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Risk assessment for enterprise merger and acquisition via multiple classifier fusion

  • * Corresponding author: Zhichao Liu

    * Corresponding author: Zhichao Liu
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  • 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.

    Mathematics Subject Classification: Primary: 58F15, 58F17; Secondary: 53C35.


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  • Figure 1.  Index system for the risk assessment for enterprise merger and acquisition

    Figure 2.  Framework of the decision system for enterprise merger and acquisition risk assessment

    Figure 3.  Settings of the multiple classifier fusion in this experiment

    Figure 4.  Value of the M&A risk for different methods

    Figure 5.  ROC curves for different methods

    Table 1.  Description of the testing dataset

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    Table 2.  Risk values of merger and acquisition for different enterprises

    Enterprise No.Risk value
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    Table 3.  Average risk assessment error rates for different methods

    MethodSVM 1SVM 2SVM 3SVM 4SVM 5SVM 6Our algorithm
    Error rate14.
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    Table 4.  Performance evaluation using MAE and MAPE

    MethodLowMediumHighVery high
    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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