American Institute of Mathematical Sciences

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

Risk assessment for enterprise merger and acquisition via multiple classifier fusion

Yinying Duan 1, , Yong Ye 2, and  Zhichao Liu 2,,

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.

Keywords: Enterprise merger and acquisition, multiple classifier fusion, SVM, feature vector, maximum membership rule.
Mathematics Subject Classification: Primary: 58F15, 58F17; Secondary: 53C35.
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
References:
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J. Qu, Z. Zhang and T. Gong, A novel intelligent method for mechanical fault diagnosis based on dual-tree complex wavelet packet transform and multiple classifier fusion, Neurocomputing, 171 (2016), 837-853.   Google Scholar

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R. Sarbast, S. Daniel and K. Mohamed, Fusion of multiple classifiers for motor unit potential sorting, Biomedical Signal Processing And Control, 3 (2008), 229-243.   Google Scholar

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T. Sumi and M. Tsuruoka, Ramp new enterprise information systems in a merger & acquisition environment: A case study, Journal of Engineering and Technology Management, 19 (2002), 93-104.   Google Scholar

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H. Wang, G. Qian and X. Feng, Intuitionistic fuzzy reasoning for multiple two-class classifiers fusion, International Journal of Pattern Recognition and Artificial Intelligence, 26 (2012), 1250016, 21pp. doi: 10.1142/S0218001412500164.  Google Scholar

[17]

W. Wen, W. K. Wang and T. H. Wang, A hybrid knowledge-based decision support system for enterprise mergers and acquisitions, Expert Systems with Applications, 28 (2005), 569-582.   Google Scholar

[18]

J. Yu and B. Xu, The game analyses to price the target enterprise of merger and acquisition based on the perspective of real options under stochastic surroundings, Economic Modelling, 28 (2011), 1587-1594.   Google Scholar

[19]

M. Zeinab, R. Saeed, G. Mohammad Mahdi, A. Vahid, T. Hamid and R. Mohsen, Classification of voice disorder in children with cochlear implantation and hearing aid using multiple classifier fusion, Biomedical Engineering Online, 10 (2011), Article No. 3. Google Scholar

[20]

Y. Zhang, G. Yu and D. Yang, Predicting non-performing loan of business bank by multiple classifier fusion algorithms, Journal of Interdisciplinary Mathematics, 19 (2016), 657-667.   Google Scholar

show all references

References:
[1]

B. Christian, Knowledge-based networks in the finance sector, The Example of The Mergers & Acquisitions Enterprise, Geographische Zeitschrift, 93 (2005), 197-199.   Google Scholar

[2]

F. Huenupan, N. B. Yoma, C. Molina and C. Garreton, Confidence based multiple classifier fusion in speaker verification, Pattern Recognition Letters, 29 (2008), 957-966.   Google Scholar

[3]

M. R. Islam, Feature and Score Fusion Based Multiple Classifier Selection for IrisRecognition, Computational Intelligence And Neuroscience, 2014, Article No. 380585. Google Scholar

[4]

G. Kling, A. Ghobadian, A. Hitt Michael, Utz Weitzel and Nicholas O'Regan, The Effects of Cross-border and Cross-industry Mergers and Acquisitions on Home-region and Global Multinational Enterprises, British Journal of Management, 25 (2014), S116-S132.   Google Scholar

[5]

O. V. Kolomiytseva, Motives and reasons for enterprises mergers and acquisitions, Actual Problemsof Economics, 132 (2012), 142-149.   Google Scholar

[6]

D. Lederman, B. Zheng, X. Wang, X. H. Wang and D. Gur, Improving breast cancer risk stratification using resonance-frequency electrical impedance spectroscopy through fusion of multiple classifiers, Annals of Biomedical Engineering, 39 (2011), 931-945.   Google Scholar

[7]

G.-R. Li, C.-H. Li, X.-H. Niu and L.-P. Yang, Risk assessment of enterprise merger and acquisition based on event tree method and fuzzy set theory, Journal of Applied Sciences, 13 (2013), 4819-4825.   Google Scholar

[8]

K. Li, X.-Y. Li, L.-L. Luan, W.-Y. Hu, Y.-H. Wang, J.-M. Li, J.-H. Li, C.-L. Lao and L.-L. Zhao, Determination of wine varieties with NIR and fusion of multiple Classifiers, Guang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis, 36 (2016), 3547-3551.   Google Scholar

[9]

W. Li, H. Leung, C. Kwan and R. Linnell Bruce, E-nose vapor identification based on Dempster-Shafer fusion of multiple classifiers, IEEE Transactions on Instrumentation and Measurement, 57 (2008), 2273-2282.   Google Scholar

[10]

J. Liu, Y. Pan, X. Zhu and W. Zhu, Using phenological metrics and the multiple classifier fusion method to map land cover types, Journal of Applied Remote Sensing, 8 (2014), Article No. 083691. Google Scholar

[11]

X. Ma, H. Shen, J. Yang, L. Zhang and P. Li, Polarimetric-spatial classification of sar images based on the fusion of multiple classifiers, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7 (2014), 961-971.   Google Scholar

