Quantifying a firm's AI engagement: Constructing objective, data-driven, AI stock indices using 10-K filings
17
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2025-03-01
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00401625
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2-s2.0-85214280198
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Technological Forecasting and Social Change
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212
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SCOPUS
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Technological Forecasting and Social Change Vol.212 (2025)
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Ante L., Saggu A. Quantifying a firm's AI engagement: Constructing objective, data-driven, AI stock indices using 10-K filings. Technological Forecasting and Social Change Vol.212 (2025). doi:10.1016/j.techfore.2024.123965 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/102729
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Quantifying a firm's AI engagement: Constructing objective, data-driven, AI stock indices using 10-K filings
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This paper proposes an objective, data-driven approach using natural language processing (NLP) techniques to classify AI stocks by analyzing annual 10-K filings from 3395 NASDAQ-listed firms between 2010 and 2022. Each company's engagement with AI is classified through binary and weighted AI scores based on the frequency 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 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.