Índice de Sentimiento Económico
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Publicado:
dic 27, 2025
Palabras clave:
Análisis de sentimiento, Procesamiento de lenguaje natural, Indicadores de confianza, Economía ecuatoriana
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Resumen
Esta investigación desarrolla un índice de sentimiento económico (ISE) para Ecuador a partir de noticias económicas diarias publicadas en plataformas web nacionales, cubriendo el periodo de 2019 a 2025. El indicador se genera mediante un proceso reproducible que aplica modelos de análisis de sentimiento en español, combinando enfoques léxicos y de aprendizaje profundo. Los resultados muestran que el modelo PySentimiento presenta el mejor desempeño y que el ISE mantiene correlaciones significativas con los principales indicadores de confianza, expectativas empresariales y actividad económica. El estudio resalta el valor del análisis de noticias como herramienta de alta frecuencia y bajo costo para el seguimiento macroeconómico en economías emergentes.
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Chasi, A., & Dávila, C. (2025). Índice de Sentimiento Económico . 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
Citas
Algaba, A., Borms, S., Boudt, K., & Verbeken, B. (2023). Daily news sentiment and monthly surveys: A mixed-frequency dynamic factor model for nowcasting consumer confidence. International Journal of Forecasting, 39(1), 266-278. https://doi.org/10.1016/j.ijforecast.2021.12.001
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
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