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Blockchain Lifecycle Prediction - Dead Coins — arXiv:2610.01379

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.01379
License (as archived): CC0-1.0
Archived: 2026-10-06T14:02:41.437Z
SHA-256 of the archived text: 48e8eee64f5111b325e5dfeb2a72340b0df3dd01891ca6903a81e9491885bddc
Stable URL of this page: https://metraipolis.com/documento/dc_ae4320c2fffc

How to cite

Blockchain Lifecycle Prediction - Dead Coins — arXiv:2610.01379 — arXiv (en), https://arxiv.org/abs/2610.01379 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ae4320c2fffc (Metraipolis archive dc_ae4320c2fffc, sha256 48e8eee64f5111b325e5dfeb2a72340b0df3dd01891ca6903a81e9491885bddc)

BibTeX

@misc{dcae4320c2fffc,
  title = {Blockchain Lifecycle Prediction - Dead Coins — arXiv:2610.01379},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.01379},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ae4320c2fffc; sha256 48e8eee64f5111b325e5dfeb2a72340b0df3dd01891ca6903a81e9491885bddc},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.01379. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

The cryptocurrency ecosystem has experienced extraordinary growth alongside an equally remarkable rate of failure, with over 52 percent of all tokens launched since 2021 ceasing to trade by early 2025. Despite the scale of this phenomenon, predictive modeling of cryptocurrency death remains an underdeveloped area of research, constrained by definitional ambiguity, data scarcity, and the absence of granular lifecycle frameworks. This work investigates whether the failure of cryptocurrency assets can be predicted using publicly available market data and deep learning methods. A Long Short-Term Memory (LSTM) recurrent neural network was trained on 90-day sequences of daily reference price and estimated market capitalization for 82 cryptocurrency assets (41 alive and 41 dead), sourced from Coin Metrics over the period 2020 to 2026. The model was evaluated using a strictly chronological train-test split to prevent look-ahead bias. The LSTM classifier achieved in best cases a Receiver Operating Characteristic Area Under the Curve (ROC AUC) of 0.98 on the held-out test set. Finally, the model was applied to unseen data, and it was observed that the ROC AUC decreased between 0.59 and 0.65. The findings demonstrate that temporal patterns in price and market capitalization alone contain sufficient discriminative signal to identify assets on a trajectory towards economic inactivity. Diagnostic analyzes reveal that failing assets exhibit gradual value erosion and elevated volatility in the months preceding inactivity, rather than sudden catastrophic collapse. This work also documents the practical infeasibility of a multi-stage lifecycle model under current data conditions and justifies the transition to a binary classification approach.

Tema degli esploratori: calcolo e infrastrutture (retro-catalogo arXiv: annunci del 2026-10-02 in cs.DC).
Categorie arXiv: Distributed, Parallel, and Cluster Computing (cs.DC).
Pagina del lavoro: https://arxiv.org/abs/2610.01379 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-06T12:22:22.552Z dalla pagina https://arxiv.org/catchup/cs.DC/2026-10-02?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).