Volume 13 • Issue 2 • PP: 18–22 • 2025
From Industry Labels to Offer Prices: Measuring AI Association Effects on IPOs
Open Access & Copyright
© 2025 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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As more companies position themselves to capitalize on becoming AI-driven innovators or market disruptors rather than traditional technology firms, this raises an important question for valuation research. The purpose of this study is to collect and analyze the various datasets, indicators, and patterns available in the current landscape of initial public offerings (IPOs) that are associated with artificial intelligence (AI). To (a) evaluate the effectiveness of econometric methods used within AI-related IPO analyses based primarily on narrative valuation and financial modeling, and (b) identify which industry indicators are the most predictive of pricing outcomes within these offerings. This paper then extends the existing literature by linking the narrative and quantitative dimensions of IPO valuation with the behavioral economics of investors and underwriters. Firms from AI-intensive sectors have a valuation premium and are relatively more appealing than non-AI peers in investor sentiment and pricing expectations. This results in a framework of factors defining AI association, valuation dynamics, and narrative influence that are considered relevant for the capital formation process. Within each model, results show differential effects for companies that belong to and do not belong to AI-related industries in price formation and fundraising outcomes. By bringing together descriptive insights and regression-based evidence on AI affiliation and IPO performance, this study reinforces the possibility of narrative bias and the symbolic influence of AI association through the combined analysis of market data from technology, financial, and innovation ecosystems. There is, however, a need for greater refinement concerning these classification measures to further improve the accuracy of IPO valuation models.
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