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

Abstract

The markets of cryptocurrencies are very volatile and vulnerable to advanced market manipulation, but current studies have historically viewed volatility prediction and manipulation detection as distinct issues, which generates a critical knowledge gap. This paper attempted to formulate a coherent model of analytical approach to explain the inherent relationship between the two phenomena. Our model incorporated a new hybrid type of deep learning network with Transformer networks to handle sequential data with Graph Neural Networks (GNNs) to learn transactional relationships. The model was trained and tested on a multi-modal data set (more than 2 million hourly observations of Bitcoin, Ethereum, and Binance Coin) of market microstructure information, social sentiment information, and on-chain data between 2020 and 2023. Our model was far superior to any known benchmarks, in volatility forecasting, it would have a Mean Absolute Error of 0.121 with a statistically significant difference with GARCH (0.198, p=0.002) and in manipulation detection it had an AUC-ROC of 0.94. More importantly, the Graph Clustering Coefficient was found to be the most significant predictor with a 200 percent growth during the times of manipulation with an odds ratio of 164.0 (p<0.001) to classify the manipulation. This study concludes that transactional coordination is a primary cause of the instability in the market and creates a new paradigm of financial surveillance. The framework gives regulators and investors a potent device of active risk control and shows that multi-modal AI is crucial to accessing and protecting decentralized financial ecosystems.