{
  "abstract": "We study sequential minimum-variance allocation when estimated market-factor loadings are uncertain. At each date, a horizon-one convex problem is solved using an ellipsoidal confidence region for the jointly estimated loading vector. The full loading covariance includes cross-asset residual dependence, and the uncertainty geometry is implemented either as a loading penalty or as an explicit worst-case exposure constraint. The principal walk-forward experiment uses the original nine U.S. sector exchange-traded funds from 2000 to 2024, yielding 6036 out-of-sample daily observations after a 252-day initial window. This fund-level opportunity set incorporates changes in underlying company constituents and therefore materially reduces the company-survivorship problem of a fixed stock panel, although the selected fund set remains subject to selection bias. An unrestricted past-only penalty selector over-regularizes: it chooses the largest normalized penalties on most dates and produces a negative long-short net return. We therefore report that frozen negative result and evaluate a separately frozen, economically guarded selector that caps the normalized penalty at 10. In long-short allocation, the guarded rule raises annualized net return from 1.13% under nominal beta targeting to 2.57%, lowers volatility from 10.72% to 8.39%, improves maximum drawdown from −38.67% to −24.42%, and reduces turnover and realized-beta dispersion. The paired Sharpe increase from 0.105 to 0.307 remains significant after false-discovery-rate correction. The result remains profitable across alternative estimation windows, transaction costs, and loading-covariance structures, and persists under a common monthly trading schedule. Its Sharpe is statistically indistinguishable from established uncertainty comparators, so universal dominance is not claimed. Long-only evidence favors zero or weak regularization. The results support economically bounded loading-uncertainty treatment in less-constrained portfolios, not unrestricted regularization or general robustness to distributional misspecification.",
  "abstract_shown": true,
  "affiliations": [
    "Independent Researcher",
    "Faculty of Engineering, Universidad de Talca, Curicó 3340000, Chile"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Leonardo P. Carvalho",
      "orcid": "0000-0003-2910-5140"
    },
    {
      "affs": [
        1
      ],
      "name": "Jonathan M. Palma",
      "orcid": "0000-0002-3924-1907"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6d927w9ksbcjq.bib",
  "cited_by": 0,
  "doi": "10.3390/math14203743",
  "fulltext_url": null,
  "id": "6d927w9ksbcjq",
  "issns": [
    "2227-7390"
  ],
  "landing_url": "https://www.mdpi.com/2227-7390/14/20/3743",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1803|Risk and Portfolio Optimization"
    ]
  ],
  "openalex": "W7221943538",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-15",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/6d927w9ksbcjq.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:29Z",
      "native_id": "10.3390/math14203743",
      "source": "crossref",
      "url": "https://www.mdpi.com/2227-7390/14/20/3743"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "issn:2227-7390",
      "subfield": "general"
    }
  ],
  "title": "Estimation-Robust Minimum-Variance Portfolio Allocation Under Factor-Loading Uncertainty",
  "title_text": "Estimation-Robust Minimum-Variance Portfolio Allocation Under Factor-Loading Uncertainty",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:29Z",
  "url": "https://research.draconitic.com/paper/6d927w9ksbcjq",
  "venue": "Mathematics",
  "versions": []
}