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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

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’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.

Original language	English
Title of host publication	Information Management and Big Data - 7th Annual International Conference, SIMBig 2020, Proceedings
Editors	Juan Antonio Lossio-Ventura, Jorge Carlos Valverde-Rebaza, Eduardo Díaz, Hugo Alatrista-Salas
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	154-168
Number of pages	15
ISBN (Print)	978-3-030-76227-8
DOIs	https://doi.org/10.1007/978-3-030-76228-5_11
State	Published - 2021
Externally published	Yes
Event	7th International Conference on Information Management and Big Data - Perú Duration: 1 Oct 2020 → 1 Oct 2020

Publication series

Name	Communications in Computer and Information Science
Volume	1410 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

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

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Keywords

Bayesian networks
S&P/BVL
Stock market index prediction

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

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

Cite this

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. In 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).

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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.",

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

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year = "2021",

doi = "10.1007/978-3-030-76228-5_11",

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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. in 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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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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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. In Lossio-Ventura JA, Valverde-Rebaza JC, Díaz E, Alatrista-Salas H, editors, 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

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

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

Cite this