用财报文本分析公司AI投入程度，构建可量化的AI股票指数。
Quantifying A Firm's AI Engagement: Constructing Objective, Data-Driven, AI Stock Indices Using 10-K Filings
- 通过NLP分析3395家上市公司年报，用词频和语境量化AI参与度。
- 新指数在收益与风险表现上优于14只现有AI ETF，且波动率不升。
- 为投资者和政策制定者提供透明、客观的科技主题投资工具。
分析现有AI主题交易型基金（ETF）发现，其选股标准常依赖模糊表述和主观判断。本文提出一种基于自然语言处理（NLP）的新方法，通过分析2011至2023年间3,395家纳斯达克上市公司的年度10-K文件，客观量化企业对人工智能的参与度，采用二值指标和加权得分衡量术语频率与上下文。据此构建四个AI股票指数：等权重指数（AII）、市值加权指数（SAII）及两个时间折扣指数（TAII05与TAII5X），从不同角度反映AI投资。通过事件研究验证，发布ChatGPT后，高AI参与度企业出现显著正向异常收益。新指数在风险-回报表现、市场响应速度和整体绩效方面达到或超越14只现有AI ETF及纳斯达克综合指数，实现更高平均日收益率与风险调整指标，同时未增加波动性。结果表明，该基于NLP的方法为现有AI ETF产品提供了可靠、灵敏且成本可控的替代方案。该方法还可用于指导投资者、资产管理者与政策制定者利用企业数据构建其他主题投资组合，推动更透明、数据驱动的投资生态。
原文摘要 · Abstract (English)
Following an analysis of existing AI-related exchange-traded funds (ETFs), we reveal the selection criteria for determining which stocks qualify as AI-related are often opaque and rely on vague phrases and subjective judgments. This paper proposes a new, objective, data-driven approach using natural language processing (NLP) techniques to classify AI stocks by analyzing annual 10-K filings from 3,395 NASDAQ-listed firms between 2011 and 2023. This analysis quantifies each company's engagement with AI through binary indicators and weighted AI scores based on the frequency and context of AI-related terms. Using these metrics, we construct four AI stock indices-the Equally Weighted AI Index (AII), the Size-Weighted AI Index (SAII), and two Time-Discounted AI Indices (TAII05 and TAII5X)-offering different perspectives on AI investment. We validate our methodology through an event study on the launch of OpenAI's ChatGPT, demonstrating that companies with higher AI engagement saw significantly greater positive abnormal returns, with analyses supporting the predictive power of our AI measures. Our indices perform on par with or surpass 14 existing AI-themed ETFs and the Nasdaq Composite Index in risk-return profiles, market responsiveness, and overall performance, achieving higher average daily returns and risk-adjusted metrics without increased volatility. These results suggest our NLP-based approach offers a reliable, market-responsive, and cost-effective alternative to existing AI-related ETF products. Our innovative methodology can also guide investors, asset managers, and policymakers in using corporate data to construct other thematic portfolios, contributing to a more transparent, data-driven, and competitive approach.
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