财经理论与实践Issue(2)：66-70,139,6.
基于人群搜索算法的上市公司的 Z-Score 模型财务预警研究
The Z-Score Model Financial Early Warning for Listed Companies Based on Seeker Optimization Algorithm
摘要
Abstract
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.关键词
Z-Score模型/人群搜索算法/寻优能力/数学模型/适应度Key words
Z-score model/Seeker optimization algorithm/Optimization capabilities/Mathe-matical model/Fitness分类
管理科学引用本文复制引用
赵海蕾,周方召,金德环..基于人群搜索算法的上市公司的 Z-Score 模型财务预警研究[J].财经理论与实践,2015,(2):66-70,139,6.基金项目
教育部人文社会科学规划基金(14YJA790089) （14YJA790089）
