An Artificial Intelligence-Based Industry Peer Grouping System
Author(s)Bonne, George; Lo, Andrew W; Prabhakaran, Abilash; Siah, Kien Wei; Singh, Manish; Wang, Xinxin; Zangari, Peter; Zhang, Howard; ... Show more Show less
In this work, 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; 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, while different clusters of companies had diverse, varying risk exposures. The authors extensively evaluated the clusters and found that companies grouped together 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.
We benefited from the methodology of MSCI Peer Similarity Scores product and the data and computing support from the MSCI Data Science Platform. We thank Roman Kouzmenko and Manuel Rueda for insightful discussions. The views and opinions expressed in this article are those of the authors only, and do not necessarily represent the views and opinions of any institution or agency, any of their affiliates or employees, or any of the individuals acknowledged above.
Journal of Financial Data Science
Bonne, George, Andrew W. Lo, Abilash Prabhakaran, Kien Wei Siah, Manish Singh, Xinxin Wang, Peter Zangari, and Howard Zhang (2022), "An Artificial Intelligence-Based Industry Peer Grouping System," Journal of Financial Data Science 4 (2), 9-36.
machine learning, artificial intelligence, peer grouping, industry classification, returns co-movement