{
  "annotation_id": "f9c86623-3616-444c-9e81-b0e133895966",
  "date_created": "2026-02-17T05:53:04.446000Z",
  "date_modified": "2026-02-17T05:53:04.446000Z",
  "file_hash": "5898847aa7f18e161a3016ad12b1fd8e0c7a508f473eab2c5e212b22f4a92082",
  "private": false,
  "record": {
    "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.",
    "arxiv_id": "2601.05428",
    "authors": [
      "Roberto Garrone"
    ],
    "categories": [
      "math.OC",
      "cs.LG"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Dynamic Inclusion and Bounded Multi-Factor Tilts for Robust Portfolio Construction",
    "url": "https://arxiv.org/abs/2601.05428",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "8546a10e-235d-4e6c-9a7e-37dc1fcd4e15",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}