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Market Risk Early Warning Based on Deep Learning and Fruit Fly Optimization

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  • Liang Chen
  • Rui Ma
  • Man Fai Leung

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.

Suggested Citation

  • Liang Chen & Rui Ma & Man Fai Leung, 2022. "Market Risk Early Warning Based on Deep Learning and Fruit Fly Optimization," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-9, May.
    • Handle: RePEc:hin:jnlmpe:4844856
    DOI: 10.1155/2022/4844856
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