# American Institute of Mathematical Sciences

August & September  2019, 12(4&5): 747-759. doi: 10.3934/dcdss.2019049

## Risk assessment for enterprise merger and acquisition via multiple classifier fusion

 1 Sichuan Agriculture University, Dujiangyan, Chengdu 611830, China 2 Southwest JiaoTong University, Chengdu 610000, China

* Corresponding author: Zhichao Liu

Received  July 2017 Revised  November 2017 Published  November 2018

This paper aims to solve the problem of Risk assessment for enterprise merger and acquisition (M&A), which is an important problem in modern company management. Firstly, we design an index system to assess risks of enterprise M&A behavior, and six risks are considered: 1) Systemic risk, 2) Law risk, 3) Financial risk, 4) Intermediary risk, 5) Integrated risk, and 6) Information risk. Furthermore, 18 indexes are chosen to cover these six aspects. Secondly, we illustrate how to utilize the proposed risk assessment in the decision system for enterprise M&A risk assessment. We separate the M&A risk assessment process to three steps, that is, 1) Before M&A, and 2) In M&A, and 3) After M&A. Particularly, after the risk assessment process, there are three decisions for enterprise managers, that is, 1) implement the original M&A plan, 2) modify the original M&A plan, and 3) refuse it. Thirdly, we propose the multiple classifier fusion based risk assessment algorithm, which aims to effectively combine the six support vector machines. To relax the limitation of the SVM classifier, we introduce the fuzzy theory in the multiple classifier fusion algorithm, and the category label assignment is determined by utilizing a maximum membership rule. Finally, we conduct an experiment to make performance evaluation by constructing a dataset which includes the M&A data of 200 enterprises, among which 185 enterprises are used as training dataset and others are regarded as testing dataset. Using ROC curve, MAE and MAPE as evaluation criterions, performance of the proposed method is compared with single SVM scheme. Experimental results demonstrate that combining multiple the SVM classifiers together, accuracy of M&A risk assessment is greatly enhanced.

