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

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  • Rickard Nyman
  • Paul Ormerod

Abstract

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.

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  • Rickard Nyman & Paul Ormerod, 2014. "Big Data, Socio-Psychological Theory, Algorithmic Text Analysis and Predicting the Michigan Consumer Sentiment Index," Papers 1405.5695, arXiv.org.
    • Handle: RePEc:arx:papers:1405.5695
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    Cited by:

    1. Lenka Mynaříková & Vít Pošta, 2023. "The Effect of Consumer Confidence and Subjective Well-being on Consumers’ Spending Behavior," Journal of Happiness Studies, Springer, vol. 24(2), pages 429-453, February.
    2. repec:hal:pseose:hal-02057279 is not listed on IDEAS
    3. David Tucket & Antoine Mandel & Diana Mangalagiu & Allen Abramson & Jochen Hinkel & Konstantinos Katsikopoulos & Alan Kirman & Thierry Malleret & Igor Mozetic & Paul Ormerod & Robert Elliot Smith & To, 2015. "Uncertainty, Decision Science, and Policy Making: A Manifesto for a Research Agenda," SciencePo Working papers Main hal-02057279, HAL.

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