A multivariate grey prediction model with grey relational analysis for bankruptcy prediction problems
- 28 Downloads
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.
KeywordsGrey prediction Time series Multi-criteria decision making Feature selection Bankruptcy prediction
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.
Compliance with ethical standards
Conflict of interest
The author declares that he has no conflict of interest.
Human and animal rights
This article does not contain any studies with human participants performed by the author.
- Abualigah LMQ, Hanandeh ES (2015) Applying genetic algorithms to information retrieval using vector space model. Int J Comput Sci Eng Appl 5(1):19–28Google Scholar
- Ali SH (2012) Miner for OACCR: case of medical data analysis in knowledge discovery. In: 2012 6th international conference on sciences of electronics, technologies of information and telecommunications, Sousse, Tunisia, 2012, pp 962–975Google Scholar
- Al-Janabi S, Abaid Mahdi M (2019) Evaluation prediction techniques to achievement an optimal biomedical analysis. Int J Grid Util Comput (forthcoming)Google Scholar
- Guo M, Lan J, Lin Z, Sun X (2012) Traffic flow data recovery algorithm based on gray residual GM(1,N) model. J Transp Syst Eng Inf Technol 12(1):42–47Google Scholar
- Hu YC, Chiu YJ, Tsai JF (2018) Establishing grey criteria similarity measures for multi-criteria recommender systems. J Grey Syst 30(1):192–205Google Scholar
- Liu S, Lin Y (2010) Grey information: theory and practical applications. Springer, BerlinGoogle Scholar
- Pei LL, Chen WM, Bai JH, Wang ZX (2015) The improved GM(1,N) models with optimal background values: a case study of Chinese high-tech industry. J Grey Syst 27(3):223–233Google Scholar
- Wang WB, Hu YC (2019) Multivariate grey prediction models for pattern classification irrespective of time series. J Grey Syst 31:135–142Google Scholar
- Weiss SM, Kulikowski CA (1991) Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems. Morgan Kaufmann, San MateoGoogle Scholar
- Yang YN (2010) Financial econometric with gretl. Compass Publishing, Taipei, TaiwanGoogle Scholar