Citation: Yinying Duan, Yong Ye, Zhichao Liu. Risk assessment for enterprise merger and acquisition via multiple classifier fusion. Discrete & Continuous Dynamical Systems - S, 2019, 12 (4&5) : 747-759. doi: 10.3934/dcdss.2019049
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##### References:
Index system for the risk assessment for enterprise merger and acquisition
Framework of the decision system for enterprise merger and acquisition risk assessment
Settings of the multiple classifier fusion in this experiment
Value of the M&A risk for different methods
ROC curves for different methods
Description of the testing dataset
 E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 E11 E12 E13 E14 E15 f1 .45 .49 .59 .46 .40 .57 .53 .59 .52 .48 .57 .60 .51 .59 .56 f2 .36 .44 .57 .35 .45 .31 .42 .44 .35 .33 .52 .30 .48 .35 .47 f3 .73 .71 .78 .73 .79 .79 .76 .78 .72 .79 .74 .73 .73 .79 .71 f4 .42 .61 .65 .56 .67 .79 .43 .41 .45 .54 .46 .50 .57 .47 .42 f5 .25 .25 .36 .26 .20 .29 .33 .37 .30 .39 .30 .25 .22 .33 .32 f6 .79 .59 .49 .73 .55 .82 .63 .64 .64 .42 .58 .45 .51 .69 .67 f7 .39 .35 .32 .20 .27 .21 .30 .34 .32 .26 .33 .39 .22 .25 .26 f8 .45 .49 .58 .55 .41 .56 .67 .65 .41 .59 .49 .50 .47 .46 .62 f9 .88 .65 .76 .81 .72 .84 .90 .66 .89 .90 .87 .67 .83 .78 .66 f10 .43 .43 .56 .46 .43 .59 .46 .48 .46 .54 .43 .55 .42 .53 .43 f11 .48 .49 .33 .23 .49 .38 .31 .20 .41 .36 .27 .29 .23 .32 .38 f12 1 1 0 0 0 0 1 1 1 0 1 1 0 0 0 f13 .61 .72 .86 .66 .76 .66 .64 .87 .72 .89 .83 .88 .66 .79 .70 f14 .40 .49 .64 .65 .67 .73 .69 .54 .50 .46 .73 .54 .71 .68 .72 f15 .36 .34 .40 .48 .39 .46 .38 .38 .48 .47 .42 .43 .33 .31 .45 f16 .47 .50 .40 .44 .46 .46 .48 .55 .44 .55 .57 .59 .45 .50 .40 f17 .55 .62 .48 .86 .67 .76 .79 .48 .62 .64 .80 .67 .86 .83 .72 f18 .87 .98 .89 .72 .92 .95 .68 .98 .76 .91 .88 .96 .93 .89 .97
 E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 E11 E12 E13 E14 E15 f1 .45 .49 .59 .46 .40 .57 .53 .59 .52 .48 .57 .60 .51 .59 .56 f2 .36 .44 .57 .35 .45 .31 .42 .44 .35 .33 .52 .30 .48 .35 .47 f3 .73 .71 .78 .73 .79 .79 .76 .78 .72 .79 .74 .73 .73 .79 .71 f4 .42 .61 .65 .56 .67 .79 .43 .41 .45 .54 .46 .50 .57 .47 .42 f5 .25 .25 .36 .26 .20 .29 .33 .37 .30 .39 .30 .25 .22 .33 .32 f6 .79 .59 .49 .73 .55 .82 .63 .64 .64 .42 .58 .45 .51 .69 .67 f7 .39 .35 .32 .20 .27 .21 .30 .34 .32 .26 .33 .39 .22 .25 .26 f8 .45 .49 .58 .55 .41 .56 .67 .65 .41 .59 .49 .50 .47 .46 .62 f9 .88 .65 .76 .81 .72 .84 .90 .66 .89 .90 .87 .67 .83 .78 .66 f10 .43 .43 .56 .46 .43 .59 .46 .48 .46 .54 .43 .55 .42 .53 .43 f11 .48 .49 .33 .23 .49 .38 .31 .20 .41 .36 .27 .29 .23 .32 .38 f12 1 1 0 0 0 0 1 1 1 0 1 1 0 0 0 f13 .61 .72 .86 .66 .76 .66 .64 .87 .72 .89 .83 .88 .66 .79 .70 f14 .40 .49 .64 .65 .67 .73 .69 .54 .50 .46 .73 .54 .71 .68 .72 f15 .36 .34 .40 .48 .39 .46 .38 .38 .48 .47 .42 .43 .33 .31 .45 f16 .47 .50 .40 .44 .46 .46 .48 .55 .44 .55 .57 .59 .45 .50 .40 f17 .55 .62 .48 .86 .67 .76 .79 .48 .62 .64 .80 .67 .86 .83 .72 f18 .87 .98 .89 .72 .92 .95 .68 .98 .76 .91 .88 .96 .93 .89 .97
Risk values of merger and acquisition for different enterprises
 Enterprise No. Risk value E4 0.4437 E5 0.4398 E13 0.4374 E15 0.4314 E10 0.4232 E3 0.4216 E14 0.4187 E9 0.4131 E1 0.4120 E2 0.4036 E7 0.3896 E12 0.3895 E8 0.3868 E6 0.3835 E11 0.3644
 Enterprise No. Risk value E4 0.4437 E5 0.4398 E13 0.4374 E15 0.4314 E10 0.4232 E3 0.4216 E14 0.4187 E9 0.4131 E1 0.4120 E2 0.4036 E7 0.3896 E12 0.3895 E8 0.3868 E6 0.3835 E11 0.3644
Average risk assessment error rates for different methods
 Method SVM 1 SVM 2 SVM 3 SVM 4 SVM 5 SVM 6 Our algorithm Error rate 14.7 9.8 9.2 17.4 12.1 18.6 5.5
 Method SVM 1 SVM 2 SVM 3 SVM 4 SVM 5 SVM 6 Our algorithm Error rate 14.7 9.8 9.2 17.4 12.1 18.6 5.5
Performance evaluation using MAE and MAPE
 Method Low Medium High Very high MAE MAPE MAE MAPE MAE MAPE MAE MAPE SVM 1 5.27 29.54 3.21 8.51 6.73 18.51 8.66 22.41 SVM 2 6.35 26.76 3.08 9.46 6.87 17.46 8.06 22.20 SVM 3 6.47 28.40 3.68 9.08 8.29 18.54 9.73 20.51 SVM 4 5.68 27.33 3.79 9.64 7.34 18.43 8.84 20.53 SVM 5 4.93 30.11 3.28 8.89 8.17 17.43 9.71 22.20 SVM 6 4.79 30.06 2.97 8.31 7.58 17.65 9.67 21.57 Our algorithm 3.12 23.54 1.97 6.89 4.59 15.65 6.85 17.85
 Method Low Medium High Very high MAE MAPE MAE MAPE MAE MAPE MAE MAPE SVM 1 5.27 29.54 3.21 8.51 6.73 18.51 8.66 22.41 SVM 2 6.35 26.76 3.08 9.46 6.87 17.46 8.06 22.20 SVM 3 6.47 28.40 3.68 9.08 8.29 18.54 9.73 20.51 SVM 4 5.68 27.33 3.79 9.64 7.34 18.43 8.84 20.53 SVM 5 4.93 30.11 3.28 8.89 8.17 17.43 9.71 22.20 SVM 6 4.79 30.06 2.97 8.31 7.58 17.65 9.67 21.57 Our algorithm 3.12 23.54 1.97 6.89 4.59 15.65 6.85 17.85
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