IDEAS home Printed from https://ideas.repec.org/p/diw/diwwpp/dp811.html
   My bibliography  Save this paper

Support Vector Machines (SVM) as a Technique for Solvency Analysis

Author

Listed:
  • Laura Auria
  • Rouslan A. Moro

Abstract

This paper introduces a statistical technique, Support Vector Machines (SVM), which is considered by the Deutsche Bundesbank as an alternative for company rating. A special attention is paid to the features of the SVM which provide a higher accuracy of company classification into solvent and insolvent. The advantages and disadvantages of the method are discussed. The comparison of the SVM with more traditional approaches such as logistic regression (Logit) and discriminant analysis (DA) is made on the Deutsche Bundesbank data of annual income statements and balance sheets of German companies. The out-of-sample accuracy tests confirm that the SVM outperforms both DA and Logit on bootstrapped samples.

Suggested Citation

  • Laura Auria & Rouslan A. Moro, 2008. "Support Vector Machines (SVM) as a Technique for Solvency Analysis," Discussion Papers of DIW Berlin 811, DIW Berlin, German Institute for Economic Research.
    • Handle: RePEc:diw:diwwpp:dp811
    as

    Download full text from publisher

    File URL: https://www.diw.de/documents/publikationen/73/diw_01.c.88369.de/dp811.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. B Baesens & T Van Gestel & S Viaene & M Stepanova & J Suykens & J Vanthienen, 2003. "Benchmarking state-of-the-art classification algorithms for credit scoring," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(6), pages 627-635, June.
    3. 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.
    4. 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.
    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. Nehrebecka Natalia, 2018. "Predicting the Default Risk of Companies. Comparison of Credit Scoring Models: Logit Vs Support Vector Machines," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 22(2), pages 54-73, June.
    2. Amarda Cano, 2021. "Evolution of Public Debt in Albania during 1990-2017 and its impact on the Economic Growth," European Journal of Marketing and Economics Articles, Revistia Research and Publishing, vol. 4, ejme_v4_i.
    3. Kyuhan Lee & Jinsoo Park & Iljoo Kim & Youngseok Choi, 2018. "Predicting movie success with machine learning techniques: ways to improve accuracy," Information Systems Frontiers, Springer, vol. 20(3), pages 577-588, June.
    4. Hossein Hassani & Emmanuel S. Silva & Marine Combe & Demetra Andreou & Mansi Ghodsi & Mohammad Reza Yeganegi & Rodolphe E. Gozlan, 2019. "A Support Vector Machine Based Approach for Predicting the Risk of Freshwater Disease Emergence in England," Stats, MDPI, vol. 2(1), pages 1-15, February.
    5. Söhnke M. Bartram & Jürgen Branke & Mehrshad Motahari, 2020. "Artificial intelligence in asset management," Working Papers 20202001, Cambridge Judge Business School, University of Cambridge.
    6. Yoon-Joo Park, 2018. "Predicting the Helpfulness of Online Customer Reviews across Different Product Types," Sustainability, MDPI, vol. 10(6), pages 1-20, May.
    7. Saeed-Ul Hassan & Mubashir Imran & Sehrish Iqbal & Naif Radi Aljohani & Raheel Nawaz, 2018. "Deep context of citations using machine-learning models in scholarly full-text articles," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(3), pages 1645-1662, December.
