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

Elena Pittsburg

Open Access

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

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Elena Pittsburg ¹ ,

⁴ Department of Financial Technology and Computational Economics, London School of Economics and Political Science

Abstract

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.

Keywords

Cryptocurrency Prediction, Hybrid Deep Learning, Attention Transformers, Sentiment Analysis

References

📄 Al-Sarayreh, A., Reis, R.E., Alkhawaldeh, A.H., Shafiq, W., Alshammari, B. A survey on artificial intelligence and machine learning applications in cryptocurrency price prediction. Algorithms (2022).

📄 Al-Sarayreh, A., Reis, R.E., Alkhawaldeh, A.H., Shafiq, W. A novel hybrid deep learning model that integrates attention transformer and gated recurrent unit (GRU) architectures to improve the accuracy of cryptocurrency price predictions. Mathematics (2025).

📄 Al-Sarayreh, A., Reis, R.E., Alkhawaldeh, A.H., and Shafiq, W. "A novel hybrid deep learning model for cryptocurrency price prediction using attention transformer and GRU," arXiv:2504.17079 (2025).

📄 Alnami, A.A. "The impact of social media sentiments on Bitcoin price prediction using machine learning models," Ph.D. dissertation, Univ. East London, London, UK (2024).

📄 Bouteska, M., Harasheh, S., and Abedin, A. "Dynamic forecasting model for cryptocurrency prices using ensemble methods," EuroMed J Manage Bus Econ (2024).

📄 Chaudhary, A., Sushil, S. A comparative analysis of ensemble-based models for predicting cryptocurrency price movements. J Inf Technol (2025).

📄 Derbentsev, A.K., Soloviev, V.N., Serdyuk, O.A. Ensemble deep learning models for forecasting cryptocurrency time-series. AIMS Math (2020).

📄 Kaddari, Z., et al. A novel self-attention enriching mechanism for biomedical question answering. Expert. Syst. Appl. (2023).

📄 Kale, A. (2025). FX Hedging Algorithms for Crypto-Native Companies. International Journal of Advanced Artificial Intelligence Research, 2(10), 09-14. https://doi.org/10.55640/ijaair-v02i10-02

📄 Kim, H.-M., et al. Predicting Ethereum prices with machine learning based on Blockchain information. Expert Syst. Appl. (2021).

📄 Li, J., Wang, X., Zhang, Y., and Chen, H. "CryptoPulse: A dual-mechanism framework for cryptocurrency price forecasting," arXiv:2502.19349 (2025).

📄 Li, Y., Zhang, Z., Wang, H. and Chen, J. "Unlocking LLM potential in cryptocurrency trading: A fact-subjectivity-aware reasoning multi-agent framework," arXiv:2410.12464 (2024).

📄 Matuszelański, K. XGBoost and random forest with upsampling for imbalanced churn prediction. Appl Sci (2023).

📄 Natashekara, K., et al. Informed trading and cryptocurrencies. New evidence using tick-by-tick data. Finance Res. Lett. (2024).

📄 Patel, R., et al. Assessing connectedness of transportation cryptocurrencies and transportation stocks: Evidence from wavelet quantile correlation. Finance Res. Lett. (2023).

📄 Patel, M.M., et al. A deep learning-based cryptocurrency price prediction scheme for financial institutions. J. Inf. Secur. Appl. (2020).

📄 Raza, A., Khan, M.A., Khan, M.A. Enhanced interpretable forecasting of cryptocurrency prices using autoencoder features and a hybrid CNN-LSTM model. Mathematics (2025).

📄 Raza, A., Khan, M.A., Khan, M.A. Using sentiment and technical analysis to predict Bitcoin with machine learning. Comput Syst Sci Eng (2023).

How to Cite

Elena Pittsburg. (2026). A Multi-Dimensional Paradigm for Cryptocurrency Valuation: Integrating Hybrid Deep Learning, Attention Transformers, And Sentiment-Aware Multi-Agent Frameworks. Global Multidisciplinary Journal, 5(01), 179-185. https://www.grpublishing.org/journals/index.php/gmj/article/view/369

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Global Multidisciplinary Journal

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

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