Patterning Mergers and Acquisitions by Network Data Envelopment Analysis in the Iranian Insurance Companies
محورهای موضوعی : Financial MathematicsElham Sadeghi 1 , Mohsen Rostamy-Malkhalifeh 2 , Mohammad Reza Miri Lavasani 3 , mohammad hamed khan mohammadi 4
1 - Department of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 - Department of Mathematics, Faculty of Science, Science and Research Branch, Islamic Azad University, Tehran, Iran
3 - Department of HSE Management, Science and Research Branch, Islamic Azad University, Tehran, Iran
4 - Department of Accounting, Damavand Branch, Islamic Azad University, Damavand, Iran
کلید واژه: Harmony or Scope Efficiency (HA), SCale or Size Efficiency (SA), Technical or Learning Efficiency (LE), Network Data Envelopment Analysis (NDEA), Modified Slack-Based Measure (MSBM),
چکیده مقاله :
One of the most important factors of the development of an economy is the mergers or acquisitions (M&A) at the level of its active companies such as insurance companies. The main purpose of this study is to examine the efficiency of merger and acquisition before doing this process in the insurance industry using network data envelopment analysis and can select the companies that potentially facilitate achieving the purposes of the merger and acquisition process and improve of this action. For this purpose, in this study, first the efficiency of 20 insurance companies was measured through the Modified Slack-Based Measure (MSBM) in the two-stage data envelopment analysis approach during three years 2017, 2018 and 2019. Then, considering the calculated efficiency, Asia Insurance Company, Parsian, Dey, Pasargad, Kowsar and Taavon, which have had efficient performance in the last three years, were excluded from the calculations and other companies were selected for M&A . After ensuring that no monopoly is considered via Herfindahl- Hirschman Index, M&A is performed and then the overall efficiency was measured and it was divided into three parts: technical, harmony and scale. The results showed that the two consolidations Dana-Mihan and Dana-Sina had the best efficiency and the three consolidations Alborz-Mellat, Sina-Arman and Sina-Sarmad had the lowest efficiency and potential for the highest improvement. Calculations also showed that if the scale effect in the composition is greater than 1, then the coordination effect is smaller than 1 and the inverse relationship are not necessarily satisfied.
One of the most important factors of the development of an economy is the mergers or acquisitions (M&A) at the level of its active companies such as insurance companies. The main purpose of this study is to examine the efficiency of merger and acquisition before doing this process in the insurance industry using network data envelopment analysis and can select the companies that potentially facilitate achieving the purposes of the merger and acquisition process and improve of this action. For this purpose, in this study, first the efficiency of 20 insurance companies was measured through the Modified Slack-Based Measure (MSBM) in the two-stage data envelopment analysis approach during three years 2017, 2018 and 2019. Then, considering the calculated efficiency, Asia Insurance Company, Parsian, Dey, Pasargad, Kowsar and Taavon, which have had efficient performance in the last three years, were excluded from the calculations and other companies were selected for M&A . After ensuring that no monopoly is considered via Herfindahl- Hirschman Index, M&A is performed and then the overall efficiency was measured and it was divided into three parts: technical, harmony and scale. The results showed that the two consolidations Dana-Mihan and Dana-Sina had the best efficiency and the three consolidations Alborz-Mellat, Sina-Arman and Sina-Sarmad had the lowest efficiency and potential for the highest improvement. Calculations also showed that if the scale effect in the composition is greater than 1, then the coordination effect is smaller than 1 and the inverse relationship are not necessarily satisfied.
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