Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/273735 
Year of Publication: 
2023
Series/Report no.: 
SAFE Working Paper No. 397
Publisher: 
Leibniz Institute for Financial Research SAFE, Frankfurt a. M.
Abstract: 
Industry classification groups firms into finer partitions to help investments and empirical analysis. To overcome the well-documented limitations of existing industry definitions, like their stale nature and coarse categories for firms with multiple operations, we employ a clustering approach on 69 firm characteristics and allocate companies to novel economic sectors maximizing the within-group explained variation. Such sectors are dynamic yet stable, and represent a superior investment set compared to standard classification schemes for portfolio optimization and for trading strategies based on within-industry mean-reversion, which give rise to a latent risk factor significantly priced in the cross-section. We provide a new metric to quantify feature importance for clustering methods, finding that size drives differences across classical industries while book-to-market and financial liquidity variables matter for clustering-based sectors.
Subjects: 
Empirical Asset Pricing
Risk Premium
Machine Learning
Industry Classification
Clustering
JEL: 
G12
C55
C58
Document Type: 
Working Paper

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