Working Paper2026SSRN

Do Narratives Crowd Out Fundamentals? Retail Investors' Response to Generative AI

Authors: Fujing Xue, Shuyang Jia, Xiaofeng Zhao, Nan Hu

Abstract

This study examines how retail investors respond to the advent of generative AI and the ensuing capital-market consequences. Using a large language model to analyze millions of investor questions from China's Investor Interactive Platforms (IIP), we document a structural reallocation of retail investors' information demand. Following the launch of ChatGPT, firms with high prior AI engagement receive significantly more AI-related questions, while questions about non-AI topics decline. This shift in information demand is associated with higher trading volume and volatility but, paradoxically, weaker price informativeness. Our findings suggest retail investors' AI information demand introduces speculative noise and crowds out their attention to non-AI fundamentals, providing a novel perspective on how generative AI transforms financial markets.

Keywords

ChatGPTInformation DemandInvestor Interactive PlatformsPrice InformativenessLarge Language Models

Tags of Social Finance

#Evolutionary Finance#Media and Textual Analysis#Archival Empirical#Financing- and Investment Decisions (Individual)#Asset Pricing & Trading Volume and Market Efficiency#Manager & Firm Behavior#Propagation of Noise & Undesirable Outcomes