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2023 , Vol. 63 >Issue 9: 1467 - 1482

DOI: https://doi.org/10.16511/j.cnki.qhdxxb.2023.21.008

经济与公共管理

回归金融原理： 企业财务危机预警研究述评与展望

  • 朱武祥 ,
  • 廖静秋 ,
  • 詹子良 ,
  • 谭智佳
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  • 1. 清华大学 经济管理学院, 北京 100084;
    2. 清华大学 五道口金融学院, 北京 100083
朱武祥(1965-),男,教授。

收稿日期: 2023-03-20

  网络出版日期: 2023-08-19

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Systematic review and future perspectives of financial distress prediction studies: Back to the principle of finance

  • ZHU Wuxiang ,
  • LIAO Jingqiu ,
  • ZHAN Ziliang ,
  • TAN Zhijia
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  • 1. School of Economics and Management, Tsinghua University, Beijing 100084, China;
    2. People's Bank of China School of Finance, Tsinghua University, Beijing 100083, China

Received date: 2023-03-20

  Online published: 2023-08-19

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摘要

财务危机预警一直受到企业、 投资者和政府的关注, 但已有预警模型方法的预警能力不能满足市场期许, 甚至引发争议, 政府部门、 市场主体对优化债券违约风险识别与预警方法的需求强烈。 该文系统梳理了1932至2020年间256篇财务危机预警文献, 从财务危机的概念基础、 预警模型的原理及迭代、 预警指标选取、 预警效率评估等维度进行了述评, 指出了现有财务危机预警模型研究的3个现象、 方法论特征及局限性。 提出了一个跨模型可比的财务危机预警模型评价框架和“一个原则、 三个方向”研究改进展望, 主张回归金融原理, 从而更加精确地进行企业财务危机绝对风险的评估、 预警与治理。

关键词： 财务危机; 预警模型; 前景展望

本文引用格式

朱武祥 , 廖静秋 , 詹子良 , 谭智佳 . 回归金融原理： 企业财务危机预警研究述评与展望[J]. 清华大学学报（自然科学版）, 2023 , 63(9) : 1467 -1482 . DOI: 10.16511/j.cnki.qhdxxb.2023.21.008

Abstract

[Significance] Because of multiple factors, such as deleveraging policy, slowing economic growth, trade friction, and the COVID-19 pandemic, debt defaults are occurring with increasing frequency, which could trigger risk contagion and even lead to systemic financial risks. However, some facts indicate that the existing financial distress prediction model is not sufficiently effective; for example, the nonperforming loan ratio of commercial banks shows a rising trend, and the downgrade of ratings usually lags considerably. Thus, government departments and market entities have a strong demand for improving and optimizing the financial distress prediction model, which is necessary to realize risk identification and early warning. An effective prediction model can provide early warnings of investment risks and help financial institutions and investors reduce losses, assist regulators in establishing a multichannel default disposal mechanism, and improve the credit environment of the capital market.[Progress] Based on an extensive literature search in top journals and conferences from 1932 to 2020, this paper reviews four topics, including the financial distress definition, statistical model, variable selection, and model efficiency evaluation method, then further summarizes three research anomalies: 1) Existing financial distress prediction models often focus on the prediction of deep crises, such as insolvency and bankruptcy, which may lead to a delayed warning and market panic. 2) The innovation of financial distress prediction research focuses on applying new computer algorithms and statistical models as well as considering nonfinancial information. One confusing fact is that the judgment of financial distress depends on the selected model, indicators, and sample set rather than the fundamental factors of the enterprise; thus, different prediction models may produce contradictory results on the judgment of the same enterprise. 3) The identification of financial distress relies on comparing an enterprise's future capital cash flow and rigid payment. However, most of the existing financial distress prediction models apply a multivariate weighting method according to common historical financial indicators.[Conclusions and Prospects] This paper proposes a cross-model evaluation framework to compare their financial distress prediction effectiveness and provides improvement suggestions including “one principle, three directions.” The one principle indicates that to accurately assess and manage the absolute risk of financial distress, the study of financial distress prediction should return to the financial principle and pay attention to future capital cash flow. The three directions that need to pay attention include: 1) early financial distress warnings, such as liquidity crisis warnings; 2) steady repayment sources, including operating cash inflows, reliable asset disposal earnings, and refinancing, rather than relying on the total assets of the balance sheet, current assets, and other indicators; 3) financing contracts and full scenario analyses of future capital cash outflows rather than just current ratio, quick ratio, asset-liability ratio, and other liability indicators. In the future, with the development of big data and the improvement in information transmission efficiency, corporate information disclosure will be considerably enhanced, allowing more accurate cash flow and repayment prediction. A prediction model assessing absolute financial distress risk has greater potential.

Key words： financial distress; prediction model; future perspectives

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