This study examines how AI-based consulting influences investors’ stock preferences in online trading by focusing on the psychological mechanisms behind risk-seeking and risk-averse behavior. Drawing on prospect theory and the planning fallacy, it argues that investment preferences depend not only on objective risk return profiles but also on whether decisions are guided by AI consulting or self-directed judgment. Across three experiments, a clear pattern emerges. AI-guided decisions consistently lead investors to prefer low-risk, low-return stocks, reflecting stronger loss aversion. In contrast, self-directed decisions increase preference for high-risk, high-return stocks, indicating reduced loss aversion, particularly among experienced investors. Study 3 further shows that this self-driven risk-seeking is rooted in focalism: prompting investors to recall past failures significantly weakens risky preferences, while AI-guided choices remain conservative. Overall, the findings suggest that self-directed risk-seeking arises from cognitive bias, whereas AI-based consulting promotes psychological distancing and loss-averse decision-making, offering important implications for behavioral finance and AI-driven advisory systems.
Kyungjin Kim (2026) studied this question.