An Artificial Intelligence-Based Industry Peer Grouping System

George Bonne, A. Lo, Abilash Prabhakaran, K. W. Siah, Manish Singh, Xinxin Wang, Peter J Zangari, Howard Zhang
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引用次数: 2

Abstract

In this article, the authors develop a data-driven peer grouping system using artificial intelligence (AI) tools to capture market perception and, in turn, group companies into clusters at various levels of granularity. In addition, they develop a continuous measure of similarity between companies; they use this measure to group companies into clusters and construct hedged portfolios. In the peer groupings, companies grouped in the same clusters had strong homogeneous risk and return profiles, whereas different clusters of companies had diverse, varying risk exposures. The authors extensively evaluated the clusters and found that companies grouped by their method had higher out-of-sample return correlation but lower stability and interpretability than companies grouped by a standard industry classification system. The authors also develop an interactive visualization system for identifying AI-based clusters and similar companies.
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基于人工智能的行业对等分组系统
在本文中,作者开发了一个数据驱动的同行分组系统,使用人工智能(AI)工具来捕捉市场感知,进而将公司按不同的粒度级别分组。此外,他们还开发了一套公司之间相似性的连续衡量标准;他们利用这一指标将公司分组,并构建对冲投资组合。在同行分组中,同一集群中的公司具有很强的同质风险和回报概况,而不同集群的公司具有不同的、不同的风险暴露。作者广泛地评估了这些集群,发现用他们的方法分组的公司比用标准行业分类系统分组的公司具有更高的样本外回报相关性,但稳定性和可解释性较低。作者还开发了一个交互式可视化系统,用于识别基于人工智能的集群和类似的公司。
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