{
  "abstract": "We develop ML models that predict when cross-factor predictability will erode, enabling prospective risk management. A decay prediction model achieves C-index $= 0.93$ on held-out factor pairs, providing early warning of signal erosion---the key predictor is initial signal strength: stronger signals decay faster (``regression to the mean''). An online regime detector enables real-time classification with 6.6-day average detection delay. Prior work explains why factors decay (arbitrage, publication effects); we predict when decay will occur. Limitation: The primary HML$\\to$SMB signal does not replicate in post-break OOS (2013--2024); this null result is consistent with genuine decay but limits confirmatory claims. Motivation: Among 30 directed Fama-French factor pairs (1990--2024), predictability targeting the Size factor (SMB) decays fastest. Of 7 pairs showing exponential decay ($R^2 > 0.3$), 4 target SMB (Mkt-RF$\\to$SMB, CMA$\\to$SMB, HML$\\to$SMB, RMW$\\to$SMB; half-lives 2.4--4.1 years). This SMB-concentration pattern motivates the predictive models. Methodology: Using a regime-conditional Granger framework with Student-$t$ HMMs, we document structural breaks clustering around June 1998 ($p < 10^{-10}$). A pre-break OOS test (HMM trained 1990--1995, tested 1996--2007) confirms signals existed: HML$\\to$SMB shows $p = 0.013$ (Elevated) and $p < 0.001$ (Crisis). The primary HML$\\to$SMB signal does not replicate in post-break OOS (2013--2024), consistent with genuine decay rather than overfitting---after ${\\sim}6$ half-lives, the expected signal is $<$1% of its 1990s peak. International analysis finds 2/4 non-US markets with Bonferroni-surviving OOS effects; correction for full specification search was not applied, so results remain exploratory.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.03668",
  "author_total": 1,
  "authors": [
    {
      "name": "Chorok Lee"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cc24bdte096gf.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.03668",
  "fulltext_url": null,
  "id": "cc24bdte096gf",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.03668",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "stat.AP"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.03668",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cc24bdte096gf.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T15:21:34Z",
      "native_id": "2611.03668",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.03668"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "Predicting Factor Decay: ML Models for Cross-Factor Predictability Erosion",
  "title_text": "Predicting Factor Decay: ML Models for Cross-Factor Predictability Erosion",
  "type": "preprint",
  "updated_at": "2026-11-04T15:21:34Z",
  "url": "https://research.draconitic.com/paper/cc24bdte096gf",
  "venue": "arXiv",
  "versions": []
}