关键词: 财务困境, 函数型Logistic模型, 风险预警, 变量选择

Abstract: Along with the development of economic globalization and the continuous intensification of market competition, not only the potential factors leading to the financial distress of enterprises are increasingly complex, but also their influence mechanisms show continuous and time-varying characteristics. The warning of financial distress is related to the crisis prevention of enterprises, the protection of investors’ and creditors’ interests, and the effective supervision of securities and capital markets. It is of great theoretical value and practical significance to find out the mechanism that affects the formation of corporate financial distress and to provide timely early warning. For corporate equity investors, early detection of crisis signals of financial distress and adjustment of investment strategies can minimize property losses. For corporate managers, early warning of financial distress risks can prevent financial crises and ensure healthy development of the corporate. As a result, how to effectively identify the key warning indicators that lead to financial distress, and establish an accurate early warning model for financial distress is the key issue that needs to be solved for corporate financial risk management.
In essence, financial distress is a long-term cumulative result of deteriorating business conditions. Traditional warning methods based on discrete data focus on measuring the average effect of indicators leading to financial distress from a static perspective, ignoring the continuous evolution of the formation of corporate financial distress. To address the continuous time-varying nature of the warning effect of financial indicators, this paper systematically extends the functional logistic model based on the financial indicator curves and the selection of variables, aiming to identify effective financial indicators from a process perspective and measure their time-varying effects on the formation of corporate financial distress. First and foremost, the Karhunen-Loève expansion based on the principal component is used to reconstruct the curves in the functional data framework. This article identifies the set of indicators that affect the financial distress of corporate by combing the literature, and the number of base functions is self-driven by the information of discrete observations. Secondly, the logistic model is extended under functional data analysis, and the optimal functional variable selection method is determined. Considering the significant of indicator warning ability, this paper systematically expands the variable selection methods under the functional logistic model, including Lasso, adaptive Lasso (based on CP statistic and based on GCV statistic) and random subspace. Finally, based on the screened financial indicators, a functional logistic model is established to portray the continuous trajectory of the indicator early warning effect, and test the relative advantages of the model.
The results of the empirical study find that only 8 indicators out of the set of 20 early warning indicators, such as return on assets, have significant early warning capability for financial distress. The early warning results further indicate that the warning model considering variable selection is significantly and robustly better than the full variable model in terms of prediction accuracy. Then, the early warning effects of financial indicators show significant differences. Total net asset margin and operating profit margin always have significant effects on corporate distress throughout the sample period, while fixed asset turnover, total asset turnover, and net asset turnover have medium-term early warning ability in the first 2-3 years of the discriminations. In contrast, the return on assets ratio exhibits significant negative warning ability only near the warning year. In addition, compared with the existing warning models, the functional logistic model incorporating random subspaces (RSFLR) in this paper has better early warning effect, exhibiting higher early warning accuracy and lower missed warning rate.
In conclusion, compared with the existing studies, this paper provides new ideas for financial distress early warning from a process perspective, continuously measures the continuous trajectory of the indicator warning effect, and enriches the screening method of existing early warning indicators. The empirical results have empirical references for enterprise managers and regulators. This paper only uses enterprise financial data to establish model, but does not consider the influence of other factors on financial distress. As a result, how to combine industry characteristics and economic factors of enterprises to build a more effective functional logistic warning model is an important issue for future research.

Key words: financial distress, functional logistic model, risk warning, variable selection method