IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v56y2020i1d10.1007_s10614-020-09975-3.html

Predicting Extreme Financial Risks on Imbalanced Dataset: A Combined Kernel FCM and Kernel SMOTE Based SVM Classifier

Author

Listed:
  • Xun Huang

    (Chengdu University
    Institute of Chinese Financial Studies, Southwest University of Finance and Economics)

  • Cheng-Zhao Zhang

    (Chengdu Polytechnic)

  • Jia Yuan

    (Chengdu Institute of Public Administration)

Abstract

Extreme financial risk prediction is an important component of risk management in financial markets. In this study, taking the China Securities Index 300 (CSI300) as an example, we set out to introduce the kernel method into fuzzy c-mean algorithm (FCM) and synthetic minority over-sampling technique (SMOTE) and combine them with support vector machine (SVM) to propose a hybrid model of KFCM-KSMOTE-SVM for predicting extreme financial risks, which is compared with other various prediction models. In addition, we investigate the influence on the prediction performance of KFCM-KSMOTE-SVM exerted by its parameters. The empirical results present that KFCM-KSMOTE-SVM outperforms other various prediction models significantly, which verifies that KFCM-KSMOTE-SVM can solve the class imbalance problem in financial markets and is more appropriate for predicting extreme financial risks. Meanwhile, parameter set plays an important role in constructing KFCM-KSMOTE-SVM prediction model. Besides, the experiment on Shanghai Stock Exchange Composite Index also proves that KFCM-KSMOTE-SVM has strong robustness on predicting extreme financial risks.

Suggested Citation

  • Xun Huang & Cheng-Zhao Zhang & Jia Yuan, 2020. "Predicting Extreme Financial Risks on Imbalanced Dataset: A Combined Kernel FCM and Kernel SMOTE Based SVM Classifier," Computational Economics, Springer;Society for Computational Economics, vol. 56(1), pages 187-216, June.
    • Handle: RePEc:kap:compec:v:56:y:2020:i:1:d:10.1007_s10614-020-09975-3
    DOI: 10.1007/s10614-020-09975-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10614-020-09975-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10614-020-09975-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. McNeil, Alexander J. & Frey, Rudiger, 2000. "Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach," Journal of Empirical Finance, Elsevier, vol. 7(3-4), pages 271-300, November.
    2. 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.
    3. 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.
    4. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 18(1), pages 109-131.
    5. Zmijewski, Me, 1984. "Methodological Issues Related To The Estimation Of Financial Distress Prediction Models," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 22, pages 59-82.
    6. Kole, Erik & Koedijk, Kees & Verbeek, Marno, 2007. "Selecting copulas for risk management," Journal of Banking & Finance, Elsevier, vol. 31(8), pages 2405-2423, August.
    7. Cumperayot, Phornchanok & Kouwenberg, Roy, 2013. "Early warning systems for currency crises: A multivariate extreme value approach," Journal of International Money and Finance, Elsevier, vol. 36(C), pages 151-171.
    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. Erdemalp Ozden & Didem Guleryuz, 2022. "Optimized Machine Learning Algorithms for Investigating the Relationship Between Economic Development and Human Capital," Computational Economics, Springer;Society for Computational Economics, vol. 60(1), pages 347-373, June.
    2. Mishra, Bhubaneswari & Chakraverty, S., 2025. "Kernel based physics-informed machine learning for approximating CEV model under nonlinear volatility regimes in real-world financial environments," Chaos, Solitons & Fractals, Elsevier, vol. 200(P3).
    3. Tang, Pan & Xu, Wei & Wang, Haosen, 2024. "Network-Based prediction of financial cross-sector risk spillover in China: A deep learning approach," The North American Journal of Economics and Finance, Elsevier, vol. 72(C).
    4. Xiangzhou Chen & Zhi Long, 2023. "E-Commerce Enterprises Financial Risk Prediction Based on FA-PSO-LSTM Neural Network Deep Learning Model," Sustainability, MDPI, vol. 15(7), pages 1-17, March.
    5. Haitao Lu & Xiaofeng Hu, 2024. "RETRACTED ARTICLE: Enhancing Financial Risk Prediction for Listed Companies: A Catboost-Based Ensemble Learning Approach," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(2), pages 9824-9840, June.
    6. Weiwei Mao & Kaijie Xu, 2024. "Enhancement of the Classification Performance of Fuzzy C-Means through Uncertainty Reduction with Cloud Model Interpolation," Mathematics, MDPI, vol. 12(7), pages 1-13, March.
    7. Zixian Liu & Guansan Du & Shuai Zhou & Haifeng Lu & Han Ji, 2022. "Analysis of Internet Financial Risks Based on Deep Learning and BP Neural Network," Computational Economics, Springer;Society for Computational Economics, vol. 59(4), pages 1481-1499, April.

