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Self-Organizing Maps Trading Strategy: Brazilian Stock Market
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Resumo(s)
This thesis presents a novel trading strategy for the Brazilian stock market, leveraging SelfOrganizing Maps (SOMs) to uncover hidden patterns in the daily behavior of the Ibovespa
index from 2000 to 2023 and tested in data from 2024 to March 2025. By transforming raw
price and technical indicators data into a set of Perceptually Important Points (PIPs), the model
captures essential market movements while discarding less relevant fluctuations. A
comprehensive time-series cross-validation approach is employed, ensuring rigorous
performance evaluation on unseen data. The proposed method automatically clusters similar
market conditions and assigns trading signals based on the expected future return within each
cluster. Experimental results show that this SOM-based strategy outperforms both a simple
buy-and-hold benchmark and other widely used technical analysis approaches in terms of
overall returns and risk-adjusted performance. Notably, the inclusion of technical indicators
provides a richer description of market trends, volatility, and momentum, enhancing the
SOM’s ability to distinguish profitable patterns. The framework also incorporates risk
management by evaluating the Sharpe ratio and other metrics, highlighting the robustness of
the strategy across diverse market regimes. These findings suggest that a well-tuned
unsupervised learning approach, combined with carefully selected financial features, can
systematically exploit market inefficiencies. Moreover, the methodology is flexible and can be
extended to other stock markets or asset classes with minimal adjustments, underlining the
versatility and practical value of the presented solution.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Business Intelligence
Palavras-chave
Self-Organizing Maps Trading Strategies Brazilian Stock Market Technical Analysis Perceptually Important Points