Modeling and predicting the Lima Stock Exchange General index with Bayesian networks and information from foreign markets

Daniel Chapi; Soledad Espezua; Julio Villavicencio; Oscar Miranda; Edwin Villanueva

doi:10.1007/978-3-030-76228-5_11

Modeling and predicting the Lima Stock Exchange General index with Bayesian networks and information from foreign markets

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

1 Cita (Scopus)

Resumen

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
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	https://doi.org/10.1007/978-3-030-76228-5_11
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

Serie de la publicación

Nombre	Communications in Computer and Information Science
Volumen	1410 CCIS
ISSN (versión impresa)	1865-0929
ISSN (versión digital)	1865-0937

Conferencia

Conferencia	7th International Conference on Information Management and Big Data
Período	1/10/20 → 1/10/20

Nota bibliográfica

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

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

ODS 8: Trabajo decente y crecimiento económico
ODS 9: Industria, innovación e infraestructura
ODS 17: Alianzas para lograr los objetivos

Acceder al documento

10.1007/978-3-030-76228-5_11

Otros archivos y enlaces

Information management and big data: 7th Annual International Conference, SIMBig 2020, Lima, Peru, October 1–3, 2020, Proceedings
Lossio-Ventura, J. A., Valverde-Rebaza, J. C. (Editor), Díaz, E. (Editor) & Alatrista-Salas, H. (Editor), 2021, Cham. 554 p. (Communications in Computer and Information Science; vol. 1410 CCIS)
Producción científica: Informe/libro › Libro › revisión exhaustiva

Citar esto

Chapi, D., Espezua, S., Villavicencio, J., Miranda, O., & Villanueva, E. (2021). Modeling and predicting the Lima Stock Exchange General index with Bayesian networks and information from foreign markets. En J. A. Lossio-Ventura, J. C. Valverde-Rebaza, E. Díaz, & H. Alatrista-Salas (Eds.), Information Management and Big Data - 7th Annual International Conference, SIMBig 2020, Proceedings (pp. 154-168). (Communications in Computer and Information Science; Vol. 1410 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-76228-5_11

Chapi, Daniel ; Espezua, Soledad ; Villavicencio, Julio et al. / Modeling and predicting the Lima Stock Exchange General index with Bayesian networks and information from foreign markets. Information Management and Big Data - 7th Annual International Conference, SIMBig 2020, Proceedings. editor / Juan Antonio Lossio-Ventura ; Jorge Carlos Valverde-Rebaza ; Eduardo Díaz ; Hugo Alatrista-Salas. Springer Science and Business Media Deutschland GmbH, 2021. pp. 154-168 (Communications in Computer and Information Science).

@inproceedings{6a67010c646a4285868a36e42744d172,

title = "Modeling and predicting the Lima Stock Exchange General index with Bayesian networks and information from foreign markets",

abstract = "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{\textquoteright}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.",

keywords = "Bayesian networks, S\&P/BVL, Stock market index prediction",

author = "Daniel Chapi and Soledad Espezua and Julio Villavicencio and Oscar Miranda and Edwin Villanueva",

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Chapi, D, Espezua, S, Villavicencio, J, Miranda, O & Villanueva, E 2021, Modeling and predicting the Lima Stock Exchange General index with Bayesian networks and information from foreign markets. En JA Lossio-Ventura, JC Valverde-Rebaza, E Díaz & H Alatrista-Salas (eds.), Information Management and Big Data - 7th Annual International Conference, SIMBig 2020, Proceedings. Communications in Computer and Information Science, vol. 1410 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 154-168, 7th International Conference on Information Management and Big Data, 1/10/20. https://doi.org/10.1007/978-3-030-76228-5_11

Modeling and predicting the Lima Stock Exchange General index with Bayesian networks and information from foreign markets. / Chapi, Daniel; Espezua, Soledad; Villavicencio, Julio et al.
Information Management and Big Data - 7th Annual International Conference, SIMBig 2020, Proceedings. ed. / Juan Antonio Lossio-Ventura; Jorge Carlos Valverde-Rebaza; Eduardo Díaz; Hugo Alatrista-Salas. Springer Science and Business Media Deutschland GmbH, 2021. p. 154-168 (Communications in Computer and Information Science; Vol. 1410 CCIS).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

TY - GEN

T1 - Modeling and predicting the Lima Stock Exchange General index with Bayesian networks and information from foreign markets

AU - Chapi, Daniel

AU - Espezua, Soledad

AU - Villavicencio, Julio

AU - Miranda, Oscar

AU - Villanueva, Edwin

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - Bayesian networks

KW - S&P/BVL

KW - Stock market index prediction

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U2 - 10.1007/978-3-030-76228-5_11

DO - 10.1007/978-3-030-76228-5_11

M3 - Conference contribution

AN - SCOPUS:85111164131

SN - 978-3-030-76227-8

T3 - Communications in Computer and Information Science

SP - 154

EP - 168

BT - Information Management and Big Data - 7th Annual International Conference, SIMBig 2020, Proceedings

A2 - Lossio-Ventura, Juan Antonio

A2 - Valverde-Rebaza, Jorge Carlos

A2 - Díaz, Eduardo

A2 - Alatrista-Salas, Hugo

PB - Springer Science and Business Media Deutschland GmbH

T2 - 7th International Conference on Information Management and Big Data

Y2 - 1 October 2020 through 1 October 2020

ER -

Chapi D, Espezua S, Villavicencio J, Miranda O, Villanueva E. Modeling and predicting the Lima Stock Exchange General index with Bayesian networks and information from foreign markets. En Lossio-Ventura JA, Valverde-Rebaza JC, Díaz E, Alatrista-Salas H, editores, Information Management and Big Data - 7th Annual International Conference, SIMBig 2020, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 154-168. (Communications in Computer and Information Science). doi: 10.1007/978-3-030-76228-5_11

Modeling and predicting the Lima Stock Exchange General index with Bayesian networks and information from foreign markets

Resumen

Serie de la publicación

Conferencia

Nota bibliográfica

ODS de las Naciones Unidas

Acceder al documento

Otros archivos y enlaces

Huella

Producción científica

Information management and big data: 7th Annual International Conference, SIMBig 2020, Lima, Peru, October 1–3, 2020, Proceedings

Citar esto