{
  "annotation_id": "07542f35-ab64-495e-a0cc-2f60c27169a7",
  "date_created": "2026-03-02T18:01:00.752000Z",
  "date_modified": "2026-03-02T18:01:00.752000Z",
  "file_hash": "71e81b39b5687b6d6c0cac084b554ef2886327ffdda3dc83b4bb522ceb864ff7",
  "private": false,
  "record": {
    "abstract": "We consider the problem of the statistical uncertainty of the correlation\nmatrix in the optimization of a financial portfolio. We show that the use of\nclustering algorithms can improve the reliability of the portfolio in terms of\nthe ratio between predicted and realized risk. Bootstrap analysis indicates\nthat this improvement is obtained in a wide range of the parameters N (number\nof assets) and T (investment horizon). The predicted and realized risk level\nand the relative portfolio composition of the selected portfolio for a given\nvalue of the portfolio return are also investigated for each considered\nfiltering method.",
    "arxiv_id": "physics/0507006",
    "authors": [
      "Vincenzo Tola",
      "Fabrizio Lillo",
      "Mauro Gallegati",
      "Rosario N. Mantegna"
    ],
    "categories": [
      "physics.soc-ph",
      "cond-mat.other",
      "q-fin.ST"
    ],
    "title": "Cluster analysis for portfolio optimization",
    "url": "https://arxiv.org/abs/physics/0507006"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "3617e781-aecf-4923-a8ff-6382721921fb",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}