A Risk Feature Recognition Method of Cross-Border Financial Derivatives’ Transaction Based on Fuzzy Support Vector Machine

Abstract

The traditional identification methods of transaction risk characteristics mostly use the amount of profit and cost to complete the risk assessment. The conflict between the two will lead to low stability. Therefore, the identification method of transaction risk characteristics of transnational financial derivatives is proposed. The fuzzy support vector machine method is used to identify the risk characteristics of cross-border financial derivatives transactions and compensate the errors in the identification process. The possibility index, severity index, and financial risk index of cross-border financial derivatives transaction risk are calculated, and the financial risks are screened and ranked. The distance from risk element to positive ideal solution and negative ideal solution is defined, and the joint identification model of transaction risk of transnational financial derivatives is constructed to realize the identification of transaction risk characteristics. The experimental results show that the designed method has good recognition effect and high recognition performance.

Optimization Design of Drilling Fluid Chemical Formula Based on Artificial Intelligence

“…In linear regression [29][30][31], the insensitive loss function of a certain precision is defined to satisfy ε ≥ 0 and relaxation factors ξ k ≥ 0ξ * k ≥ 0 and parameter C are introduced (penalty factor C meets C ≥ 0, indicating the degree of penalty for samples exceeding ε). e problem of the optimal hyperplane that is difficult to solve is transformed into an easy-toimplement quadratic programming problem.…”

“…Although the choice of the kernel function will lead to different prediction performances of the support vector machine, it is found that the selection of the kernel parameter has a more noticeable impact on the results in the practical application of the regression prediction of the support vector machine. In many cases, it plays a crucial role in the performance of the learning machine [ 28 , 30 ]. Many scholars have used random search algorithms to determine nuclear parameters.…”