Company rating with support vector machines
Author
Abstract
Suggested Citation
DOI: 10.1515/strm-2012-1141
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Glennon, Dennis & Nigro, Peter, 2005. "Measuring the Default Risk of Small Business Loans: A Survival Analysis Approach," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(5), pages 923-947, October.
- João Fernandes, 2005. "Corporate Credit Risk Modeling: Quantitative Rating System And Probability Of Default Estimation," Finance 0505013, University Library of Munich, Germany.
- Engelmann, Bernd & Hayden, Evelyn & Tasche, Dirk, 2003. "Measuring the Discriminative Power of Rating Systems," Discussion Paper Series 2: Banking and Financial Studies 2003,01, Deutsche Bundesbank.
- 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.
- Merton, Robert C, 1974.
"On the Pricing of Corporate Debt: The Risk Structure of Interest Rates,"
Journal of Finance, American Finance Association, vol. 29(2), pages 449-470, May.
- Merton, Robert C., 1973. "On the pricing of corporate debt: the risk structure of interest rates," Working papers 684-73., Massachusetts Institute of Technology (MIT), Sloan School of Management.
- Chen, Andrew H. & Ju, Nengjiu & Mazumdar, Sumon C. & Verma, Avinash, 2006. "Correlated Default Risks and Bank Regulations," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 38(2), pages 375-398, March.
- Martin, Daniel, 1977. "Early warning of bank failure : A logit regression approach," Journal of Banking & Finance, Elsevier, vol. 1(3), pages 249-276, November.
- Karatzoglou, Alexandros & Meyer, David & Hornik, Kurt, 2006. "Support Vector Machines in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 15(i09).
- 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.
- de Leeuw, Jan & Hornik, Kurt & Mair, Patrick, 2009. "Isotone Optimization in R: Pool-Adjacent-Violators Algorithm (PAVA) and Active Set Methods," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 32(i05).
- Mark J Manning, 2004. "Exploring the relationship between credit spreads and default probabilities," Bank of England working papers 225, Bank of England.
- Martens, David & Baesens, Bart & Van Gestel, Tony & Vanthienen, Jan, 2007. "Comprehensible credit scoring models using rule extraction from support vector machines," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1466-1476, December.
- Duan, Jin-Chuan & Sun, Jie & Wang, Tao, 2012. "Multiperiod corporate default prediction—A forward intensity approach," Journal of Econometrics, Elsevier, vol. 170(1), pages 191-209.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Wosnitza, Jan Henrik, 2022. "Calibration alternatives to logistic regression and their potential for transferring the dispersion of discriminatory power into uncertainties of probabilities of default," Discussion Papers 04/2022, Deutsche Bundesbank.
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.- Wolfgang Karl Härdle & Dedy Dwi Prastyo & Christian Hafner, 2012.
"Support Vector Machines with Evolutionary Feature Selection for Default Prediction,"
SFB 649 Discussion Papers
SFB649DP2012-030, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
- Hardle, Wolfgang Karl & Prastyo, Dedy Dwi & Hafner, Christian, 2013. "Support Vector Machines with Evolutionary Feature Selection for Default Prediction," LIDAM Discussion Papers ISBA 2013040, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
- 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.
- Enrique Batiz‐Zuk & Fabrizio López‐Gallo & Abdulkadir Mohamed & Fátima Sánchez‐Cajal, 2022. "Determinants of loan survival rates for small and medium‐sized enterprises: Evidence from an emerging economy," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 4741-4755, October.
- Xu, Si & He, Xiaoyi & Cao, Chunfang, 2023. "Struggle for survival in credit crunch: The effect of interest rate deregulation in China," China Economic Review, Elsevier, vol. 77(C).
- Asis, Gonzalo & Chari, Anusha & Haas, Adam, 2021.
"In search of distress risk in emerging markets,"
Journal of International Economics, Elsevier, vol. 131(C).
