2018/05/10 by Melody Y. Huang, Melody Huang, Randall R. Rojas +4 · 1 citation
Economics, Econometrics and Finance · Mathematics · Social Sciences · #Applications (stat.AP) #Artificial intelligence #Computer science #Construct (python library) #Data mining #Econometrics #Econometrics (econ.EM) #Economic indicator #Economics #FOS: Computer and information sciences #FOS: Economics and business #Index (typography) #Macroeconomics #Measure (data warehouse) #Media Influence and Politics #Metric (unit) #Purchasing #Recession #Sentiment analysis #World Wide Web #econ.EM #stat.AP
paper · pdf · doi:10.48550/arxiv.1805.04160
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2018/05/10 · arxiv created 2018/05/31 · arxiv updated 2018/06/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
In the following paper, we use a topic modeling algorithm and sentiment scoring methods to construct a novel metric that serves as a leading indicator in recession prediction models. We hypothesize that the inclusion of such a sentiment indicator, derived purely from unstructured news data, will improve our capabilities to forecast future recessions because it provides a direct measure of the polarity of the information consumers and producers are exposed to. We go on to show that the inclusion of our proposed news sentiment indicator, with traditional sentiment data, such as the Michigan Index of Consumer Sentiment and the Purchasing Manager's Index, and common factors derived from a large panel of economic and financial indicators helps improve model performance significantly.