Matias D. Cattaneo, Richard K. Crump, Weining Wang
arXiv 23 Aug 2022 · Econometrics · 2 citations (OpenAlex)
arXiv:2208.10974 · PDF · DOI · OpenAlex · Extracted main text
Beta-sorted portfolios -- portfolios comprised of assets with similar covariation to selected risk factors -- are a popular tool in empirical finance to analyze models of (conditional) expected returns. Despite their widespread use, little is known of their statistical properties in contrast to comparable procedures such as two-pass regressions. We formally investigate the properties of beta-sorted portfolio returns by casting the procedure as a two-step nonparametric estimator with a nonparametric first step and a beta-adaptive portfolios construction. Our framework rationalize the well-known estimation algorithm with precise economic and statistical assumptions on the general data generating process and characterize its key features. We study beta-sorted portfolios for both a single cross-section as well as for aggregation over time (e.g., the grand mean), offering conditions that ensure consistency and asymptotic normality along with new uniform inference procedures allowing for uncertainty quantification and testing of various relevant hypotheses in financial applications. We also highlight some limitations of current empirical practices and discuss what inferences can and cannot be drawn from returns to beta-sorted portfolios for either a single cross-section or across the whole sample. Finally, we illustrate the functionality of our new procedures in an empirical application.
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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 | Gagliardini, P., Ossola, E., Scaillet, O (2016) Time-varying risk premium in large cross-sectional equity data sets | 1.000 | 9 | 3 | 100% |
| 2 | Adrian, T., Crump, R. K., Moench, E (2015) Regression-based estimation of dynamic asset pricing models self | 1.000 | 6 | 3 | 100% |
| 3 | Cattaneo, M. D., Crump, R. K., Farrell, M. H., Schaumburg, E (2020) a self | 0.928 | 4 | 3 | 100% |
| 4 | Hall, P., Heyde, C. C (2014) Martingale limit theory and its application | 0.928 | 4 | 3 | 100% |
| 5 | Gagliardini, P., Ossola, E., Scaillet, O (2020) Estimation of large dimensional conditional factor models in finance | 0.811 | 4 | 2 | 100% |
| 6 | Bali, T. G., Engle, R. F., Murray, S (2016) Empirical Asset Pricing: The Cross Section of Stock Returns | 0.737 | 3 | 2 | 100% |
| 7 | Welch, I., Goyal, A (2008) A comprehensive look at the empirical performance of equity premium prediction | 0.644 | 4 | 1 | 100% |
| 8 | Ang, A., Kristensen, D (2012) Testing conditional factor models | 0.644 | 2 | 2 | 100% |
| 9 | Zhang, T., Wu, W. B (2012) Inference of time-varying regression models | 0.644 | 2 | 2 | 100% |
| 10 | Chava, S., Gallmeyer, M., Park, H (2015) Credit conditions and stock return predictability | 0.585 | 3 | 1 | 100% |
Showing the top 10 of 60 scored citations.