考虑多维效率的上市公司财务困境预警研究

doi:10.16381/j.cnki.issn1003-207x.2019.1366

摘要/Abstract

摘要： 已有的财务困境预警研究一般基于财务指标，或在财务指标基础上引入单一效率指标，而多维效率指标能够更加全面有效地反映不同行业、不同资产规模的上市公司整体状况，从而对上市公司财务困境产生更好的预警效果。本文从经营效率、财务效率、融资效率和人力资本效率这四个维度分别提出相对应的投入产出指标体系，并采用数据包络分析对上市公司各个维度的相对有效性进行评价。在此基础上，将得到的多维效率指标与财务指标相融合，建立上市公司财务困境预警模型。为了验证所提出模型的有效性，采用支持向量机、人工神经网络和决策树这三种常用的财务困境预警技术，并基于不同的财务指标体系对我国上市公司进行实证研究。结果表明，考虑多维效率指标的上市公司财务困境预警模型能够有效提高预测准确度。

关键词: 财务困境预警, 数据包络分析, 支持向量机, 人工神经网络, 决策树

Abstract: Accurate financial distress prediction models are of critical importance to various stakeholders, i.e. management, investors, employees, shareholders and other interested parties, as the models provide them with timely warnings. The selection of appropriate indicators, which has significant impact on the accuracy of the prediction model, has been widely studied by researchers. Generally, most of the prediction models presented in the literature select indicators directly from various financial ratios based on information that appears in the corporations' financial statements. However, it is widely recognized that a main cause of financial distress is poor management. Therefore, it is believed that the efficiency of business operations should be included in the financial distress prediction model.
Different from previous researches that construct a prediction model based on the integration of financial ratios and a single-dimensional efficiency indicator, it is argued that multi-dimensional efficiency indicators can comprehensively and effectively reflect the overall situation of corporations in various industries with different scales. Therefore, they should be considered in financial distress prediction in order to achieve better performance. In this study, four input-output systems are put forward for efficiency evaluation with respect to four dimensions, i.e., operational efficiency, financial efficiency, financing efficiency and human capital efficiency. The data envelopment analysis is employed to evaluate the relative efficiencies of corporations in each dimension. And then, the financial distress prediction model, which integrates the multi-dimensional efficiency indicators and financial ratios, is established.
In order to verify the validity of the proposed model, 148 listed corporations in Shanghai Stock Exchange are selected and Shenzhen Stock Exchange for empirical study. Among them, 74 corporations are financial healthy, while the other 74 corporations fall into financial distress. Three commonly used financial distress prediction techniques, i.e., support vector machine, artificial neural network and decision tree, are embedded in the model. To further investigate the effectiveness of the proposed model, we adopt three different financial ratios systems are adopted as benchmarks. The results show that the proposed model that combines multi-dimensional efficiency indicators and financial ratios can effectively improve the accuracy of financial distress prediction. The findings of the empirical study also reveal that when different financial ratios are used as the input variables, adding multi-dimensional efficiency indicators could constantly improve the prediction performance, which exhibit the robustness of the proposed approach.

Key words: financial distress prediction, data envelopment analysis, support vector machine, artificial neural network, decision tree

中图分类号:

F224

王昱, 杨珊珊. 考虑多维效率的上市公司财务困境预警研究[J]. 中国管理科学, 2021, 29(2): 32-41.

WANG Yu, YANG Shan-shan. Corporate Financial Distress Prediction Based on Multi-Dimensional Efficiency Indicators[J]. Chinese Journal of Management Science, 2021, 29(2): 32-41.

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