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Forecasting stock market index daily direction: A Bayesian Network approach - 2018

Forecasting Stock Market Index Daily Direction: A Bayesian Network Approach

Research Paper on Forecasting Stock Market Index Daily Direction: A Bayesian Network Approach

Research Area:  Machine Learning


In this work, we investigate the feasibility of Bayesian Networks as a way to verify the extent to which stock market indices from around the globe influence iBOVESPA – the main index at the São Paulo Stock Exchange, Brazil. To do so, index directions were input to a network designed to reflect some intuitive dependencies amongst continental markets, moving through 24 and 48 h cycles, and outputting iBOVESPAs next day closing direction. Two different network topologies were tested, with different numbers of stock indices used in each test. Best results were obtained with the model that accounts for a single index per continent, up to 24 h before iBOVESPAs closing time. Mean accuracy with this configuration was around 71% (with almost 78% top accuracy). With results comparable to those of the related literature, our model has the further advantage of being simpler and more tractable for its users. Also, along with the fact that it not only gives the next day closing direction, but also furnishes the set of indices that influence iBovespa the most, the model lends itself both to academic research purposes and as one of the building blocks in more robust decision support systems.

Stock Market
Bayesian Network Approach
Machine Learning
Deep Learning

Author(s) Name:  Luciana S.Malagrino,Norton T.Roman and Ana M.Monteirob

Journal name:  Expert Systems with Applications

Conferrence name:  

Publisher name:  ELSEVIER

DOI:  10.1016/j.eswa.2018.03.039

Volume Information:  Volume 105, 1 September 2018, Pages 11-22