Does Finance Benefit Society? A Language Embedding Approach

Abstract

We measure popular sentiment toward finance using a computational linguistics approach applied to millions of books published in eight countries over hundreds of years. We document persistent differences in finance sentiment across countries despite ample time-series variation. Finance sentiment declines after epidemics and earthquakes, but rises following droughts, floods, and landslides. These heterogeneous effects of natural disasters suggest finance sentiment responds differently to the realization of insured versus uninsured risks. Using local projections, we find that positive shocks to finance sentiment have positive and persistent effects on economic growth. Our estimates predict a contraction in finance sentiment due to the COVID-19 pandemic that will exacerbate its long-term economic damage.

Keywords: sentiment, natural disasters, text analysis, word embedding, COVID-19

JEL Classification: C82, E44, G52, N40, Q54