从专利数据中预测并购的基于机器学习的相似性测量方法

Giambattista Albora, Matteo Straccamore, Andrea Zaccaria
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引用次数: 0

摘要

并购(M&A)的定义和最终确定需要复杂的人工技能,因此很难自动找到最佳合作伙伴或预测哪些公司将达成交易。在这项工作中,我们提出了 MASS 算法,这是一种专门设计的公司间相似性度量方法,我们将其应用于专利活动数据,以预测并购交易。MASS 算法基于对基于树的机器学习算法的极度简化,自然地融入了交易的直观标准;因此,它是完全可解释和可说明的。通过将 MASS 应用于基于 Zephyr 和 Crunch 的数据集,我们发现它优于 "黑盒 "图卷积网络算法 LightGCN。相反,当相似公司的专利活动互不关联时,LightGCN 则是最有效的算法。这项研究为模拟和预测并购交易提供了一个简单而强大的工具,为管理者和从业者做出明智决策提供了有价值的见解。
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Machine learning-based similarity measure to forecast M&A from patent data
Defining and finalizing Mergers and Acquisitions (M&A) requires complex human skills, which makes it very hard to automatically find the best partner or predict which firms will make a deal. In this work, we propose the MASS algorithm, a specifically designed measure of similarity between companies and we apply it to patenting activity data to forecast M&A deals. MASS is based on an extreme simplification of tree-based machine learning algorithms and naturally incorporates intuitive criteria for deals; as such, it is fully interpretable and explainable. By applying MASS to the Zephyr and Crunchbase datasets, we show that it outperforms LightGCN, a "black box" graph convolutional network algorithm. When similar companies have disjoint patenting activities, on the contrary, LightGCN turns out to be the most effective algorithm. This study provides a simple and powerful tool to model and predict M&A deals, offering valuable insights to managers and practitioners for informed decision-making.
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