Tilly, Sonja, Ebner, Markus and Livan, Giacomo (2021) Macroeconomic forecasting through news, emotions and narrative. Expert Systems With Applications, 175. ISSN 0957-4174
Full text not available from this repository.Abstract
This study proposes a new method of incorporating emotions from newspaper articles into macroeconomic forecasts, attempting to forecast industrial production and consumer prices leveraging narrative and sentiment from global newspapers. For the most part, existing research includes positive and negative tone only to improve macroeconomic forecasts, focusing predominantly on large economies such as the US. These works use mainly anglophone sources of narrative, thus not capturing the entire complexity of the multitude of emotions contained in global news articles. This study expands the existing body of research by incorporating a wide array of emotions from newspapers around the world – extracted from the Global Database of Events, Language and Tone (GDELT) – into macroeconomic forecasts. We present a thematic data filtering methodology based on a bi-directional long short term memory neural network (Bi-LSTM) for extracting emotion scores from GDELT and demonstrate its effectiveness by comparing results for filtered and unfiltered data. We model industrial production and consumer prices across a diverse range of economies using an autoregressive framework, and find that including emotions from global newspapers significantly improves forecasts compared to three autoregressive benchmark models. We complement our forecasts with an interpretability analysis on distinct groups of emotions and find that emotions associated with happiness and anger have the strongest predictive power for the variables we predict.
| Item Type: | Article |
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| Additional Information: | Funding Information: GL acknowledges support from an EPSRC Early Career Fellowship [Grant No. EP/N006062/1]. The authors thank Simone Righi and David Tuckett for very helpful feedback on preliminary versions of our manuscript. Funding Information: The GDELT Project is a research collaboration of Google Ideas, Google Cloud, Google and Google News, the Yahoo! Fellowship at Georgetown University, BBC Monitoring, the National Academies Keck Futures Program, Reed Elsevier’s LexisNexis Group, JSTOR, DTIC and the Internet Archive. The project monitors world media from a multitude of perspectives, identifying and extracting items such as themes, emotions, locations and events. GDELT version two incorporates real-time translation from 65 languages and measures over 2,300 emotions and themes from every news article, updated every 15 min ( Gdelt project, 2015 ). It is a public data set available on the Google Cloud Platform. Publisher Copyright: © 2021 Elsevier Ltd |
| Divisions: | Systemic Risk Centre |
| Date Deposited: | 07 May 2024 23:19 |
| Last Modified: | 16 Nov 2024 07:00 |
| URI: | http://eprints.lse.ac.uk/id/eprint/122984 |
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