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AdaBoost Models for Corporate Bankruptcy Prediction with Missing Data

Author

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  • Ligang Zhou

    (Macau University of Science and Technology)

  • Kin Keung Lai

    (The University of Hong Kong
    Shaanxi Normal University)

Abstract

Very little existing research in corporate bankruptcy prediction discusses modeling where there are missing values. This paper investigates AdaBoost models for corporate bankruptcy prediction with missing data. Three AdaBoost models integrated with different imputation methods are tested on two data sets with very different sample sizes. The experimental results show that the AdaBoost algorithm combined with imputation methods has strong predictive accuracy in both data sets and it is a useful alternative for bankruptcy prediction with missing data.

Suggested Citation

  • Ligang Zhou & Kin Keung Lai, 2017. "AdaBoost Models for Corporate Bankruptcy Prediction with Missing Data," Computational Economics, Springer;Society for Computational Economics, vol. 50(1), pages 69-94, June.
    • Handle: RePEc:kap:compec:v:50:y:2017:i:1:d:10.1007_s10614-016-9581-4
    DOI: 10.1007/s10614-016-9581-4
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    Cited by:

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    4. Tsai, Chih-Fong & Sue, Kuen-Liang & Hu, Ya-Han & Chiu, Andy, 2021. "Combining feature selection, instance selection, and ensemble classification techniques for improved financial distress prediction," Journal of Business Research, Elsevier, vol. 130(C), pages 200-209.
    5. Jung-Kai Tsai & Chih-Hsing Hung, 2021. "Improving AdaBoost Classifier to Predict Enterprise Performance after COVID-19," Mathematics, MDPI, vol. 9(18), pages 1-10, September.
    6. Arnab Mitra & Arnav Jain & Avinash Kishore & Pravin Kumar, 2022. "A Comparative Study of Demand Forecasting Models for a Multi-Channel Retail Company: A Novel Hybrid Machine Learning Approach," SN Operations Research Forum, Springer, vol. 3(4), pages 1-22, December.
    7. Xiangjun Hong & Xian Lin & Laitan Fang & Yuchen Gao & Ruipeng Li, 2022. "Application of Machine Learning Models for Predictions on Cross-Border Merger and Acquisition Decisions with ESG Characteristics from an Ecosystem and Sustainable Development Perspective," Sustainability, MDPI, vol. 14(5), pages 1-27, February.
    8. Haidong Huang & Zhixiong Zhang & Fengxuan Song, 2021. "An Ensemble-Learning-Based Method for Short-Term Water Demand Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(6), pages 1757-1773, April.

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