Previsão de Tendência de Preço no Mercado Brasileiro de Ações usando Cadeias de Markov de Tempo Discreto

João Queiroz; Martha Torres

doi:10.14295/vetor.v34i1.16774

Authors

João Queiroz Universidade Estadual de Santa Cruz, Ilhéus, Brasil
Martha Torres Universidade Estadual de Santa Cruz, Ilhéus, Brasil https://orcid.org/0000-0002-8056-8493

DOI:

https://doi.org/10.14295/vetor.v34i1.16774

Keywords:

Discrete-Time Markov Chain, Time-Series Prediction, Stock Market Prediction

Abstract

Understanding the stock market trend in order to predict price movement is very important for investment decisions given that stock prices are affected not only by the financial state of the company, but also by political, social, economic, global and local, plus many other factors. Markov Chains provide a powerful tool for performing mathematical and computational modeling and also have been used to predict trends in the stock market. From this, the following work brings a computational tool modeled based on the knowledge obtained through studies on discrete-time Markov Chains capable of making predictions of price trends of stocks on the Brazilian stock exchange using the 3-state method. Analyzes were carried out on 50 BOVESPA stocks in order to observe whether the forecast success percentage has any relation to the length of the period for building the transition matrix. These tests were carried out for the years 2019, 2020 and 2021 in order to observe whether there were impacts on the effectiveness of the methods during the period of the COVID-19 pandemic.

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