我国上市公司高管背景特征对并购绩效的影响研究

发布时间：2018-02-11 21:30

本文关键词： 高管背景特征企业绩效并购绩效　出处：《天津工业大学》2017年硕士论文　论文类型：学位论文

【摘要】：近年来,为了适应全球经济转型和产业调整的需要,并购活动呈现出快速发展的趋势,有关并购绩效问题的研究成为学术界研究的热点。本文基于高管团队的视角,研究高管背景特征对企业并购绩效的影响,该研究结论对于上市公司合理配置高管团队,进行内部治理,提升并购绩效具有一定的现实指导意义。本文在对相关文献进行回顾的基础上,首先对高管背景特征和企业并购绩效的关系进行了理论分析和推理,其次选取了 2012年发生并购的108家上市企业2010～2015年的数据为样本,.运用包络超效率模型对样本企业并购前后的绩效进行测度,并进一步对高管背景特征和并购企业的绩效及绩效变化进行描述性统计分析,再次运用灰色关联模型对并购企业的高管背景特征与并购绩效的关联度进行分析,最后重点建立二元logistic回归模型,分析了高管背景特征对并购绩效的影响,并对我国上市公司如何配置高管团队以提升并购绩效提出建议。通过分析得出如下结论:第一,通过对并购绩效的描述性统计分析发现,并购后企业的绩效呈现出先上升后下降,随后又上升的波动趋势,综合来看,并购后企业的绩效有一定的提升。第二,通过对高管背景特征与并购绩效进行灰色关联分析发现,高管背景特征与并购绩效有很强的关联性。第三,通过二元logistic回归模型对二者进行进一步分析发现,高管团队的各个特征对并购绩效都有不同程度的影响,其中,高管团队中女性比例过高和平均年龄过大都不利于并购绩效的提升;高管团队的平均任期越长,企业的并购绩效越好;高管团队的平均教育水平和团队规模有利于并购绩效的提升;年龄异质性和教育水平异质性对并购绩效也是正向影响,但是影响不大。因此,企业在并购后配备高管团队时,要着重关注对并购绩效影响比较大的特征,将有利的特征高效的组合在一起,高管团队中女性比例不宜过高,并且尽量保持高管团队的稳定性,组建一支高学历年轻化的高管团队,以最大限度的提升企业的并购绩效。
[Abstract]:In recent years, in order to adapt to the needs of global economic transformation and industrial adjustment, M & A activities show a trend of rapid development, and the research on M & A performance has become a hot topic in academic circles. This paper studies the influence of executive background characteristics on corporate M & A performance. The conclusion of this study is that the listed companies should allocate the executive team reasonably and carry out internal governance. On the basis of reviewing the relevant literature, this paper first makes theoretical analysis and reasoning on the relationship between executive background characteristics and corporate M & A performance. Secondly, the data of 108 listed enterprises from 2010 to 2015 in 2012 are selected as samples. The performance of the sample enterprises before and after M & A is measured by envelope super-efficiency model. Furthermore, it makes a descriptive statistical analysis of the background characteristics of senior executives and the performance and performance changes of M & A enterprises, and then analyzes the correlation degree between executive background characteristics and M & A performance of M & A enterprises by using grey relational model. In the end, the paper establishes a binary logistic regression model, analyzes the influence of executive background on M & A performance, and puts forward some suggestions on how to configure senior management team to improve M & A performance in China's listed companies. The conclusions are as follows: first, Through the descriptive statistical analysis of M & A performance, it is found that the corporate performance after M & A shows a fluctuation trend of first rising, then decreasing, and then rising. In a word, the corporate performance after M & A has a certain improvement. Second, Through the grey correlation analysis of executive background characteristics and M & A performance, it is found that the executive background characteristics have strong correlation with M & A performance. Third, through the dual logistic regression model to further analyze the two findings. Each characteristic of senior management team has different influence on M & A performance. Among them, the overrepresentation of women and the excessive average age of senior management team are not conducive to the improvement of M & A performance; the longer the average tenure of senior management team, the longer the performance of M & A performance. The better the M & A performance is, the better the average education level and the team size of the senior management team are. The age heterogeneity and the educational level heterogeneity also have a positive effect on the M & A performance, but the impact is not significant. After M & A, the enterprise should pay more attention to the characteristics that have a great impact on the performance of M & A, combine the favorable characteristics efficiently, the proportion of female in the senior management team should not be too high, and keep the stability of the senior management team as far as possible. Set up a highly educated and young executive team to maximize M & A performance.
【学位授予单位】：天津工业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：F272.91;F271

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