ARTIFICIAL INTELLIGENCE IN STOCK MARKETS: IMPLICATIONS FOR VOLATILITY, MARKET LIQUIDITY, AND INVESTMENT RETURNS
ARTIFICIAL INTELLIGENCE IN STOCK MARKETS: IMPLICATIONS FOR VOLATILITY, MARKET LIQUIDITY, AND INVESTMENT RETURNS

Summary/Abstract: This study aims to analyse how artificial intelligence, integrated and communicated differently across large, listed companies, is related to expectations and perceptions of price volatility, market liquidity, and investment returns. The methodology is based on three case studies in the European context: NatWest Group in the banking and financial services sector, Iberdrola in the energy and utilities sector, and Vodafone Group in the telecommunications and technology sector. The data are entirely secondary and include annual reports, consolidated financial documents, sustainability reports, press releases, and investor relations materials in the recent period, which were analysed through qualitative content analysis, thematic analysis, and a cross-case synthesis. The results showed that artificial intelligence in NatWest is mainly framed as an instrument for increasing efficiency, managing risk and strengthening regulatory compliance, suggesting a more stabilising role for liquidity and market confidence; at Iberdrola it is linked to the energy transition, smart grids and long-term sustainability, emerging as a source of improving the risk-return profile; while at Vodafone artificial intelligence is positioned as an engine of commercial growth and digital innovation, with expectations for higher return potential and more significant short-term volatility. The main conclusions emphasize that the impact of artificial intelligence on the stock market should not be seen solely through quantitative models, but also through the strategic narratives that companies build in their official documents, since the way it is portrayed shapes investors' expectations for risk, liquidity, and returns. Based on these findings, the study recommends that company management more clearly link artificial intelligence to risk management and performance; that investors analyze not only the intensity of reporting but also its strategic framing; and that regulators and researchers encourage more structured disclosures and combine qualitative analysis of narratives with quantitative market indicators. As additional data, the study provides a reproducible analytical framework for future work that seeks to combine official company documents with real price and liquidity behavior in stock markets.

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