SOLVING LONG-TERM FINANCIAL-PLANNING PROBLEMS VIA GLOBAL OPTIMIZATION

A significant multi-stage financial planning problem is posed as a sto chastic program with decision rules. The decision rule - called dynami cally balanced - requires the purchase and sale of assets at each time stage so as to keep constant asset proportions in the portfolio compo sition. It leads to a nonconvex objective function. We show that the r ule performs well as compared with other dynamic investment strategies . We specialize a global optimization algorithm for this problem class - guaranteeing finite E-optimal convergence. Computational results de monstrate the procedure's efficiency on a real-world financial plannin g problem. The tests confirm that local optimizers are prone to errone ously underestimate the efficient frontier. The concepts can be readil y extended for other classes of long-term investment strategies.