Interpretable Factors of Firm Characteristics

Abstract

We develop a new framework for constructing factors from firm characteristics that balances statistical efficiency and economic interpretability. Instead of using all characteristics equally, our method groups related characteristics and derives one factor per group. The grouping combines economic intuition with data-driven clustering. Applied to the IPCA model by Kelly et al. (2019), our approach yields economically meaningful factors that match or exceed standard IPCA in pricing performance. Using 94 characteristics from Gu et al. (2020), we show that our parsimonious, transparent factors outperform benchmarks in out-of-sample tests, demonstrating the value of embedding economic structure into statistical modeling.

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