Research On Financial Risk Early Warning Of YT Bus Company Based On Efficacy Coefficient Method

With the expiration of the new energy subsidy policy,the profits of most bus enterprises are declining due to the impact of subsidy withdrawal,and a few bus enterprises are facing severe financial risks.With the growing demand of overseas bus market,more and more countries are vigorously promoting the delivery and use of new energy bus,domestic new energy bus companies began to explore overseas markets,seeking new business growth points.When enterprises explore new overseas markets,it is particularly important to maintain the healthy development of enterprises and prevent financial risks.Based on this,this paper applies the efficacy coefficient method to the financial risk early warning research of YT bus company,and constructs a financial risk early warning model based on the efficacy coefficient method for YT bus.On the one hand,it can effectively identify the financial risk of YT bus,and on the other hand,it also has certain reference significance for the financial risk early warning work of the same industry companies.This paper combs the relevant literature at home and abroad,mainly focuses on the research results of financial risk and financial risk early warning model,and conducts indepth research on the application of efficacy coefficient method in financial risk early warning.Combined with the existing theoretical research results,YT bus company is selected as the case study object,through the research,it is found that there are many problems in the company’s financial risk warning.Under the guidance of risk management theory,economic early warning theory and crisis management theory,the financial risk early warning system is constructed for YT bus company,and the early warning results are evaluated by the efficiency coefficient method.Specifically,through the study of YT Bus company’s financial status and financial risk status,it is found that the company’s financial risk early warning has the following problems: insufficient awareness of financial risk early warning,imperfect financial risk early warning system,early warning model is not applicable.Therefore,the necessity and feasibility of building the financial risk early warning model system are put forward.According to the entropy method and Pearson correlation coefficient analysis method,12 early warning indicators such as return on equity and technology investment ratio were selected and determined.Then use the entropy method to carry on the objective weight to each index to construct the financial early warning model;The optimized efficiency coefficient method is used to determine the evaluation standard and early warning level of each index.Finally,the financial risk early warning model system is used to calculate the individual indicators and the overall early warning results of YT Bus Company from 2017 to 2021,and the early warning results are studied,the existing financial risks are found,and targeted preventive measures such as setting up financial risk early warning posts and strengthening cash flow management are put forward.Combined with the current situation of the financial risk of YT Bus Company and the research on the financial risk early warning,the following conclusions are drawn: first,it is feasible to construct the financial early warning model to predict the financial risk of YT Bus Company;Secondly,according to the early warning results,the comprehensive early warning results of YT Bus Company in the past five years generally show a downward trend,and there are certain financial risks.From the detailed study of early warning results,it is found that the profitability of YT Bus Company has declined seriously,inventory turnover speed needs to be improved in operating capacity,debt paying ability has decreased after2020,and the development of the enterprise has slowed down.However,there are still some shortcomings in this paper.For example,the financial data of YT Bus Company mainly come from the public annual report of the enterprise,and the authenticity of the data will have a certain impact on the results of the model.