{
  "abstract": "Forecasters often score the same units per date against one standardized realized outcome. We show that every standardized forecast splits exactly into a component aligned with this common target and a component uncorrelated with it. Three consequences follow: forecast-error correlation largely mirrors forecast correlation and is therefore a poor measure of diversity; an equally weighted combination beats a no-information forecast only when average alignment is large relative to the combination's dispersion; and the gain from adding a forecaster separates into genuine improvement and mere dilution, which equal-weight admission can mistakenly reward. We develop a cautious selection rule, study it in simulations, and apply it to language-model forecasts of US equity rankings and mechanical signals ranking exchange-traded funds. Selection removes most dilution losses, but no combination beats the no-information forecast.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.26303",
  "author_total": 1,
  "authors": [
    {
      "name": "Masoud Soleimani"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fvprfc107xqcb.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.26303",
  "fulltext_url": null,
  "id": "fvprfc107xqcb",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.26303",
  "language": null,
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "econ.EM"
    ],
    [
      "arxiv",
      "q-fin.ST"
    ],
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "openalex",
      "1803|Forecasting Techniques and Applications"
    ]
  ],
  "openalex": "W7214093364",
  "pdf_url": "https://arxiv.org/pdf/2609.26303",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-22",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/fvprfc107xqcb.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:49Z",
      "native_id": "2609.26303",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.26303"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "Target alignment, dilution and forecast selection when cross-sectional forecasts share a common target",
  "title_text": "Target alignment, dilution and forecast selection when cross-sectional forecasts share a common target",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:49Z",
  "url": "https://research.draconitic.com/paper/fvprfc107xqcb",
  "venue": "arXiv",
  "versions": []
}