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AI-Driven Predictive Analytics for Cryptocurrency Price Volatility and Market Manipulation Detection

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This model demonstrates improved volatility prediction and manipulation detection in cryptocurrencies, indicating new regulatory tools.

The model showcased superior performance in forecasting cryptocurrency volatility with a Mean Absolute Error of 0.121.
In manipulation detection, it achieved a highly impressive AUC-ROC of 0.94, outperforming existing benchmarks.
Implementation of a hybrid deep learning model combines transformer networks and graph neural networks for advanced analytics.
Identifying the Graph Clustering Coefficient as a key predictor highlights the complexity of market transactions during manipulation.

Zerine et al. (2025) studied this question.

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View Full Paper

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Authors

This model demonstrates improved volatility prediction and manipulation detection in cryptocurrencies, indicating new regulatory tools.

The model showcased superior performance in forecasting cryptocurrency volatility with a Mean Absolute Error of 0.121.
In manipulation detection, it achieved a highly impressive AUC-ROC of 0.94, outperforming existing benchmarks.
Implementation of a hybrid deep learning model combines transformer networks and graph neural networks for advanced analytics.
Identifying the Graph Clustering Coefficient as a key predictor highlights the complexity of market transactions during manipulation.

Zerine et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark