Keywords: M&A risk assessment
M&A will have many positive effects on enterprises, but there are also huge risks, so enterprises should pay attention to the identification and evaluation of M&A risks. This paper mainly studies the evaluation method of enterprise merger and acquisition.
Basic evaluation method
Model 1: R=P(r0), where r represents the risk of M&A project, p represents the probability, r represents the return on investment of M&A project (a random variable), and r0 represents the benchmark return on investment before M&A. This model comes from probability statistics, and holds that M&A is also an investment behavior, which requires enterprises to improve the return on investment through M&A and invest in it.
Model 2: R=p(f), where f is a random event, which means "M&A plan failed". This model actually defines risk as the probability of M&A project failure, and its essence comes from the probability method.
Model 3: R= 1-α 1α2, where R is the risk probability of enterprise merger and acquisition, α 1 is the success rate of enterprise merger and acquisition transaction period, and α2 is the success rate of enterprise merger and acquisition integration. This model considers the success rate of M&A in two different periods and the risk of M&A from the other side. As can be seen from the formula, in order to reduce the risk of M&A, it is necessary to improve the success rate of M&A trading period and M&A integration period, thus requiring entrepreneurs to consider the risk of M&A from a longer-term perspective, which has practical value.
Model 4: In the formula, R is the mean square error of investment return rate r 1, r2, ... rn is the possible state return rate of M&A project, p 1, p2, ... pn is the probability of various states, and -r is the expected value of investment return rate. The model is based on variance method, and the difference of M&A rate of return among states is taken as the measure of M&A risk. The greater the standard deviation of M&A rate of return, the greater the M&A risk. On the contrary, the smaller the risk of mergers and acquisitions. This requires that after the merger, no matter what happens, the merger must achieve a stable rate of return.
Model 5: where μ is the average return on investment (i.e. expected value). This model is obviously the application of mean-variance method in risk assessment of M&A project, and its calculation result is a relative index, which reflects the uncertainty of M&A rate of return more accurately than model 4, thus showing the risk of M&A more clearly. The greater the proportion, the greater the risk of mergers and acquisitions.
Model 6: R =1-θ 1 (1-θ2), where R is the risk probability of M&A, θ1is the support rate of M&A enterprises, and θ 2 is the opposition rate of M&A enterprises. This model considers two important ratios of M&A, and the success of M&A is inseparable from the support of both parties. The higher the support rate of M&A enterprises, the lower the opposition rate of M&A enterprises, and the lower the risk of M&A. This requires that the merger behavior of enterprises is in the interests of both parties.
Comprehensive evaluation method
The above model only evaluates and measures the risk from one aspect, while there are three methods to evaluate the overall risk of M&A. ..
Fuzzy evaluation
Fuzzy evaluation method, by introducing fuzzy mathematics theory, establishes the fuzzy set of enterprise merger risk factors, the membership function of enterprise merger risk nature and the fuzzy matrix for evaluating enterprise merger risk factors, evaluates enterprise merger risk, judges the feasibility of candidate target enterprises and makes a choice.
The application scope of fuzzy evaluation: judging whether a candidate target enterprise is feasible and how to choose between different candidate target enterprises; Or whether an alternative merger and acquisition scheme can be used as a merger and acquisition scheme, and how to choose the best scheme among different alternatives for the same target enterprise. But it can't accurately measure the risk of enterprise merger and acquisition. If M&A experts can identify the main risk factors in the process of M&A through analysis and demonstration according to certain methods, and estimate their uncertainty, they can evaluate their M&A risks through fuzzy evaluation method.
Grey relational evaluation
According to the basic idea of grey system theory, a running enterprise can be regarded as a complex grey system, and enterprise merger and acquisition is an important way of grey system operation. From the analysis of risk sources, there are many risk factors that affect the effectiveness of enterprise mergers and acquisitions. In different M&A schemes, various risk factors have different effects on M&A effect. Grey relational evaluation method is to evaluate the risk degree of different M&A schemes by using grey system theory.
Application scope of grey relational evaluation: Like fuzzy evaluation method, the application of grey relational evaluation method can quantitatively evaluate the risk of mergers and acquisitions. When an enterprise decides to implement the M&A strategy and initially selects two or more candidate target enterprises through risk identification, it can evaluate its M&A risk by using the grey correlation method and make a further choice between the two. If the same target enterprise has different alternatives to choose from, the grey correlation method can be used to evaluate its M&A risk and choose the best one. If M&A experts can identify the main risk factors of M&A through analysis and demonstration according to certain methods, and make statistical estimation of their uncertainty through expert investigation, then their M&A risks can be evaluated by grey correlation method. However, the grey relational evaluation method can't accurately measure the M&A risk.
References:
1. Zhao Min. The risk of enterprise merger and the integration after merger and acquisition. Business economics magazine, May 2000.
2. Chen * * * Rong, Ai Zhiqun. On the financial risk of enterprise merger and acquisition. Financial Theory and Practice, 2003.3