eISSN: 2791-173X editor@grpublishing.org
All Journals
Submit your article

Global Multidisciplinary Journal

Open Access Peer Review International
Open Access

Adaptive FX Hedging and Predictive Learning Architectures for Crypto-Native Enterprises: Integrating Soft Computing, Deep Predictive Coding, and Game-Theoretic Decision Frameworks

DOI
pdf
Dr. Alejandro M. Rivas 1 ,
4 Department of Computational Finance and Intelligent Systems Universidad Autónoma de Madrid, Spain

Abstract

The rapid emergence of crypto-native companies has fundamentally altered the landscape of foreign exchange exposure, risk management, and algorithmic decision-making. Unlike traditional multinational enterprises, crypto-native firms operate at the intersection of volatile digital assets, fiat currencies, decentralized financial infrastructures, and real-time global markets. This creates a uniquely complex foreign exchange (FX) risk environment that cannot be adequately addressed using conventional hedging strategies or static econometric models. In response to this challenge, this article develops a comprehensive, theoretically grounded synthesis of adaptive FX hedging algorithms for crypto-native enterprises by integrating soft computing techniques, deep learning-based time series forecasting, reinforcement learning, predictive coding architectures, and game-theoretic learning frameworks. Drawing strictly on established research in FX prediction, soft computing hybrids, deep neural forecasting, predictive coding theory, and online learning with expert advice, the study constructs a unified conceptual framework that explains how modern hedging systems can dynamically learn, adapt, and self-correct under persistent uncertainty. The methodology emphasizes descriptive and theoretical integration rather than mathematical formalism, detailing how data structuring, agent behavior modeling, and loss-sensitive optimization interact in real-world hedging contexts. The results section provides an extensive descriptive analysis of how such integrated systems outperform static hedging paradigms in terms of adaptability, robustness, and behavioral transparency. The discussion critically examines limitations related to model interpretability, regime shifts, and ethical considerations, while outlining future research directions that bridge neuro-inspired predictive coding with financial decision systems. The article concludes by positioning adaptive, learning-based FX hedging as an essential strategic capability for crypto-native firms navigating an increasingly fragmented and uncertain global monetary ecosystem.

Keywords

Foreign exchange hedging, crypto-native firms, soft computing, predictive coding

References

📄 Ahmed, S., Hassan, S. U., Aljohani, N. R., & Nawaz, R. (2020). FLF-LSTM: A novel prediction system using Forex Loss Function. Applied Soft Computing, 97, 106780.
📄 Al-Baghdadi, N., Lindsay, D., Kalnishkan, Y., & Lindsay, S. (2020). Practical investment with the long-short game. Proceedings of Machine Learning Research, 128, 209–228.
📄 Al-Baghdadi, N., Wisniewski, W., Lindsay, D., Lindsay, S., Kalnishkan, Y., & Watkins, C. (2019). Structuring time series data to gain insight into agent behaviour. Proceedings of the IEEE International Workshop on Big Data for Financial News and Data.
📄 Carapuço, J., Neves, R., & Horta, N. (2018). Reinforcement learning applied to Forex trading. Applied Soft Computing, 73, 783–794.
📄 Cesa-Bianchi, N., & Lugosi, G. (2006). Prediction, Learning, and Games. Cambridge University Press.
📄 Chernov, A. (2010). On Theorem 2.3 in “Prediction, learning, and games”. CoRR, abs/1011.5668.
📄 Chernov, A., & Zhdanov, F. (2010). Prediction with expert advice under discounted loss. Lecture Notes in Artificial Intelligence, 6331, 255–269.
📄 Dora, S., Pennartz, C., & Bohte, S. (2018). A deep predictive coding network for inferring hierarchical causes underlying sensory inputs. Lecture Notes in Computer Science, 11141.
📄 FX Hedging Algorithms for Crypto-Native Companies. (2025). International Journal of Advanced Artificial Intelligence Research, 2(10), 09–14.
📄 Islam, M. S., & Hossain, E. (2021). Foreign exchange currency rate prediction using a GRU-LSTM hybrid network. Soft Computing Letters, 3, 100009.
📄 Mohan, A., Luckey, A., Weisz, N., & Vanneste, S. (2022). Predisposition to domain-wide maladaptive changes in predictive coding in auditory phantom perception. NeuroImage, 248, 118813.
📄 Panda, M. M., Panda, S. N., & Pattnaik, P. K. (2021). Multi currency exchange rate prediction using convolutional neural network. Materials Today Proceedings.
📄 Pang, Z., O’May, C. B., Choksi, B., & VanRullen, R. (2021). Predictive coding feedback results in perceived illusory contours in a recurrent neural network. Neural Networks, 144, 164–175.
📄 PradeepKumar, D., & Ravi, V. (2018). Soft computing hybrids for FOREX rate prediction: A comprehensive review. Computers and Operations Research, 99, 262–284.
📄 Sezer, O. B., Gudelek, M. U., & Ozbayoglu, A. M. (2020). Financial time series forecasting with deep learning: A systematic literature review. Applied Soft Computing, 90, 106181.
📄 Sledge, I. J., & Principe, J. C. (2021). Faster convergence in deep-predictive-coding networks to learn deeper representations. IEEE Transactions on Neural Networks and Learning Systems, 1–15.
📄 Wen, H., Han, K., Shi, J., Zhang, Y., Culurciello, E., & Liu, Z. (2018). Deep predictive coding network for object recognition. Proceedings of the International Conference on Machine Learning, 80, 5266–5275.

How to Cite

Dr. Alejandro M. Rivas. (2025). Adaptive FX Hedging and Predictive Learning Architectures for Crypto-Native Enterprises: Integrating Soft Computing, Deep Predictive Coding, and Game-Theoretic Decision Frameworks. Global Multidisciplinary Journal, 4(11), 151-156. https://www.grpublishing.org/journals/index.php/gmj/article/view/264

Most read articles by the same author(s)

1 2 3 4 5 6 7 8 9 10 > >> 

Similar Articles

31-40 of 48

You may also start an advanced similarity search for this article.