A Multi-Dimensional Paradigm for Cryptocurrency Valuation: Integrating Hybrid Deep Learning, Attention Transformers, And Sentiment-Aware Multi-Agent Frameworks

• Elena Pittsburg

  Department of Financial Technology and Computational Economics, London School of Economics and Political Science

  Author

The digital asset landscape has undergone a radical transformation from a niche cryptographic experiment to a systemic component of the global financial architecture. However, the inherent volatility and non-linear dynamics of cryptocurrency price movements pose significant challenges to traditional econometric forecasting models. This research article presents a comprehensive investigation into the next generation of predictive frameworks, specifically focusing on the integration of hybrid deep learning architectures and Large Language Model (LLM) reasoning. By synthesizing contemporary research on Attention Transformers, Gated Recurrent Units (GRU), and Convolutional Neural Networks (CNN) combined with Long Short-Term Memory (LSTM) networks, this study elaborates on the mechanisms required to capture high-frequency temporal dependencies. Furthermore, the research delves into the socio-technical dimensions of market valuation, analyzing the impact of social media sentiment and fact-subjectivity-aware reasoning through multi-agent systems. The article explores the application of autoencoder features for interpretable forecasting and the role of upsampling techniques in addressing the imbalanced nature of market churn. By bridging the gap between technical blockchain metrics and psychological market drivers, this study provides a robust theoretical foundation for financial institutions and crypto-native companies seeking to implement sophisticated hedging algorithms and predictive schemes. The findings suggest that hybrid models, which leverage both structural time-series data and qualitative sentiment reasoning, significantly outperform monolithic architectures in both accuracy and interpretability.

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