IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v183y2007i1p472-478.html
   My bibliography  Save this article

Random effects logistic regression model for default prediction of technology credit guarantee fund

Author

Listed:
  • Sohn, So Young
  • Kim, Hong Sik

Abstract

No abstract is available for this item.

Suggested Citation

  • Sohn, So Young & Kim, Hong Sik, 2007. "Random effects logistic regression model for default prediction of technology credit guarantee fund," European Journal of Operational Research, Elsevier, vol. 183(1), pages 472-478, November.
    • Handle: RePEc:eee:ejores:v:183:y:2007:i:1:p:472-478
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377-2217(06)01039-3
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Sohn, S. Y., 1996. "Random effects meta analysis of military recruiting," Omega, Elsevier, vol. 24(2), pages 141-151, April.
    2. Gentry, Ja & Newbold, P & Whitford, Dt, 1985. "Classifying Bankrupt Firms With Funds Flow Components," Journal of Accounting Research, Wiley Blackwell, vol. 23(1), pages 146-160.
    3. Tam, KY, 1991. "Neural network models and the prediction of bank bankruptcy," Omega, Elsevier, vol. 19(5), pages 429-445.
    4. Molinero, C Mar & Ezzamel, M, 1991. "Multidimensional scaling applied to corporate failure," Omega, Elsevier, vol. 19(4), pages 259-274.
    5. S Y Sohn, 2006. "Random effects logistic regression model for ranking efficiency in data envelopment analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(11), pages 1289-1299, November.
    6. Pompe, Paul P.M. & Bilderbeek, Jan, 2005. "The prediction of bankruptcy of small- and medium-sized industrial firms," Journal of Business Venturing, Elsevier, vol. 20(6), pages 847-868, November.
    7. Abdul Aziz & David C. Emanuel & Gerald H. Lawson, 1988. "Bankruptcy Prediction ‐ An Investigation Of Cash Flow Based Models[1]," Journal of Management Studies, Wiley Blackwell, vol. 25(5), pages 419-437, September.
    8. Altman, Edward I. & Haldeman, Robert G. & Narayanan, P., 1977. "ZETATM analysis A new model to identify bankruptcy risk of corporations," Journal of Banking & Finance, Elsevier, vol. 1(1), pages 29-54, June.
    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. 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.
    11. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    12. Johnsen, Thomajean & Melicher, Ronald W., 1994. "Predicting corporate bankruptcy and financial distress: Information value added by multinomial logit models," Journal of Economics and Business, Elsevier, vol. 46(4), pages 269-286, October.
    13. Zopounidis, Constantin & Doumpos, Michael, 1999. "A Multicriteria Decision Aid Methodology for Sorting Decision Problems: The Case of Financial Distress," Computational Economics, Springer;Society for Computational Economics, vol. 14(3), pages 197-218, December.
    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. Ju, Yong Han & Sohn, So Young, 2014. "Updating a credit-scoring model based on new attributes without realization of actual data," European Journal of Operational Research, Elsevier, vol. 234(1), pages 119-126.
    2. Yonghan Ju & So Young Sohn, 2015. "Stress test for a technology credit guarantee fund based on survival analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(3), pages 463-475, March.
    3. Mihaela NICOLAU & Nicoleta BARBUTA-MISU, 2011. "Aggregated Index For Modelling The Influence Of Fiscal, Financial And Social Policies On Enterprise Financial Performance," EuroEconomica, Danubius University of Galati, issue 5(30), pages 13-20, December.
    4. Bravo, Cristián & Maldonado, Sebastián & Weber, Richard, 2013. "Granting and managing loans for micro-entrepreneurs: New developments and practical experiences," European Journal of Operational Research, Elsevier, vol. 227(2), pages 358-366.
    5. Shu Hui Lan & Jia Yi Cheng & Sheng Guo, 2016. "How to Build up the Loan - Evaluation System toward Small and Medium Enterprises between Taiwan and China’s Commercial Banks？ The Application for Multi Criteria Decision Making," International Business Research, Canadian Center of Science and Education, vol. 9(3), pages 121-142, March.
    6. Zhang, Wen & Yan, Shaoshan & Li, Jian & Tian, Xin & Yoshida, Taketoshi, 2022. "Credit risk prediction of SMEs in supply chain finance by fusing demographic and behavioral data," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).
    7. S Y Sohn & Y Kim & B T Kim, 2009. "Cost of ownership model for spare engines purchase for the Korean navy acquisition program," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(12), pages 1674-1682, December.
    8. Nicoleta BARBUTA-MISU, 2011. "A Specific Model for Assessing the Financial Performance:Case study on Building Sector Enterprises of Galati County - Romania," Risk in Contemporary Economy, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, pages 318-325.
    9. Medina-Olivares, Victor & Calabrese, Raffaella & Dong, Yizhe & Shi, Baofeng, 2022. "Spatial dependence in microfinance credit default," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1071-1085.
    10. Nicoleta Barbuta-Misu, 2012. "Aggregated Index for Modelling the Influence of Financial Variables on Enterprise Performance," EuroEconomica, Danubius University of Galati, issue 2(31), pages 155-165, May.
    11. Yonghan Ju & So Young Sohn, 2017. "Technology Credit Scoring Based on a Quantification Method," Sustainability, MDPI, vol. 9(6), pages 1-16, June.
    12. Kim, Hong Sik & Sohn, So Young, 2010. "Support vector machines for default prediction of SMEs based on technology credit," European Journal of Operational Research, Elsevier, vol. 201(3), pages 838-846, March.
    13. Lisa Crosato & Caterina Liberati & Marco Repetto, 2021. "Look Who's Talking: Interpretable Machine Learning for Assessing Italian SMEs Credit Default," Papers 2108.13914, arXiv.org, revised Sep 2021.
    14. Yu, Jian & Peng, Fanjia & Shi, Xunpeng & Yang, Longjian, 2022. "Impact of credit guarantee on firm performance: Evidence from China’s SMEs," Economic Analysis and Policy, Elsevier, vol. 75(C), pages 624-636.
    15. Bo Kyeong Lee & So Young Sohn, 2017. "A Credit Scoring Model for SMEs Based on Accounting Ethics," Sustainability, MDPI, vol. 9(9), pages 1-15, September.
    16. Chen, Cathy W.S. & Dong, Manh Cuong & Liu, Nathan & Sriboonchitta, Songsak, 2019. "Inferences of default risk and borrower characteristics on P2P lending," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    17. Shi, Xiaojun & Zhang, Shunming, 2010. "An incentive-compatible solution for trade credit term incorporating default risk," European Journal of Operational Research, Elsevier, vol. 206(1), pages 178-196, October.
    18. Tao Wu, 2022. "Predictive Search for Capacitated Multi-Item Lot Sizing Problems," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 385-406, January.
    19. Ye Chen & Ilya O. Ryzhov, 2020. "Technical Note—Consistency Analysis of Sequential Learning Under Approximate Bayesian Inference," Operations Research, INFORMS, vol. 68(1), pages 295-307, January.
    20. So Sohn & Yoon Kim, 2013. "Behavioral credit scoring model for technology-based firms that considers uncertain financial ratios obtained from relationship banking," Small Business Economics, Springer, vol. 41(4), pages 931-943, December.