    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. Li, Chunyu & Lou, Chenxin & Luo, Dan & Xing, Kai, 2021. "Chinese corporate distress prediction using LASSO: The role of earnings management," International Review of Financial Analysis, Elsevier, vol. 76(C).
    2. Elizabeth Demers & Philip Joos, 2007. "IPO Failure Risk," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 45(2), pages 333-371, May.
    3. 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.
    4. Huang, Hsing-Hua & Lee, Han-Hsing, 2013. "Product market competition and credit risk," Journal of Banking & Finance, Elsevier, vol. 37(2), pages 324-340.
    5. Balios, Dimitris & Thomadakis, Stavros & Tsipouri, Lena, 2016. "Credit rating model development: An ordered analysis based on accounting data," Research in International Business and Finance, Elsevier, vol. 38(C), pages 122-136.
    6. 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.
    7. Ruey-Ching Hwang & K. F. Cheng & Jack C. Lee, 2007. "A semiparametric method for predicting bankruptcy," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(5), pages 317-342.
    8. John Y. Campbell & Jens Hilscher & Jan Szilagyi, 2008. "In Search of Distress Risk," Journal of Finance, American Finance Association, vol. 63(6), pages 2899-2939, December.
    9. Duc Hong Vo, 2023. "Market risk, financial distress and firm performance in Vietnam," PLOS ONE, Public Library of Science, vol. 18(7), pages 1-15, July.
    10. McGurr, Paul T. & DeVaney, Sharon A., 1998. "Predicting Business Failure of Retail Firms: An Analysis Using Mixed Industry Models," Journal of Business Research, Elsevier, vol. 43(3), pages 169-176, November.
    11. Alexander Hölzl & Sebastian Lobe, 2016. "Predicting above-median and below-median growth rates," Review of Managerial Science, Springer, vol. 10(1), pages 105-133, January.
    12. Hui Li & Ting Sun & Jinquan Zhang, 2024. "Prediction of corporate financial distress based on corporate social responsibility: New evidence from DANP, VWP and MEOWA weights methodologies," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 64(5), pages 4537-4565, December.
    13. 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.
    14. Megginson, William L. & Meles, Antonio & Sampagnaro, Gabriele & Verdoliva, Vincenzo, 2019. "Financial distress risk in initial public offerings: How much do venture capitalists matter?," Journal of Corporate Finance, Elsevier, vol. 59(C), pages 10-30.
    15. Charitou, Andreas & Dionysiou, Dionysia & Lambertides, Neophytos & Trigeorgis, Lenos, 2013. "Alternative bankruptcy prediction models using option-pricing theory," Journal of Banking & Finance, Elsevier, vol. 37(7), pages 2329-2341.
    16. Anand Deo & Sandeep Juneja, 2021. "Credit Risk: Simple Closed-Form Approximate Maximum Likelihood Estimator," Operations Research, INFORMS, vol. 69(2), pages 361-379, March.
    17. Ruize Gao & Shaoze Cui & Yu Wang & Wei Xu, 2025. "Predicting financial distress in high-dimensional imbalanced datasets: a multi-heterogeneous self-paced ensemble learning framework," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-34, December.
    18. García-Gallego, Ana & Mures-Quintana, María-Jesús, 2013. "La muestra de empresas en los modelos de predicción del fracaso: influencia en los resultados de clasificación || The Sample of Firms in Business Failure Prediction Models: Influence on Classification Results," 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. 15(1), pages 133-150, June.
    19. du Jardin, Philippe, 2015. "Bankruptcy prediction using terminal failure processes," European Journal of Operational Research, Elsevier, vol. 242(1), pages 286-303.
    20. Chen, Peter F. & He, Shaohua & Ma, Zhiming & Stice, Derrald, 2016. "The information role of audit opinions in debt contracting," Journal of Accounting and Economics, Elsevier, vol. 61(1), pages 121-144.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    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:kap:compec:v:56:y:2020:i:1:d:10.1007_s10614-020-09975-3. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.