A novel neural network model of mergers and acquisitions performance measurement based on multistage dynamic fuzzy judgement

The mergers and acquisitions (M&A) performance measurement is an important tool to test the M&A effects, evaluate the validity of M&A decision-making, and which is an important part in the M&A management. But how to measure the M&A performance is a major issue that troubled many enterprises. This paper overcomes the shortcoming of tradition linear M&A measuring method, proposes a measuring method which unifies the improved BP neural network algorithm and the multistage dynamic fuzzy judgment, takes the multistage dynamic fuzzy judgment as the sampling foundation, uses the improved BP neural network principle to establish measuring model. This method not only can exert the unique advantages of improved BP neural network, but also overcome the difficulty of seeking the high grade training sample data. The M&A performance measurement of 12 enterprises, indicates that the method to evaluate the M&A performance is stable and reliable, and improves the evaluating efficiency and accuracy.