arXiv 4 Nov 2025 · Econometrics
arXiv:2511.03076 · PDF · DOI · OpenAlex · Extracted main text
We study factor models that combine latent factors with firm characteristics and propose a new framework for modeling, estimating, and inferring pricing errors. Following Zhang (2024), our approach decomposes mispricing into two distinct components: inside alpha, explained by firm characteristics but orthogonal to factor exposures, and outside alpha, orthogonal to both factors and characteristics. Our model generalizes those developed recently such as Kelly et al. (2019) and Zhang (2024), resolving issues of orthogonality, basis dependence, and unit sensitivity. Methodologically, we develop estimators grounded in low-rank methods with explicit debiasing, providing closed-form solutions and a rigorous inferential theory that accommodates a growing number of characteristics and relaxes standard assumptions on sample dimensions. Empirically, using U.S. stock returns from 2000-2019, we document strong evidence of both inside and outside alphas, with the former showing industry-level co-movements and the latter reflecting idiosyncratic shocks beyond firm fundamentals. Our framework thus unifies statistical and characteristic-based approaches to factor modeling, offering both theoretical advances and new insights into the structure of pricing errors.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Chen, Qihui and Roussanov, Nikolai and Wang, Xiaoliang (2023) Semiparametric conditional factor models: Estimation and inference | 1.000 | 8 | 3 | 100% |
| 2 | Belloni, Alexandre and Chernozhukov, Victor and Chetverikov, Denis a… (2018) High-dimensional econometrics and regularized GMM | 0.928 | 5 | 3 | 80% |
| 3 | Kelly, Bryan T and Pruitt, Seth and Su, Yinan (2019) Characteristics are covariances: A unified model of risk and return | 0.916 | 13 | 7 | 77% |
| 4 | Fan, Jianqing and Liao, Yuan and Wang, Weichen (2016) Projected principal component analysis in factor models | 0.874 | 5 | 2 | 100% |
| 5 | Bai, Jushan (2003) Inferential theory for factor models of large dimensions | 0.843 | 5 | 3 | 60% |
| 6 | Zhang, Chu (2024) Testing Pricing Errors of Models with Latent Factors and Firm Characteristics as Covariances | 0.830 | 21 | 9 | 57% |
| 7 | Fama, Eugene F and French, Kenneth R (1993) Common risk factors in the returns on stocks and bonds | 0.644 | 2 | 2 | 100% |
| 8 | Kim, Soohun and Korajczyk, Robert A and Neuhierl, Andreas (2021) Arbitrage portfolios | 0.644 | 2 | 2 | 100% |
| 9 | Chernozhukov, Victor and Hansen, Christian and Liao, Yuan and Zhu, Y… (2023) Inference for low-rank models | 0.511 | 2 | 1 | 100% |
| 10 | Benjamini, Yoav and Yekutieli, Daniel (2001) The control of the false discovery rate in multiple testing under dependency | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 28 scored citations.