- Gonzalo Asis & Anusha Chari & Adam Haas, 2020. "In Search of Distress Risk in Emerging Markets," NBER Working Papers 27213, National Bureau of Economic Research, Inc.
- Wolfgang Härdle & Yuh-Jye Lee & Dorothea Schäfer & Yi-Ren Yeh, 2009. "Variable selection and oversampling in the use of smooth support vector machines for predicting the default risk of companies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(6), pages 512-534.
- Wolfgang Härdle & Yuh-Jye Lee & Dorothea Schäfer & Yi-Ren Yeh, 2007.
"The Default Risk of Firms Examined with Smooth Support Vector Machines,"
Discussion Papers of DIW Berlin
757, DIW Berlin, German Institute for Economic Research.
- Wolfgang Härdle & Yuh-Jye Lee & Dorothea Schäfer & Yi-Ren Yeh, 2008. "The Default Risk of Firms Examined with Smooth Support Vector Machines," SFB 649 Discussion Papers SFB649DP2008-005, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
- En-Der Su & Shih-Ming Huang, 2010. "Comparing Firm Failure Predictions Between Logit, KMV, and ZPP Models: Evidence from Taiwan’s Electronics Industry," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 17(3), pages 209-239, September.
- 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.
- Abinzano, Isabel & Gonzalez-Urteaga, Ana & Muga, Luis & Sanchez, Santiago, 2020. "Performance of default-risk measures: the sample matters," Journal of Banking & Finance, Elsevier, vol. 120(C).
- Anand Deo & Sandeep Juneja, 2021. "Credit Risk: Simple Closed-Form Approximate Maximum Likelihood Estimator," Operations Research, INFORMS, vol. 69(2), pages 361-379, March.
- Zhang, Xuan & Ouyang, Ruolan & Liu, Ding & Xu, Liao, 2020. "Determinants of corporate default risk in China: The role of financial constraints," Economic Modelling, Elsevier, vol. 92(C), pages 87-98.
- Ijaz Hussain, 2013. "Estimating Firms’ Vulnerability to Short-Term Financing Shocks: The Case of Foreign Exchange Companies in Pakistan," Lahore Journal of Economics, Department of Economics, The Lahore School of Economics, vol. 18(2), pages 147-163, July-Dec.
- Alexandros Benos & George Papanastasopoulos, 2005. "Extending the Merton Model: A Hybrid Approach to Assessing Credit Quality," Finance 0505020, University Library of Munich, Germany, revised 18 Nov 2005.
- Anand Deo & Sandeep Juneja, 2019. "Credit Risk: Simple Closed Form Approximate Maximum Likelihood Estimator," Papers 1912.12611, arXiv.org.
- Zhou Lu & Zhuyao Zhuo, 2021. "Modelling of Chinese corporate bond default – A machine learning approach," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 61(5), pages 6147-6191, December.
- Hyeongjun Kim & Hoon Cho & Doojin Ryu, 2020. "Corporate Default Predictions Using Machine Learning: Literature Review," Sustainability, MDPI, vol. 12(16), pages 1-11, August.
- Ruey-Ching Hwang & Chih-Kang Chu, 2013. "Forecasting forward defaults: a simple hazard model with competing risks," Quantitative Finance, Taylor & Francis Journals, vol. 14(8), pages 1467-1477, August.
- Yeh, Chung-Ying & Hsu, Junming & Wang, Kai-Li & Lin, Che-Hui, 2015. "Explaining the default risk anomaly by the two-beta model," Journal of Empirical Finance, Elsevier, vol. 30(C), pages 16-33.
- Nan Hu & Jian Li & Alexis Meyer-Cirkel, 2019. "Completing the Market: Generating Shadow CDS Spreads by Machine Learning," IMF Working Papers 2019/292, International Monetary Fund.
More about this item
Keywords
Bankruptcy; company rating; probability of default; support vector machines; 62-07; 62G05; 62P05;All these keywords.
JEL classification:
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:bpj:strimo:v:34:y:2017:i:1-2:p:55-67:n:1. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.