## Evolving Themescapes: Powerful Auto-ML for Thematic Investment with tidyfit

The recent years have been marked by an unusual amount of geopolitical upheaval and crisis. In this post, I explore the change in importance that this period has elicited in different investment themes. Which trends have grown in importance? What can be discovered about evolving market priorities and the brave new world ahead?

To explore these questions, I draw on a data set of MSCI Thematic and Sector index returns, and calculate the regression-based importance of each theme for each sector over time. The analytical workflow is typical to the quantitative finance setting, essentially requiring the estimation of a large number of linear regressions that provide orthogonal exposures to different investment themes. Here the R package tidyfit (available on CRAN) can be extremely helpful, since it automates much of the machine learning pipeline for regularized regressions .

MSCI provides thematic equity indexes for 17 different themes that range from digital health and cybersecurity to millennials and future education. The following plot shows the average change in each theme’s importance — measured as the change in the absolute standardized beta — from before the COVID-19 pandemic to after the pandemic. The regression betas are estimated using an elastic net regression (discussed below). A positive value suggests that the theme has, on average, increased in recent years: