Machine Learning-Based Time Series Prediction at Brazilian Stocks Exchange

This study proposes a novel method for forecasting the returns of assets comprising the Ibovespa from January 1, 2016, to December 30, 2020, by integrating machine learning algorithms-Gradient Boosting Machine, k-Nearest Neighbor, and Bayesian Regularized Neural Networks. Employing an ensemble strategy with diverse data modeling approaches, the method includes a pre-processing stage for variable selection, ranking their importance using statistical techniques such as OneR, Information Gain, and Chi-Square. This approach aims to overcome common challenges such as overfitting, high dimensionality, and computational efficiency, thus enhancing the robustness of the machine learning model and reducing susceptibility to biases and fluctuations. Empirical results demonstrate that, compared to the ARIMA model, the machine learning algorithm shows superior performance in forecast error and forecast hit rate and precision (R2, Willmott, and Kurtosis). Furthermore, the results suggest that the proposed algorithm can significantly improve predictive precision when applied to the ARIMA model and generalized to various datasets that include various markets and assets.