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 The “New Generation” High-tech Enterprise's Early Warning Model and Its Empirical Research Based on the Perspective of Organizational Commitment Theory

2021 ◽  
Vol 14 (10) ◽  
pp. 96
Author(s):  
Huabai Bu ◽  
Jiaqi Bu ◽  
Naifu Shi ◽  
Yanglingli Ou ◽  
Jingyi Wang
Keyword(s):  
Organizational Commitment ◽  
Early Warning ◽  
Theoretical Research ◽  
High Tech ◽  
High Quality ◽  
Healthy Development ◽  
The Core ◽  
Early Warning Model ◽  
New Generation ◽  
Warning Model

With the continuous advancement of emerging technologies such as big data, cloud computing, Internet of Things, blockchain, artificial intelligence, and 5G communications, China's “new generation” high-tech companies are developing rapidly, but the loss of core employees restricts their healthy development. How to manage the core employees of “new generation” high-tech enterprises is a grim reality in front of theorists and industrialists. Based on the results of the current theoretical research on organizational commitment, the research group proposed a “new generation” high-tech enterprise core employee resignation early warning model to provide decision-making basis and methodological reference for the “new generation” high-tech enterprise high-quality development.

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.

scholarly journals Financial distress in Brazilian banks: an early warning model,

2017 ◽  
Vol 29 (77) ◽  
pp. 312-331
Author(s):  
Paulo Sérgio Rosa ◽  
Ivan Ricardo Gartner
Keyword(s):  
Early Warning ◽  
Financial Distress ◽  
Financial Stability ◽  
Balance Sheet ◽  
Warning Signs ◽  
Capital Adequacy ◽  
Financial Indicators ◽  
Forecast Horizon ◽  
Early Warning Model ◽  
Warning Model

ABSTRACT This study aims to propose an early warning model for predicting financial distress events in Brazilian banking institutions. Initially, a set of economic-financial indicators is evaluated, suggested by the risk management literature for identifying situations of bank insolvency and exclusively taking public information into account. For this, multivariate logistic regressions are performed, using as independent variables financial indicators involving capital adequacy, asset quality, management quality, earnings, and liquidity. The empirical analysis was based on a sample of 142 financial institutions, including privately and publicly held and state-owned companies, using monthly data from 2006 to 2014, which resulted in panel data with 12,136 observations. In the sample window there were nine cases of Brazilian Central Bank intervention or mergers and acquisitions motivated by financial distress. The results were evaluated based on the estimation of the in-sample parameters, out-of-sample tests, and the early warning model signs for a 12-month forecast horizon. These obtained true positive rates of 81%, 94%, and 89%, respectively. We conclude that typical balance-sheet indicators are relevant for the early warning signs of financial distress in Brazilian banks, which contributes to the literature on financial intermediary credit risk, especially from the perspective of bank supervisory agencies acting towards financial stability.

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