引入2000-2020年新的最终并购协议语料库

IF 1.2 2区 社会学 Q1 LAW
Peter Adelson, Matthew Jennejohn, Julian Nyarko, Eric Talley
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引用次数: 0

摘要

合同设计和架构是经济学、金融学和法学中的一个重要课题。然而,由于公共高质量数据的有限可用性,对其进行研究的尝试受到了极大的限制。在本文中,我们介绍了2000年至2020年间提交给美国证券交易委员会的7929份最终合并协议的新语料库,涉及的交易超过1亿美元。通过机器学习和人类评估的结合,我们将这些协议与其他元数据联系起来,例如交易规模、行业分类和向律师事务所提供咨询。此外,我们确定并提供这些协定中所载个别条款的案文。在最后一步,我们提供了一个例子,说明如何使用这些数据来生成M&; a合同设计和起草实践的新见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Introducing a New Corpus of Definitive M&A Agreements, 2000–2020

Contract design and architecture is an important topic within economics, finance, and law. However, attempts to study it are significantly constrained by the limited availability of public, high quality data. In this paper, we introduce a new corpus of 7929 Definitive Merger Agreements submitted to the SEC between 2000 and 2020 involving a transaction in excess of $100 million. Through a combination of machine learning and human evaluation, we associate these agreements with other metadata, such as deal size, industry classification, and advising law firms. In addition, we identify and make available the text of individual clauses contained in these agreements. In a final step, we provide an illustration of how these data can be used to generate novel insights into M&A contract design and drafting practices.

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来源期刊
CiteScore
2.30
自引率
11.80%
发文量
34
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