Wealth Management - Goals-Based Application of Fragility Detection Theory
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.
The purpose of this framework is twofold. First, we aim to provide a tool for better understanding the risks that sit within the traditional tools and techniques of portfolio management used by practitioners. Second, we aim to provide a framework for ascertaining which portfolio management inputs are the most sensitive to errors—and thus require the most attention from the practitioner. Investors are, after all, busy people with many items requiring their attention. Knowing, therefore, which items take priority and which are not as pressing is useful.
From here, we will discuss the theoretical background on which we base our analysis. After the background, we will lay out the framework itself, and finally we will discuss the implications for portfolio management in a goals-based setting.