Soft Set Theory Oriented Forecast Combination Method for Business Failure Prediction

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.

Explanatory Factors of Business Failure: Literature Review and Global Trends

“…The main contribution of this study consists in the new insights it provides into bibliometric trends in the research of business failure. The techniques applied to predict financial insolvency and business failure scenarios have evolved over the years: from univariate analysis proposed in 1966 [65] to artificial intelligence techniques and, within these, machine learning [1,150,[165][166][167][168][169][170][171][172], which currently has become a preeminent methodological approach to identify the explanatory factors of business failure and whose results are auspicious. Therefore, and intending to discover new research niches on this topic, it is necessary to understand and identify the intellectual structure of the trend in business failure prediction studies.…”

“…Table 11 presents other indicators for this term; for example, it occurred in 105 papers and had 1249 citations. Another basic topic that attracted the attention of researchers is the support vector machine, which, combined with the log-linear embedding rhythm, was used to predict business failure; this methodology analyses data on the performance of companies from previous years [141,[145][146][147][148][149][150][151][152]. The last two basic themes, business and finance, have high centrality, indicating the importance of these themes in the overall development of business failure.…”

On the solution of games with arbitrary payoffs: An application to an over‐the‐counter financial market

“…The proposed method combines elements from statistical logistic regression and soft set decision theory and is applied to real data sets from Chinese listed firms. Xu and Xiao (2016) propose a new forecasting method to predict business failures that is based on soft set theory. The authors introduce a new weighted scheme based on the receiver operating characteristic curve theory to obtain suitable weight coefficients for their model.…”