Dynamic Inclusion and Bounded Multi-Factor Tilts for Robust Portfolio Construction

TL;DR

This paper introduces a robust portfolio construction framework that dynamically adjusts asset eligibility and factor tilts based on market conditions, avoiding traditional estimation methods to enhance stability and operational feasibility.

Contribution

It presents a novel, fully algorithmic approach combining dynamic eligibility, deterministic rebalancing, and bounded tilts, improving robustness without relying on expected return or covariance estimates.

Findings

Enhances portfolio stability under estimation errors and non-stationarity.

Controls concentration, turnover, and fragility through structural bounds.

Suitable for long-horizon allocations prioritizing operational stability.

Abstract

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.