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http://dx.doi.org/10.3745/JIPS.04.0016

Soft Set Theory Oriented Forecast Combination Method for Business Failure Prediction  

Xu, Wei (School of Economics and Business Administration, Chongqing University)
Xiao, Zhi (School of Economics and Business Administration, Chongqing University)
Publication Information
Journal of Information Processing Systems / v.12, no.1, 2016 , pp. 109-128 More about this Journal
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.
Keywords
Business Failure Prediction; Combined Forecasting Method; Qualitative Analysis; Quantitative Analysis; Receiver Operating Characteristic Curve; Soft Set Theory;
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