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’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.