arXiv 19 Oct 2024 · Finance — Portfolio Management
arXiv:2410.16333 · PDF · DOI · OpenAlex · Extracted main text
This study examines portfolio selection using predictive models for portfolio returns. Portfolio selection is a fundamental task in finance, and a variety of methods have been developed to achieve this goal. For instance, the mean-variance approach constructs portfolios by balancing the trade-off between the mean and variance of asset returns, while the quantile-based approach optimizes portfolios by considering tail risk. These methods often depend on distributional information estimated from historical data using predictive models, each of which carries its own uncertainty. To address this, we propose a framework for predictive portfolio selection via conformal prediction , called Conformal Predictive Portfolio Selection (CPPS). Our approach forecasts future portfolio returns, computes the corresponding prediction intervals, and selects the portfolio of interest based on these intervals. The framework is flexible and can accommodate a wide range of predictive models, including autoregressive (AR) models, random forests, and neural networks. We demonstrate the effectiveness of the CPPS framework by applying it to an AR model and validate its performance through empirical studies, showing that it delivers superior returns compared to simpler strategies.
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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 | Victor Chernozhukov, Kaspar Wüthrich, and Zhu Yinchu (2018) Exact and robust conformal inference methods for predictive machine learning with dependent data | 0.874 | 8 | 2 | 100% |
| 2 | Michal Klein, Louis Bethune, Eugene Ndiaye, and Marco Cuturi (2025) Multivariate conformal prediction using optimal transport, 2025 | 0.644 | 2 | 2 | 100% |
| 3 | Gauthier Thurin, Kimia Nadjahi, and Claire Boyer (2025) Optimal transport-based conformal prediction, 2025 | 0.644 | 2 | 2 | 100% |
| 4 | Christopher B. Barry (1974) Portfolio analysis under uncertain means, variances, and covariances | 0.405 | 1 | 1 | 100% |
| 5 | Nestor Parolya David Bauder, Taras Bodnar and Wolfgang Schmid (2021) Bayesian mean–variance analysis: optimal portfolio selection under parameter uncertainty | 0.405 | 1 | 1 | 100% |
| 6 | Taras Bodnar, Mathias Lindholm, Erik Thorsén, and Joanna Tyrcha (2021) Quantile-based optimal portfolio selection | 0.405 | 1 | 1 | 100% |
| 7 | James Douglas Hamilton (1994) Time series analysis | 0.405 | 1 | 1 | 100% |
| 8 | Harry Markowitz (1952) Portfolio selection | 0.405 | 1 | 1 | 100% |
| 9 | Harry Markowitz (1959) Portfolio selection: efficient diversification of investments | 0.405 | 1 | 1 | 100% |
| 10 | Harry M Markowitz and G Peter Todd (2000) Mean-Variance Analysis in Portfolio Choice and Capital Markets, volume 66 | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 18 scored citations.