English(EN) When News Gets Old: Portfolio Allocation with Reinforcement Learning and Large Language Models

大型语言模型通过过滤陈旧财经新闻在投资组合配置中优于强化学习

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-23 04:00

研究人员利用强化学习（RL）和大型语言模型（LLMs）探索了新闻时效性对投资组合配置的影响。一项涉及2023-2025年历史数据的测试发现，虽然RL策略可以响应时效信息，但保留陈旧新闻信号并未带来持续的收益。对于直接选择投资组合的LLMs，过滤掉旧新闻被证明是最有效的方法，当新闻递送延迟四周时，平均年化净回报率为18.43%，优于所有纳入新闻的RL策略。 AI

影响展示了LLMs在金融决策中的实际应用，有望改进投资策略。

排序理由关于将AI方法应用于特定领域的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.LG 阅读 →

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本文如何被排名

Signal score: 3 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 关于将AI方法应用于特定领域的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, other; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: Same-day; Cluster formed today. Ranking reflects the current source set at time of score.

完整方法见我们的编辑标准。

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arXiv cs.LG TIER_1 English(EN) · Bolin Song (Cornell University), Surui Xiang (United Nations) · 2026-10-23 04:00

When News Gets Old: Portfolio Allocation with Reinforcement Learning and Large Language Models

arXiv:2610.30564v1 Announce Type: new Abstract: Financial news can influence investment decisions long after publication. Timely news may reach an investor before prices fully adjust, whereas older news may already be reflected in prices. We examine two ways to handle aging signa…

本文如何被排名

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When News Gets Old: Portfolio Allocation with Reinforcement Learning and Large Language Models

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