Multiperiod Dynamic Portfolio Choice: When High Dimensionality Meets Return Predictability
多期动态投资组合选择：高维资产与收益可预测性AI 解读 ↓
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多期动态投资组合选择：高维资产与收益可预测性
- 研究问题
- 当可交易资产和状态预测变量都很多时，如何利用条件收益可预测性，求解计算困难的多期动态投资组合问题？
- 方法与思路
- 作者先用风险溢价投影主成分法压缩资产维度，再在动态规划递推中采用调整后的半参数模型平均，组成 DRAMA 方法。
- 材料陈述的贡献
- 摘要称，该方法兼顾收益时间序列变动与预期收益信息，在一定假设下构造近似最优组合，并在美国股票数据上取得良好样本外表现。
- 阅读关注点
- 应核对因子降维保留哪些投资信息、近似最优所需条件，以及模型平均误差如何传递至动态决策和样本外评价。
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- 仅依据所给 OpenAlex 版本摘要；其中未列出具体假设、最优性误差界和绩效数值，不能据此推断刊本变化或普遍优势。
原文摘要
Multiperiod portfolio choice is the central problem in active asset management. Multiperiod dynamic portfolios are notoriously difficult to solve, especially when there are hundreds of tradable assets as well as a large number of state variables. In this article, we develop a novel two-step methodology to solve the multiperiod dynamic portfolio choice problem with high dimensional assets in the presence of return predictability conditional on a large number of state predictors. Specifically, in the first step, we propose the new Risk-Premium Projected-PCA (RP-PPCA) method to reduce the dimension of tradable assets. This method achieves Dimension Reduction (DR) by estimating latent factors with explanatory power in both time series variation and expected return in high-dimension-low-sample-size data. In the second step, we use dynamic programming to solve the multiperiod portfolio choice problem, and in each recursive step, we adopt an Adjusted semiparametric Model Averaging (AMA) method to avoid the curse of dimensionality associated with a large set of state variables while remaining computationally efficient. Thus, we name this two-step approach DRAMA, which stands for a combination of a new dimension reduction method and an adjusted semiparametric model averaging method. Analytically, we show that the portfolios constructed by the DRAMA are approximately optimal under mild assumptions. Moreover, our numerical results based on empirical data from US stock markets show that the proposed portfolios have excellent out-of-sample performances.