Forecasting Core Business Transformation Risk Using the Optimal Rough Set and the Neural Network

Delu Wang, Xuefeng Song, Wenying Yin and Jingying Yuan

Journal of Forecasting, 2015, vol. 34, issue 6, 478-491

Abstract: In the process of enterprise growth, core business transformation is an eternal theme. Enterprise risk forecasting is always an important concern for stakeholders. Considering the completeness and accuracy of the information in the early‐warning index, this paper presents a new risk‐forecasting method for enterprises to use for core business transformation by using rough set theory and an artificial neural network. First, continuous attribute values are discretized using the fuzzy clustering algorithm based on the maximum discernibility value function and information entropy. Afterwards, the major attributes are reduced by the rough sets. The core business transformation risk rank judgement is extracted to define the connection between network nodes and determine the structure of the neural networks. Finally, the improved back‐propagation (BP) neural network learning and training are used to judge the risk level of the test samples. The experiments are based on 265 listed companies in China, and the results show that the proposed risk‐forecasting model based on rough sets and the neural network provides higher prediction accuracy rates than do other widely developed baselines including logistic regression, neural networks and association rules mining. Copyright © 2015 John Wiley & Sons, Ltd.

Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:wly:jforec:v:34:y:2015:i:6:p:478-491

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