Predicting daily trends in the Lima Stock Exchange general index using economic indicators and financial news sentiments

Adrian Ulloa, Soledad Espezua, Julio Villavicencio, Oscar Miranda, Edwin Villanueva

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Predicting the future trend of the Lima Stock Exchange market is challenging because of its high volatility, transaction costs, and illiquidity. In this work, we investigate machine learning models able to use technical indicators, economic variables, and financial news sentiments to forecast the daily return trend of the S&P/BVL Peru General Index. To the best of our knowledge, no other published S&P/BVL predicting tool considered these joint sources of information as relevant input features. To do so, fifteen economic indicators relevant to the local market and sentiment-tagged financial news headlines were used as extra input features for multiple machine learning classification models and feature selection methods. In addition, the performance of the static learning approach (the only one used for this particular problem so far) was compared against an online learning approach, which could dynamically better adapt to such a volatile, emergent market. The results showed an increase in performance when using the economic variables and news sentiment in comparison to existing predicting tools of the local market. When comparing both learning approaches, online learning yielded better predictive accuracy than its static counterpart. To the best of our knowledge, this is the first effort to include all these novel features for predicting trends in the Lima Stock Exchange.

Idioma originalInglés
Título de la publicación alojadaInformation Management and Big Data - 8th Annual International Conference, SIMBig 2021, Proceedings
EditoresJuan Antonio Lossio-Ventura, Jorge Valverde-Rebaza, Eduardo Díaz, Denisse Muñante, Carlos Gavidia-Calderon, Alan Demétrius Valejo, Hugo Alatrista-Salas
EditorialSpringer Science and Business Media Deutschland GmbH
Número de páginas16
ISBN (versión impresa)978-3-031-04446-5
EstadoPublicada - 2022
Publicado de forma externa
Evento8th International Conference on Information Management and Big Data - virtual conference, Lima, Perú
Duración: 1 dic. 20213 dic. 2021
Número de conferencia: 8

Serie de la publicación

NombreCommunications in Computer and Information Science
Volumen1577 CCIS
ISSN (versión impresa)1865-0929
ISSN (versión digital)1865-0937


Conferencia8th International Conference on Information Management and Big Data
Título abreviadoSIMBig 2021
Dirección de internet

Nota bibliográfica

Funding Information:
Acknowledgment. The authors gratefully acknowledge financial support by Pontifical Catholic University of Peru (CAP program, project ID 735).

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.


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