    8. Abdulkadir Atalan, 2023. "Forecasting drinking milk price based on economic, social, and environmental factors using machine learning algorithms," Agribusiness, John Wiley & Sons, Ltd., vol. 39(1), pages 214-241, January.
    9. Junlong Zhang & Youbin He & Yuan Zhang & Weifeng Li & Junjie Zhang, 2022. "Well-Logging-Based Lithology Classification Using Machine Learning Methods for High-Quality Reservoir Identification: A Case Study of Baikouquan Formation in Mahu Area of Junggar Basin, NW China," Energies, MDPI, vol. 15(10), pages 1-15, May.
    10. Wang, Xinlin & Ahn, Sung-Hoon, 2020. "Real-time prediction and anomaly detection of electrical load in a residential community," Applied Energy, Elsevier, vol. 259(C).
    11. Wang, Xinlin & Wang, Hao & Ahn, Sung-Hoon, 2021. "Demand-side management for off-grid solar-powered microgrids: A case study of rural electrification in Tanzania," Energy, Elsevier, vol. 224(C).
    12. Changju Lee & Sunghoon Lee, 2022. "Exploring the Contributions by Transportation Features to Urban Economy: An Experiment of a Scalable Tree-Boosting Algorithm with Big Data," Land, MDPI, vol. 11(4), pages 1-30, April.
    13. Hemraj Verma & Garima Verma, 2020. "Prediction Model for Bollywood Movie Success: A Comparative Analysis of Performance of Supervised Machine Learning Algorithms," The Review of Socionetwork Strategies, Springer, vol. 14(1), pages 1-17, April.
    14. Thierry Delahaye & Rodrigo Acuna-Agost & Nicolas Bondoux & Anh-Quan Nguyen & Mourad Boudia, 2017. "Data-driven models for itinerary preferences of air travelers and application for dynamic pricing optimization," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 16(6), pages 621-639, December.
    15. Mostafaei, Kamran & maleki, Shaho & Zamani Ahmad Mahmoudi, Mohammad & Knez, Dariusz, 2022. "Risk management prediction of mining and industrial projects by support vector machine," Resources Policy, Elsevier, vol. 78(C).
    16. Sagnik Anupam & Arpan Kumar Kar, 2021. "Phishing website detection using support vector machines and nature-inspired optimization algorithms," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 76(1), pages 17-32, January.
    17. 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.
    18. Petrakova Aleksandra & Merkurjeva Galina & Affenzeller Michael, 2015. "Heterogeneous versus Homogeneous Machine Learning Ensembles," Information Technology and Management Science, Sciendo, vol. 18(1), pages 135-140, December.
    19. Mukhtar Sani & Maxime Piffard & Vincent Heiries, 2023. "Fault Detection for PEM Fuel Cells via Analytical Redundancy: A Critical Review and Prospects," Energies, MDPI, vol. 16(14), pages 1-16, July.
    20. Yee-Fan Tan & Lee-Yeng Ong & Meng-Chew Leow & Yee-Xian Goh, 2021. "Exploring Time-Series Forecasting Models for Dynamic Pricing in Digital Signage Advertising," Future Internet, MDPI, vol. 13(10), pages 1-24, September.
    21. Swati Anand & Kushendra Mishra, 2022. "Identifying potential millennial customers for financial institutions using SVM," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 27(4), pages 335-345, December.
    22. Aurelia Rybak & Aleksandra Rybak & Spas D. Kolev, 2023. "Modeling the Photovoltaic Power Generation in Poland in the Light of PEP2040: An Application of Multiple Regression," Energies, MDPI, vol. 16(22), pages 1-17, November.
    23. Ni, Ji & Chen, Bowei & Allinson, Nigel M. & Ye, Xujiong, 2020. "A hybrid model for predicting human physical activity status from lifelogging data," European Journal of Operational Research, Elsevier, vol. 281(3), pages 532-542.

