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
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

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
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
 [1] Liming Yang, Yannan Chao. A new semi-supervised classifier based on maximum vector-angular margin. Journal of Industrial & Management Optimization, 2017, 13 (2) : 609-622. doi: 10.3934/jimo.2016035 [2] Wei Feng, Xin Lu, Richard John Donovan Jr.. Population dynamics in a model for territory acquisition. Conference Publications, 2001, 2001 (Special) : 156-165. doi: 10.3934/proc.2001.2001.156 [3] A. Domoshnitsky. About maximum principles for one of the components of solution vector and stability for systems of linear delay differential equations. Conference Publications, 2011, 2011 (Special) : 373-380. doi: 10.3934/proc.2011.2011.373 [4] Henk van Tilborg, Josef Pieprzyk, Ron Steinfeld, Huaxiong Wang. New constructions of anonymous membership broadcasting schemes. Advances in Mathematics of Communications, 2007, 1 (1) : 29-44. doi: 10.3934/amc.2007.1.29 [5] Tadeusz Antczak, Najeeb Abdulaleem. Optimality conditions for $E$-differentiable vector optimization problems with the multiple interval-valued objective function. Journal of Industrial & Management Optimization, 2020, 16 (6) : 2971-2989. doi: 10.3934/jimo.2019089 [6] Andreas Widder. On the usefulness of set-membership estimation in the epidemiology of infectious diseases. Mathematical Biosciences & Engineering, 2018, 15 (1) : 141-152. doi: 10.3934/mbe.2018006 [7] Wei Li, Yun Teng. Enterprise inefficient investment behavior analysis based on regression analysis. Discrete & Continuous Dynamical Systems - S, 2019, 12 (4&5) : 1015-1025. doi: 10.3934/dcdss.2019069 [8] Hanbing Liu, Yongdong Huang, Chongjun Li. Weaving K-fusion frames in hilbert spaces. Mathematical Foundations of Computing, 2020, 3 (2) : 101-116. doi: 10.3934/mfc.2020008 [9] Z. G. Feng, Kok Lay Teo, N. U. Ahmed, Yulin Zhao, W. Y. Yan. Optimal fusion of sensor data for Kalman filtering. Discrete & Continuous Dynamical Systems, 2006, 14 (3) : 483-503. doi: 10.3934/dcds.2006.14.483 [10] Xiao Lan Zhu, Zhi Guo Feng, Jian Wen Peng. Robust design of sensor fusion problem in discrete time. Journal of Industrial & Management Optimization, 2017, 13 (2) : 825-834. doi: 10.3934/jimo.2016048 [11] Kevin Kuo, Phong Luu, Duy Nguyen, Eric Perkerson, Katherine Thompson, Qing Zhang. Pairs trading: An optimal selling rule. Mathematical Control & Related Fields, 2015, 5 (3) : 489-499. doi: 10.3934/mcrf.2015.5.489 [12] Mehmet Onur Olgun, Osman Palanci, Sirma Zeynep Alparslan Gök. On the grey Baker-Thompson rule. Journal of Dynamics & Games, 2020, 7 (4) : 303-315. doi: 10.3934/jdg.2020024 [13] Piotr Jaworski, Marcin Pitera. The 20-60-20 rule. Discrete & Continuous Dynamical Systems - B, 2016, 21 (4) : 1149-1166. doi: 10.3934/dcdsb.2016.21.1149 [14] Raluca Felea, Venkateswaran P. Krishnan, Clifford J. Nolan, Eric Todd Quinto. Common midpoint versus common offset acquisition geometry in seismic imaging. Inverse Problems & Imaging, 2016, 10 (1) : 87-102. doi: 10.3934/ipi.2016.10.87 [15] Jianguo Dai, Wenxue Huang, Yuanyi Pan. A category-based probabilistic approach to feature selection. Big Data & Information Analytics, 2018  doi: 10.3934/bdia.2017020 [16] Caifang Wang, Tie Zhou. The order of convergence for Landweber Scheme with $\alpha,\beta$-rule. Inverse Problems & Imaging, 2012, 6 (1) : 133-146. doi: 10.3934/ipi.2012.6.133 [17] Vladimir Georgiev, Koichi Taniguchi. On fractional Leibniz rule for Dirichlet Laplacian in exterior domain. Discrete & Continuous Dynamical Systems, 2019, 39 (2) : 1101-1115. doi: 10.3934/dcds.2019046 [18] Regina S. Burachik, C. Yalçın Kaya. An update rule and a convergence result for a penalty function method. Journal of Industrial & Management Optimization, 2007, 3 (2) : 381-398. doi: 10.3934/jimo.2007.3.381 [19] Andreas Asheim, Alfredo Deaño, Daan Huybrechs, Haiyong Wang. A Gaussian quadrature rule for oscillatory integrals on a bounded interval. Discrete & Continuous Dynamical Systems, 2014, 34 (3) : 883-901. doi: 10.3934/dcds.2014.34.883 [20] Hoi Tin Kong, Qing Zhang. An optimal trading rule of a mean-reverting asset. Discrete & Continuous Dynamical Systems - B, 2010, 14 (4) : 1403-1417. doi: 10.3934/dcdsb.2010.14.1403

2020 Impact Factor: 2.425

Metrics

• PDF downloads (954)
• HTML views (957)
• Cited by (0)

Other articlesby authors

• on AIMS
• on Google Scholar

[Back to Top]