Do AI Assistants Reinforce Investor Beliefs? Evidence from Human and AI Replies

Swaminathan Balasubramaniam (), Jorge Sabat () and Soumyajit Ray ()
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Swaminathan Balasubramaniam: NEOMA Business School, Rouen, France
Jorge Sabat: Universidad Andrés Bello – Institute of Economic Policy, Santiago, Chile
Soumyajit Ray: Department of Electrical Engineering, Johns Hopkins University, Baltimore, MD

No 2026-133, Working Papers from Becker Friedman Institute for Research In Economics

Abstract: Using Reddit r/wallstreetbets posts, we compare the sentiment of AI-generated replies with actual human top-level replies to the same investor-authored posts. We find that counterfactual replies generated by a user-facing AI model are systematically more bullish than human replies. Prompting the model to respond as a professional investor narrows the gap, but does not eliminate it. To study potential mechanisms, we compare the user-facing model with a matched base model that shares the same architecture and pretraining corpus but lacks instruction-following post-training and chat-format deployment. The instruction-tuned model is consistently more bullish than the matched base model, suggesting that the post-trained conversational layer amplifies bullishness beyond what is already present in the base model. We rationalize the evidence with a reduced-form model in which replies reflect a learned bullish default, responsiveness to the user’s thesis, and categorical expression. Finally, we examine whether AI-augmented signals retain return-relevant content. The return evidence is descriptive: AI-augmented signals remain positively associated with near-horizon returns in the main specifications, but the relation is not uniform across samples or horizons.

Keywords: AI and LLMs; Investor Communication; Information Intermediaries; Sentiment; Retail Investors (search for similar items in EconPapers)
JEL-codes: C55 G10 G12 G41 (search for similar items in EconPapers)
Pages: 62 pages
Date: 2026-10
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