19. Josep Patau & Antonio Somoza & Salvador Torra, 2020. "Diagnosis of the Domino Effect in Bankruptcy Situations Through Positioning Maps and Their Evolution 10 Years Later," SAGE Open, , vol. 10(4), pages 21582440209, December.
    20. Joye Khoo & Adrian (Wai Kong) Cheung, 2024. "Vintage capital and trade credit," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 64(1), pages 507-537, March.

    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:gam:jrisks:v:7:y:2019:i:2:p:67-:d:239504. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.