<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Relying on advanced technology, the financial shared management model realizes a breakthrough for the enhancement of enterprise financial management capability, and provides effective support for enterprise decision makers while mining financial risks. In this paper, the support vector machine algorithm (SVM) is used to construct a financial risk prediction model for e-commerce enterprises, and the radial basis kernel function (RBF) is selected among various kernel functions to enhance the discriminative ability of SVM. At the same time, the particle swarm algorithm (PSO) is introduced to optimize parameter <italic>γ</italic> and penalty factor <italic>C</italic> of the RBF kernel function, and the PSO-SVM fusion model is constructed, so as to improve the prediction accuracy of the e-commerce enterprise financial risk prediction model. After the experimental analysis of this paper on the various capabilities of enterprise financial management, it is found that the operating cash flow ratio of the e-commerce enterprise is less than 40% in 2017-2021, and reaches a minimum of 3% in 2020, which indicates that the Z e-commerce enterprise has insufficient ability to raise debt to operate, which may cause problems such as a decrease in the rate of return on assets. In addition, the cash flow of the e-commerce enterprise is highly variable, and the cash outflow from operating activities is significantly higher than the cash inflow in 2020, resulting in a negative net inflow of −111,451,575,000 yuan. It indicates that the Z e-commerce enterprise has a low degree of control over cash flow, and there is a financial risk of a sudden break in the capital chain. Therefore, the risk prediction model constructed in this paper can uncover potential hidden financial dangers and risks by analyzing the financial data of e-commerce enterprises.</p>
</abstract>

Relying on advanced technology, the financial shared management model realizes a breakthrough for the enhancement of enterprise financial management capability, and provides effective support for enterprise decision makers while mining financial risks. In this paper, the support vector machine algorithm (SVM) is used to construct a financial risk prediction model for e-commerce enterprises, and the radial basis kernel function (RBF) is selected among various kernel functions to enhance the discriminative ability of SVM. At the same time, the particle swarm algorithm (PSO) is introduced to optimize parameter γ and penalty factor C of the RBF kernel function, and the PSO-SVM fusion model is constructed, so as to improve the prediction accuracy of the e-commerce enterprise financial risk prediction model. After the experimental analysis of this paper on the various capabilities of enterprise financial management, it is found that the operating cash flow ratio of the e-commerce enterprise is less than 40% in 2017-2021, and reaches a minimum of 3% in 2020, which indicates that the Z e-commerce enterprise has insufficient ability to raise debt to operate, which may cause problems such as a decrease in the rate of return on assets. In addition, the cash flow of the e-commerce enterprise is highly variable, and the cash outflow from operating activities is significantly higher than the cash inflow in 2020, resulting in a negative net inflow of −111,451,575,000 yuan. It indicates that the Z e-commerce enterprise has a low degree of control over cash flow, and there is a financial risk of a sudden break in the capital chain. Therefore, the risk prediction model constructed in this paper can uncover potential hidden financial dangers and risks by analyzing the financial data of e-commerce enterprises.

Construction and validation of a financial risk prediction model for e-commerce enterprises oriented to financial sharing

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Relying on advanced technology, the financial shared management model realizes a breakthrough for the enhancement of enterprise financial management...

Construction and validation of a financial risk prediction model for e-commerce enterprises oriented to financial sharing

Online veröffentlicht: 15. Mai 2024

Seitenbereich: -

Eingereicht: 09. Feb. 2024

Akzeptiert: 20. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-1075

Schlüsselwörter
SVM, Particle swarm algorithm, Kernel function, Parameter optimization, Financial risk

© 2024 Sheng Wang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Construction and validation of a financial risk prediction model for e-commerce enterprises oriented to financial sharing

Online veröffentlicht: 15. Mai 2024

Seitenbereich: -

Eingereicht: 09. Feb. 2024

Akzeptiert: 20. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-1075

SchlüsselwörterSVM, Particle swarm algorithm, Kernel function, Parameter optimization, Financial risk

© 2024 Sheng Wang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
SVM, Particle swarm algorithm, Kernel function, Parameter optimization, Financial risk