A Paradigm Shift in Cryptocurrency Market Analysis The cryptocurrency market has established itself as a central pillar of the global financial landscape, with various digital assets, starting with Bitcoin, becoming increasingly integrated into the economy. Cryptocurrencies are now moving beyond mere investment vehicles, finding practical applications in a wide range of areas such as payment settlements, decentralized finance (DeFi), and smart contracts, standing at the forefront of financial technology innovation. However, the market's inherent volatility and complexity continue to pose significant challenges for investors and analysts alike. Against this backdrop, a paper introducing the DM-MSTP (Dhouib-Matrix-MSTP) methodology for analyzing the cryptocurrency market was published in the Frontiers in Blockchain journal on April 1, 2026. This methodology opens new avenues for understanding the dynamics of the digital asset market more clearly through the application of artificial intelligence (AI). DM-MSTP is an innovative framework that combines traditional Minimum Spanning Tree (MST)-based approaches with AI and machine learning (ML). The MST method, pioneered by Mantegna in 1999, has proven its value in traditional financial markets by filtering noisy correlations and revealing hierarchical structures, establishing itself as a crucial tool for financial network analysis. However, the integration of such frameworks into digital asset optimization remained insufficient, and limitations became apparent in the cryptocurrency environment due to data noise and complexity. Concurrently, while artificial intelligence (AI) and machine learning (ML) models have achieved remarkable success in financial portfolio prediction and optimization, they often tended to prioritize model performance alone. DM-MSTP was developed to overcome the limitations of these two approaches. The core innovation of this framework lies in directly embedding AI within the MST network topology, which not only enhances model performance but also transparently represents the dependencies between cryptocurrencies. This goes beyond merely improving prediction accuracy, helping investors and portfolio managers better understand and control risk propagation mechanisms and market co-movements. For instance, when attempting to cluster and predict dynamic changes in the cryptocurrency market in real-time, DM-MSTP demonstrated effectiveness in refining noisy data while simultaneously segmenting market trends according to various use cases such as payment settlements, stablecoins, and decentralized finance (DeFi). This study applied the DM-MSTP methodology to 35 major cryptocurrencies from 2018 to perform clustering. It explored patterns of dynamic cluster formation within the market, revealing that cryptocurrencies tend to form dynamic clusters based on use cases such as payments, DeFi, or stablecoins, as recent studies have shown. The research results emphasized that these clusters can rapidly reconfigure in response to speculative trends or regulatory events, underscoring the increased importance of investment risk management and predictive capabilities. Such capabilities provide investors with a clear information base and can make a decisive contribution to reducing market uncertainty. The innovation focus highlighted by DM-MSTP technology extends beyond mere clustering or predictive analysis to uncover hidden patterns within the cryptocurrency market. Notably, this methodology prioritizes interpretability as a core value. In a landscape where many AI-based financial models are criticized for their 'black box' nature, DM-MSTP transparently presents the relationships between cryptocurrencies and the logic behind cluster formation, enabling investors to trust and understand the analysis results. This transparency further enhances its value as a reliable decision-support tool for navigating the complex and evolving digital asset environment. Financial Technology Innovation Brought by the DM-MSTP Framework The paper also emphasizes that DM-MSTP is a practical tool capable of optimizing portfolio construction and enhancing risk-return management. While traditional cryptocurrency market analysis primarily focused on individual asset performance or simple correlations, DM-MSTP enables more strategic portfolio management by capturing the overall structural characteristics and dynamic changes of the market. Through this, investors can clearly identify which cryptocurrencies move together and which assets are effective for risk diversification. However, every innovation requires careful evaluation. Some might point out that DM-MSTP is overly technology-centric, and general investors may require additional education or tool development to directly utilize this methodology. Furthermore, the cryptocurrency market still contains a mix of regulatory uncertainties and excessive speculative elements, so DM-MSTP's analysis alone may have limitations in fully predi
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