人工智能金融建议：供给、需求与生命周期含义

中文摘要

我们要求一个代表性样本撰写向大语言模型（LLM）寻求消费与投资建议的提示词，随后在现实的资产与劳动力市场条件下，模拟遵循该建议的终身效应。将此方法应用于 GPT-5.2，我们发现遵循该建议将使受访者趋向生命周期理论：更广泛地参与多元化股票基金、随年龄递减的股票份额，以及更大的储蓄缓冲。建议因性别、先前的 AI 经验与金融素养而产生系统性差异。就性别而言，推荐股票份额差异的三分之二源于男性和女性撰写了不同的提示词（需求），而三分之一源于附加在其他方面相同的提示词上的性别标签（供给）。

Abstract

We ask a representative sample to write prompts seeking spending and investing advice from LLMs, then simulate the lifetime effects of following the advice under realistic asset and labor market conditions. Applying this method to GPT-5.2, we find following the advice would move respondents toward life cycle theory: broader participation in diversified equity funds, age-declining equity shares, and larger savings buffers. Recommendations vary systematically by gender, prior AI experience, and financial literacy. For gender, two-thirds of recommended equity-share differences arise from men and women writing different prompts (demand), while one-third arise from gender labels attached to otherwise identical prompts (supply).