论文标题
通过部分信息和深度学习分辨率在最大缩减约束下优化离散时间组合
Discrete-time portfolio optimization under maximum drawdown constraint with partial information and deep learning resolution
论文作者
论文摘要
我们研究一个离散的时间组合选择问题，其中有部分信息和最大\ -MUM缩减约束。多维框架中的漂移不确定性是由先前概率分布建模的。在此贝叶斯框架中，我们使用适当的度量更改得出动态编程方程，并在高斯案例中获得半明确的结果。后一种情况，具有CRRA效用功能的情况是使用最新的最佳最佳控制问题的最新深度学习技术来完全求解的。我们强调学习策略的信息价值与非学习策略的信息价值，通过提供经验性绩效和敏感性分析，以相对于漂移的不确定性。此外，我们通过说明前者对后者的收敛，因为非学习策略与无短额的约束默顿问题之间的密切关系显示了数值证据，这是最大的下降约束消失。
We study a discrete-time portfolio selection problem with partial information and maxi\-mum drawdown constraint. Drift uncertainty in the multidimensional framework is modeled by a prior probability distribution. In this Bayesian framework, we derive the dynamic programming equation using an appropriate change of measure, and obtain semi-explicit results in the Gaussian case. The latter case, with a CRRA utility function is completely solved numerically using recent deep learning techniques for stochastic optimal control problems. We emphasize the informative value of the learning strategy versus the non-learning one by providing empirical performance and sensitivity analysis with respect to the uncertainty of the drift. Furthermore, we show numerical evidence of the close relationship between the non-learning strategy and a no short-sale constrained Merton problem, by illustrating the convergence of the former towards the latter as the maximum drawdown constraint vanishes.
