Drawing from the theory of nonlinear risk and fragility detection, we analyze which portfolio management attributes should be of most concern to the practitioner in a goal-based setting. We begin by constructing a risk measurement mechanism using a Gaussian stochastic Monte Carlo process. We then analyze which portfolio inputs are most sensitive to errors. Counterintuitively, we find that portfolio variance is of least concern, whereas factors affecting the future required minimum wealth level are of greatest concern. We show how goal-based practitioners get the most bang for their buck by focusing on the accuracy of their inflation projections (not often a first focus) and portfolio variance last (often the first focus). These results can help both the practitioner and researcher dedicate resources to answering the questions that are of most importance when a future goal is at stake.
Goals-based investing, portfolio management, financial planning