Abstract:
This empirical research seeks to demonstrate that we can decentralize, crowd source and aggregate signals of uncertainty coming from market agents and organizations, by using the Internet -and more specifically Twitter- as an information archive from which we extract the wisdom of the crowds concerning the state of uncertainty of a specific target system, like a country or a particular uncertainty source, in a given moment in time. We extract and aggregate these signals, constructing a set of specialized uncertainty indexes, by topic and/or by geographic-area. We model the dependence among these uncertainty indexes and other pre-existing uncertainty proxies and highlight differences in their reactiveness to real-world events that occurred in the year 2016, like the EU-referendum vote and the US presidential elections. Finally, we analyze and model the dynamics across time and space of uncertainty signals by geographic-area, to discriminate between, area specific feedback mechanisms, contagion among geographic-areas and international (multi-area) uncertainty shocks. Our results show that crowd-sourced uncertainty signals coming from Twitter may be fruitfully used to improve our understanding of uncertainty contagion and amplification mechanisms across geographic-areas and among market and non-market systems;
