标题： 金融监管与人工智能：一场弗洛斯蒂安的交易吗？ 显示英文标题

标题： Financial Regulation and AI: A Faustian Bargain?

摘要： 我们考察了是否以及如何将粒度化、实时的预测模型整合到中央银行的宏观审慎工具箱中。 首先，我们开发了一个易于处理的框架，该框架形式化了监管者在选择能够准确预测系统性风险但因果内容不确定的模型与具有相反特点的模型时所面临的权衡。 我们在一个私人投资组合内生地对监管者的模型选择和政策规则做出反应的环境中，推导出监管者的最优政策。 我们证明，即使纯粹的预测模型也能为监管者带来福利收益，并且预测精度和对政策干预因果影响的知识是互补的。 其次，我们引入了一种专为金融持仓数据设计的深度学习架构——图变换器，并讨论了它为何最适合这个问题。 该模型通过显式建模持仓的关系结构，为资产和投资者学习向量嵌入表示，并在包括交易预测在内的样本外预测任务中达到了最先进的预测准确性。 显示英文摘要

摘要： We examine whether and how granular, real-time predictive models should be integrated into central banks' macroprudential toolkit. First, we develop a tractable framework that formalizes the tradeoff regulators face when choosing between implementing models that forecast systemic risk accurately but have uncertain causal content and models with the opposite profile. We derive the regulator's optimal policy in a setting in which private portfolios react endogenously to the regulator's model choice and policy rule. We show that even purely predictive models can generate welfare gains for a regulator, and that predictive precision and knowledge of causal impacts of policy interventions are complementary. Second, we introduce a deep learning architecture tailored to financial holdings data--a graph transformer--and we discuss why it is optimally suited to this problem. The model learns vector embedding representations for both assets and investors by explicitly modeling the relational structure of holdings, and it attains state-of-the-art predictive accuracy in out-of-sample forecasting tasks including trade prediction.