Economic state classification and portfolio optimisation with application to stagflationary environments

Abstract

Motivated by the current fears of a potentially stagflationary global economic environment, this paper uses new and recently introduced mathematical techniques to study multivariate time series pertaining to country inflation (CPI), economic growth (GDP) and equity index behaviours. We start by assessing the temporal evolution among various economic phenomena, and complement this analysis with "economic driver analysis", where we decouple country economic trajectories and determine what is most important in their association. Next, we study the temporal self-similarity of global inflation, growth and equity index returns to identify the most anomalous historic periods, and windows in the past that are most similar to current market dynamics. We then introduce a new algorithm to construct economic state classifications and compute an economic state integral, where countries are determined to belong in one of four candidate states based on their inflation and growth behaviours. Finally, we implement a decade-by-decade portfolio optimization to determine which equity indices and portfolio assets have been most beneficial in maximizing portfolio risk-adjusted returns in various market conditions. This could be of great interest to those looking for asset allocation guidance in the current period of high economic uncertainty.

Collective infectivity of the pandemic over time and association with vaccine coverage and economic development

“…We begin by choosing a window of τ = 90 days over which we compute correlations between rolling R 0 time series. This approach is inspired by analyses of correlations and distances between financial time series [89][90][91][92][93][94][95]. These are defined as follows: for t = τ, ..., T , define an N × N matrix Ψ(t) by…”