This work presents a novel application of the Stochastic Dual Dynamic Problem (SDDP) to large-scale asset allocation. We construct a model that delivers allocation policies based on how the portfolio performs with respect to user-defined (synthetic) indexes, and implement it in a SDDP open-source package. Based on US economic cycles and ETF data, we generate Markovian regime-dependent returns to solve an instance of multiple assets and 28 time periods. Results show our solution outperforms its benchmark, in both profitability and tracking error. Objectives and ContributionsMultistage stochastic programming (MSP) is a well-known framework for modeling large-scale problems under uncertainty, and has been widely used in several fields, including asset allocation problems. 1 MSP solutions have the quality of enabling planning not only for today's positions, but also for future portfolio rebalancing, depending on the evolution of market events and prices. That advantage avoids increases in turnover (which leads to higher transaction costs) and augment the & Lorenzo Reus
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