Measuring Financial Sentiment to Predict Financial Instability: A New Approach based on Text Analysis

Abstract

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.