Machine learning method for return direction forecasting of Exchange Traded Funds using classification and regression models
This work addresses investment strategy decisions for financial traders by applying standard machine learning models to ETF data, but it is incremental as it uses existing methods on new datasets without introducing novel techniques.
The authors tackled the problem of forecasting return directions for Exchange Traded Funds (ETFs) using historical component data, finding that their machine learning models, such as linear regression and classification methods, outperformed control metrics with returns up to two times higher and Sharpe ratios up to four times higher than buy & hold.
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 Naïve 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.