Affiliation:
1. School of Economics and Management, Southwest Petroleum University, Chengdu 610500, China
2. Health and Rehabilitation College, Chengdu University of Traditional Chinese Medicine, Chengdu 610032, China
Abstract
To improve the ability of market to avoid and prevent credit risk and strengthen the awareness of market risk early warning, SMOTE is used to process the unbalanced sample, and fruit fly optimization algorithm (FOA) is utilized to optimize the parameters of support vector machine (SVM), and thus an improved SVM market risk early warning model is proposed. The simulation results show that the proposed model has excellent stability and generalization ability, and it can predict market credit risk accurately. Compared with the prediction model based on FOA-SMOTE-BP and FOA-SMOTE-Logit, the proposed model performs better on the indicators of G value, F value, and AUC value, which provides a reference for market credit risk prediction.
Subject
General Engineering,General Mathematics
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