Academic Journal of Business & Management, 2022, 4(16); doi: 10.25236/AJBM.2022.041616.
Jingyang Cui1, Haixin Huang2
1Shaanxi Branch Sales Department, Agricultural Development Bank of China, Xi'an, Shaanxi, 710061, China
2Faculty of Business, Economics & Law, The University of Queensland, Brisbane, Queensland, 4072, Australia
With the deepening of economic globalization, China's participation in global foreign trade activities is constantly diversified, and at the same time it faces more and more risks. China's long-term accumulated risks in the future may be released intensively, causing high incidence. Potential risks in macro-economy, business environment, sovereign credit, debt and other fields will bring certain losses to enterprises and even hinder their development. Support Vector Machine (SVM) based on statistical learning theory is a new machine learning algorithm, which can successfully deal with classification and regression problems. Because of the excellent learning performance of SVM, this technology has become a research hotspot in current academic circles. This paper expounds the basic theory of support vector machine in detail, constructs the basic framework of economic investment risk prediction model based on support vector machine, and gives the concrete steps to realize the model and the key problems to be solved. The algorithm is very practical. Using support vector machine regression theory to predict economic risk not only enriches the existing risk prediction methods in theory, but also has important value in practical application.
support vector machine algorithm; economic risk; risk assessment and control
Jingyang Cui, Haixin Huang. Analysis of Economic Risk Identification Evaluation and Management and Control Strategy Based on Support Vector Machine Algorithm. Academic Journal of Business & Management (2022) Vol. 4, Issue 16: 95-98. https://doi.org/10.25236/AJBM.2022.041616.
 Chen Shanshan. Credit Risk Assessment Model of Commercial Banks Based on Rough Sets and Support Vector Machines [J]. Value Engineering, 2018, 27(5):4.
 Hou Ningning. Research on Support Vector Machine Learning Algorithm Based on Optimization Theory [J]. Science and Technology Economics Guide, 2020, v.28;No.730(32):20-21.
 Wang Jianguo, Zhao Pengfei, Zhang Wenxing, Qin Bo, Liu Wenjing. Multi-scale Gaussian Kernel Support Vector Machine Algorithm [J]. Machine Tool and Hydraulics, 2020, 48(20):4.
 Huang Qi. Financing Risk Evaluation of Small and Medium-sized High-tech Enterprises at the Start-up Stage: Financing Risk Evaluation Method Based on Fuzzy Support Vector Machine [J]. Value Engineering, 2018, 33(33):3.
 Wang Xiaohui, Li Yunfei. Research on personal credit risk assessment based on support vector machine [J]. Journal of West China Normal University (Natural Science Edition), 2017(2).
 Xia Bing. Credit risk assessment of SMEs from the perspective of supply chain based on SLS-SVM [J]. Industrial Technology Economics, 2021, 40(6):6.
 Zhao Rui, Jia Runan. A Brief Discussion on Country Risk Management and Control in the "One Belt, One Road" Strategy: A Study Based on Country Economic Risk Assessment Model [J]. Shanghai Finance, 2017, 000(003):91-95.
 Cheng Yunjie, Wu Jie. Economic Risk Assessment and Prevention of China's Investment in Transition Economies——Based on the Background of "One Belt One Road" [J]. Decision and Information, 2018(9):11.