Massively parallel processing of recursive multi-period portfolio models

Abstract A recursive portfolio decision system is extended with parallel processing capability monitored by the Genetic Hybrid Algorithm (GHA). Massively parallel portfolio efficiency testing is conducted using stochastic simulation. Genuine out-of-sample forecasts are generated for all titles in the universe using fast cutting-edge time series algorithms. The computation of dynamic optimal portfolio weights is done within an affine multi-period setting. The terminal wealth within the planning horizon forms a moving target as the system evolves through time and only current transactions are carried out. Fixed and variable transaction costs are recognized without increasing computational complexity. We show using a recursive multi-period portfolio framework ( RMP ) that robust grid search and stochastic simulation with thousands of parallel processors can be conducted to provide evidence on portfolio efficiency. The downside risk of the RMP -strategy is significantly lower than that of the corresponding buy-and-hold strategy. The upside potential of RMP is much better than that of buy-and-hold. The non-parametric test procedure is independent of the underlying model and hence completely general. The modular structure of the system allows new forecasting techniques and optimization formulations to be introduced and tested in future development efforts.