A Bayesian Network Model to Improve Stock Market Trend Following Strategies

  • Fabio Katsumi USP
  • Edson S. Gomi USP


Traditional trend-following models have been used for many years to invest in various asset classes. In this paper we propose a Bayesian Network architecture to enhance classical trend following performance applied to the US stock index. The results show that a Bayesian Network that considers other market variables outperforms both traditional trend following and buy and hold strategies.
Palavras-chave: Bayesian Network, Stock Market, Trend Following Strategy


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KATSUMI, Fabio; GOMI, Edson S.. A Bayesian Network Model to Improve Stock Market Trend Following Strategies. In: BRAZILIAN WORKSHOP ON ARTIFICIAL INTELLIGENCE IN FINANCE (BWAIF), 1. , 2022, Niterói. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2022 . p. 81-92. DOI: https://doi.org/10.5753/bwaif.2022.223291.