Blockchain Lifecycle Prediction - Dead Coins

Abstract

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.