Market Risk Early Warning Based on Deep Learning and Fruit Fly Optimization
Open Access
- 9 May 2022
- journal article
- research article
- Published by Hindawi Limited in Mathematical Problems in Engineering
- Vol. 2022, 1-9
- https://doi.org/10.1155/2022/4844856
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.Keywords
This publication has 28 references indexed in Scilit:
- Multi-step medical image segmentation based on reinforcement learningJournal of Ambient Intelligence and Humanized Computing, 2020
- Research on real-time analysis technology of urban land use based on support vector machinePattern Recognition Letters, 2020
- Efficient classification of chronic kidney disease by using multi‐kernel support vector machine and fruit fly optimization algorithmInternational Journal of Imaging Systems and Technology, 2020
- Does machine learning help us predict banking crises?Journal of Financial Stability, 2019
- A Fault Diagnosis Method Based on Improved Pattern Spectrum and FOA-SVMThe International Journal of Acoustics and Vibration, 2019
- Short-term load forecasting of urban gas using a hybrid model based on improved fruit fly optimization algorithm and support vector machineEnergy Reports, 2019
- Preaching water but drinking wine? Relative performance evaluation in international bankingSwiss Journal of Economics and Statistics, 2019
- Reducing model risk in early warning systems for banking crises in the euro areaInternational Economics, 2018
- Solvency prediction for small and medium enterprises in bankingDecision Support Systems, 2017
- Dynamics in Bank Crisis ModelMathematical Problems in Engineering, 2015