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The Journal of Financial Data Science

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An Artificial Intelligence–Based Industry Peer Grouping System

George Bonne, Andrew W. Lo, Abilash Prabhakaran, Kien Wei Siah, Manish Singh, Xinxin Wang, Peter Zangari and Howard Zhang
The Journal of Financial Data Science Spring 2022, jfds.2022.1.090; DOI: https://doi.org/10.3905/jfds.2022.1.090
George Bonne
is executive director at MSCI, Inc., in Berkeley, CA
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Andrew W. Lo
is the Charles E. and Susan T. Harris Professor at the MIT Sloan School of Management, director of the MIT Laboratory for Financial Engineering, a principal investigator at the MIT Computer Science and Artificial Intelligence Laboratory, an affiliated faculty member of the MIT Department of Electrical Engineering and Computer Science in Cambridge, MA, and an external faculty member at the Santa Fe Institute in Santa Fe, NM
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Abilash Prabhakaran
is a U.G. researcher at MIT Laboratory for Financial Engineering in Cambridge, MA
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Kien Wei Siah
is a research affiliate at MIT Laboratory for Financial Engineering in Cambridge, MA
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Manish Singh
is a Ph.D. student at MIT Electrical Engineering and Computer Science in Cambridge, MA
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Xinxin Wang
is executive director at MSCI, Inc., in Boston, MA
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Peter Zangari
is global head of research and product development at MSCI, Inc., in New York, NY
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Howard Zhang
is vice president at MSCI, Inc., in New York, NY
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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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The Journal of Financial Data Science: 4 (2)
The Journal of Financial Data Science
Vol. 4, Issue 2
Spring 2022
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An Artificial Intelligence–Based Industry Peer Grouping System
George Bonne, Andrew W. Lo, Abilash Prabhakaran, Kien Wei Siah, Manish Singh, Xinxin Wang, Peter Zangari, Howard Zhang
The Journal of Financial Data Science Apr 2022, jfds.2022.1.090; DOI: 10.3905/jfds.2022.1.090

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An Artificial Intelligence–Based Industry Peer Grouping System
George Bonne, Andrew W. Lo, Abilash Prabhakaran, Kien Wei Siah, Manish Singh, Xinxin Wang, Peter Zangari, Howard Zhang
The Journal of Financial Data Science Apr 2022, jfds.2022.1.090; DOI: 10.3905/jfds.2022.1.090
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  • Article
    • Abstract
    • DATA AND FEATURE EXTRACTION
    • DISTANCE METRICS
    • CLUSTERING
    • EVALUATING PERFORMANCE
    • CONCLUSION
    • ACKNOWLEDGMENTS
    • APPENDIX A
    • APPENDIX B
    • APPENDIX C
    • APPENDIX D
    • ENDNOTES
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