doi: 10.3934/dcdss.2020271

Analytic expression of optimal solution of M & A matching based on fuzzy number method

1. 

College of Accounting and Finance, Jiangxi University of Engineering, Xinyu 338000, China

2. 

School of Marxism, Hainan University, Haikou 570228, China

*Corresponding author: Hongping Yan

Received  May 2019 Revised  May 2019 Published  February 2020

Research on the merger and acquisition matching focuses on strategic matching, organizational matching and resource matching in academic circles, but merger and acquisition matching is more in line with the rational decision from the efficiency and scale, and the existing DEA research has not paid sufficient attention to this topic. In order to increase the flexibility of M & A matching, this paper studies the M & A matching problem from efficiency and scale using DEA model and fuzzy number method, and gives Analytic Expression of Optimal Solution of M & A Matching of two methods. The example shows that the method is feasible and effective and has important practical significance for strategic M & A.

Citation: Manwen Tian, Shurong Yan, Lina Wang, Weihong Li, Hongping Yan. Analytic expression of optimal solution of M & A matching based on fuzzy number method. Discrete & Continuous Dynamical Systems - S, doi: 10.3934/dcdss.2020271
References:
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show all references

References:
[1]

S. Cartwright and C. Cooper, Organizational marriage: "Hard" versus "soft" issue, Personnel Review, 24 (1995), 32-42.  doi: 10.1108/00483489510089632.  Google Scholar

[2]

T. K. Das and B. S. Teng, The dynamics of alliance conditions in the alliance development process, Journal of Management Studies, 39 (2002), 725-746.  doi: 10.1111/1467-6486.00006.  Google Scholar

[3]

J. Down, The m & a game is often won or lost after the deal, Management Review Executive Forum, 9. Google Scholar

[4]

W. GaoM. K. SiddiquiM. ImranM. K. Jamil and M. R. Farahani, Forgotten topological index of chemical structure in drugs, Saudi Pharmaceutical Journal, 24 (2016), 258-264.  doi: 10.1016/j.jsps.2016.04.012.  Google Scholar

[5]

P. Haspeslagh and D. Jemison, Managing acquisitions: Creating value through corporate renewal. Google Scholar

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J. Kitching, Why do mergers miscarry, Harvard Business Review, 45 (1967), 84-101.   Google Scholar

[7]

C. MiJ. WangW. J. MiY. F. HuangZ. W. ZhangY. S. YangJ. Jiang and P. Octavian, Research on regional clustering and two-stage svm method for container truck recognition, Discrete and Continuous Dynamical Systems Series S, 12 (2019), 1117-1133.   Google Scholar

[8]

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[11]

R. A. Tao and Q. Y. Zhang, Research on the choice of target enterprise based on manda fit, Journal of Beijing Technology and Business University, 26 (2011), 52-57.   Google Scholar

[12]

G. WeiM. R. FarahaniA. Aslam and S. Hosamani, Distance learning techniques for ontology similarity measuring and ontology mapping, Cluster Computing, 20 (2017), 959-968.   Google Scholar

[13]

A. YangX. YangW. LiuY. Han and H. Zhang, Research on 3d positioning of handheld terminal based on particle swarm optimization, Journal of Internet Technology, 20 (2019), 565-574.   Google Scholar

