Modelo de previsão do mercado financeiro utilizando técnicas de aprendizado de máquina e árvores de decisão

Abstract

This work examines the development and application of a predictive model based on machine learning, using decision trees, to assist investors in making assertive decisions in the stock market. The tool was designed to predict price movements of financial assets by employing technical analysis techniques and indicators. The main goal is to provide a decision support base for investors, particularly for those with less experience, contributing to the reduction of errors and improving the performance of their investment portfolios. Additionally, the tool helps in managing investor emotions, reducing behavioral biases that often lead to impulsive and potentially harmful decisions. The model uses historical asset data, processed with machine learning algorithms to identify patterns and future trends. The model’s performance was evaluated against a passive investment strategy, demonstrating that the proposed approach generated returns superior to the benchmark. The combination of machine learning with technical indicators allowed for an increase in forecast accuracy, providing a competitive edge in a volatile market. Furthermore, the study highlights the importance of using technologies such as machine learning and artificial intelligence in the current financial context, where they have the capacity to process large volumes of data and extract valuable insights that are crucial for more efficient decisionmaking. The use of this tool proves promising for democratizing access to financial analysis, benefiting investors of all experience levels.