发表机构
Tailstate Intelligence Ltd.; Zanista AI Ltd.（泰尔斯特特智能有限公司; 扎尼斯特人工智能有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出将大语言模型预测的分解型风险输入投资组合协方差矩阵的方法，在罗素2000股票上验证分离纯阿尔法与纯贝塔选股机制的策略，其在40天持有期下夏普比率达2.33，表现优于双渠道一致机制。
AI 中文摘要
大型语言模型能够从财经新闻中提取比固定情绪词典更丰富的信号，近期研究已探索将此类信号用于投资组合构建。本研究提出一种不确定性感知的构建方法，将模型预测的风险（分解为偶然风险与认知风险分量）直接输入投资组合分配器的协方差矩阵，而非将投资组合风险视为固定值或仅调整预期收益。我们在罗素2000股票上针对三种选股机制评估该流程：纯阿尔法触发机制（分离宏观指标无法解释的异常股价波动）、纯贝塔触发机制（在股票自身变动前捕捉宏观指标变动）、双渠道一致的贝塔触发机制。在整个持有期网格中，分离后的纯阿尔法与纯贝塔组合在夏普比率和收益上通常优于贝塔交集组合。两个时间区间的表现尤为关键：1天时，纯贝塔在低至中等交易成本下有效，因其捕捉了流动性宏观与行业指标向小盘股的即时领先-滞后溢出效应，但当换手率与微观结构噪声主导时，该优势在100个基点（bps）时消失；40天时，纯贝塔因较慢的宏观重定价超过公司层面的纯阿尔法渠道而有效。表现最佳的保守策略为采用GPT-4o mini情绪、学生t目标、40天持有期与风险平价分配的纯贝塔组合，在100 bps时夏普比率达2.33。结果表明，选股机制与分配器选择的重要性至少不亚于情绪模型，且分离公司层面与宏观敞口触发机制比要求两者同时触发更具信息价值。
英文摘要
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.
CommentsSome technical mistakes in the paper, we will re-submit the new version soon
