Investigating the impact of investor attention on AI-based stocks: A comprehensive analysis using quantile regression, GARCH, and ARIMA models - Sweena Ravichandran; Mohd Afjal (PLoS ONE, 2025)
The literature implies an increased interest in AI-based companies, but it is unclear how investor attention affects their volatility. This study fills the gap by investigating the relationship between investor attention, as measured by Google Trends data, and the volatility of AI-based stocks. Using weekly adjusted closing stock price data for 8 AI-based stocks from 2015 to 2024, quantile regression analysis was used to identify the impact of investor attention at various volatility levels. Though the direction of the effect differs, the data shows that investor attention has a considerable impact on the volatility of AI-based companies. Although most stocks show a positive relationship, Tencent Holding’s unique traits or market dynamics impact its response to investor attention. The study uses GARCH and ARIMA models to investigate stock volatility dynamics across time. The findings of this study show that market information changes are critical in driving volatility variations. This study provides insights into the intricate relationship between investor attention and market volatility, with substantial implications for investors and policymakers. Understanding these processes can help investors make educated decisions and allocate resources more effectively, while regulators can devise policies to reduce possible risks and promote market stability.