财经理论与实践
財經理論與實踐
재경이론여실천
The Theory and Practice of Finance and Economics
2015年
2期
66~70
,共null页
Z-Score模型 人群搜索算法 寻优能力 数学模型 适应度
Z-Score模型 人群搜索算法 尋優能力 數學模型 適應度
Z-Score모형 인군수색산법 심우능력 수학모형 괄응도
Z-score model; Seeker optimization algorithm; Optimization capabilities; Mathematical model; Fitness
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。
針對傳統的Z-Score財務預警模型預警能力的不足,導緻無法準確判定上市公司的財務風險狀況,將SOA算法的良好尋優能力和Z-Score財務預警模型結閤起來,提齣一種改進的Z-Score財務預警模型,構建齣SOA算法優化Z-Score財務預警模型的適應度函數。倣真對比髮現,改進的Z-Score財務預警模型其平均識彆率高達96.33%,遠遠高于SVM算法和AdaBoost算法的平均識彆率,改進的算法極大地提升瞭Z-Score財務預警模型的預測能力,使其更具適應性。
침대전통적Z-Score재무예경모형예경능력적불족,도치무법준학판정상시공사적재무풍험상황,장SOA산법적량호심우능력화Z-Score재무예경모형결합기래,제출일충개진적Z-Score재무예경모형,구건출SOA산법우화Z-Score재무예경모형적괄응도함수。방진대비발현,개진적Z-Score재무예경모형기평균식별솔고체96.33%,원원고우SVM산법화AdaBoost산법적평균식별솔,개진적산법겁대지제승료Z-Score재무예경모형적예측능력,사기경구괄응성。
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 average 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 adaptable.