[12]

N. Gi Pyo, L. Thi Thu Trang, H. N. Hyun, R. Park Kang and S.-J. Park, Intelligent query by humming system based on score level fusion of multiple classifiers, Eurasip Journal on Advances in Signal Processing, 2011, Article No. 21. Google Scholar

[13]

J. Qu, Z. Zhang and T. Gong, A novel intelligent method for mechanical fault diagnosis based on dual-tree complex wavelet packet transform and multiple classifier fusion, Neurocomputing, 171 (2016), 837-853.   Google Scholar

[14]

R. Sarbast, S. Daniel and K. Mohamed, Fusion of multiple classifiers for motor unit potential sorting, Biomedical Signal Processing And Control, 3 (2008), 229-243.   Google Scholar

[15]

T. Sumi and M. Tsuruoka, Ramp new enterprise information systems in a merger & acquisition environment: A case study, Journal of Engineering and Technology Management, 19 (2002), 93-104.   Google Scholar

[16]

H. Wang, G. Qian and X. Feng, Intuitionistic fuzzy reasoning for multiple two-class classifiers fusion, International Journal of Pattern Recognition and Artificial Intelligence, 26 (2012), 1250016, 21pp. doi: 10.1142/S0218001412500164.  Google Scholar

[17]

W. Wen, W. K. Wang and T. H. Wang, A hybrid knowledge-based decision support system for enterprise mergers and acquisitions, Expert Systems with Applications, 28 (2005), 569-582.   Google Scholar

[18]

J. Yu and B. Xu, The game analyses to price the target enterprise of merger and acquisition based on the perspective of real options under stochastic surroundings, Economic Modelling, 28 (2011), 1587-1594.   Google Scholar

[19]

M. Zeinab, R. Saeed, G. Mohammad Mahdi, A. Vahid, T. Hamid and R. Mohsen, Classification of voice disorder in children with cochlear implantation and hearing aid using multiple classifier fusion, Biomedical Engineering Online, 10 (2011), Article No. 3. Google Scholar

[20]

Y. Zhang, G. Yu and D. Yang, Predicting non-performing loan of business bank by multiple classifier fusion algorithms, Journal of Interdisciplinary Mathematics, 19 (2016), 657-667.   Google Scholar

Figure 1.  Index system for the risk assessment for enterprise merger and acquisition
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Figure 2.  Framework of the decision system for enterprise merger and acquisition risk assessment
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Figure 3.  Settings of the multiple classifier fusion in this experiment
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Figure 4.  Value of the M&A risk for different methods
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Figure 5.  ROC curves for different methods
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Table 1.  Description of the testing dataset
E1E2E3E4E5E6E7E8E9E10E11E12E13E14E15
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
f12110000111011000
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
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E1E2E3E4E5E6E7E8E9E10E11E12E13E14E15
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
f12110000111011000
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
Table 2.  Risk values of merger and acquisition for different enterprises
Enterprise No.Risk value
E40.4437
E50.4398
E130.4374
E150.4314
E100.4232
E30.4216
E140.4187
E90.4131
E10.4120
E20.4036
E70.3896
E120.3895
E80.3868
E60.3835
E110.3644
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Enterprise No.Risk value
E40.4437
E50.4398
E130.4374
E150.4314
E100.4232
E30.4216
E140.4187
E90.4131
E10.4120
E20.4036
E70.3896
E120.3895
E80.3868
E60.3835
E110.3644
Table 3.  Average risk assessment error rates for different methods
MethodSVM 1SVM 2SVM 3SVM 4SVM 5SVM 6Our algorithm
Error rate14.79.89.217.412.118.65.5
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MethodSVM 1SVM 2SVM 3SVM 4SVM 5SVM 6Our algorithm
Error rate14.79.89.217.412.118.65.5
Table 4.  Performance evaluation using MAE and MAPE
MethodLowMediumHighVery high
MAEMAPEMAEMAPEMAEMAPEMAEMAPE
SVM 15.2729.543.218.516.7318.518.6622.41
SVM 26.3526.763.089.466.8717.468.0622.20
SVM 36.4728.403.689.088.2918.549.7320.51
SVM 45.6827.333.799.647.3418.438.8420.53
SVM 54.9330.113.288.898.1717.439.7122.20
SVM 64.7930.062.978.317.5817.659.6721.57
Our algorithm3.1223.541.976.894.5915.656.8517.85
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MethodLowMediumHighVery high
MAEMAPEMAEMAPEMAEMAPEMAEMAPE
SVM 15.2729.543.218.516.7318.518.6622.41
SVM 26.3526.763.089.466.8717.468.0622.20
SVM 36.4728.403.689.088.2918.549.7320.51
SVM 45.6827.333.799.647.3418.438.8420.53
SVM 54.9330.113.288.898.1717.439.7122.20
SVM 64.7930.062.978.317.5817.659.6721.57
Our algorithm3.1223.541.976.894.5915.656.8517.85
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