通过数字孪生访谈，捕捉金融博主隐含观点并预测大盘股收益。
Talking to Digital Twins: Selective Disclosure and Belief Measurement in Financial Social Media
- 构建金融博主数字孪生体，按固定流程实时访谈获取隐藏观点。
- 访谈数据能有效预测大市值股票收益，方向符合预期。
- 解决事后查询导致的前瞻偏差，适合研究市场情绪与信息隐藏。
社交媒体影响金融市场，但金融类媒体人发布的内容为自愿披露，未披露的信息通常不可观测。本文通过在固定协议下对监控的金融博主X账号构建的‘数字孪生体’进行重复实时访谈，恢复了即使无公开推荐也存在的个股层面公众人物信念代理指标。由于访谈生成与归档发生在相关收益窗口之前，该设计避免了事后用大模型查询时产生的前瞻偏差。实证表明，这些数字孪生体访谈获得的信息可有效预测大市值股票收益的横截面表现，方向符合预期。重复实时访谈揭示了如何将选择性披露转化为可观测的市场观点面板。
原文摘要 · Abstract (English)
Social media affect financial markets, but public posts by financial media personas are voluntary disclosures. What is not disclosed is therefore usually unobserved. We address this measurement problem by conducting repeated, real-time interviews of "digital twins" built from monitored finfluencers' X accounts under a fixed protocol. The interviews recover stock-level public-persona belief proxies even when no public recommendation is made. Because the interviews are generated and archived before the relevant return windows, the design avoids the look-ahead bias that arises when LLMs are queried ex post. The evidence shows that information obtained from these digital-twin interviews predicts the cross section of large-cap stock returns in the expected direction. Repeated real-time interviews therefore show how selective disclosure can be turned into measurable panels of market views.
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