The blockchain ecosystem has seen a huge growth since 2009, with the introduction of
Bitcoin, driven by conceptual and algorithmic innovations, along with the emergence of numerous new
cryptocurrencies. While significant attention has been devoted to established cryptocurrencies like
Bitcoin and Ethereum, the continuous introduction of new tokens requires a nuanced examination. In
this article, we contribute a comparative analysis encompassing deep learning and quantum methods
within neural networks and genetic algorithms, incorporating the innovative integration of EGARCH
(Exponential Generalized Autoregressive Conditional Heteroscedasticity) into these methodologies. In
this study, we evaluated how well Neural Networks and Genetic Algorithms predict “buy” or “sell”
decisions for different cryptocurrencies, using F1 score, Precision, and Recall as key metrics. Our
findings underscored the Adaptive Genetic Algorithm with Fuzzy Logic as the most accurate and
precise within genetic algorithms. Furthermore, neural network methods, particularly the Quantum
Neural Network, demonstrated noteworthy accuracy. Importantly, the X2Y2 cryptocurrency
consistently attained the highest accuracy levels in both methodologies, emphasizing its predictive
strength. Beyond aiding in the selection of optimal trading methodologies, we introduced the potential
of EGARCH integration to enhance predictive capabilities, offering valuable insights for reducing
risks associated with investing in nascent cryptocurrencies amidst limited historical market data. This
research provides insights for investors, regulators, and developers in the cryptocurrency market.
Investors can utilize accurate predictions to optimize investment decisions, regulators may consider implementing guidelines to ensure fairness, and developers play a pivotal role in refining neural
network models for enhanced analysis.
