论文标题
在约束下的投资组合分配策略的政策优化
Off-Policy Optimization of Portfolio Allocation Policies under Constraints
论文作者
论文摘要
金融中的动态投资组合优化问题经常需要学习政策，这些政策遵守各种约束，这是由投资者的偏好和风险驱动的。我们激发了这个问题，即在顺序决策框架内找到分配策略，并研究以下方面的影响：（a）使用先前使用的策略收集的数据，这些数据可能是最佳和约束 - 侵略性的，以及（b）强加的期望约束，同时计算近乎最佳的政策。我们的框架依赖于解决最小值目标，其中一位玩家通过非政策估计器评估政策，并且对手使用在线学习策略来控制约束违规行为。我们广泛研究了各种选择，以进行非政策估计及其相应的优化子调查，并量化它们对计算约束意识分配策略的影响。我们的研究显示，在经营，维度和约束方面对历史股票数据进行回顾时，构建此类政策的结果有希望的结果。
The dynamic portfolio optimization problem in finance frequently requires learning policies that adhere to various constraints, driven by investor preferences and risk. We motivate this problem of finding an allocation policy within a sequential decision making framework and study the effects of: (a) using data collected under previously employed policies, which may be sub-optimal and constraint-violating, and (b) imposing desired constraints while computing near-optimal policies with this data. Our framework relies on solving a minimax objective, where one player evaluates policies via off-policy estimators, and the opponent uses an online learning strategy to control constraint violations. We extensively investigate various choices for off-policy estimation and their corresponding optimization sub-routines, and quantify their impact on computing constraint-aware allocation policies. Our study shows promising results for constructing such policies when back-tested on historical equities data, under various regimes of operation, dimensionality and constraints.