Table 1.  CCR Efficiency of 20 Listed Chinese New Energy Companies
DMU1 DMU2 DMU3 DMU4 DMU5 DMU6 DMU7 DMU8 DMU9 DMU10
0.9986 1 0.8967 1 1 1 0.9873 1 0.9978 0.9986
DMU11 DMU12 DMU13 DMU14 DMU15 DMU16 DMU17 DMU18 DMU19 DMU20
0.9968 1 0.9885 1 1 0.8637 0.9786 0.6543 1 0.9769
DMU1 DMU2 DMU3 DMU4 DMU5 DMU6 DMU7 DMU8 DMU9 DMU10
0.9986 1 0.8967 1 1 1 0.9873 1 0.9978 0.9986
DMU11 DMU12 DMU13 DMU14 DMU15 DMU16 DMU17 DMU18 DMU19 DMU20
0.9968 1 0.9885 1 1 0.8637 0.9786 0.6543 1 0.9769
Table 2.  Relative Efficiency Matrix ($ \theta ) $ of M & A Match
DMU1 DMU3 DMU4 DMU5 DMU6 DMU8 DMU10 DMU14 DMU15 DMU19
DMU2 0.7896 1 0.8795 0.8897 0.8973 0.8794 0.7968 0.7947 1 0.8969
DMU7 0.9876 0.9974 0.8975 0.9828 1 0.9756 0.9984 0.8756 1 1
DMU9 0.9683 1 0.9567 0.9357 1 0.9875 0.9684 0.8896 1 1
DMU11 0.9354 1 0.9785 0.9657 1 0.9165 0.9145 0.9831 1 1
DMU12 0.8796 1 0.9867 0.9375 1 0.8673 0.8891 0.9456 1 1
DMU13 0.9843 1 0.9781 0.9680 1 0.9147 0.9602 0.9063 1 1
DMU16 0.8731 1 0.8834 0.9601 1 0.9364 0.8798 0.8634 1 0.9934
DMU17 0.8897 1 0.8986 0.9976 0.9912 0.9671 0.9561 0.8697 1 1
DMU18 0.8354 0.9934 0.8613 0.9182 1 0.8794 0.8672 0.8437 1 0.9634
DMU20 0.8679 0.9987 0.8891 0.9683 1 0.9861 1 0.8793 1 1
DMU1 DMU3 DMU4 DMU5 DMU6 DMU8 DMU10 DMU14 DMU15 DMU19
DMU2 0.7896 1 0.8795 0.8897 0.8973 0.8794 0.7968 0.7947 1 0.8969
DMU7 0.9876 0.9974 0.8975 0.9828 1 0.9756 0.9984 0.8756 1 1
DMU9 0.9683 1 0.9567 0.9357 1 0.9875 0.9684 0.8896 1 1
DMU11 0.9354 1 0.9785 0.9657 1 0.9165 0.9145 0.9831 1 1
DMU12 0.8796 1 0.9867 0.9375 1 0.8673 0.8891 0.9456 1 1
DMU13 0.9843 1 0.9781 0.9680 1 0.9147 0.9602 0.9063 1 1
DMU16 0.8731 1 0.8834 0.9601 1 0.9364 0.8798 0.8634 1 0.9934
DMU17 0.8897 1 0.8986 0.9976 0.9912 0.9671 0.9561 0.8697 1 1
DMU18 0.8354 0.9934 0.8613 0.9182 1 0.8794 0.8672 0.8437 1 0.9634
DMU20 0.8679 0.9987 0.8891 0.9683 1 0.9861 1 0.8793 1 1
Table 3.  Return-to-Scale Matrix (T) of M & A Match
DMU1 DMU3 DMU4 DMU5 DMU6 DMU8 DMU10 DMU14 DMU15 DMU19
DMU2 Decreasing Decreasing Unchanged Unchanged Unchanged Increasing Decreasing Increasing Decreasing Decreasing
DMU7 Decreasing Unchanged Unchanged Unchanged Unchanged Decreasing Decreasing Increasing Unchanged Unchanged
DMU9 Decreasing Decreasing Decreasing Decreasing Decreasing Decreasing Decreasing Increasing Unchanged Increasing
DMU11 Decreasing Decreasing Decreasing Decreasing Decreasing Decreasing Decreasing Increasing Unchanged Unchanged
DMU12 Increasing Unchanged Decreasing Increasing Decreasing Decreasing Decreasing Unchanged Increasing Decreasing
DMU13 Decreasing Unchanged Unchanged Unchanged Unchanged Unchanged Unchanged Increasing Decreasing Increasing
DMU16 Decreasing Unchanged Unchanged Unchanged Unchanged Unchanged Unchanged Decreasing Increasing Unchanged
DMU17 Decreasing Decreasing Unchanged Decreasing Increasing Decreasing Decreasing Increasing Decreasing Unchanged
DMU18 Decreasing Unchanged Decreasing Decreasing Decreasing Decreasing Decreasing Unchanged Unchanged Decreasing
DMU20 Decreasing Unchanged Unchanged Unchanged Unchanged Unchanged Unchanged Increasing Unchanged Unchanged
DMU1 DMU3 DMU4 DMU5 DMU6 DMU8 DMU10 DMU14 DMU15 DMU19
DMU2 Decreasing Decreasing Unchanged Unchanged Unchanged Increasing Decreasing Increasing Decreasing Decreasing
DMU7 Decreasing Unchanged Unchanged Unchanged Unchanged Decreasing Decreasing Increasing Unchanged Unchanged
DMU9 Decreasing Decreasing Decreasing Decreasing Decreasing Decreasing Decreasing Increasing Unchanged Increasing
DMU11 Decreasing Decreasing Decreasing Decreasing Decreasing Decreasing Decreasing Increasing Unchanged Unchanged
