Feasibility-First Satellite Integration in Robust Portfolio Architectures

ArXiv ID: 2601.08721 “View on arXiv”

Authors: Roberto Garrone

Abstract

The integration of thematic satellite allocations into core-satellite portfolio architectures is commonly approached using factor exposures, discretionary convictions, or backtested performance, with feasibility assessed primarily through liquidity screens or market-impact considerations. While such approaches may be appropriate at institutional scale, they are ill-suited to small portfolios and robustness-oriented allocation frameworks, where dominant constraints arise not from return predictability or trading capacity, but from fixed costs, irreversibility risk, and governance complexity. This paper develops a feasibility-first, non-predictive framework for satellite integration that is explicitly scale-aware. We formalize four nested feasibility layers (physical, economic, structural, and epistemic) that jointly determine whether a satellite allocation is admissible. Physical feasibility ensures implementability under concave market-impact laws; economic feasibility suppresses noise-dominated reallocations via cost-dominance threshold constraints; structural feasibility bounds satellite size through an explicit optionality budget defined by tolerable loss under thesis failure; and epistemic feasibility limits satellite breadth and dispersion through an entropy-based complexity budget. Within this hierarchy, structural optionality is identified as the primary design principle for thematic satellites, with the remaining layers acting as robustness lenses rather than optimization criteria. The framework yields closed-form feasibility bounds on satellite size, turnover, and breadth without reliance on return forecasts, factor premia, or backtested performance, providing a disciplined basis for integrating thematic satellites into small, robustness-oriented portfolios.

Keywords: Feasibility Framework, Market Impact, Optionality Budget, Entropy-based Complexity, Thematic Satellites, Equities

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 2.0/10
• Quadrant: Lab Rats
• Why: The paper introduces several mathematically advanced constructs like nested feasibility layers, concave market-impact laws, and entropy-based complexity budgets, which require formal modeling and derivation. However, it explicitly avoids backtesting, empirical data, or performance metrics, focusing instead on theoretical feasibility bounds without implementation details or statistical validation.

  flowchart TD
    A["Research Goal: Feasibility-First Satellite Integration<br>Scale-Aware Framework for Small/Robust Portfolios"] --> B["Key Methodology: Four Nested Feasibility Layers<br>(Physical, Economic, Structural, Epistemic)"]
    B --> C["Data/Inputs: Market Impact Parameters, Cost Data, Thesis Risk Tolerance, Complexity Budget"]
    C --> D["Computational Processes: Closed-Form Bounding (No Return Forecasts)<br>• Concave Impact Models<br>• Cost-Dominance Thresholds<br>• Optionality Budgeting<br>• Entropy-Based Complexity"]
    D --> E["Key Outcomes: Feasibility Bounds for Satellite Size, Turnover, & Breadth<br>• Admissibility Criteria<br>• Robust Integration Principles"]