Yuan Liao, Xinjie Ma, Andreas Neuhierl, Linda Schilling
arXiv 1 Mar 2025 · Econometrics
arXiv:2503.00549 · PDF · DOI · OpenAlex · Extracted main text
Machine learning in asset pricing typically predicts expected returns as point estimates, ignoring uncertainty. We develop new methods to construct forecast confidence intervals for expected returns obtained from neural networks. We show that neural network forecasts of expected returns share the same asymptotic distribution as classic nonparametric methods, enabling a closed-form expression for their standard errors. We also propose a computationally feasible bootstrap to obtain the asymptotic distribution. We incorporate these forecast confidence intervals into an uncertainty-averse investment framework. This provides an economic rationale for shrinkage implementations of portfolio selection. Empirically, our methods improve out-of-sample performance.
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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 | Gu, S., B. Kelly, and D. Xiu (2020) Empirical asset pricing via machine learning | 0.928 | 4 | 3 | 100% |
| 2 | Garlappi, L., R. Uppal, and T. Wang (2007) Portfolio selection with parameter and model uncertainty: A multi-prior approach | 0.874 | 7 | 2 | 100% |
| 3 | Allena, R (2021) Confident risk premiums and investments using machine learning uncertainties | 0.811 | 4 | 2 | 100% |
| 4 | Fan, J., Z. T. Ke, Y. Liao, and A. Neuhierl (2022) Structural deep learning in conditional asset pricing | 0.811 | 4 | 2 | 100% |
| 5 | Bianchi, D., M. Büchner, and A. Tamoni (2021) Bond risk premiums with machine learning | 0.737 | 3 | 2 | 100% |
| 6 | Didisheim, A., S. B. Ke, B. T. Kelly, and S. Malamud (2023) Complexity in factor pricing models | 0.737 | 3 | 2 | 100% |
| 7 | Kelly, B. T., S. Malamud, and K. Zhou (2021) The virtue of complexity in return prediction | 0.737 | 3 | 2 | 100% |
| 8 | Kozak, S., S. Nagel, and S. Santosh (2020) Shrinking the cross-section | 0.737 | 3 | 2 | 100% |
| 9 | Andrews, D. W (2002) Higher-order improvements of a computationally attractive k-step bootstrap for extremum estimators | 0.644 | 2 | 2 | 100% |
| 10 | Ao, M., Y. Li, and X. Zheng (2019) Approaching mean-variance efficiency for large portfolios | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 77 scored citations.