中文摘要：

当前财务预警的相关研究主要集中于依托财务指标构建模型以预测公司的财务状况,难以深入解释财务困境发生的原因,对财务危机的早期预警有较大局限。在财务指标的基础上,本文引入公司治理、EVA等非财务指标因素,利用统计分析方法对备选指标进行筛选后构建上市公司的财务预警指标体系,通过建立PSO-SVM的预测模型,利用PSO算法自动寻找最优参数组合,并与其他方法进行比较以验证基于非财务指标的PSO-SVM预测模型的有效性,结果表明这种模型的预测准确率比之其他方法有了明显的提高,能够为我国上市公司的财务预警提供理论依据。

英文摘要：

Present literatures on corporate financial early warning are mainly based on financial index to build model to forest financial situation,so it is difficult to further explain the financial difficulties and hardly be applied to forecast the early financial distress. Based on the analysis of financial index,the paper introduces corporate governance,EVA,etc.non-financial indicators to build the financial early warning index system of listed companies after selecting the candidate index with statistical analysis method; by building PSO- SVM prediction model to find the optimal combination of parameters automatically with PSO algorithm,the paper tests validation of PSO-SVM prediction model based on non- financial index. Research results show the prediction accuracy of this model has been significantly improved compared with other methods,which can provide theoretical basis for the financial early warning of listed companies in China.