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An Effective Computational Model for Bankruptcy Prediction Using Kernel Extreme Learning Machine Approach

Author

Listed:
  • Dong Zhao

    (Changchun Normal University)

  • Chunyu Huang

    (Changchun University of Science Technology)

  • Yan Wei

    (Wenzhou Vocational College of Science and Technology)

  • Fanhua Yu

    (Changchun Normal University)

  • Mingjing Wang

    (Wenzhou University)

  • Huiling Chen

    (Wenzhou University)

Abstract

Bankruptcy prediction is becoming more and more important issue in financial decision-making. It is essential to make the companies prevent from bankruptcy through building effective corporate bankruptcy prediction model in time. This study proposes an effective bankruptcy prediction model based on the kernel extreme learning machine (KELM). A two-step grid search strategy which integrates the coarse search with the fine search is adopted to train KELM. The resultant bankruptcy prediction model is compared with other five competitive methods including support vector machines, extreme learning machine, random forest, particle swarm optimization enhanced fuzzy k-nearest neighbor and Logit model on the real life dataset via 10-fold cross validation analysis. The obtained results clearly confirm the superiority of the developed model in terms of classification accuracy, Type I error, Type II error and area under the receiver operating characteristic curve (AUC) criterion. Promisingly, the proposed KELM can serve as a new candidate of powerful early warning systems for bankruptcy prediction with excellent performance.

Suggested Citation

  • Dong Zhao & Chunyu Huang & Yan Wei & Fanhua Yu & Mingjing Wang & Huiling Chen, 2017. "An Effective Computational Model for Bankruptcy Prediction Using Kernel Extreme Learning Machine Approach," Computational Economics, Springer;Society for Computational Economics, vol. 49(2), pages 325-341, February.
    • Handle: RePEc:kap:compec:v:49:y:2017:i:2:d:10.1007_s10614-016-9562-7
    DOI: 10.1007/s10614-016-9562-7
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Xiangxing Tao & Mingxin Wang & Yanting Ji, 2023. "The Application of Graph-Structured Cox Model in Financial Risk Early Warning of Companies," Sustainability, MDPI, vol. 15(14), pages 1-16, July.
    3. Sami Ben Jabeur & Nicolae Stef & Pedro Carmona, 2023. "Bankruptcy Prediction using the XGBoost Algorithm and Variable Importance Feature Engineering," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 715-741, February.
    4. Misankova Maria & Zvarikova Katarina & Kliestikova Jana, 2017. "Bankruptcy Practice in Countries of Visegrad Four," Economics and Culture, Sciendo, vol. 14(1), pages 108-118, June.
    5. Johan Eklund & Nadine Levratto & Giovanni B. Ramello, 2020. "Entrepreneurship and failure: two sides of the same coin?," Small Business Economics, Springer, vol. 54(2), pages 373-382, February.
    6. Chen, Jian & Katchova, Ani L. & Zhou, Chenxi, 2021. "Agricultural loan delinquency prediction using machine learning methods," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 24(5), May.
    7. Vicente García & Ana I. Marqués & J. Salvador Sánchez & Humberto J. Ochoa-Domínguez, 2019. "Dissimilarity-Based Linear Models for Corporate Bankruptcy Prediction," Computational Economics, Springer;Society for Computational Economics, vol. 53(3), pages 1019-1031, March.
    8. Abbas Haider & Hui Wang & Bryan Scotney & Glenn Hawe, 2022. "Predictive Market Making via Machine Learning," SN Operations Research Forum, Springer, vol. 3(1), pages 1-21, March.
    9. repec:ctc:sdimse:dime21_01 is not listed on IDEAS
    10. Veganzones, David & Séverin, Eric & Chlibi, Souhir, 2023. "Influence of earnings management on forecasting corporate failure," International Journal of Forecasting, Elsevier, vol. 39(1), pages 123-143.
    11. Doering, Jana & Kizys, Renatas & Juan, Angel A. & Fitó, Àngels & Polat, Onur, 2019. "Metaheuristics for rich portfolio optimisation and risk management: Current state and future trends," Operations Research Perspectives, Elsevier, vol. 6(C).
    12. Abang Zainoren Abang Abdurahman & Wan Fairos Wan Yaacob & Syerina Azlin Md Nasir & Serah Jaya & Suhaili Mokhtar, 2022. "Using Machine Learning to Predict Visitors to Totally Protected Areas in Sarawak, Malaysia," Sustainability, MDPI, vol. 14(5), pages 1-16, February.
    13. Marko Špiler & Tijana Matejić & Snežana Knežević & Marko Milašinović & Aleksandra Mitrović & Vesna Bogojević Arsić & Tijana Obradović & Dragoljub Simonović & Vukašin Despotović & Stefan Milojević & Mi, 2022. "Assessment of the Bankruptcy Risk in the Hotel Industry as a Condition of the COVID-19 Crisis Using Time-Delay Neural Networks," Sustainability, MDPI, vol. 15(1), pages 1-54, December.
    14. Philippe Jardin & David Veganzones & Eric Séverin, 2019. "Forecasting Corporate Bankruptcy Using Accrual-Based Models," Computational Economics, Springer;Society for Computational Economics, vol. 54(1), pages 7-43, June.

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