Artificial Intelligence for Detecting Price Surges Based on Network Features of Crypto Asset Transactions

Yuichi Ikeda, Hideaki Aoyama, Tetsuo Hatsuda, Tomoyuki Shirai, Taro Hasui, Yoshimasa Hidaka, Krongtum Sankaewtong, Hiroshi Iyetomi, Yuta Yarai, Abhijit Chakraborty, Yasushi Nakayama, Akihiro Fujihara, Pierluigi Cesana and Wataru Souma

Discussion papers from Research Institute of Economy, Trade and Industry (RIETI)

Abstract: This study proposes an artificial intelligence framework to detect price surges in crypto assets by leveraging network features extracted from transaction data. Motivated by the challenges in Anti-Money Laundering, Countering the Financing of Terrorism, and Counter-Proliferation Financing, we focus on structural features within crypto asset networks that may precede extreme market events. Building on theories from complex network analysis and rate-induced tipping, we characterize early warning signals. Granger causality is applied for feature selection, identifying network dynamics that causally precede price movements. To quantify surge likelihood, we employ a Boltzmann machine as a generative model to derive nonlinear indicators that are sensitive to critical shifts in transactional topology. Furthermore, we develop a method to trace back and identify individual nodes that contribute significantly to price surges. The findings have practical implications for investors, risk management officers, regulatory supervision by financial authorities, and the evaluation of systemic risk. This framework presents a novel approach to integrating explainable AI, financial network theory, and regulatory objectives in crypto asset markets.

Pages: 72 pages
Date: 2025-12
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Persistent link: https://EconPapers.repec.org/RePEc:eti:dpaper:25113

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