Soft Set Theory Oriented Forecast Combination Method for Business Failure Prediction


Wei Xu, Zhi Xiao, Journal of Information Processing Systems Vol. 12, No. 1, pp. 109-128, Feb. 2016  

10.3745/JIPS.04.0016
Keywords: Business Failure Prediction, Combined Forecasting Method, Qualitative Analysis, Quantitative Analysis, Receiver Operating Characteristic Curve, Soft Set Theory
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Abstract

This paper presents a new combined forecasting method that is guided by the soft set theory (CFBSS) to predict business failures with different sample sizes. The proposed method combines both qualitative analysis and quantitative analysis to improve forecasting performance. We considered an expert system (ES), logistic regression (LR), and support vector machine (SVM) as forecasting components whose weights are determined by the receiver operating characteristic (ROC) curve. The proposed procedure was applied to real data sets from Chinese listed firms. For performance comparison, single ES, LR, and SVM methods, the combined forecasting method based on equal weights (CFBEWs), the combined forecasting method based on neural networks (CFBNNs), and the combined forecasting method based on rough sets and the D-S theory (CFBRSDS) were also included in the empirical experiment. CFBSS obtains the highest forecasting accuracy and the second-best forecasting stability. The empirical results demonstrate the superior forecasting performance of our method in terms of accuracy and stability.


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Cite this article
[APA Style]
Wei Xu and Zhi Xiao (2016). Soft Set Theory Oriented Forecast Combination Method for Business Failure Prediction. Journal of Information Processing Systems, 12(1), 109-128. DOI: 10.3745/JIPS.04.0016.

[IEEE Style]
W. Xu and Z. Xiao, "Soft Set Theory Oriented Forecast Combination Method for Business Failure Prediction," Journal of Information Processing Systems, vol. 12, no. 1, pp. 109-128, 2016. DOI: 10.3745/JIPS.04.0016.

[ACM Style]
Wei Xu and Zhi Xiao. 2016. Soft Set Theory Oriented Forecast Combination Method for Business Failure Prediction. Journal of Information Processing Systems, 12, 1, (2016), 109-128. DOI: 10.3745/JIPS.04.0016.