 Ranking professional forecasters by the predictive power of their narrativesInternational Journal of Forecasting, 2021, vol. 37, issue 1, 186-204 Abstract: This article presents the first ever ranking of professional forecasters based on the predictive power of the narrative of their regular research reports. The ranking is generated by applying the fully automated four-step procedure – called NLP-ForRank – developed in this article. The four steps are data scraping from the internet; data preparation; application of the natural language processing (NLP) models; and evaluation of the predictive power of the NLP indexes with linear regression, Granger causality, vector autoregression (VAR), and random forest forecasting models. Applying this procedure to five large Polish banks and to many time series shows that including the constructed NLP indexes in the forecasting models lowers the forecast errors, and that the optimal model almost always contains the NLP index. The financial news agencies could consider publishing this type of ranking on a regular basis as it would foster accountability, transparency, and a more competitive environment in the professional forecasting industry. Keywords: Economic research; Forecasting; Ranking; NLP; Machine learning; Poland (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:37:y:2021:i:1:p:186-204 DOI: 10.1016/j.ijforecast.2020.04.003 Access Statistics for this article International Journal of Forecasting is currently edited by R. J. Hyndman More articles in International Journal of Forecasting from Elsevier |
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