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
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
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
UR - http://www.scopus.com/inward/record.url?scp=85111164131&partnerID=8YFLogxK
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 - 9783030762278
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 Annual International Conference on Information Management and Big Data, SIMBig 2020
Y2 - 1 October 2020 through 3 October 2020
ER -