Off-Policy Optimization of Portfolio Allocation Policies under Constraints
This work is significant for financial investors and institutions seeking to optimize portfolio allocations while respecting complex constraints, especially when relying on historical, potentially non-compliant data.
This paper addresses the challenge of dynamic portfolio optimization by learning allocation policies that adhere to various constraints, using data collected under potentially sub-optimal and constraint-violating prior policies. The authors propose a minimax objective framework where one player evaluates policies via off-policy estimators and the opponent controls constraint violations, demonstrating promising results when back-tested on historical equities data.
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.