Financial Management Early Warning Model Based on Two-layer Kriging Element Model Algorithm

Li, Sheng; Liu, Hongzhen; Zhang, Aihua

doi:10.1109/aiars57204.2022.00015

Cited by 1 publication

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is particularly important to establish a comprehensive, flexible, scientific, and accurate enterprise financial risk management and early-warning model in China (Li et al 2022). This is especially true for investors, governments, banks, creditors, managers, employees, and other stakeholders.…”

Section: Introductionmentioning

confidence: 99%

Financial Risk Management Early-Warning Model for Chinese Enterprises

Wei,

Wang

2024

JRFM

View full text Add to dashboard Cite

As enterprises face increasing competitive pressures, financial crises can significantly impact on their capital operations, potentially leading to operational difficulties and, ultimately, market exclusion. Consequently, many enterprises have begun to utilize financial early-warning systems to guide and control risks. Currently, there is neither a universal nor comprehensive enterprise financial risk management model in China, nor a unified classification standard for enterprise financial risk management levels. This article takes financial data on A-share listed companies in 2020 as the data sample, including those with special treatment (represented by ST) or non-ST status. We establish an independent indicator system within the framework of profitability, solvency, operational capability, development potential, shareholders’ retained earnings, cash flow, and asset growth. The model is constructed employing the factor–logistic fusion algorithm. The factor part addresses the issue of collinearity among risk indicators, and the logistic part presents the results in probabilistic form, enhancing the interpretability of the model. The prediction accuracy of this model exceeds 89%. Finally, by applying the principles of interval estimation theory to statistical hypothesis testing, we categorize the risk levels into Grade A, representing significant risk; Grade B, representing moderate risk; Grade C, representing minor risk; and Grade D, representing no risk. This article aims to provide a comprehensive definition of a universal financial risk management early-warning model applicable to all enterprises in China.

show abstract

Section: Introductionmentioning

confidence: 99%