{
  "abstract": "We evaluate the information and model complexity needed to forecast first closing-price exits from pre-issued dynamic zones. Established competing-risk principles encode no exit within 60 min and first upper/lower exits in four intervals as nine outcomes. Annual historical analysis uses 502,443 DZ-LSTM-zone and 474,591 causal moving-quantile (MQ60) test origins for BTC, ETH, and XRP from 2020 through June 2026. Multinomial logit, XGBoost, survival forest, and an auxiliary MLP receive 22 non-zone predictors, 2 added distances, or all 32 inputs. Exact-calendar reconstruction repeats all 504 learner/information-set fits on unchanged historical targets and samples. Full inputs have lower Brier loss than non-zone inputs in 4/4 DZ-LSTM and 4/4 MQ60 learners, with signed relative reductions of 10.02–11.85% and 13.58–14.53%, respectively. Two distances recover most historical input gains. A locked follow-up applies stored 2026 MQ60 models and training scalers without tuning to 19,056 unused origins from 30 June through 30 September 2026; 4/4 learners have lower full-input Brier, and 4/4 reductions remain Holm-significant under all tested date-block choices. The contribution is empirical evidence about boundary information and learner choice. These retrospective checks do not establish a new output layer or universal neural superiority. Existing volatility benchmarks and negative external-market results delimit interpretation. Episode alerts remain optional illustrations under assumed policies and costs.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Computer Software Engineering, Soonchunhyang University, 22 Soonchunhyang-ro, Asan-si 31538, Chungcheongnam-do, Republic of Korea",
    "Department of Software Convergence, Soonchunhyang University, 22 Soonchunhyang-ro, Asan-si 31538, Chungcheongnam-do, Republic of Korea"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Giwon Kim"
    },
    {
      "affs": [
        1
      ],
      "name": "Yoojeong Song",
      "orcid": "0000-0003-1666-6803"
    },
    {
      "affs": [
        1
      ],
      "name": "Sang Ik Han",
      "orcid": "0000-0002-5263-4932"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7pnpaksws0qne.bib",
  "cited_by": 0,
  "doi": "10.3390/math14213963",
  "fulltext_url": null,
  "id": "7pnpaksws0qne",
  "issns": [
    "2227-7390"
  ],
  "landing_url": "https://www.mdpi.com/2227-7390/14/21/3963",
  "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": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-30",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/7pnpaksws0qne.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T00:20:46Z",
      "native_id": "10.3390/math14213963",
      "source": "crossref",
      "url": "https://www.mdpi.com/2227-7390/14/21/3963"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "issn:2227-7390",
      "subfield": "general"
    }
  ],
  "title": "Information Sets and Learning Models for First Exits from Dynamic Price Zones: An Empirical Competing-Risk Study",
  "title_text": "Information Sets and Learning Models for First Exits from Dynamic Price Zones: An Empirical Competing-Risk Study",
  "type": "peer-reviewed",
  "updated_at": "2026-11-01T00:20:44Z",
  "url": "https://research.draconitic.com/paper/7pnpaksws0qne",
  "venue": "Mathematics",
  "versions": []
}