Post-tax optimization with stochastic programming

In this paper, we consider a stochastic programming approach to multi-stage posttax portfolio optimization. Asset performance information is speci ed as a scenario tree generated by two alternative methods based on simulation and optimization. We assume three tax wrappers involving the same instruments for an eÆcient investment strategy and determine optimal allocations to di erent instruments and wrappers. The tax rules are integrated with the linear and mixed integer stochastic models to yield an overall tax and return-eÆcient multistage portfolio. The computational performance of these models is tested using a case study with di erent scenario trees.