Economic Sentiment Index
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Published:
Dec 27, 2025
Keywords:
Sentiment analysis, Natural language processing, Confidence indicators, Ecuadorian economy
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Abstract
This research develops an Economic Sentiment Index (ESI) for Ecuador, based on daily economic news published on national web platforms between 2019 and 2025. The indicator is generated through a reproducible process that applies sentiment analysis models in Spanish, combining lexical and deep learning approaches. The results show that the PySentimiento model achieves the best performance and that the ESI exhibits significant correlations with key indicators of consumer confidence, business expectations, and economic activity. The study highlights the value of news analysis as a high-frequency and low-cost tool for macroeconomic monitoring in emerging economies.
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How to Cite
Chasi, A., & Dávila, C. (2025). Economic Sentiment Index. Cuestiones Económicas, 35(2), Autores: Carlos Dávila y Andrea Chasi. https://doi.org/10.47550/RCE/35.2.1
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Artículos de Investigación
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
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Powers, D. M. W. (2011). Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation. Journal of Machine Learning Technologies, 2(1), 37-63.
Romero, P., López, R., & Herrera, P. (2025). Índice incertidumbre de economía y política de Ecuador: U-Index USFQ [doi:10.13140/RG.2,2,20947.08484]. [Pre-Print]. https://doi.org/10.13140/RG.2,2,20947.08484
Shapiro, A. H., Sudhof, M., & Wilson, D. (2020). Measuring news sentiment. Federal Reserve Bank of San Francisco (Working Paper 2017-01). https://doi.org/10.24148/wp2017-01
Van Binsbergen, J. H., Bryzgalova, S., Mukhopadhyay, M., & Sharma, V.(2024). (Almost) 200 years of news-based economic sentiment (inf. téc.Índice de sentimiento económico N.o NBER Working Paper No. 32026). National Bureau of Economic Research. https://doi.org/10.3386/w32026
Zhang, Y. (2025). Interpretable machine learning for macro alpha: A news sentiment case study. https://arxiv.org/abs/2505.16136
Zhao, W. (2025). Machine learning for economic forecasting: A sentiment and timeseries approach. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.5127104
Angeletos, G.-M., & La’O, J. (2013). Sentiments. Econometrica, 81(2), 739-779. https://doi.org/10.3982/ECTA10008
Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. The Quarterly Journal of Economics, 131(4), 1593-1636. https://doi.org/10.1093/qje/qjw024
Banco Central de Reserva del Perú (BCRP). (2022). Un índice de sentimiento de las noticias para el Perú. Revista Moneda, 193, 33-44. https://www.bcrp. gob.pe/docs/Publicaciones/Revista-Moneda/moneda-193/moneda-193-03. pdf
Barbaresi, A. (2021). Trafilatura: A web scraping library and command-line tool for text discovery and extraction. Proceedings of the Joint Conference of the 59th annual meeting of the Association for Computational Linguistics and the 11th International Joint Conference On Natural Language Processing: system demonstrations, 122-131. https://aclanthology. org/2021.acl-demo.15
Beygelzimer, A., Dasgupta, S., & Langford, J. (2009). Importance weighted active learning. Proceedings of the 26th International Conference on Machine Learning (ICML). https://icml.cc/ Brier, G. W. (1950). Verification of forecasts expressed in terms of probability. Monthly Weather Review, 78(1), 1-3. https://doi. org/10.1175/1520-0493(1950)078<0001:VOFEIT>2,0.CO;2
Cajner, T., Morin, N., Crane, L., Soto, P. E., & Vrankovich, B. (2024). Manufacturing sentiment: Forecasting industrial production with text análisis (inf. téc. Nº FEDS Working Paper No. 2024-026). Board of Governors of the Federal Reserve System. https://doi.org/10.17016/FEDS.2024.026
Centro de Estudios Fiscales. (2010). Índice de actividad empresarial no petrolera (IAE-NP). (2010-05).
Del Pilar Cruz, M., Peralta, H., & Cova, J. P. (2022). Using the press as a real-time economic confidence indicator (Working Paper No. 938). Banco Central de Chile. https://www.bcentral.cl/en/web/banco-central/content/-/detalle/documento-de-trabajo-n-938
Devlin, J., Chang, M.-W., & Lee, K., Kenton y Toutanova. (2019). BERT: Pretraining of deep bidirectional transformers for language understanding. NAACL-HLT.
Gentzkow, M., Kelly, B., & Taddy, M. (2019). Text as data. Journal of Economic Literature, 57(3), 535-574. https://doi.org/10,1257/jel.20181020 Grob-Klubmann, A. (2024). Learning deep news sentiment representations for macrofinancial forecasting. Financial Innovation, 10(1), 1-24. https://doi.org/10.1007/s42521-024-00107-2
Guo, C., Pleiss, G., Sun, Y., & Weinberger, K. Q. (2017). On calibration of modern neural networks. Proceedings of the 34th International Conference on Machine Learning, 70, 1321-1330. http://proceedings.mlr.press/v70/guo17a.html
Hutto, C. J., & Gilbert, E. (2014). VADER: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media, 8(1).
Kahneman, D., Krueger, A. B., Schkade, D., Schwarz, N., & Stone, A. A. (2004). A survey method for characterizing daily life experience: The day reconstruction method. Science, 306(5702), 1776-1780. https://doi.org/10.1126/science.1103572
Loughran, T., & McDonald, B. (2011). When is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10-Ks. The Journal of Finance, 66(1), 35-65. https://doi.org/10.1111/j.1540-6261,2010.01625.x Nguyen, D. P. (2025). Sentiment index as a predictor of CPI: A lexicon-based approach in Vietnam. Journal of Asian Economics, 87, 101694. https://doi.org/10.1016/j.asieco.2025,101694
Nguyen, V.-L., Yamasaki, T., & Ishikawa, H. (2022). How to measure uncertainty in uncertainty sampling for active learning. Machine Learning. https://doi.org/10.1007/s10994-021-06090-6
Pérez, J. M., Giudici, J. C., & Luque, F. (2021). Pysentimiento: A Python toolkit for sentiment analysis and social NLP tasks.
Powers, D. M. W. (2011). Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation. Journal of Machine Learning Technologies, 2(1), 37-63.
Romero, P., López, R., & Herrera, P. (2025). Índice incertidumbre de economía y política de Ecuador: U-Index USFQ [doi:10.13140/RG.2,2,20947.08484]. [Pre-Print]. https://doi.org/10.13140/RG.2,2,20947.08484
Shapiro, A. H., Sudhof, M., & Wilson, D. (2020). Measuring news sentiment. Federal Reserve Bank of San Francisco (Working Paper 2017-01). https://doi.org/10.24148/wp2017-01
Van Binsbergen, J. H., Bryzgalova, S., Mukhopadhyay, M., & Sharma, V.(2024). (Almost) 200 years of news-based economic sentiment (inf. téc.Índice de sentimiento económico N.o NBER Working Paper No. 32026). National Bureau of Economic Research. https://doi.org/10.3386/w32026
Zhang, Y. (2025). Interpretable machine learning for macro alpha: A news sentiment case study. https://arxiv.org/abs/2505.16136
Zhao, W. (2025). Machine learning for economic forecasting: A sentiment and timeseries approach. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.5127104