Bankruptcy prediction for SMEs using relational data
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Chae Woo Nam & Tong Suk Kim & Nam Jung Park & Hoe Kyung Lee, 2008. "Bankruptcy prediction using a discrete-time duration model incorporating temporal and macroeconomic dependencies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(6), pages 493-506.
- 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.
- Delen, Dursun & Cogdell, Douglas & Kasap, Nihat, 2012. "A comparative analysis of data mining methods in predicting NCAA bowl outcomes," International Journal of Forecasting, Elsevier, vol. 28(2), pages 543-552.
- Zhang, Guoqiang & Y. Hu, Michael & Eddy Patuwo, B. & C. Indro, Daniel, 1999. "Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis," European Journal of Operational Research, Elsevier, vol. 116(1), pages 16-32, July.
- Cielen, Anja & Peeters, Ludo & Vanhoof, Koen, 2004. "Bankruptcy prediction using a data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 154(2), pages 526-532, April.
- 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.
- Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
- Zmijewski, Me, 1984. "Methodological Issues Related To The Estimation Of Financial Distress Prediction Models," Journal of Accounting Research, Wiley Blackwell, vol. 22, pages 59-82.
- STANKOVA, Marija & MARTENS, David & PROVOST, Foster, 2015. "Classification over bipartite graphs through projection," Working Papers 2015001, University of Antwerp, Faculty of Business and Economics.
- Selwyn Piramuthu & Harish Ragavan & Michael J. Shaw, 1998. "Using Feature Construction to Improve the Performance of Neural Networks," Management Science, INFORMS, vol. 44(3), pages 416-430, March.
- 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.
- Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
- Baldwin, John R., 1998. "Failing Concerns: Business Bankruptcy in Canada," Failing Concerns: Business Bankruptcy in Canada, Statistics Canada, Economic Analysis Division, number stcb1e, March.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Ge Gao & Hongxin Wang & Pengbin Gao, 2021. "Establishing a Credit Risk Evaluation System for SMEs Using the Soft Voting Fusion Model," Risks, MDPI, vol. 9(11), pages 1-12, November.
- Sanjiv Das & Xin Huang & Soji Adeshina & Patrick Yang & Leonardo Bachega, 2023. "Credit Risk Modeling with Graph Machine Learning," INFORMS Joural on Data Science, INFORMS, vol. 2(2), pages 197-217, October.
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.- 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.
- 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.
- 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.
- Mohammad Mahdi Mousavi & Jamal Ouenniche & Kaoru Tone, 2023. "A dynamic performance evaluation of distress prediction models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 756-784, July.
- Nawaf Almaskati & Ron Bird & Yue Lu & Danny Leung, 2019. "The Role of Corporate Governance and Estimation Methods in Predicting Bankruptcy," Working Papers in Economics 19/16, University of Waikato.
- 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.
- Virág, Miklós & Kristóf, Tamás, 2005. "Az első hazai csődmodell újraszámítása neurális hálók segítségével [Recalculation of the first Hungarian bankruptcy-prediction model using neural networks]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(2), pages 144-162.
- Alam, Nurul & Gao, Junbin & Jones, Stewart, 2021. "Corporate failure prediction: An evaluation of deep learning vs discrete hazard models," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 75(C).
- Mohammad Mahdi Mousavi & Jamal Ouenniche, 2018. "Multi-criteria ranking of corporate distress prediction models: empirical evaluation and methodological contributions," Annals of Operations Research, Springer, vol. 271(2), pages 853-886, December.
- Andrzej Geise & Magdalena Kuczmarska & Jarosław Pawlowski, 2021. "Corporate Failure Prediction of Construction Companies in Poland: Evidence from Logit Model," European Research Studies Journal, European Research Studies Journal, vol. 0(1), pages 99-116.
- Almaskati, Nawaf & Bird, Ron & Yeung, Danny & Lu, Yue, 2021. "A horse race of models and estimation methods for predicting bankruptcy," Advances in accounting, Elsevier, vol. 52(C).
- Hyeongjun Kim & Hoon Cho & Doojin Ryu, 2020. "Corporate Default Predictions Using Machine Learning: Literature Review," Sustainability, MDPI, vol. 12(16), pages 1-11, August.
- 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.
- 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.
- Zeineb Affes & Rania Hentati-Kaffel, 2016. "Forecast bankruptcy using a blend of clustering and MARS model - Case of US banks," Post-Print halshs-01314553, HAL.
- 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.
- Mousavi, Mohammad M. & Ouenniche, Jamal & Xu, Bing, 2015. "Performance evaluation of bankruptcy prediction models: An orientation-free super-efficiency DEA-based framework," International Review of Financial Analysis, Elsevier, vol. 42(C), pages 64-75.
- David Alaminos & Agustín del Castillo & Manuel Ángel Fernández, 2016. "A Global Model for Bankruptcy Prediction," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-18, November.
- Chih‐Chun Chen & Chun‐Da Chen & Donald Lien, 2020. "Financial distress prediction model: The effects of corporate governance indicators," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1238-1252, December.
- Hyeongjun Kim & Hoon Cho & Doojin Ryu, 2022. "Corporate Bankruptcy Prediction Using Machine Learning Methodologies with a Focus on Sequential Data," Computational Economics, Springer;Society for Computational Economics, vol. 59(3), pages 1231-1249, March.
More about this item
NEP fields
This paper has been announced in the following NEP Reports:- NEP-ENT-2016-05-21 (Entrepreneurship)
Statistics
Access and download statisticsCorrections
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:ant:wpaper:2016004. 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: Joeri Nys (email available below). General contact details of provider: https://edirc.repec.org/data/ftufsbe.html .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.