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 begin 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 optimisation to determine which equity indices and portfolio assets have been most beneficial in maximising 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.
翻译:由于目前担心全球经济环境可能陷入停滞,本文件利用最近引进的新的数学技术,研究国家通货膨胀、经济增长以及股权指数行为等多重时间序列。我们首先评估各种经济现象之间的时间演变,并以“经济驱动分析”作为这一分析的补充,在“经济驱动分析”中,我们将各国的经济轨迹脱钩,并确定它们之间最重要的关系。接着,我们研究全球通货膨胀、增长和股权指数回报的时间自相矛盾性,以确定过去最异常的历史时期和窗口,与当前的市场动态最为相似。我们然后采用一种新的算法,以构建经济国家分类,并计算经济整体状态,其中各国决心根据它们的通货膨胀和增长行为,属于四个候选国家之一。最后,我们实行十年一次的投资组合优化,以确定哪些股权指数和投资组合资产最有助于在各种市场条件下实现投资组合风险调整后回报最大化。这可能对那些在当前经济高度不稳定时期寻求资产分配指导的人有很大的兴趣。