Financial Early Warning Analysis Of Animal Husbandry Enterprises Based On GRU Neural Network Model

In the face of an uncertain economic environment,China’s listed companies continue to experience financial crises such as default,bankruptcy,and forced delisting.The frequent occurrence of financial crises of listed companies directly harms the vital interests of the company’s creditors and relevant investors,and is also not conducive to market development.At the same time,in recent years,China’s pig market prices have been showing the cyclical fluctuation characteristics of "sharp rise and fall",which makes livestock breeding enterprises face strong profit fluctuations,and even leads to financial crises in serious cases,how to deal with the risks brought by market cycle fluctuations has become an urgent problem for listed companies in the livestock industry.Therefore,it is an urgent need for animal husbandry enterprises to do a good job in the prevention and control of financial risks in the animal husbandry industry and establish a model suitable for the financial risks of the livestock industry.Based on the empirical analysis method,this thesis uses animal husbandry enterprises as analyzing samples.By analyzing the financial risk characteristics of animal husbandry enterprises,the appropriate financial indicators are selected,and the main component is extracted with the main component analysis method.GRU neural network warning model of animal husbandry enterprises.Based on the case analysis method,the Zhengbang Technology Company was used as a case research object to verify the practicality of the model.By reference to existing research,this thesis has defined financial analysis,and explains the basic principles,processes,and its application advantages in corporate financial early warning in detail.Secondly,the status and industry characteristics of animal husbandry enterprises were introduced,and the causes of the financial risks of animal husbandry enterprises were analyzed to illustrate the need for its financial risk warning.Then,this article uses 13 animal husbandry listed companies as a sample,using the main component analysis method to reduce the dimension of financial indicators,obtains 4 main factors,combined with two non-financial indicators,the degree of diversified development and the pig cycle as an early warning as an early warning.Factors,the CRU neural network model is used as the core to establish a financial risk warning model of animal husbandry.Later,in the case study,the model company Zhengbang Technology predicted the case company Zhengbang Technology,and found that the prediction results were basically in line with the truth,and then the profitability,operation ability,debt repayment capacity,development capacity,diversified development,and pig cycle and pig cycle and pig cycle and pig cycle and pig cycle and pig cycle and pig cycle and pig cycle and pig cycle.Specific analysis of existing financial risks in6 aspects of investment in investment.In the end,this thesis concludes that the GRU neural network model built in this article has a high level in the accuracy of the training set and test set,indicating that the GRU neural network model predicts the accuracy of the financial risk of animal husbandry companies;second,Parameter settings and function selection will also affect the accuracy of the model.According to the results of the case analysis,the corresponding financial risk prevention countermeasures are proposed: moderate diversified development,deepening the core industry chain,and reducing operating risks;optimizing the capital structure,promoting resource allocation,and avoiding funding risks;attaching importance to market laws,rational planning projects,and resistance to external risks;Strengthen internal control and improve awareness of prevention and control.Finally,this thesis proposes two application inspiration:increase the amount of sample data,increase the accuracy of forecasting;continuously update the early warning system and increase model applicability.