Dynamic Inclusion and Bounded Multi-Factor Tilts for Robust Portfolio Construction
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arXiv
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866909985530707968 |
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| author | Garrone, Roberto |
| author_facet | Garrone, Roberto |
| contents | This paper proposes a portfolio construction framework designed to remain robust under estimation error, non-stationarity, and realistic trading constraints. The methodology combines dynamic asset eligibility, deterministic rebalancing, and bounded multi-factor tilts applied to an equal-weight baseline. Asset eligibility is formalized as a state-dependent constraint on portfolio construction, allowing factor exposure to adjust endogenously in response to observable market conditions such as liquidity, volatility, and cross-sectional breadth. Rather than estimating expected returns or covariances, the framework relies on cross-sectional rankings and hard structural bounds to control concentration, turnover, and fragility. The resulting approach is fully algorithmic, transparent, and directly implementable. It provides a robustness-oriented alternative to parametric optimization and unconstrained multi-factor models, particularly suited for long-horizon allocations where stability and operational feasibility are primary objectives. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_05428 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Dynamic Inclusion and Bounded Multi-Factor Tilts for Robust Portfolio Construction Garrone, Roberto Optimization and Control Machine Learning 2020, 91G10, 90C90 F.2.2; I.2.8 This paper proposes a portfolio construction framework designed to remain robust under estimation error, non-stationarity, and realistic trading constraints. The methodology combines dynamic asset eligibility, deterministic rebalancing, and bounded multi-factor tilts applied to an equal-weight baseline. Asset eligibility is formalized as a state-dependent constraint on portfolio construction, allowing factor exposure to adjust endogenously in response to observable market conditions such as liquidity, volatility, and cross-sectional breadth. Rather than estimating expected returns or covariances, the framework relies on cross-sectional rankings and hard structural bounds to control concentration, turnover, and fragility. The resulting approach is fully algorithmic, transparent, and directly implementable. It provides a robustness-oriented alternative to parametric optimization and unconstrained multi-factor models, particularly suited for long-horizon allocations where stability and operational feasibility are primary objectives. |
| title | Dynamic Inclusion and Bounded Multi-Factor Tilts for Robust Portfolio Construction |
| topic | Optimization and Control Machine Learning 2020, 91G10, 90C90 F.2.2; I.2.8 |
| url | https://arxiv.org/abs/2601.05428 |