  This article aims to propose and apply a machine learning method to analyze
the direction of returns from Exchange Traded Funds (ETFs) using the historical
return data of its components, helping to make investment strategy decisions
through a trading algorithm. In methodological terms, regression and
classification models were applied, using standard datasets from Brazilian and
American markets, in addition to algorithmic error metrics. In terms of
research results, they were analyzed and compared to those of the Na\"ive
forecast and the returns obtained by the buy & hold technique in the same
period of time. In terms of risk and return, the models mostly performed better
than the control metrics, with emphasis on the linear regression model and the
classification models by logistic regression, support vector machine (using the
LinearSVC model), Gaussian Naive Bayes and K-Nearest Neighbors, where in
certain datasets the returns exceeded by two times and the Sharpe ratio by up
to four times those of the buy & hold control model.
