2025/08/04 by Yuxiao Jiao, Guofu Zhou, Jiao, Yuxiao +6 · 1 voice
Business, Management and Accounting · Computer Science · Economics, Econometrics and Finance · Social Sciences · #Computational and Text Analysis Methods #Consumer Market Behavior and Pricing #Economic indicator #Embedding #Explainable Artificial Intelligence (XAI) #FOS: Economics and business #Intuition #Pricing of Securities (q-fin.PR) #Statistical analysis #Statistical model #Value (mathematics) #q-fin.PR
paper · pdf · doi:10.48550/arxiv.2508.02253
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2025/08/04 · arxiv published 2025/08/04 · arxiv updated 2025/08/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.