An Empirical Study On The Valuation Model Of China's Listed Companies' M&A Synergy Value

The number of companies which participate in the M&A became larger recently.Some researches indicate that most M&A activities haven't reached expected purpose and many of them have become heavy cloth-wrappers for M&A enterprises.The bad performance had something to do with laking synergy value appraisal before M&A.It's difficult to appraise synergy value of the synergy research.So far,few scholars appraised synergy value and there's no agreed model in the value aspect.So the research on appraisal of M&A synergy value in listed companies is of important guidance significance and practical value for the both sides of M&A.Based on a brief review of these research qualitatively or quantitatively analyzing the M&A performance, this paper points out that we try to use the M&A deals data of China's listed companies to directly discuss the creature of the M&A value in the view of asset valuation. Firstly, the author discuss how to determine the key parameters in the income approach which is a very important appraisal method and we use to evaluate the change of the enterprise values of the M&A company and the target company. Secondly, this paper detailedly analyses eight factors which could affect the M&A value. These factors are state share proportion, M&A enterprise size increase, M&A enterprise operating profit growth rate, industry relativity, related transaction, premium payment and so on. In order to meet the requirement of the practice of asset valuation and evaluate the M&A value before the M&A deal is completed, the author use the multiple regression method and the neural network technique to analyze the relationship between the M&A value and the factors which we believe would affect the value. Based on the compare of the predictive powers of multiple regression model and the neural network models, we screen out the one which has the strongest predictive power.This paper draws three main conclusions as follows: (1) In the general, the merger and acquisition of China's listed companies do create the synergy value; (2) Eight factors may nonlinearly affect the create of synergy value and the mechanism can't not be described by the multiple regression model, however, the neural network can fit the relationship between the synergy value and the factors quite well; (3) based on the compare of the predictive powers of multiple regression model and the neural network models using the non-estimate data, the author finds the BP neural network is the best model. The model has practical value because it can be used to evaluate the synergy value before the accomplishment of M&A.