基于人群搜索算法的上市公司的 Z-Score 模型财务预警研究

针对传统的 Z-Score 财务预警模型预警能力的不足，导致无法准确判定上市公司的财务风险状况，将 SOA 算法的良好寻优能力和 Z-Score 财务预警模型结合起来，提出一种改进的 Z-Score 财务预警模型，构建出 SOA 算法优化 Z-Score 财务预警模型的适应度函数。仿真对比发现，改进的 Z-Score 财务预警模型其平均识别率高达96.33%，远远高于 SVM 算法和 AdaBoost 算法的平均识别率，改进的算法极大地提升了 Z-Score 财务预警模型的预测能力，使其更具适应性。

The Z-Score Model Financial Early Warning for Listed Companies Based on Seeker Optimization Algorithm

The conventional Z-Score model financial early warning lacks predicative power, making it impossible to accurately determine the financial risk profile of listed companies.It needs to be further optimized to enhance its power.This article combines the optimization ability of SOA with Z-Score financial early warning model algorithms,and proposes an improved Z-Score financial early warning model to construct a SOA algorithm optimization fitness function for the new early warning model.Our simulation result show that the improved Z-Score financial early warning model increases the average recognition rate up to 96.33%,much higher than the aver-age recognition rate of SVM algorithm and AdaBoost algorithm;and the improved algorithm greatly enhances the ability of the Z-Score Financial Early Warning Model by making it more a-daptable.