A Simulation-based Portfolio Optimization Approach with Least Squares Learning

This paper introduces a simulation-based numerical method for solving dynamic portfolio optimization problem. We describe a recursive numerical approach that is based on the Least Squares Monte Carlo method to calculate the conditional value functions of investors for a sequence of discrete decision dates. The method is data driven rather than restricted to specific asset model, also importantly intermediate transaction costs associated with portfolio rebalancing is considered in the dynamic optimisation method, and investors’ risk preferences and risk management constraints are also taken into account in the current implementation. In this paper, the presented method is used for a case study on a global equity portfolio invested in five equity markets, and foreign exchange risks are also included. We examine the portfolio performance with three optimizers in a out-of-sample simulation study together with a benchmark portfolio which is passively managed with equal weighted position.