标题： 战术资产配置与宏观经济状态检测 显示英文标题

标题： Tactical Asset Allocation with Macroeconomic Regime Detection

摘要： 本文通过使用机器学习技术进行制度建模，扩展了战术资产配置文献。我们提出了一种新模型，该模型可以对当前制度进行分类，预测未来制度的分布，并将这些预测与单个资产的历史表现相结合，以优化投资组合配置。利用来自FRED-MD数据库的宏观经济数据集，我们的方法采用修改后的k均值算法，以确保随时间推移的制度分类一致。然后，我们利用这些制度预测来估计预期收益和波动率，随后使用各种规模方案将这些结果映射到投资组合配置中。我们的方法优于传统的基准，如等权重、买入并持有和随机制度模型。此外，我们首次将来自大型宏观经济数据集的制度检测模型应用于战术资产配置，展示了投资组合绩效的显著改善。我们的工作有几个关键贡献，包括一种针对预测制度不确定性的数据驱动制度检测算法，以及将FRED-MD数据集应用于战术资产配置。 显示英文摘要

摘要： This paper extends the tactical asset allocation literature by incorporating regime modeling using techniques from machine learning. We propose a novel model that classifies current regimes, forecasts the distribution of future regimes, and integrates these forecasts with the historical performance of individual assets to optimize portfolio allocations. Utilizing a macroeconomic data set from the FRED-MD database, our approach employs a modified k-means algorithm to ensure consistent regime classification over time. We then leverage these regime predictions to estimate expected returns and volatilities, which are subsequently mapped into portfolio allocations using various sizing schemes. Our method outperforms traditional benchmarks such as equal-weight, buy-and-hold, and random regime models. Additionally, we are the first to apply a regime detection model from a large macroeconomic dataset to tactical asset allocation, demonstrating significant improvements in portfolio performance. Our work presents several key contributions, including a novel data-driven regime detection algorithm tailored for uncertainty in forecasted regimes and applying the FRED-MD data set for tactical asset allocation.