Publicaciones por año
Publicaciones por año
Daniel Chapi, Soledad Espezua, Julio Villavicencio, Oscar Miranda, Edwin Villanueva
Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva
This paper presents a Bayesian Network approach to model and forecast the daily return direction of the Lima stock Exchange general index using foreign market’s information. Thirteen worldwide stock market indices were used along with the copper future that is negotiated in New York. The proposed approach was compared against popular machine learning methods, including decision tree, SVM, Multilayer Perceptron and Long short-term memory networks. The results showed competitive results at classifying both positive and negative classes. The approach allows graphical representation of the relationships between the markets, which facilitate the understanding on the target market in the global context. A web application was developed to demonstrate the advantages of the proposed approach. To the best of our knowledge, this is the first effort to model the influences of the main stock markets around the world on the Lima Stock Exchange general index.
Idioma original | Inglés |
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Título de la publicación alojada | Information Management and Big Data - 7th Annual International Conference, SIMBig 2020, Proceedings |
Editores | Juan Antonio Lossio-Ventura, Jorge Carlos Valverde-Rebaza, Eduardo Díaz, Hugo Alatrista-Salas |
Editorial | Springer Science and Business Media Deutschland GmbH |
Páginas | 154-168 |
Número de páginas | 15 |
ISBN (versión impresa) | 978-3-030-76227-8 |
DOI | |
Estado | Publicada - 2021 |
Publicado de forma externa | Sí |
Evento | 7th International Conference on Information Management and Big Data - Perú Duración: 1 oct. 2020 → 1 oct. 2020 |
Nombre | Communications in Computer and Information Science |
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Volumen | 1410 CCIS |
ISSN (versión impresa) | 1865-0929 |
ISSN (versión digital) | 1865-0937 |
Conferencia | 7th International Conference on Information Management and Big Data |
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Período | 1/10/20 → 1/10/20 |
Producción científica: Informe/libro › Libro › revisión exhaustiva