Online supply chain financial risk assessment based on improved random forest

Zhang, Hao; Shi, Yuxin; Tong, Jianbo

doi:10.1007/s42488-021-00042-6

Cited by 13 publications

(6 citation statements)

References 7 publications

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“…Both linear regression and nonlinear regression belong to SVM regression [27,28]. For linear regression, there is a function shown in Eq (9):…”

Section: Svm Model Analysis and Implementationmentioning

confidence: 99%

“…The minimum value ω needs to be found to ensure the smoothness of Eq (9). Therefore, this paper assumes that there is a function f that can estimate all (x i , y i ) within the accuracy ε, and solving the minimum value ω becomes a convex optimization problem, as shown in Eqs (10) and (11):…”

Section: Svm Model Analysis and Implementationmentioning

confidence: 99%

“…[8] proposed an Internet-based SCF risk management model based on data science. [9] established the improved Random Forest (RF)based online SCF-oriented Risk Assessment Index System (RAIS); the data analysis proved the feasibility and accuracy of the proposed system and provided a new method for the SC risk assessment. Sreedharan et al (2019) [10] argued that SC risks were the major obstacles in operational excellence of organizations; the purpose was to evaluate the SC risk of pharmaceutical industry and its impact on SC operational performance; based on literature review, 44 projects were divided into five aspects: supplier risk, production risk, demand risk, infrastructure risk, and macro risk, based on which a structured Questionnaire Survey (QS) was designed and was distributed over the Internet on the pharmaceutical industry, and the response rate was 66.20%; afterward, the corresponding assumptions between these structures and production risk were tested using structural equation model; the results of structural equation model suggested that except demand risk, all other threats were negatively correlated with production risk; additionally, the organizations were ranked according to their production risk by proposing a SC risk assessment index, which was based on the rating obtained by experts using fuzzy technology.…”

Section: Introductionmentioning

confidence: 99%

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Internet-based supply chain financing-oriented risk assessment using BP neural network and SVM

Zhang

Huang

2022

PLoS ONE

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To better prevent the potential risks in Internet-based Supply Chain Financing (SCF) products, this paper optimizes and evaluates the Internet-based SCF-oriented Credit Risk Evaluation (CRE) method. Firstly, this paper summarizes 12 risk factors of SCF business, establishes a Risk Assessment Index System (RAIS) with good consistency and stability; then, the principles of Backpropagation (BP) Neural Network (NN) is expounded together with Support Vector Machines (SVM) and Genetic Algorithm (GA) model. Consequently, a CRE model is implemented by the NN tools in MATLAB based on the collection of multiple groups of SCF-oriented risk assessment samples. Subsequently, the assessment samples are trained and tested. Finally, the SCF-oriented CRE model is proposed and verified. The results show that the BP-GA model has presented high prediction consistency with the actual classification. According to the comparison of classification results of SVM, BP model, and BP-GA model, the classification accuracy of test samples of the proposed Internet-based SCF-oriented CRE system using BP-GA model reaches 97.19%; the Type I and Type II error rate of the CRE system based on BP-GA model is 7.2% and 14.21%, respectively. Therefore, a suitable SCF-oriented CRE method is put forward for China’s commercial banks along with scientific and feasible suggestions to manage SCF-oriented credit risks more reasonably and effectively.

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“…Both linear regression and nonlinear regression belong to SVM regression [27,28]. For linear regression, there is a function shown in Eq (9):…”

Section: Svm Model Analysis and Implementationmentioning

confidence: 99%

Section: Svm Model Analysis and Implementationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Internet-based supply chain financing-oriented risk assessment using BP neural network and SVM

Zhang

Huang

2022

PLoS ONE

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“…Zhao used the quantitative analysis method to evaluate financial risks, but this method has certain subjectivity [7]. Zhang et al used the random forest algorithm to evaluate financial risks, which provides a reference for the evaluation of financial risks of supply chain [8]. rough the above research, it can be seen that trying to apply different deep learning algorithms in financial abnormal recognition and risk warning can not only improve the accuracy of financial recognition but also become the focus of discussing and studying the application of deep learning.…”

Section: Introductionmentioning

confidence: 99%

Construction and Simulation of the Enterprise Financial Risk Diagnosis Model by Using Dropout and BN to Improve LSTM

Yang

Jia

Liu

2022

Security and Communication Networks

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In view of the financial risks faced by listed enterprises, how to accurately predict the risks is an important work. However, the traditional LSTM financial diagnosis model has the disadvantage of low accuracy; the specific reason is that the LSTM model has the problems of overfitting and gradient disappearance in risk diagnosis. Therefore, Dropout is adopted to solve the overfitting problem in the process of premodel prediction, and the BN algorithm is used to solve the gradient disappearance problem in the process of iteration. In order to verify the feasibility of above improvements, the financial data of China’s A-share listed enterprises from 2017 to 2020 are taken as samples to analyze the financial data of listed enterprises through single-step dimension and multistep dimension. The experimental results show that under the analysis of two dimensions, the financial prediction accuracy of the improved LSTM for T-2∼T-3 years can reach 83.96% and 91.19%, respectively, which indicates that through the above improvements, the model can be improved and has certain reference value.

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“…Capital asset pricing model is a very classic measure of market risk, which is developed based on Markowitz's portfolio theory [9,10]. e calculation of the commonly used method mainly depends on the historical data to calculate the VAR value, that is, to calculate the VAR of all financial assets in the portfolio and the income distribution of the entire portfolio [11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation Method of Online Supply Chain Finance Logistics Enterprises Based on GARCH-VAR

Xia

Yang

2022

Security and Communication Networks

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In recent years, China’s core enterprises have been plagued by high operating costs, difficult bank loans, and other practical problems in the supply chain. As a new financing model emerged in recent years, it has opened up new markets in finance and logistics industry and brought new ideas and vitality to all parties in the supply chain. Since the strengthening of supervision, the operation of the industry has gradually changed to the direction of normalization. The study of online loan interest rate fluctuation is helpful to understand the source and characteristics of online loan risk, which has important reference significance for China’s Internet lending market. The measurement of interest rate risk can help to monitor market risk in time and improve financial supervision. This paper takes the online loan interest rate under the background of supervision as the research object, combining theoretical analysis and empirical research to explore the characteristics of interest rate fluctuations and the reasons behind the P2P online loan, and discusses the online loan business model. Based on the influencing factors of interest rate fluctuation, this paper analyzes the main characteristics of network loan interest rate from the aspects of interest rate trend interval distribution, regional distribution, and background distribution. This paper adopts the daily closing price data of domestic R insurance company to establish the daily logarithmic return rate series of stock prices. The results show that (1) GARCH-VAR model can effectively reflect the stock price fluctuation and market risk degree of the insurance company; (2) the company’s VAR showed a trend of first rise and then slight decline, with the highest VAR in 2018; and (3) the interpretation of VAR value should be combined with financial indicators. The increase in net profit rate will also lead to the decline of VAR value in the next period. Based on this, countermeasures and suggestions for market risk management are proposed for insurance companies.

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Online supply chain financial risk assessment based on improved random forest

Cited by 13 publications

References 7 publications

Internet-based supply chain financing-oriented risk assessment using BP neural network and SVM

Internet-based supply chain financing-oriented risk assessment using BP neural network and SVM

Construction and Simulation of the Enterprise Financial Risk Diagnosis Model by Using Dropout and BN to Improve LSTM

Performance Evaluation Method of Online Supply Chain Finance Logistics Enterprises Based on GARCH-VAR

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