欧洲的收购:目标特征与收购可能性

IF 3.4 3区 经济学 Q1 ECONOMICS
Hicham Meghouar
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引用次数: 0

摘要

与美国和英国市场相比,欧洲市场对收购目标的研究相对较少,本文分析了欧洲市场收购目标的特征,从而建立了一个收购预测模型。我们的样本包括 320 家欧洲公司,其中 140 家为目标公司,180 家为非目标公司,时间跨度为 1994-2007 年,涵盖两次并购浪潮。在这项研究中,我们测试了许多相关变量的判别能力,包括可能在潜在决定(价值创造)方面具有判别能力的新变量。我们的研究结果表明,欧洲目标公司的特点是增长与资源不平衡、FCF 较少、有增长机会、竞购前股票交易量较高、经济绩效较低、破坏价值。此外,我们还利用目标公司被收购前 1 年、2 年和 3 年的财务数据以及 3 年的平均值建立了多个预测模型。原始样本的正确分类率为 70%(样本内)。至于预测能力,对照样本的正确分类率为 79.4%(样本外)。我们还注意到,使用投标前 1 年或 2 年数据的预测模型似乎显示出更显著的预测能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Takeover in Europe: Target characteristics and acquisition likelihood

This article analyzes characteristics of takeover targets in the European market—relatively less studied compared with US and UK markets—to develop a takeover prediction model. Our sample includes 320 European companies with 140 targets and 180 non-targets over the period 1994–2007, covering two M&A waves. In this study, we test the discriminating power of many relevant variables including new one that could have a discriminating power in potentially determining (value creation). Our results show that European targets are characterized by a growth-resource imbalance, are less rich in FCF, have growth opportunities, have a higher level of transaction volume of shares prior to the bid, achieve lower economic performance, and destroy value. Furthermore, we develop several predictive models using targets' financial data from 1 year, 2 years, and 3 years before takeover, along with the 3-year average. The correct classification power in the original sample is 70% (in-sample). As for predictive ability, the correct classification power in a control sample is 79.4% (out-of-sample). We also noted that predictive models using data from 1 or 2 years before the bid appear to display more significant predictive ability.

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来源期刊
CiteScore
5.40
自引率
5.90%
发文量
91
期刊介绍: The Journal of Forecasting is an international journal that publishes refereed papers on forecasting. It is multidisciplinary, welcoming papers dealing with any aspect of forecasting: theoretical, practical, computational and methodological. A broad interpretation of the topic is taken with approaches from various subject areas, such as statistics, economics, psychology, systems engineering and social sciences, all encouraged. Furthermore, the Journal welcomes a wide diversity of applications in such fields as business, government, technology and the environment. Of particular interest are papers dealing with modelling issues and the relationship of forecasting systems to decision-making processes.
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