    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. Dangxing Chen & Weicheng Ye & Jiahui Ye, 2022. "Interpretable Selective Learning in Credit Risk," Papers 2209.10127, arXiv.org.
    2. Hoffmann, F. & Baesens, B. & Mues, C. & Van Gestel, T. & Vanthienen, J., 2007. "Inferring descriptive and approximate fuzzy rules for credit scoring using evolutionary algorithms," European Journal of Operational Research, Elsevier, vol. 177(1), pages 540-555, February.
    3. Kristóf, Tamás, 2008. "A csődelőrejelzés és a nem fizetési valószínűség számításának módszertani kérdéseiről [Some methodological questions of bankruptcy prediction and probability of default estimation]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(5), pages 441-461.
    4. 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.
    5. Loterman, Gert & Brown, Iain & Martens, David & Mues, Christophe & Baesens, Bart, 2012. "Benchmarking regression algorithms for loss given default modeling," International Journal of Forecasting, Elsevier, vol. 28(1), pages 161-170.
    6. Dean Fantazzini & Silvia Figini, 2009. "Random Survival Forests Models for SME Credit Risk Measurement," Methodology and Computing in Applied Probability, Springer, vol. 11(1), pages 29-45, March.
    7. Gunnarsson, Björn Rafn & vanden Broucke, Seppe & Baesens, Bart & Óskarsdóttir, María & Lemahieu, Wilfried, 2021. "Deep learning for credit scoring: Do or don’t?," European Journal of Operational Research, Elsevier, vol. 295(1), pages 292-305.
    8. Tong, Edward N.C. & Mues, Christophe & Thomas, Lyn, 2013. "A zero-adjusted gamma model for mortgage loan loss given default," International Journal of Forecasting, Elsevier, vol. 29(4), pages 548-562.
    9. Fraisse, Henri & Laporte, Matthias, 2022. "Return on investment on artificial intelligence: The case of bank capital requirement," Journal of Banking & Finance, Elsevier, vol. 138(C).
    10. Dirick, Lore & Claeskens, Gerda & Baesens, Bart, 2015. "An Akaike information criterion for multiple event mixture cure models," European Journal of Operational Research, Elsevier, vol. 241(2), pages 449-457.
    11. Casado Yusta, Silvia & Nœ–ez Letamendía, Laura & Pacheco Bonrostro, Joaqu’n Antonio, 2018. "Predicting Corporate Failure: The GRASP-LOGIT Model || Predicci—n de la quiebra empresarial: el modelo GRASP-LOGIT," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 26(1), pages 294-314, Diciembre.
    12. Tsukahara, Fábio Yasuhiro & Kimura, Herbert & Sobreiro, Vinicius Amorim & Zambrano, Juan Carlos Arismendi, 2016. "Validation of default probability models: A stress testing approach," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 70-85.
    13. Richard Chamboko & Jorge M. Bravo, 2016. "On the modelling of prognosis from delinquency to normal performance on retail consumer loans," Risk Management, Palgrave Macmillan, vol. 18(4), pages 264-287, December.
    14. Adnan Dželihodžić & Dženana Đonko & Jasmin Kevrić, 2018. "Improved Credit Scoring Model Based on Bagging Neural Network," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 17(06), pages 1725-1741, November.
    15. Crone, Sven F. & Lessmann, Stefan & Stahlbock, Robert, 2006. "The impact of preprocessing on data mining: An evaluation of classifier sensitivity in direct marketing," European Journal of Operational Research, Elsevier, vol. 173(3), pages 781-800, September.
    16. Matthias Bogaert & Lex Delaere, 2023. "Ensemble Methods in Customer Churn Prediction: A Comparative Analysis of the State-of-the-Art," Mathematics, MDPI, vol. 11(5), pages 1-28, February.
    17. Jones, Stewart & Johnstone, David & Wilson, Roy, 2015. "An empirical evaluation of the performance of binary classifiers in the prediction of credit ratings changes," Journal of Banking & Finance, Elsevier, vol. 56(C), pages 72-85.
    18. Gao, Zheming & Fang, Shu-Cherng & Luo, Jian & Medhin, Negash, 2021. "A kernel-free double well potential support vector machine with applications," European Journal of Operational Research, Elsevier, vol. 290(1), pages 248-262.
    19. 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.
    20. Wei-Chang Yeh & Yuan-Ming Yeh & Cheng-Wei Chiu & Yuk Chung, 2013. "A Wrapper-Based Combined Recursive Orthogonal Array and Support Vector Machine for Classification and Feature Selection," Modern Applied Science, Canadian Center of Science and Education, vol. 8(1), pages 1-11, February.

    More about this item

    Keywords

    Company rating; bankruptcy analysis; support vector machines;
    All these keywords.

    JEL classification:

    • 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 fields

    This paper has been announced in the following NEP Reports:

    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:diw:diwwpp:dp811. 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: Bibliothek (email available below). General contact details of provider: https://edirc.repec.org/data/diwbede.html .

    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.