{
  "annotation_id": "3a45eb19-b6a3-461a-b448-b4e4e9294ed6",
  "date_created": "2026-02-17T05:53:12.654000Z",
  "date_modified": "2026-02-17T05:53:12.654000Z",
  "file_hash": "963e9187b513617466eab5a7cdb39f62f24c130e67db70c6b129d45b0679fb9b",
  "private": false,
  "record": {
    "abstract": "Our work focuses on deep learning (DL) portfolio optimization, tackling challenges in long-only, multi-asset strategies across market cycles. We propose training models with limited regime data using pre-training techniques and leveraging transformer architectures for state variable inclusion. Evaluating our approach against traditional methods shows promising results, demonstrating our models\u0027 resilience in volatile markets. These findings emphasize the evolving landscape of DL-driven portfolio optimization, stressing the need for adaptive strategies to navigate dynamic market conditions and improve predictive accuracy.",
    "arxiv_id": "2601.07942",
    "authors": [
      "Brandon Luo",
      "Jim Skufca"
    ],
    "categories": [
      "q-fin.PM",
      "cs.LG"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Enhancing Portfolio Optimization with Deep Learning Insights",
    "url": "https://arxiv.org/abs/2601.07942",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "69461523-e84e-4d13-bd6e-f7c2c413d395",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}