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DeePM：面向系统化宏观投资组合的稳健深度学习

文章 arXiv papers · 作者: Kieran Wood et al.
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总结

DeePM被描述为一个端到端深度学习系统，用于管理多元化宏观投资组合。其设计通过基于时滞的因果机制处理异步市场数据，利用融入经济学信息的图结构约束跨资产关系，并以稳健的风险调整目标进行训练；该目标会惩罚表现不佳的历史窗口，以此作为熵意义下尾部风险的代理指标。文中所述输入为每日收盘价，投资组合范围由多元化期货构成。

文章报告了涵盖2010–2025并计入交易成本的大规模回测，声称其净风险调整后表现优于趋势跟随方法、被动基准和Momentum Transformer。文章将模型在不同市场环境中的韧性归因于滞后横截面注意力机制、图先验、成本处理和极小极大优化。这些是研究报告的发现，并非经过独立证实的结果；简短描述没有说明具体工具、评估方案、实施细节或比较结果的不确定性。回测表现不能证明未来结果。

核心观点

  • DeePM使用时滞机制处理异步提供的宏观和市场信息。
  • 融入经济学信息的图先验对模型表示跨资产关系的方式进行正则化。
  • 训练时使用最差窗口惩罚，以实现稳健的风险调整后投资组合表现。
  • 报告中的期货回测计入交易成本，并将模型与趋势跟随和被动基准进行比较。
  • 消融结果据称将滞后注意力、图先验、成本建模和稳健优化与模型泛化联系起来。

标签

机器学习期货投资组合构建风险管理回测
全文
# DeePM: Regime-Robust Deep Learning for Systematic Macro Portfolio Management


# DeePM: Regime-Robust Deep Learning for Systematic Macro Portfolio Management









We propose DeePM (Deep Portfolio Manager), a structured deep-learning macro portfolio manager trained end-to-end to maximize a robust, risk-adjusted utility. DeePM addresses three fundamental challenges in financial learning: (1) it resolves the asynchronous "ragged filtration" problem via a Directed Delay (Causal Sieve) mechanism that prioritizes causal impulse-response learning over information freshness; (2) it combats low signal-to-noise ratios via a Macroeconomic Graph Prior, regularizing cross-asset dependence according to economic first principles; and (3) it optimizes a distributionally robust objective where a smooth worst-window penalty serves as a differentiable proxy for Entropic Value-at-Risk (EVaR) - a window-robust utility encouraging strong performance in the most adverse historical subperiods. In large-scale backtests from 2010-2025 on 50 diversified futures with highly realistic transaction costs, DeePM attains net risk-adjusted returns that are roughly twice those of classical trend-following strategies and passive benchmarks, solely using daily closing prices. Furthermore, DeePM improves upon the state-of-the-art Momentum Transformer architecture by roughly fifty percent. The model demonstrates structural resilience across the 2010s "CTA (Commodity Trading Advisor) Winter" and the post-2020 volatility regime shift, maintaining consistent performance through the pandemic, inflation shocks, and the subsequent higher-for-longer environment. Ablation studies confirm that strictly lagged cross-sectional attention, graph prior, principled treatment of transaction costs, and robust minimax optimization are the primary drivers of this generalization capability.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。