Bayesian Networks for Stock Value Prediction: Applying the K2 and PC Algorithms
DOI:
https://doi.org/10.5753/reic.2023.2344Keywords:
Machine learning, Bayesian networks, Stock marketAbstract
Aiming to support the investors' decisions in stock market, in this paper, the application of Bayesian networks for stock market value prediction is proposed. The applied data were obtained through the Yahoo! Finance and they included daily quotations from Petrobras, Telefonica Brasil and Embraer between 02/01/2020 and 11/27/2020. The learning of the Bayesian networks was carried out by the PC and K2 algorithms. For the inference, the exact Lauritzen algorithm was used, which generated good results, with hits of up to 94%, obtaining an average hit rate of 73.66% for the network induced by the PC algorithm and 70.8% for the network induced by K2. Thus, the proposal is promising and manages to satisfactorily forecast the stock price.
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