Off-Policy Optimization of Portfolio Allocation Policies under Constraints

TL;DR

This paper develops a framework for off-policy optimization of portfolio allocation policies that satisfy constraints, using a minimax approach with off-policy estimators and online learning, validated on historical equities data.

Contribution

It introduces a novel minimax framework for constraint-aware portfolio policy optimization using off-policy data and online learning strategies.

Findings

Effective constraint satisfaction in portfolio policies.

Robust performance across different market regimes.

Promising back-test results on equities data.

Abstract

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.