论文标题
Dirichlet政策的增强因子投资组合
Dirichlet policies for reinforced factor portfolios
论文作者
论文摘要
本文旨在结合因素投资和加强学习（RL）。代理通过依赖公司特征的顺序随机分配来学习。使用Dirichlet分布作为驾驶政策，我们为策略梯度和性能度量的分析属性提供了封闭式。这使我们能够在美国股票的大型数据集上执行加强方法。在各种参数选择中，我们的结果表明基于RL的投资组合非常接近同样加权的（1/N）分配。这意味着代理在因素方面学会了 *不可知论 *，可以通过横截面回归来部分解释，表明回报和公司特征之间关系的时间差异很强。
This article aims to combine factor investing and reinforcement learning (RL). The agent learns through sequential random allocations which rely on firms' characteristics. Using Dirichlet distributions as the driving policy, we derive closed forms for the policy gradients and analytical properties of the performance measure. This enables the implementation of REINFORCE methods, which we perform on a large dataset of US equities. Across a large range of parametric choices, our result indicates that RL-based portfolios are very close to the equally-weighted (1/N) allocation. This implies that the agent learns to be *agnostic* with regard to factors, which can partly be explained by cross-sectional regressions showing a strong time variation in the relationship between returns and firm characteristics.
