Paper: arXiv 2510.08268

Authors: 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.

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 5.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper is mathematically dense with formal theoretical guarantees and optimal property proofs for its fusion mechanism and multi-agent system, but it focuses more on architectural design and statistical significance from experiments rather than providing exhaustive backtesting details like code, datasets, or detailed performance metrics, placing it in a high-math, moderate-rigor category.

Research Flowchart

  flowchart TD
  A["Research Goal: Predict crypto trends<br>using off-exchange public info"] --> B["Input: Heterogeneous Data Streams<br>(News, Social, Technical, On-chain)"]
  B --> C["Step 1: Multi-LLM News Analysis<br>Extract 6-Impact Features<br>(Sentiment, Regulatory, Volume, Risk, Technical, Temporal)"]
  C --> D["Step 2: Multi-Agent Coordination<br>Distributed Processing with<br>Low Communication Overhead"]
  D --> E["Step 3: Adaptive Volatility-Conditional Fusion<br>Dynamically combine signals based on<br>Market Regime Detection (Bull/Bear/Sideways)"]
  E --> F["Key Findings & Outcomes<br>Significant accuracy improvements<br>over SOTA NLP baselines<br>Enhanced sideways market detection<br>for risk management"]

Cited References (40)

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  2. Deep Learning and NLP in Cryptocurrency Forecasting: Integrating Financial, Blockchain, and Social Media Data (2024) — link
  3. Multilevel Analysis of Cryptocurrency News using RAG Approach with Fine-Tuned Mistral Large Language Model (2025) — link
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and 32 more
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  2. Controllable and Reliable Knowledge-Intensive Task-Oriented Conversational Agents with Declarative Genie Worksheets (2025) — link
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0 of 40 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.