TY  - UNPD
A1  - Bagnara, Matteo
A1  - Goodarzi, Milad
T1  - Clustering-based sector investing
N2  - 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.
T3  - SAFE working paper - 397 
KW  - Empirical Asset Pricing
KW  - Risk Premium
KW  - Machine Learning
KW  - Industry Classification
KW  - Clustering
Y1  - 2023
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/70395
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-703952
UR  - https://ssrn.com/abstract=4528879
N1  - JEL-Klassifikation:
C Mathematical and Quantitative Methods / C5 Econometric Modeling / C55 Large Data Sets: Modeling and Analysis

C Mathematical and Quantitative Methods / C5 Econometric Modeling / C58 Financial Econometrics
N1  - We gratefully acknowledge research support from the Leibniz Institute for Financial Research SAFE.
PB  - SAFE
CY  - Frankfurt am Main
ER  -