Financial and Economic Review | 2026
Authors: Temesvári C.; Horváth B.; Ónozó L.R.
DOI: 10.33893/FER.25.1.27
Journal: Financial and Economic Review
Year: 2026
Publisher: Magyar Nemzeti Bank
Document Type: Article
Open Access: All Open Access; Gold Open Access; Green Open Access
Cited by: 0
Economic text data, such as news articles or retail trade item names, are an alternative, feature-rich, high frequency information source that can provide insight into economic trends and generate timelier and more accurate estimates. We trained multiple deep learning models for two distinct research tasks: 1) the creation of a sentiment index derived from the categorisation of financial and economic articles into three sentiment categories; and 2) the classification of retail trade item names into appropriate tariff categories. Our models consistently outperformed their baseline counterparts for retail trade item classification, while our sentiment index was able to accurately predict economic downturns where high-frequency data were not available. © 2026, Magyar Nemzeti Bank. All rights reserved.
classification; Deep Learning; macroeconomic nowcasting; Natural Language Processing