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Modeling and Predicting the Lima Stock Exchange General Index with Bayesian Networks and Information from Foreign Markets

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Information Management and Big Data (SIMBig 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1410))

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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’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.

Supported by Pontifical Catholic University of Peru.

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Acknowledgment

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

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Authors and Affiliations

  1. Pontifical Catholic University of Peru, Lima, Peru

    Daniel Chapi, Soledad Espezua, Julio Villavicencio, Oscar Miranda & Edwin Villanueva

Authors
  1. Daniel Chapi
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  2. Soledad Espezua
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  3. Julio Villavicencio
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  4. Oscar Miranda
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  5. Edwin Villanueva
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Corresponding author

Correspondence to Edwin Villanueva .

Editor information

Editors and Affiliations

  1. Stanford University, Stanford, CA, USA

    Juan Antonio Lossio-Ventura

  2. Visibilia, São Paulo, Brazil

    Jorge Carlos Valverde-Rebaza

  3. University of Valencia, Valencia, Spain

    Eduardo Díaz

  4. Universidad del Pacífico, Lima, Peru

    Hugo Alatrista-Salas

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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: Lossio-Ventura, J.A., Valverde-Rebaza, J.C., Díaz, E., Alatrista-Salas, H. (eds) Information Management and Big Data. SIMBig 2020. Communications in Computer and Information Science, vol 1410. Springer, Cham. https://doi.org/10.1007/978-3-030-76228-5_11

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

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-76227-8

  • Online ISBN: 978-3-030-76228-5

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