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English(EN) Are LLMs Good Financial User Simulators? A Preliminary Study

大型语言模型难以模拟复杂的金融交易行为

一项初步研究调查了大型语言模型(LLMs)在模拟个人金融交易决策方面的有效性。在一个有120名志愿者的受控模拟交易环境中,研究人员利用LLMs根据预设截止日期前的信息来预测参与者的行为、交易的证券以及交易数量。虽然市场背景提高了对行为和股票代码的预测准确性,但交易规模的预测仍然具有挑战性。研究还发现LLMs存在系统性的行为压缩,包括过度生成持有行为和低估卖出决策。 AI

影响 这项研究突显了大型语言模型在准确模拟复杂人类金融决策方面存在的局限性,表明当前模型可能不适用于复杂的金融模拟任务。

排序理由 在arXiv上发表的学术论文,详细介绍了一项研究。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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大型语言模型难以模拟复杂的金融交易行为

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在arXiv上发表的学术论文,详细介绍了一项研究。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Jiajie He, Jiangyuan Hong, Dongling Ni, Wenjin Liu, Xintong Chen ·

    大型语言模型是优秀的金融用户模拟器吗?一项初步研究

    arXiv:2609.15727v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as user simulators, but their ability to reproduce evolving individual financial decisions remains unclear. We present a preliminary study in a controlled paper-trading environment …