    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. Westgaard, Sjur & van der Wijst, Nico, 2001. "Default probabilities in a corporate bank portfolio: A logistic model approach," European Journal of Operational Research, Elsevier, vol. 135(2), pages 338-349, December.
    2. Ana Paula Matias Gama & Helena Susana Amaral Geraldes, 2012. "Credit risk assessment and the impact of the New Basel Capital Accord on small and medium‐sized enterprises," Management Research Review, Emerald Group Publishing Limited, vol. 35(8), pages 727-749, July.
    3. fernández, María t. Tascón & gutiérrez, Francisco J. Castaño, 2012. "Variables y Modelos Para La Identificación y Predicción Del Fracaso Empresarial: Revisión de La Investigación Empírica Reciente," Revista de Contabilidad - Spanish Accounting Review, Elsevier, vol. 15(1), pages 7-58.
    4. du Jardin, Philippe, 2015. "Bankruptcy prediction using terminal failure processes," European Journal of Operational Research, Elsevier, vol. 242(1), pages 286-303.
    5. Ha-Thu Nguyen, 2014. "Default Predictors in Credit Scoring - Evidence from France’s Retail Banking Institution," EconomiX Working Papers 2014-26, University of Paris Nanterre, EconomiX.
    6. Şaban Çelik, 2013. "Micro Credit Risk Metrics: A Comprehensive Review," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 20(4), pages 233-272, October.
    7. Francesco Ciampi & Valentina Cillo & Fabio Fiano, 2020. "Combining Kohonen maps and prior payment behavior for small enterprise default prediction," Small Business Economics, Springer, vol. 54(4), pages 1007-1039, April.
    8. Balcaen, Sofie & Ooghe, Hubert, 2006. "35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems," The British Accounting Review, Elsevier, vol. 38(1), pages 63-93.
    9. Layla Khoja & Maxwell Chipulu & Ranadeva Jayasekera, 2016. "Analysing corporate insolvency in the Gulf Cooperation Council using logistic regression and multidimensional scaling," Review of Quantitative Finance and Accounting, Springer, vol. 46(3), pages 483-518, April.
    10. Dimitras, A. I. & Zanakis, S. H. & Zopounidis, C., 1996. "A survey of business failures with an emphasis on prediction methods and industrial applications," European Journal of Operational Research, Elsevier, vol. 90(3), pages 487-513, May.
    11. Kim, Hong Sik & Sohn, So Young, 2010. "Support vector machines for default prediction of SMEs based on technology credit," European Journal of Operational Research, Elsevier, vol. 201(3), pages 838-846, March.
    12. 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.
    13. 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.
    14. Carlos Serrano-Cinca & Yolanda Fuertes-Call鮠 & Bego uti鲲ez-Nieto & Beatriz Cuellar-Fernᮤez, 2014. "Path modelling to bankruptcy: causes and symptoms of the banking crisis," Applied Economics, Taylor & Francis Journals, vol. 46(31), pages 3798-3811, November.
    15. 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.
    16. Ha-Thu Nguyen, 2015. "How is credit scoring used to predict default in China?," EconomiX Working Papers 2015-1, University of Paris Nanterre, EconomiX.
    17. Jones, Stewart & Hensher, David A., 2007. "Modelling corporate failure: A multinomial nested logit analysis for unordered outcomes," The British Accounting Review, Elsevier, vol. 39(1), pages 89-107.
    18. du Jardin, Philippe, 2012. "The influence of variable selection methods on the accuracy of bankruptcy prediction models," MPRA Paper 44383, University Library of Munich, Germany.
    19. du Jardin, Philippe, 2009. "Bankruptcy prediction models: How to choose the most relevant variables?," MPRA Paper 44380, University Library of Munich, Germany.
    20. Zhichao Luo & Pingyu Hsu & Ni Xu, 2020. "SME Default Prediction Framework with the Effective Use of External Public Credit Data," Sustainability, MDPI, vol. 12(18), pages 1-18, September.

    More about this item

    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:eee:ejores:v:183:y:2007:i:1:p:472-478. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    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.