RT Journal Article
SR Electronic
T1 An Artificial Intelligence–Based Industry Peer Grouping System
JF The Journal of Financial Data Science
FD Institutional Investor Journals
SP jfds.2022.1.090
DO 10.3905/jfds.2022.1.090
A1 George Bonne
A1 Andrew W. Lo
A1 Abilash Prabhakaran
A1 Kien Wei Siah
A1 Manish Singh
A1 Xinxin Wang
A1 Peter Zangari
A1 Howard Zhang
YR 2022
UL https://pm-research.com/content/early/2022/04/02/jfds.2022.1.090.abstract
AB 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.