Clustering-based sector investing

Matteo Bagnara and Milad Goodarzi

No 397, SAFE Working Paper Series from Leibniz Institute for Financial Research SAFE

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.

Keywords: Empirical Asset Pricing; Risk Premium; Machine Learning; Industry Classification; Clustering (search for similar items in EconPapers)
JEL-codes: C55 C58 G12 (search for similar items in EconPapers)
Date: 2023
New Economics Papers: this item is included in nep-cmp
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:safewp:397

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