Do People Follow AI Advice? Evidence from a Pension Portfolio Choice Experiment

2026-08-11

AI Summary

This study investigates how individuals respond to AI-generated financial advice in real-world pension investment decisions and its causal impact on asset allocation. Through a 2×2 randomized controlled trial involving 400 Korean workplace pension participants, the research delivered either aggressive or conservative AI recommendations—with or without explanatory rationales—and combined behavioral economics tasks with econometric analysis to identify the causal effects of AI advice in an authentic pension context. The findings reveal that approximately 37% of the recommended portfolio shifts were transmitted to participants’ final allocations, significantly altering expected returns, volatility, and risk profiles. While 81% of participants adjusted their choices—95% of whom moved in the direction of the advice—they implemented only about half of the suggested change on average. Notably, providing explanatory rationales did not significantly enhance compliance. The results highlight selective adoption and partial adherence to AI advice, offering empirical insights for the design of robo-advisory systems.

Abstract

We study how differences in AI-generated financial recommendations are transmitted into individual portfolio choices. In an experiment with 400 employed adults enrolled in workplace defined contribution pension plans in South Korea, participants allocate a hypothetical pension balance across eleven products and may revise it after receiving one of two fixed AI-generated recommendations. A $2 \times 2$ design randomizes recommendation content and whether the recommendation includes a short rationale. Approximately 37$\%$ of the experimentally induced difference between the aggressive and conservative recommendations passes through to final portfolios. This causal contrast changes expected portfolio return, volatility, allocations across risk grades, and the number of products held, but produces no detectable difference in computed Sharpe ratios. 81$\%$ of participants revise. Among revisers, 95$\%$ move toward the assigned recommendation and implement about half of the suggested adjustment. Rationales do not detectably alter pass-through. These results show that users partially and selectively transmit recommendation content into economically meaningful differences in risk exposure while retaining substantial weight on their initial choices.

Problem

• AI advice
• portfolio choice
• pension
• behavioral response
• financial recommendations

Innovation

• AI advice
• portfolio choice
• behavioral experiment
• recommendation adherence
• pension investment