The value and cost of more stages in stochastic programing: a statistical analysis on a set of portfolio choice problems

Sequential decision problems under uncertainty are commonly studied with stochastic programing. An important modeling choice is the number of stages. More stages allow additional information to be captured, but associated a coarser representation may worsen solution quality. In this paper, we study trade-off, objective advance understanding how affect quality in We show: (i) optimistic bounds from programing gradually suggest improved performance more stages, while real simultaneously deteriorates; and (ii) that can by adding only up some point, after which detrimental. Further, highlight importance what creates value problem interest, particularly if captured models few The numerical experiments based on classic portfolio an investor constant relative risk aversion preferences, maximizing expected utility terminal wealth. instances proportional transaction costs predictability returns, takes into inherently multi-stage nature.