Research on risk early warning algorithm for asymmetric samples in multifractal financial market

Bao, Rong; Lin, Jianyi

doi:10.3233/jifs-219020

Cited by 4 publications

(2 citation statements)

References 23 publications

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“…However, due to the nonoperability of nonfinancial indicators, it is still difficult to apply them [ 21 ]. Bao and Lin proposed to apply the multivariate linear research method to the financial risk early warning for the first time, adopt the statistical analysis method, and finally build the Z model with the result of minimum error judgment [ 22 ].…”

Section: Related Workmentioning

confidence: 99%

Prediction and Analysis of Corporate Financial Risk Assessment Using Logistic Regression Algorithm in Multiple Uncertainty Environment

Li¹,

Yan²,

Lu³

et al. 2022

Journal of Environmental and Public Health

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Currently, the level of economic globalisation is expanding, which gives organizations more room to grow while also subjecting them to an increasing amount of pressure from the market. Companies are forced to deal with an increasing number of unclear aspects due to the unstable internal and external environments, which also increases the risks they confront. A management system for corporate financial risk is according to studies on early warning systems for financial risks. Its goals are to raise the standard of corporate financial management and boost economic advantages, identify concerns and potential hazards in the corporate financial management process, stop corporate financial crises in their tracks, and lessen the losses brought on by such crises. The financial risk management of the organization is predicted and examined in this research using the logistic regression model. The use of a logistic regression model allows for the simultaneous analysis of various risk factors, such as discrete and continuous variables, as well as the analysis of external variables’ interactions and confounding. This method is suited for widespread usage in practice because it has shown exceptional outcomes in study that are 16.24% better than those of the conventional method.

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Section: Related Workmentioning

confidence: 99%

Prediction and Analysis of Corporate Financial Risk Assessment Using Logistic Regression Algorithm in Multiple Uncertainty Environment

Li¹,

Yan²,

Lu³

et al. 2022

Journal of Environmental and Public Health

View full text Add to dashboard Cite

show abstract

“…e authors of [6] design a nancial risk control model based on support vector machine (SVM). Using the risk status of six-carbon nance pilot markets as the study sample each month, this work develops a nancial risk early warning model based on SVM to control nancial risk.…”

Section: Introductionmentioning

confidence: 99%

A Big Data Technique for Internet Financial Risk Control

Xie

Shi

2022

Mobile Information Systems

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The danger of losing funds on an investment or business enterprise is referred to as financial risk. Credit risk, liquidity risk, and operational risk are some of the most prevalent and different financial hazards. Financial risk control is an organizational activity that seeks to detect, monitor, and manage exposure to different risks associated with the usage of financial services. Conventional accounting risk control has relatively great timeliness and unpredictability; therefore, traditional accounting risk identification has numerous difficulties, such as high error rate, low precision, lengthy time, and risk. As a result, this research provides a unique financial risk control strategy in the Internet environment based on big data. First, the decision tree algorithm is used to classify the users who can face financial risks. Simultaneously, the user’s personal credit information is established and represented mathematically, and the 5C mathematical model is obtained. In this way, the user’s credit degree can be judged more accurately, and the user’s credit rating can be obtained, allowing quantitative analysis of users to be realized. Second, construct and weight the financial risk system index of the Internet in accordance with the existing financial system. Finally, the Internet financial risk control model based on big data is built, and the financial risk early warning threshold range is determined to achieve Internet financial risk control. The experimental findings reveal that the method’s risk identification error rate is minimal, accuracy is high, and control time is quick. It can match the actual risk level, which shows that the control effect of this method is good.

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Research on a Financial Crisis Early Warning Model Based on a Stochastic Forest Algorithm

Wang¹

2021

2021 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI)

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Research on risk early warning algorithm for asymmetric samples in multifractal financial market

Cited by 4 publications

References 23 publications

Prediction and Analysis of Corporate Financial Risk Assessment Using Logistic Regression Algorithm in Multiple Uncertainty Environment

Prediction and Analysis of Corporate Financial Risk Assessment Using Logistic Regression Algorithm in Multiple Uncertainty Environment

A Big Data Technique for Internet Financial Risk Control

Research on a Financial Crisis Early Warning Model Based on a Stochastic Forest Algorithm

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