Intertopic Distances as Leading Indicators
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We use a topic modeling algorithm and sentiment scoring methods to construct a novel metric to use as a leading indicator in recession prediction models. We hypothesize that due to non-instantaneous information flows, the inclusion of such a sentiment indicator derived purely from unstructured news data will improve our capabilities to forecast future recessions. We go on to show that the use of this proposed metric, even when included with consumer survey data, helps improve model performance significantly. This metric, in combination with consumer survey data, S&P 500 returns, and the yield curve, produces forecasts that significantly outperform models of higher complexity, containing traditional economic indicators.
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