Artificial Intelligence, Demand Elasticity, and Asset Prices
A new study finds that artificial intelligence is changing how big investors like banks and funds react to price shifts in financial markets. Contrary to expectations, exposure to AI-focused firms makes institutional demand less elastic, meaning portfolios adjust more slowly. Using a novel measure of AI exposure and decades of data, researchers show that markets become more volatile and slower to recover after shocks, especially for banks. The link between AI adoption and market behavior is a key insight.
What it examines
This paper studies how artificial intelligence (AI) changes asset prices by affecting how institutions buy and sell stocks. Using data on institutional holdings and a demand-system asset pricing model, the authors show that AI adoption inside firms makes institutional demand less responsive to price changes, increasing market price impact.
What it concludes
AI in firms makes markets less flexible, causing prices to react more strongly and adjust more slowly to shocks. This can affect market stability and liquidity. The findings help investors, regulators, and policymakers understand how technology changes market behavior and suggest future research on AI's broader financial effects.
Evidence objects
Researchers developed a novel measure of AI exposurebased on how much of a firm's work can be done by AIand tracked institutional portfolio shifts using decades of regulatory filings and stock data.
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AI is transforming financial markets, but surprisingly, exposure to AI-intensive firms makes big investors like banks and funds less responsive to price changes, reducing market flexibility instead of increasing it.
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When institutions hold more AI-exposed firms, market prices react more sharply to shocks and take longer to stabilize, especially after major events like ChatGPTs release; this effect is strongest among regulated banks.
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This paper uniquely examines firm-side AI adoptions structural effects on institutional demand elasticity and asset prices, diverging from typical investor-focused studies. By embedding AI exposure into a demand-system asset-pricing model and using novel generative AI measures, it provides compelling, original empirical evidence that challenges established views on market elasticity and price dynamics.
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Raw abstract and provenance
- … The return predictability results in this section complete the empirical validation of the … In this sense, return predictability is not an independent phenomenon but a direct …
Source row: 232 · abstract type: snippet