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结合新闻、宏观与技术信号的不确定性感知小盘股投资组合

文章 arXiv papers · 作者: Alireza Kargarzadeh et al.
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总结

本研究将财经新闻中的语言模型情绪与宏观经济指标和技术信号结合起来，构建 Russell 2000 股票投资组合。该不确定性感知方法将预测风险拆分为偶然不确定性和认知不确定性，并将这些估计值纳入投资组合配置器使用的协方差矩阵。研究比较了三种选股状态：归因于个股特有变动的信号、由宏观因素驱动的变动，以及两个渠道信号一致的情形。

报告结果显示，与要求两个信号同时触发相比，个股特有和宏观两条独立策略腿表现更好。在交易成本较低或适中的情况下，宏观驱动选股在一天时距下似乎有用，但高成本会抹去这一优势；在40天时距下，报告称宏观重新定价较慢有利于宏观策略腿。报告称，最稳健的保守结果来自一个40天宏观触发策略，该策略使用GPT-4o 情绪、Student-t 目标和风险平价，在100个基点的成本下夏普比率为2.33。摘要未说明完整测试设计，也未证明这些结果可推广到评估范围之外的股票和假设条件。

核心观点

  • 投资组合流程将偶然不确定性和认知不确定性的独立风险估计纳入协方差矩阵。
  • 该流程区分个股特有触发因素、宏观驱动触发因素及二者交集。
  • 通常，个股特有和宏观两条独立策略腿优于要求两个信号渠道一致。
  • 宏观触发因素可能捕捉短时距的快速溢出，但交易成本可能消除这一优势。
  • 报告的最佳保守配置采用40天持有期和风险平价，但测试细节有限。

标签

股票美国市场市场情绪投资组合构建风险管理
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# Large Language Model-Driven Small-Capitalization Trading: Integrating Financial News Sentiment, Macroeconomic Indicators, and Technical Signals









Large language models can extract richer signals from financial news than fixed sentiment lexicons, and recent work has explored feeding such signals into portfolio construction. We study an uncertainty-aware construction that feeds model-predicted risk -- decomposed into aleatoric and epistemic components -- directly into the covariance matrix of portfolio allocators, rather than treating portfolio risk as fixed or adjusting only expected returns. We evaluate the pipeline on Russell 2000 equities under three stock-selection regimes: a pure-alpha trigger that isolates abnormal stock moves not explained by macro indicators, a pure-beta trigger that captures macro-indicator moves before the stock itself fires, and a beta trigger in which both channels agree. Across the full holding-period grid, the separated pure-alpha and pure-beta legs usually dominate the beta intersection on Sharpe and return. Two horizons are especially informative. At one day, pure beta can work under low and moderate transaction costs because it captures immediate lead-lag spillovers from liquid macro and sector indicators into exposed small-cap stocks, but this advantage disappears at 100 bps when turnover and microstructure noise dominate. At 40 days, pure beta works for a different reason: slower macro repricing overtakes the firm-specific pure-alpha channel. The strongest conservative row is pure beta with GPT-4o mini sentiment, a Student-t target, a 40-day holding period, and risk parity allocation, reaching Sharpe 2.33 at 100 bps. The results suggest that stock-selection regime and allocator choice matter at least as much as the sentiment model, and that separating firm-specific and macro-exposure triggers is more informative than requiring both to fire simultaneously.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。