Evaluation Of Corporate Goodwill Value Based On Grey Neural Network Method

In recent years,due to the active trading of property rights,the listed companies have reported an increase in goodwill,most of which are generated by mergers and acquisitions.Goodwill,as an organic part of the value of the enterprise,obviously affects the profit and loss relationship of the enterprise.If the loss of goodwill impairment is too large,it will even lead to the loss of profit from the enterprise,which will seriously disrupt the market order.In this context,goodwill gradually degenerates as an accomplice to the main body of the listed company to manipulate the company's profits,seriously damaging the interests of investors.In the application of the specific method of goodwill evaluation,the domestic and foreign research results can be found to be unanimously recognized by experts and scholars as the cut-off method and the excess return method.Because the excess return method is closer to the essential attribute that goodwill brings the excess return of the future greater than the benchmark return,it is theoretically better than the cut-off method.However,in the assessment of the company's actual business and the announcement of the listed company,the author found that the evaluation of the company's goodwill usually adopts the cut-off method,that is,the cash flow generated by the future income of the evaluated company's operating assets and liabilities by using the income method.The value is estimated,and the value of non-operating assets,surplus assets and interest-bearing liabilities is measured,and the equity value of the enterprise measured by the income method is obtained.Secondly,identify the scope of the identifiable net assets of the evaluated company and determine its fair value.Finally,the difference between the overall value of the enterprise and the fair value of the identifiable net assets of the enterprise is calculated,and the value of the goodwill on the evaluation base date is obtained.The excess income method is difficult to use in the scientific,reasonable and objective prediction of future income,and it is less used in practice.The case study using the excess return method is even blank.What are the factors that affect the value of goodwill? How to evaluate the value ofgoodwill in traditional goodwill evaluation? What advanced prediction methods can be applied to the assessment field under the artificial intelligence wave?In the theoretical part of this paper,in order to lay a theoretical foundation for the evaluation of the value of goodwill selected in the case study of goodwill evaluation,we first explore the driving factors of the value of goodwill.Then study the applicability of traditional evaluation methods in the evaluation of goodwill,and try to study the applicability of grey prediction,neural network and Markov chain in asset evaluation in view of the dilemma of the theoretical basis of excess return method and the difficulty of practical prediction..Taking Kweichow Moutai wine industry as the research object,combined with DuPont analysis formula to judge the income index,in-depth analysis of the operation of Kweichow Moutai wine industry and the market expectation of liquor industry to analyze the impact of various fields on goodwill.With the help of MATLAB software,the gray neural network and Markov chain model are realized,which realizes good human-computer interaction and solves the "pain point" in the forecast of income.Scientifically calculate the value of goodwill and analyze the results of the method rationally.Finally,it is a review of the full text,combining the theoretical research and practical conditions to expound the basic conclusions and make corresponding suggestions,and finally explain the shortcomings of this paper.Based on the full-text content,the innovation of this paper focuses on the “modernization” of revenue forecasting and the evaluation path of innovative assets.It integrates intelligent algorithms in finance and engineering fields,uses MATLAB programming methods,selects gray prediction vertically,and BP neural network in horizontal direction,using different database storage.In the past ten years,the enterprises and the industry have reported the data of the annual report and the research report.Dynamic corrections in time series through the Markov chain,in order to make the value of goodwill evaluation more fair and easier to operate.