Clustering-based sector investing

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: SAFE Working Paper ; No. 397

Classification and Topics

• Classification: Wirtschaft ; Asset Pricing; Trading Volume; Bond Interest Rates ; Large Data Sets: Modeling and Analysis ; Financial Econometrics
• Subject: Empirical Asset Pricing ; Risk Premium ; Machine Learning ; Industry Classification ; Clustering

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Bagnara, Matteo ; Goodarzi, Milad
• Event: Veröffentlichung
• (who): Leibniz Institute for Financial Research SAFE
• (where): Frankfurt a. M.
• (when): 2023

Further information

• Handle: http://hdl.handle.net/10419/273735
• Last update: 10.03.2025, 11:43 AM CET