Predicting the Risk of Death for Cryptocurrencies Using Deep Learning
Bibliographic record
Abstract
The rapid rise in the popularity of cryptocurrencies has drawn increasing attention from investors, entrepreneurs, and the public in recent years. However, this rapid growth comes with risk: many coins fail early and become what are known as “dead coins”, defined by a lack of recorded activity for more than a year. This study applies deep learning techniques to estimate the short-term risk of a cryptocurrency’s death. Specifically, three Recurrent Neural Network architectures, Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Gated Recurrent Unit (GRU), were trained on 18-month time series of daily closing prices and trading volumes using a stratified five-fold cross-validation framework. The models’ predictive performances were compared across input windows ranging from 10 to 180 days. Using the previous 180 days of data as input, GRU achieved the highest point accuracy of 0.7134, whereas BiLSTM exhibited the best performance when evaluated across input sequence lengths varying from 10 to 180 days, reaching an average accuracy of 0.676. These findings show the ability of recurrent architectures to anticipate short-term failure risks in cryptocurrency markets. Theoretically, the study contributes to financial risk modeling by extending time series classification methods to cryptocurrency failure prediction. Practically, it provides investors and analysts with a data-driven early-warning tool to manage portfolio risk and reduce potential losses.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".