DMU12 Increasing Unchanged Decreasing Increasing Decreasing Decreasing Decreasing Unchanged Increasing Decreasing
DMU13 Decreasing Unchanged Unchanged Unchanged Unchanged Unchanged Unchanged Increasing Decreasing Increasing
DMU16 Decreasing Unchanged Unchanged Unchanged Unchanged Unchanged Unchanged Decreasing Increasing Unchanged
DMU17 Decreasing Decreasing Unchanged Decreasing Increasing Decreasing Decreasing Increasing Decreasing Unchanged
DMU18 Decreasing Unchanged Decreasing Decreasing Decreasing Decreasing Decreasing Unchanged Unchanged Decreasing
DMU20 Decreasing Unchanged Unchanged Unchanged Unchanged Unchanged Unchanged Increasing Unchanged Unchanged
Table 4.  Goodness or Badness of M & A Match When $ \theta _{dk}^L = 0.6 $
DMU1 DMU3 DMU4 DMU5 DMU6 DMU8 DMU10 DMU14 DMU15 DMU19
DMU2 Inferior Inferior Neutral Neutral Neutral Superior Inferior Superior Inferior Inferior
DMU7 Inferior Neutral Neutral Neutral Neutral Inferior Inferior Superior Neutral Neutral
DMU9 Inferior Inferior Inferior Inferior Inferior Inferior Inferior Superior Neutral Superior
DMU11 Inferior Inferior Inferior Inferior Inferior Inferior Inferior Superior Neutral Neutral
DMU12 Superior Neutral Inferior Superior Inferior Inferior Inferior Neutral Superior Inferior
DMU13 Inferior Neutral Neutral Neutral Neutral Neutral Neutral Superior Inferior Superior
DMU16 Inferior Neutral Neutral Neutral Neutral Neutral Neutral Inferior Superior Neutral
DMU17 Inferior Inferior Neutral Inferior Superior Inferior Inferior Superior Inferior Neutral
DMU18 Inferior Neutral Inferior Inferior Inferior Inferior Inferior Neutral Neutral Inferior
DMU20 Inferior Neutral Neutral Neutral Neutral Neutral Neutral Superior Neutral Neutral
DMU1 DMU3 DMU4 DMU5 DMU6 DMU8 DMU10 DMU14 DMU15 DMU19
DMU2 Inferior Inferior Neutral Neutral Neutral Superior Inferior Superior Inferior Inferior
DMU7 Inferior Neutral Neutral Neutral Neutral Inferior Inferior Superior Neutral Neutral
DMU9 Inferior Inferior Inferior Inferior Inferior Inferior Inferior Superior Neutral Superior
DMU11 Inferior Inferior Inferior Inferior Inferior Inferior Inferior Superior Neutral Neutral
DMU12 Superior Neutral Inferior Superior Inferior Inferior Inferior Neutral Superior Inferior
DMU13 Inferior Neutral Neutral Neutral Neutral Neutral Neutral Superior Inferior Superior
DMU16 Inferior Neutral Neutral Neutral Neutral Neutral Neutral Inferior Superior Neutral
DMU17 Inferior Inferior Neutral Inferior Superior Inferior Inferior Superior Inferior Neutral
DMU18 Inferior Neutral Inferior Inferior Inferior Inferior Inferior Neutral Neutral Inferior
DMU20 Inferior Neutral Neutral Neutral Neutral Neutral Neutral Superior Neutral Neutral
Table 5.  Cross Efficiency Matrix of M & A Match when $ \theta _{dk}^L = 0.6 $
DMU1 DMU3 DMU4 DMU5 DMU6 DMU8 DMU10 DMU14 DMU15 DMU19
DMU2 0.6985 0.6491 0.7819 0.8943
DMU7
DMU9 0.9051
DMU11 0.5163 0.6897 0.6015 0.8652
DMU12 0.9325 0.6135 0.8465 0.8956
DMU13 0.7614 0.8616 0.9824 0.7635 0.8165 0.8357
DMU16 0.8156 0.8952 0.9102 0.7024 0.8167
DMU17 0.8014
DMU18 0.6354 0.9164
DMU20 0.9861 0.8816 0.8165
DMU1 DMU3 DMU4 DMU5 DMU6 DMU8 DMU10 DMU14 DMU15 DMU19
DMU2 0.6985 0.6491 0.7819 0.8943
DMU7
DMU9 0.9051
DMU11 0.5163 0.6897 0.6015 0.8652
DMU12 0.9325 0.6135 0.8465 0.8956
DMU13 0.7614 0.8616 0.9824 0.7635 0.8165 0.8357
DMU16 0.8156 0.8952 0.9102 0.7024 0.8167
DMU17 0.8014
DMU18 0.6354 0.9164
DMU20 0.9861 0.8816 0.8165
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