Support Vector Machines (SVM) as a Technique for Solvency Analysis
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References listed on IDEAS
- 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.
- Wolfgang K. Härdle & Rouslan A. Moro & Dorothea Schäfer, 2004. "Rating Companies with Support Vector Machines," Discussion Papers of DIW Berlin 416, DIW Berlin, German Institute for Economic Research.
- Wolfgang K. Härdle & Rouslan A. Moro & Dorothea Schäfer, 2004. "Support Vector Machines: eine neue Methode zum Rating von Unternehmen," DIW Wochenbericht, DIW Berlin, German Institute for Economic Research, vol. 71(49), pages 759-765.
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- repec:spr:infosf:v:20:y:2018:i:3:d:10.1007_s10796-016-9689-z is not listed on IDEAS
- Kyuhan Lee & Jinsoo Park & Iljoo Kim & Youngseok Choi, 0. "Predicting movie success with machine learning techniques: ways to improve accuracy," Information Systems Frontiers, Springer, vol. 0, pages 1-12.
More about this item
KeywordsCompany rating; bankruptcy analysis; support vector machines;
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
- C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
NEP fieldsThis paper has been announced in the following NEP Reports:
- NEP-ALL-2008-08-31 (All new papers)
- NEP-ECM-2008-08-31 (Econometrics)
- NEP-ORE-2008-08-31 (Operations Research)
- NEP-RMG-2008-08-31 (Risk Management)
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