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

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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.

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Acknowledgments

This study was financially supported by the National Natural Science Foundation of China (61303113), the Science and Technology Plan Project of Wenzhou, China (G20140048), Scientific Research fund of Zhejiang Provincial Education Department (Y201533884) and the Science and Technology Plan Project of Changchun (2012091), Jilin Province Development and Reform Commission High-tech ([2014]817).

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Authors and Affiliations

  1. College of Computer Science and Technology, Changchun Normal University, Changchun, 130032, China

    Dong Zhao & Fanhua Yu

  2. College of Computer Science and Technology, Changchun University of Science Technology, Changchun, 130032, China

    Chunyu Huang

  3. Wenzhou Vocational College of Science and Technology, Wenzhou, Zhejiang, 325006, China

    Yan Wei

  4. College of Physics and Electronic Information, Wenzhou University, Wenzhou, Zhejiang, 325035, China

    Ming**g Wang & Huiling Chen

Authors
  1. Dong Zhao
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  2. Chunyu Huang
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  3. Yan Wei
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  4. Fanhua Yu
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  5. Ming**g Wang
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  6. Huiling Chen
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Correspondence to Huiling Chen.

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Zhao, D., Huang, C., Wei, Y. et al. An Effective Computational Model for Bankruptcy Prediction Using Kernel Extreme Learning Machine Approach. Comput Econ 49, 325–341 (2017). https://doi.org/10.1007/s10614-016-9562-7

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