论文标题
使用经济复杂性对合并和收购的预测和可视化
Prediction and visualization of Mergers and Acquisitions using Economic Complexity
论文作者
论文摘要
合并和收购代表着重要的业务交易形式，这既是由于交易中涉及的数量和公司创新活动的作用。然而，经济复杂性方法尚未应用于该领域的研究。通过考虑大约一千家公司的专利活动，我们通过假设公司更频繁地处理技术相关的产品来开发一种预测未来收购的方法。我们既解决预测一对公司的未来交易的问题，又是找到目标公司给予收购方的问题。我们比较了不同的预测方法，包括机器学习和基于网络的算法，表明简单的角度距离与行业部门信息的添加优于其他方法。最后，我们介绍了连续的公司空间，这是公司的二维表示，以形象化其技术接近性和可能的交易。公司和政策制定者可以使用这种方法来识别最有可能追求交易的公司或探索可能的创新策略。
Mergers and Acquisitions represent important forms of business deals, both because of the volumes involved in the transactions and because of the role of the innovation activity of companies. Nevertheless, Economic Complexity methods have not been applied to the study of this field. By considering the patent activity of about one thousand companies, we develop a method to predict future acquisitions by assuming that companies deal more frequently with technologically related ones. We address both the problem of predicting a pair of companies for a future deal and that of finding a target company given an acquirer. We compare different forecasting methodologies, including machine learning and network-based algorithms, showing that a simple angular distance with the addition of the industry sector information outperforms the other approaches. Finally, we present the Continuous Company Space, a two-dimensional representation of firms to visualize their technological proximity and possible deals. Companies and policymakers can use this approach to identify companies most likely to pursue deals or to explore possible innovation strategies.
