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首页> 外文期刊>Knowledge-Based Systems >The prediction for listed companies' financial distress by using multiple prediction methods with rough set and Dempster-Shafer evidence theory
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The prediction for listed companies' financial distress by using multiple prediction methods with rough set and Dempster-Shafer evidence theory

机译：基于粗糙集和Dempster-Shafer证据理论的多种预测方法对上市公司财务困境的预测

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摘要

It is critical to build an effective prediction model to improve the accuracy of financial distress prediction. Some existing literatures have demonstrated that single classifier has limitations and combination of multiple prediction methods has advantages in financial distress prediction. In this paper, we extend the research of multiple predictions to integrate with rough set and Dempster-Shafer evidence theory. We use rough set to determine the weight of each single prediction method and utilize Dempster-Shafer evidence theory method as the combination method. We discuss the research process for the financial distress prediction based on the proposed method. Finally, we provide an empirical experiment with Chinese listed companies' real data to demonstrate the accuracy of the proposed method. We find that the performance of the proposed method is superior to those of single classifier and other multiple classifiers.
机译：建立有效的预测模型以提高财务困境预测的准确性至关重要。现有文献表明，单一分类器具有局限性，多种预测方法的组合在财务困境预测中具有优势。在本文中，我们将多重预测的研究扩展到与粗糙集和Dempster-Shafer证据理论相结合。我们使用粗糙集确定每种预测方法的权重，并使用Dempster-Shafer证据理论方法作为组合方法。在此基础上，我们讨论了财务困境预测的研究过程。最后，我们对中国上市公司的真实数据进行了实验，以证明所提方法的准确性。我们发现该方法的性能优于单个分类器和其他多个分类器。

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