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A Hybrid Business Failure Prediction Model Using Locally Linear Embedding And Support Vector Machines

Author

Listed:
  • Lin, Fengyi

    (Department of Business Management, National Taipei University of Technology, Taipei, Taiwan.)

  • Yeh, Ching Chiang

    (Department of Business Administration, National Taipei College of Business, Taipei, Taiwan.)

  • Lee, Meng Yuan

    (Institute of Commerce Automation and Management, National Taipei University of Technology, Taipei, Taiwan.)

Abstract

The purpose of this paper is to propose a hybrid model which combines locally linear embedding (LLE) algorithm and support vector machines (SVM) to predict the failure of firms based on past financial performance data. By making use of the LLE algorithm to perform dimension reduction for feature extraction, is then utilized as a preprocessor to improve business failure prediction capability by SVM. The effectiveness of the methodology was verified by comparing principal component analysis (PCA) and SVM with our proposed hybrid approach. The results show that our hybrid approach not only has the best classification rate, but also produces the lowest incidence of Type I and Type II errors, and is capable to provide on time signals for better investment and government decisions with timely warnings.

Suggested Citation

  • Lin, Fengyi & Yeh, Ching Chiang & Lee, Meng Yuan, 2013. "A Hybrid Business Failure Prediction Model Using Locally Linear Embedding And Support Vector Machines," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 82-97, March.
    • Handle: RePEc:rjr:romjef:v::y:2013:i:1:p:82-97
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    References listed on IDEAS

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    Cited by:

    1. Iulian Viorel Brasoveanu & Florin Dobre & Laura Brad, 2014. "Increasing Financial Audit Quality Using A New Model To Estimate Financial Performance," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 88-107, October.

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    More about this item

    Keywords

    business failure; manifold learning; locally linear embedding; support vector machines;
    All these keywords.

    JEL classification:

    • 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

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