Selection Of Variables And Indicators In Financial Distress Prediction Model-Svm Method Based On Sparse Principal Component Analysis

S. Zeng, Wanjun Yang
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引用次数: 1

Abstract

How to screen out the early warning indicators from a large number of alternative financial indicators is an important step in the prediction of financial distress. In order to design the financial distress prediction model more effectively, this paper proposes a novel method that combines the sparse algorithm and support vector machine. Firstly, according to the financial management theory, financial indicators are divided into several groups, and then variable screening was conducted for each group of financial indicators by sparse principal component analysis. Finally, after variable screening, the data set is input to the SVM for classification and prediction (classifier prediction). The empirical results show that this method can more effectively identify companies in financial distress, improve the prediction results of the model, and reduce the risk of investors facing financial distress.
财务困境预测模型中变量和指标的选择——基于稀疏主成分分析的svm方法
如何从大量可供选择的财务指标中筛选出预警指标,是财务困境预测的重要一步。为了更有效地设计财务困境预测模型,本文提出了一种将稀疏算法与支持向量机相结合的新方法。首先,根据财务管理理论,将财务指标分成若干组,然后利用稀疏主成分分析对每组财务指标进行变量筛选。最后,经过变量筛选,将数据集输入支持向量机进行分类和预测(分类器预测)。实证结果表明,该方法可以更有效地识别财务困境企业,提高模型的预测结果,降低投资者面临财务困境的风险。
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