We use least-square Monte Carlo method to allocate assets via dynamic programming. We've finished two papers on the following topic and are planning to publish one on some theoretical research on the method.
We propose a dynamic portfolio optimisation strategy that takes liquidity cost into account. Our liquidity cost model is built upon the so-called Marginal Supply-Demand Curve which describes the asset price as a function of the trading volume. We extend the least-squares Monte Carlo algorithm to a stochastic control problem with switching costs and endogenous state variables. This approach is simulation-based, with great flexibility in the choice of investor objective, asset dynamics, portfolio constraints, intermediate consumption and incorporation of different sources of costs, making it easy to implement in practice. We study a portfolio investing in four major sectors in the U.S. market. We benchmark our dynamic strategy against several alternative portfolio strategies in terms of the out-of-sample distribution of terminal wealth and the real market performance. Overall, our dynamic strategy outperforms other standard asset allocation strategies.