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Multi-Agent Analysis of Off-Exchange Public Information for Cryptocurrency Market Trend Prediction

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Listed:
  • Kairan Hong
  • Jinling Gan
  • Qiushi Tian
  • Yanglinxuan Guo
  • Rui Guo
  • Runnan Li

Abstract

Cryptocurrency markets present unique prediction challenges due to their extreme volatility, 24/7 operation, and hypersensitivity to news events, with existing approaches suffering from key information extraction and poor sideways market detection critical for risk management. We introduce a theoretically-grounded multi-agent cryptocurrency trend prediction framework that advances the state-of-the-art through three key innovations: (1) an information-preserving news analysis system with formal theoretical guarantees that systematically quantifies market impact, regulatory implications, volume dynamics, risk assessment, technical correlation, and temporal effects using large language models; (2) an adaptive volatility-conditional fusion mechanism with proven optimal properties that dynamically combines news sentiment and technical indicators based on market regime detection; (3) a distributed multi-agent coordination architecture with low communication complexity enabling real-time processing of heterogeneous data streams. Comprehensive experimental evaluation on Bitcoin across three prediction horizons demonstrates statistically significant improvements over state-of-the-art natural language processing baseline, establishing a new paradigm for financial machine learning with broad implications for quantitative trading and risk management systems.

Suggested Citation

  • Kairan Hong & Jinling Gan & Qiushi Tian & Yanglinxuan Guo & Rui Guo & Runnan Li, 2025. "Multi-Agent Analysis of Off-Exchange Public Information for Cryptocurrency Market Trend Prediction," Papers 2510.08268, arXiv.org.
    • Handle: RePEc:arx:papers:2510.08268
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    References listed on IDEAS

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    3. Banerjee, Ameet Kumar & Akhtaruzzaman, Md & Dionisio, Andreia & Almeida, Dora & Sensoy, Ahmet, 2022. "Nonlinear nexus between cryptocurrency returns and COVID-19 news sentiment," Journal of Behavioral and Experimental Finance, Elsevier, vol. 36(C).
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    5. Vincent Gurgul & Stefan Lessmann & Wolfgang Karl Hardle, 2023. "Deep Learning and NLP in Cryptocurrency Forecasting: Integrating Financial, Blockchain, and Social Media Data," Papers 2311.14759, arXiv.org, revised Oct 2024.
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