AdaBoost Models for Corporate Bankruptcy Prediction with Missing Data

Ligang Zhou () and Kin Keung Lai ()
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Ligang Zhou: Macau University of Science and Technology
Kin Keung Lai: The University of Hong Kong

Computational Economics, 2017, vol. 50, issue 1, No 3, 69-94

Abstract: 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.

Keywords: AdaBoost algorithms; Bankruptcy prediction; Missing data (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (8)

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DOI: 10.1007/s10614-016-9581-4

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