  Following the financial crisis of the late 2000s, policy makers have shown
considerable interest in monitoring financial stability. Several central banks
now publish indices of financial stress, which are essentially based upon
market related data. In this paper, we examine the potential for improving the
indices by deriving information about emotion shifts in the economy. We report
on a new approach, based on the content analysis of very large text databases,
and termed directed algorithmic text analysis. The algorithm identifies, very
rapidly, shifts through time in the relations between two core emotional
groups. The method is robust. The same word-list is used to identify the two
emotion groups across different studies. Membership of the words in the lists
has been validated in psychological experiments. The words consist of everyday
English words with no specific economic meaning. Initial results show promise.
An emotion index capturing shifts between the two emotion groups in texts
potentially referring to the whole US economy improves the one-quarter ahead
consensus forecasts for real GDP growth. More specifically, the same indices
are shown to Granger cause both the Cleveland and St Louis Federal Reserve
Indices of Financial Stress.
