An Optimized Z-Score Financial Early-Warning Model for Foreign Trade Enterprises Using Stochastic Optimization Algorithm
Hong Guo, Lifang Liu
Journal of Information Security Research, Vol. 16, No. 4, 2025.
DOI: https://doi.org/10.6025/jisr/2025/16/4/163-172
Link: https://www.dline.info/jisr/fulltext/v16n4/jisrv16n4_3.pdf
Abstract:
The paper proposed a model to assess and predict financial risks in Chinese foreign trade listed companies.
Recognizing limitations in the traditional Altman Z-Score model such as low diagnostic accuracy the authors
integrate the Stochastic Optimization Algorithm (SOA) to fine tune the model's coefficients. Using financial
data from 20 listed firms and a five year case study of Jiangsu Sainty Co., Ltd. (2012-2016), they demonstrate
that the SOA optimized Z-Score model significantly improves prediction accuracy, achieving a 96.33%
recognition rate outperforming SVM and AdaBoost algorithms. The analysis reveals key financial indicators
(e.g., retained earnings, EBIT, and asset turnover) critically influence risk levels. Based on findings, the
authors recommend enterprises establish robust risk control systems, optimize capital structures, improve
working capital management, and enhance profitability. The study underscores the value of combining
classical financial models with modern optimization techniques for more reliable, adaptive financial risk
assessment in dynamic market environments.