发表机构
Mays Business School, Texas A&M University（得克萨斯农工大学梅斯商学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过对金融网红的X账号构建的数字孪生开展重复实时访谈，解决金融社交媒体中未披露信息的测量问题，其信息可预测大盘股收益横截面，避免了大语言模型的前瞻性偏差。
AI 中文摘要
社交媒体会影响金融市场，但金融媒体角色的公开帖子属于自愿披露，未披露的内容通常无法观测。我们通过对由受监测金融网红的X账号构建的“数字孪生”开展重复、实时访谈（遵循固定协议），解决这一测量问题。即便未发布公开推荐，这些访谈仍能恢复个股层面的公开角色信念代理指标。由于访谈在相关收益窗口之前生成并归档，该设计避免了事后查询大语言模型（LLM）时出现的前瞻性偏差。证据表明，从这些数字孪生访谈中获取的信息，能按预期方向预测大盘股收益的横截面。因此，重复实时访谈展示了如何将选择性披露转化为可测量的市场观点面板。
英文摘要
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.
