检索增强扩散建模用于随机贴现因子投资组合
Retrieval-Augmented Diffusion Modeling for Stochastic Discount Factor Portfolios
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中文总结 AI 辅助
提出检索增强扩散框架RADAR，通过条件于相似历史机制学习市场表示，解决金融非平稳性和噪声问题，在风险调整指标上达到最先进性能并产生经济意义信号。
中文摘要 AI 辅助
在这项工作中，我们研究在随机贴现因子（SDF）框架下的投资组合优化问题，通过学习捕捉金融数据底层风险结构的市场状态表示。这面临多个挑战：金融市场表现出具有转移机制的非平稳动态；价格和新闻等多模态输入通常包含随机噪声；现有的基于扩散的方法虽然能有效建模随机动态，但依赖于各向同性高斯噪声等假设，无法捕捉金融不确定性的状态依赖性质。为解决这些挑战，我们提出了RADAR，一种检索增强扩散框架，通过以相似历史机制为条件来学习市场表示。RADAR利用检索构建上下文相关的噪声分布，应用条件扩散对多模态表示进行去噪，并使用经验统计初始化扩散过程以反映状态依赖的不确定性。实验表明，RADAR在关键风险调整指标上取得了最先进的性能，同时在资产收益和相关性上产生了具有经济意义的信号。
英文摘要
In this work, we study portfolio optimization under the stochastic discount factor (SDF) framework by learning market state representations that capture the underlying risk structures of financial data. This is challenging due to several factors: financial markets exhibit non-stationary dynamics with shifting regimes, multimodal inputs such as price and news data often contain stochastic noise, and existing diffusion-based approaches, while effective for modeling stochastic dynamics, rely on assumptions such as isotropic Gaussian noise that fail to capture the state-dependent nature of financial uncertainty. To address these challenges, we introduce RADAR, a retrieval-augmented diffusion framework that learns market representations by conditioning on similar historical regimes. RADAR leverages retrieval to construct context-dependent noise distributions, applies conditional diffusion to denoise multimodal representations, and initializes the diffusion process using empirical statistics to reflect state-dependent uncertainty. Experiments show that RADAR achieves state-of-the-art performance on key risk-adjusted metrics while producing economically meaningful signals on asset returns and correlations.
发表机构
- National University of Singapore（新加坡国立大学）
- Asian Institute of Digital Finance（亚洲数字金融研究所）
机构由 AI 辅助整理，请以论文原文为准。
