美国市场内累积并购交易价值的宏观经济决定因素

I.  V. Tregub , A.  F.  Butyrskiy 
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引用次数: 1

摘要

企业智慧提出并购是在市场上广泛(非有机)增长的重要战略,从而获得财务优势和战略优势和优势。因此,这一工具无可否认地占据了企业管理工具的最高地位。考虑到上述所有因素,本研究的目标是研究宏观经济指标对1985-2021年美国境内年度并购总量的影响范围。本研究的对象被定义为美国并购市场。采用多元线性回归方程的普通最小二乘法,确定对数GDP增长与贴现率对因变量的解释有显著的正作用,而10年期美国国债收益率对因变量的解释呈负相关。此外,还进行了几项统计检验,以确保所获得结果的真实性和利用该模型估计正向值的潜力。本研究的实际意义在于揭示了用于预测并购规模的计量经济学模型,从而在考虑时机和市场情绪的情况下做出有效的企业管理决策。
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Macroeconomic Determinants of Accumulated M&A Transaction Values within the U.S. Market
The corporate wisdom propounds mergers and acquisitions (M&A) as a notable strategy for extensive (non-organic) growth in the market and, as a result, gaining financial advantage and strategic superiority and ascendancy along the way. Hence, this instrument undeniably occupies the highest ranks of instruments for corporate management. Considering all aforementioned, the work object is to research to which extend the macroeconomic indicators have an effect on the annually aggregated volume of mergers and acquisitions, entered within the United States of America in the historical period of 1985–2021. The subject of the study is defined as U.S. mergers and acquisitions market. By employing the ordinary least square method of a multiple linear regression equation, it was determined that logarithmic GDP growth together with the discount rate had a significant positive effect on the explanation of the dependent variable, while the 10-year US Treasury bond yield had a negative relationship. Further, several statistical tests were conducted to ensure the authenticity of results obtained and potential for utilization of the model for the purpose of estimation forward values. The practical significance of the research is recognized as uncovering econometric model for the purpose of forecasting mergers and acquisitions volumes, resulting in effective corporate management decisions with regard to timing and market sentiment.
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来源期刊
Periodica Polytechnica, Social and Management Sciences
Periodica Polytechnica, Social and Management Sciences Social Sciences-Social Sciences (all)
CiteScore
1.50
自引率
0.00%
发文量
26
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