Vector forecasting and dynamic portfolio selection: Empirical efficiency of recursive multiperiod strategies

The paper presents a dynamic portfolio selection system. The system combines recent results from the domains of time series analysis, portfolio theory and multiperiod linear programming under uncertainty. The performance of the decision support system is tested empirically against a dynamic super criterion defined in the study. The paper demonstrates that suitable combination of ex ante and ex post information with initial balances for the planning horizon yields better portfolio efficiency through time than a strategy relying purely on ex ante information. The system was programmed on Åbo Academy’s