Read between the headlines: Can news data predict inflation?

Handle

https://riunet.upv.es/handle/10251/208401

Cita bibliográfica

Arcin, AC.; Guliman, MEJ.; Centeno, GP.; Herbo, JM.; Parmanand, S.; Mapa, C. (2024). Read between the headlines: Can news data predict inflation?. Editorial Universitat Politècnica de València. https://doi.org/10.4995/CARMA2024.2024.17441

Titulación

Resumen

[EN] Big data and machine learning applications are increasingly gaining traction in central bank operations. Among8 others, central banks have tapped big data in their nowcasting exercises. In this study, we construct inflation news indices and examine if such indices can help predict inflation. The indices are developed using lexicon-based sentiment analysis refined using reinforcement learning and supervised machine learning methods, particularly artificial neural networks and long short-term memory models. These indices are then used as additional feature variables in time series models and machine learning models for nowcasting regional and nationwide inflation in the Philippines. We find empirical evidence that our constructed inflation news indices can improve the predictive capability of these forecasting models.

Fuente

6th International Conference on Advanced Research Methods and Analytics (CARMA 2024) isbn: 9788413962016

Editorial

Editorial Universitat Politècnica de València

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