统一模型同时预测个股波动与金融系统风险，提升决策准确性。
Uni-FinLLM: A Unified Multimodal Large Language Model with Modular Task Heads for Micro-Level Stock Prediction and Macro-Level Systemic Risk Assessment
- 共享主干+模块化任务头，融合文本、时序、财务与图像数据
- 股票方向预测准确率达67.4%，信用风险识别达84.1%，系统性风险预警达82.3%
- 适合金融风控、量化交易与监管机构使用
金融机构与监管机构需要整合异构数据以评估从个股波动到系统性脆弱性的风险。现有方法通常孤立处理这些任务，无法捕捉跨尺度依赖。我们提出Uni-FinLLM，一种统一的多模态大语言模型，采用共享Transformer主干与模块化任务头，联合处理金融文本、数值时间序列、基本面数据与视觉信息。通过跨模态注意力与多任务优化，模型学习微、中、宏观预测的一致表征。在股票预测、信用风险评估与系统性风险检测上评估，显著优于基线：股票方向准确率提升至67.4%（原61.7%），信用风险准确率84.1%（原79.6%），宏观早期预警准确率82.3%。结果验证了统一多模态大模型可协同建模资产行为与系统性脆弱性，为金融领域提供可扩展的决策支持引擎。
原文摘要 · Abstract (English)
Financial institutions and regulators require systems that integrate heterogeneous data to assess risks from stock fluctuations to systemic vulnerabilities. Existing approaches often treat these tasks in isolation, failing to capture cross-scale dependencies. We propose Uni-FinLLM, a unified multimodal large language model that uses a shared Transformer backbone and modular task heads to jointly process financial text, numerical time series, fundamentals, and visual data. Through cross-modal attention and multi-task optimization, it learns a coherent representation for micro-, meso-, and macro-level predictions. Evaluated on stock forecasting, credit-risk assessment, and systemic-risk detection, Uni-FinLLM significantly outperforms baselines. It raises stock directional accuracy to 67.4% (from 61.7%), credit-risk accuracy to 84.1% (from 79.6%), and macro early-warning accuracy to 82.3%. Results validate that a unified multimodal LLM can jointly model asset behavior and systemic vulnerabilities, offering a scalable decision-support engine for finance.
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