AI 中文总结
通过韩国400名在职成年人的养老金投资组合实验，发现AI金融建议的差异约37%传导至最终投资组合，多数调整者会向建议方向调整，理由对传导无显著影响。
AI 中文摘要
我们研究AI生成的金融建议的差异如何转化为个人投资组合选择。在一项针对韩国400名参与职场固定缴款养老金计划的在职成年人的实验中，参与者需在11种产品间分配一笔虚拟养老金余额，且在收到两条固定AI生成建议中的一条后可进行调整。采用2×2设计，随机分配建议内容及建议是否包含简短理由。实验诱导的激进型与保守型建议之间的差异，约37%传导至最终投资组合。这一因果对比改变了投资组合的预期收益、波动率、风险等级间的分配及持有产品数量，但在计算出的夏普比率上未产生可检测到的差异。81%的参与者进行了调整，在调整者中，95%的人向分配的建议方向调整，并实施了约一半的建议调整量。理由未对传导产生可检测到的影响。这些结果表明，用户会部分且有选择性地将建议内容转化为具有经济意义的风险暴露差异，同时对初始选择保留了重要权重。
英文摘要
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.
