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

Big Data, Socio-Psychological Theory, Algorithmic Text Analysis and Predicting the Michigan Consumer Sentiment Index