Enterprise Value Assessment Of Power Battery Based On Combination Prediction Model

With global warming and resource shortage,people’s awareness of environmental protection has been improved.Hot words such as green travel and clean energy have become the focus of attention.As a key component of green travel,the development of power battery is also favored by the capital market.The share prices of relevant companies have reached new highs,the number of enterprises has continued to increase,and merger and investment activities have become more and more frequent.In this context,a reasonable value evaluation of power battery enterprises can provide decision-making basis for both parties of industrial M & A and promote the research and development of value evaluation practice of domestic power battery enterprises.After nearly a decade of development,power battery enterprises have a certain profitability and can generate stable cash flow under normal operating conditions.Different from the high proportion of tangible assets of traditional enterprises,the intangible assets and R & D investment of power battery enterprises account for a relatively high proportion,and the growth rate of enterprises is very fast.Using traditional evaluation methods alone may not be able to fully evaluate the enterprise value.Therefore,after clarifying the characteristics of power battery enterprises,this study attempts to improve and optimize the value evaluation methods and models of power battery enterprises.This thesis uses a combination of theoretical analysis and case verification to study.Firstly,it combs the literature content of enterprise value evaluation methods,influencing factors and prediction models;Secondly,it briefly introduces the development of power battery industry,and analyzes the characteristics and evaluation status of power battery industry;Then,according to the value composition of power battery enterprises,this thesis discusses the applicability of each evaluation method.Through the above analysis,this thesis divides the value of power battery enterprises into two parts: entity value and potential value.The free cash flow method can better evaluate the entity value of the enterprise,while the real option theory regards the potential value of the enterprise as an option,and the option pricing method can more accurately evaluate the value of this part.In the process of free cash flow prediction,scholars used to predict mainly by analyzing the development trend of enterprises or constructing a single statistical model,which has a certain subjectivity,and failed to fully consider the impact of internal and external factors on their value.Based on the free cash flow method,this study puts forward the combined prediction method of grey prediction and BP neural network,considers a variety of influencing factors,constructs the evaluation index system,and improves the objectivity and accuracy of the evaluation results.Finally,this thesis selects Yiwei lithium energy as a case enterprise for empirical research,establishes the value evaluation index system by analyzing the data of 16 power battery enterprises in the same industry,calculates the entity value of the enterprise with the free cash flow method based on the combination prediction model,evaluates the potential value with the physical term weight method,and finally sums up the overall value of the enterprise.This study constructs the value evaluation index system of power battery enterprises,introduces the combination prediction model to optimize and upgrade the free cash flow method,and explores the applicability of the real option method in the field of enterprise value evaluation.This can help appraisers better understand the value composition of power battery enterprises,solve practical problems in evaluation with the help of mathematical models,and provide new ideas and methods for evaluation practice;In the operation process of power battery enterprises,these information can also be used as a reference for investment decisions,so as to further enhance the value of enterprises.