Financial Crisis Early Warning Model of Small and Medium Sized Enterprises Based on Big Data

2021 ◽  
pp. 445-453
Author(s):  
Tao Yu
Keyword(s):  
Big Data ◽  
Financial Crisis ◽  
Early Warning ◽  
Early Warning Model ◽  
Warning Model

scholarly journals Analysis of Financial Risk Early Warning Systems of High-Tech Enterprises under Big Data Framework

2022 ◽  
Vol 2022 ◽  
pp. 1-9
Author(s):  
Maotao Lai
Keyword(s):  
Big Data ◽  
Financial Crisis ◽  
Early Warning ◽  
Logistic Model ◽  
High Tech ◽  
Financial Indicators ◽  
Early Warning Model ◽  
Early Warning Indicators ◽  
Warning Model ◽  
Warning Indicators

With the further development of China's market economy, the competition faced by companies in the market has become more intense, and many companies have difficulty facing pressure and risks. Among the many types of enterprises, high-tech enterprises are the riskiest. The emergence of big data technologies and concepts in recent years has provided new opportunities for financial crisis early warning. Through in-depth study of the theoretical feasibility and practical value of big data indicators, the use of big data indicators to develop an early warning system for financial crises has important theoretical value for breaking through the stagnant predicament of financial crisis early warning. As a result of the preceding context, this research focuses on the influence of big data on the financial crisis early warning model, selects and quantifies the big data indicators and financial indicators, designs the financial crisis early warning model, and verifies its accuracy. The specific research design ideas include the following: (1) We make preliminary preparations for model construction. Preliminary determination and screening of training samples and early warning indicators are carried out, the samples needed to build the model and the early warning indicator system are determined, and the principles of the model methods used are briefly described. First, we perform a significant analysis of financial indicators and screen out early warning indicators that can clearly distinguish between financial crisis companies and nonfinancial crisis companies. (2) We analyze the sentiment tendency of the stock bar comment data to obtain big data indicators. Then, we establish a logistic model based on pure financial indicators and a logistic model that introduces big data indicators. Finally, the two models are tested and compared, the changes in the model's early warning effect before and after the introduction of big data indicators are analyzed, and the optimization effect of big data indicators on financial crisis early warning is tested.

scholarly journals Research on Pump Inspection Cycle Early Warning Method Based on Big Data

2021 ◽  
Vol 245 ◽  
pp. 02026
Author(s):  
Du Lihong ◽  
Liu Yufang ◽  
Cao Fei ◽  
Li Fang ◽  
Min Guizhi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Influencing Factors ◽  
Early Warning ◽  
Learning Technology ◽  
Inspection Period ◽  
Engineering Technology ◽  
Early Warning Model ◽  
Single Well ◽  
Warning Model

At present, the existing indicator diagram can only be used for expost judgment and can not give early warning, and the influencing factors of pump inspection period are nonlinear, multi constrained and multi variable. In this paper, big data machine learning method is used to carry out relevant research. Firstly, around the influencing factors of pump inspection cycle, relevant data are collected and the evaluation index of pump inspection cycle is designed. Then, based on feature engineering technology, the production parameters of oil wells in different pump inspection periods are calculated to form the analysis sample set of pump inspection period. Finally, the early warning model of pump inspection period is established by using machine learning technology. The experimental results show that: the pump inspection cycle early warning model established by stochastic forest algorithm can identify the pump inspection status of single well, and the accuracy rate is about 85%.

Evaluate the sustainable reuse strategy of the corporate financial management based on the big data model

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Weige Yang ◽  
Yuqin Zhou ◽  
Wenhai Xu ◽  
Kunzhi Tang
Keyword(s):  
Big Data ◽  
Development Strategy ◽  
Early Warning ◽  
Financial Management ◽  
Prediction Models ◽  
Sustainable Growth ◽  
Management Model ◽  
Content Type ◽  
Early Warning Model ◽  
Warning Model

PurposeThe purposes are to explore corporate financial management optimization in the context of big data and provide a sustainable financial strategy for corporate development.Design/methodology/approachFirst, the shortcomings of the traditional financial management model are analyzed under the background of big data analysis. The big data analytic technology is employed to extract financial big data information and establish an efficient corporate financial management model. Second, the deep learning (DL) algorithm is applied to implement a corporate financial early-warning model to predict the potential risks in corporate finance, considering the predictability of corporate financial risks. Finally, a corporate value-centered development strategy based on sustainable growth is proposed for long-term development.FindingsThe experimental results demonstrate that the financial early-warning model based on DL has an accuracy of 90.7 and 88.9% for the two-year financial alert, which is far superior to the prediction effect of the traditional financial risk prediction models.Originality/valueThe obtained results can provide a reference for establishing a sustainable development pattern of corporate financial management under the background of big data.

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