  We describe an exercise of using Big Data to predict the Michigan Consumer
Sentiment Index, a widely used indicator of the state of confidence in the US
economy. We carry out the exercise from a pure ex ante perspective. We use the
methodology of algorithmic text analysis of an archive of brokers' reports over
the period June 2010 through June 2013. The search is directed by the
social-psychological theory of agent behaviour, namely conviction narrative
theory. We compare one month ahead forecasts generated this way over a 15 month
period with the forecasts reported for the consensus predictions of Wall Street
economists. The former give much more accurate predictions, getting the
direction of change correct on 12 of the 15 occasions compared to only 7 for
the consensus predictions. We show that the approach retains significant
predictive power even over a four month ahead horizon.
