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A multivariate grey prediction model with grey relational analysis for bankruptcy prediction problems

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Abstract

Regarding bankruptcy prediction as a kind of grey system problem, this study aims to develop multivariate grey prediction models based on the most representative GM(1, N) for bankruptcy prediction. There are several distinctive features of the proposed grey prediction model. First, to improve the prediction performance of the GM(1, N), grey relational analysis is used to sift relevant features that have the strongest relationship with the class feature. Next, the proposed model effectively extends the multivariate grey prediction model for time series to bankruptcy prediction irrespective of time series. It turns out that the proposed model uses the genetic algorithms to avoid indexing by time and using the ordinary least squares with statistical assumptions for the traditional GM(1, N). The empirical results obtained from the financial data of Taiwanese firms in the information and technology industry demonstrated that the proposed prediction model performs well compared with other GM(1, N) variants considered.

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Acknowledgements

The author would like to thank the anonymous referees for their valuable comments. This research is supported by the Ministry of Science and Technology, Taiwan, under grant MOST 106-2410-H-033-006-MY2.

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  1. Department of Business Administration, Chung Yuan Christian University, Taoyuan City, Taiwan

    Yi-Chung Hu

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  1. Yi-Chung Hu
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Correspondence to Yi-Chung Hu.

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Hu, YC. A multivariate grey prediction model with grey relational analysis for bankruptcy prediction problems. Soft Comput 24, 4259–4268 (2020). https://doi.org/10.1007/s00500-019-04191-0

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