用多模态分析中国上市公司AI夸大宣传，揭示其资本市场的后果。
Talk, Walk, and Market Response: Multimodal Measurement of AI Washing and Its Capital Market Consequences in China
- 通过图文一致性与实际投入匹配度构建AI虚报风险评分
- 虚报公司创新被挤出，长期机构投资者会减持并引发估值下调
- 研究为监管科技提供依据，适合关注AI投资泡沫者阅读
随着人工智能和生成式大模型推动产业升级，资本市场愈发关注以AI为主题的上市公司。信息不对称和技术不透明降低了夸大AI能力的成本，导致广泛存在的AI虚报现象。基于2018年第一季度至2025年第二季度中国A股市场数据，本文推进了测量与机制检验的研究。利用Qwen-VL构建多模态AI虚报风险评分（AWRS），评估年报与路演中文字与图像的一致性；并通过主成分分析（PCA）整合专利质量、AI无形资产资本化及技术人员薪酬，构建实质性投资匹配指数（MRMI）。研究发现：(1) AWRS对未来MRMI无预测力，财务受限企业存在更大言行差距；(2) 实质性AI投入促进高质量专利产出，而空泛宣传则挤出行业创新；(3) 长期机构投资者通过实地考察识别虚报行为并减少持股；(4) 这类减持在180天内触发分析师下调评级、散户抛售及显著估值修正。结果经工具变量-两阶段最小二乘法（IV-2SLS）和分段双重差分法（staggered DID）验证，使用ChatGPT冲击作为外生事件。本研究深化了信息披露与定价效率研究，支持监管科技（RegTech）以遏制主题炒作。
原文摘要 · Abstract (English)
As artificial intelligence and generative large language models drive industrial upgrading, capital markets increasingly focus on AI-themed listed firms. Information asymmetry and technological opacity lower the cost of exaggerating AI capabilities relative to genuine R&D, spurring widespread AI Washing. Using China's A-share market from 2018Q1 to 2025Q2, we advance literature in measurement and mechanism testing. We construct a multimodal AI Washing Risk Score (AWRS) via Qwen-VL to assess text-image consistency in annual reports and roadshows, and a Material Real-Investment Matching Index (MRMI) from patent quality, AI intangible asset capitalization, and technical personnel compensation using PCA. Four findings emerge: (1) AWRS lacks predictive power for future MRMI, with a wider rhetoric-action gap among financially constrained firms; (2) substantive AI investment boosts high-quality patents, while empty rhetoric crowds out industry innovation; (3) long-horizon institutional investors detect AI Washing through site visits and reduce holdings; (4) such divestment triggers analyst downgrades, retail selling, and sharp valuation corrections within 180 days. Results are robust to IV-2SLS and staggered DID using the ChatGPT shock. This study enhances disclosure and pricing-efficiency research and supports RegTech for curbing thematic speculation.
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