{
  "annotation_id": "59878896-2ced-4212-bead-248a6293d414",
  "date_created": "2026-03-02T18:01:03.858000Z",
  "date_modified": "2026-03-02T18:01:03.858000Z",
  "file_hash": "5e0559966c70cb568da0bd2b6c9a9c6246d3ba12ca6c5565d927dc55f802793d",
  "private": false,
  "record": {
    "abstract": "We study the dynamical behavior of high-frequency data from the Korean Stock\nPrice Index (KOSPI) using the movement of returns in Korean financial markets.\nThe dynamical behavior for a binarized series of our models is not completely\nrandom. The conditional probability is numerically estimated from a return\nseries of KOSPI tick data. Non-trivial probability structures can be\nconstituted from binary time series of autoregressive (AR), logit, and probit\nmodels, for which the Akaike Information Criterion shows a minimum value at the\n15th order. From our results, we find that the value of the correct match ratio\nfor the AR model is slightly larger than the findings of other models.",
    "arxiv_id": "physics/0512225",
    "authors": [
      "Kyungsik Kim",
      "Seong-Min Yoon",
      "Soo Yong Kim",
      "Ki-Ho Chang",
      "Yup Kim"
    ],
    "categories": [
      "physics.soc-ph",
      "q-fin.ST"
    ],
    "title": "Dynamical Structures of High-Frequency Financial Data",
    "url": "https://arxiv.org/abs/physics/0512225"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "1e58e1a8-0375-444a-9dcd-e21c2237c489",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}