Developing Practical Investment Resilience

Abstract

While seeking practical tools for resilient investors, a myopic ability for single-period decisions to reflect outcomes at multiple time horizons without dynamic programming is developed. It captures tail risk and, with multiple overlapping observations compounded at different frequencies, captures time-series properties as well. These benefits rely on pairing multilevel representation of the utility probability distribution with the use of Rubinstein utility—logarithmic in investor surplus. Other tools demonstrated for practical use include top-down hierarchical cluster analysis to provide qualitative information, simplified shrinkage of observed return means (but not covariances or higher moments), and better measures of potential tail risk. The resulting asset allocation examples drawn from 28 mutual funds and exchange-traded funds vary by investor risk aversion, degree of anticipation of different regimes, and whether they consider short, longer, or multiple time horizons. For single-horizon problems, resilient methods appear most helpful to conservative investors or those who trade infrequently. In contrast, multiple time horizons appear potentially helpful to all investors. <b>Key Findings</b> ▪ A toolset for investors seeking greater resilience against systemic risk stemming from nontraditional sources, such as pandemics, revolutionary technology, climate change, and political upheavals, is created, with an example based on the impact of poorly understood derivatives in the financial crisis of 2007–2009. ▪ Confronting the wavelike character of systemic disruptions with time stages that may include a sudden shock, rolling disruption, or gradual recovery as winners and losers are revealed leads to a method of making single-period allocations that reflect multiple time horizon outcomes while staying within a simple and easily implemented decision-theory framework. ▪ Other tools for resilient investing include top-down hierarchical cluster analysis, measures of tail risk customized by risk aversion, preserving more useful information by shrinking means but not covariances, and transparent open-source software.