One belt, one road, the early warning model of financial risk analysis for small and medium sized enterprises based on big data analysis

2020 ◽  
Author(s):  
Boyi Xiao ◽  
Yuping Zhang
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Risk Analysis ◽  
Early Warning ◽  
Financial Risk ◽  
Big Data Analysis ◽  
Early Warning Model ◽  
One Belt One Road ◽  
Warning Model

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%.

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 How to Determine the Early Warning Threshold Value of Meteorological Factors on Influenza through Big Data Analysis and Machine Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Hui Ge ◽  
Debao Fan ◽  
Ming Wan ◽  
Lizhu Jin ◽  
Xiaofeng Wang ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Infectious Diseases ◽  
Early Warning ◽  
Meteorological Factors ◽  
Threshold Value ◽  
Big Data Analysis ◽  
Meteorological Conditions ◽  
Medical Big Data ◽  
Influenza Outbreaks

Infectious diseases are a major health challenge for the worldwide population. Since their rapid spread can cause great distress to the real world, in addition to taking appropriate measures to curb the spread of infectious diseases in the event of an outbreak, proper prediction and early warning before the outbreak of the threat of infectious diseases can provide an important basis for early and reasonable response by the government health sector, reduce morbidity and mortality, and greatly reduce national losses. However, if only traditional medical data is involved, it may be too late or too difficult to implement prediction and early warning of an infectious outbreak. Recently, medical big data has become a research hotspot and has played an increasingly important role in public health, precision medicine, and disease prediction. In this paper, we focus on exploring a prediction and early warning method for influenza with the help of medical big data. It is well known that meteorological conditions have an influence on influenza outbreaks. So, we try to find a way to determine the early warning threshold value of influenza outbreaks through big data analysis concerning meteorological factors. Results show that, based on analysis of meteorological conditions combined with influenza outbreak history data, the early warning threshold of influenza outbreaks could be established with reasonable high accuracy.

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.

Application of Artificial Neural Network in Corporate Financial Risk Early-Warning

2013 ◽  
Vol 336-338 ◽  
pp. 2476-2479 ◽  
Author(s):  
Hong Xiao Zhou ◽  
Sai Hua Xu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Early Warning ◽  
Bp Neural Network ◽  
Financial Risk ◽  
Financial Data ◽  
Early Warning Model ◽  
Risk Warning ◽  
Artificial Neural ◽  
Warning Model

The traditional financial risk warning model are all based on probability theory and statistical analysis, but the precisions of the results are usually not satisfied in practice. This paper studies the application of artificial neural network in corporate financial risk early-warning. It designs an early warning model of financial risk based on BP neural network. And then selects financial data from 30 enterprises as samples to train and test the network. The result indicates that the risk early warning model is very effective. It can solve some problems of the traditional early warning methods such as difficult to deal with highly non-linear and lack of adaptive capacity.

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