Brazilian Selic Rate Forecasting with Deep Neural Networks

Abstract

Artificial intelligence has shortened edges in many areas, especially the economy, to support long-term and accurate forecasting of financial indicators. Traditional statistical methods perform poorly compared to those based on artificial intelligence, which can achieve higher rates even with high-dimensional datasets. This method still needs evolution and studies. In emerging countries, decision-makers and investors must follow the basic interest rate, such as in Brazil, with a Special System of Settlement and Custody (Selic). Prior works used deep neural networks (DNNs) for forecasting time series economic indicators such as interest rates, inflation, and the stock market. However, there is no empirical evaluation of the prediction models for the Selic interest rate, especially the impact of training time and the optimization of hyperparameters. In this paper, we shed light on these issues and evaluate, through a fair comparison, the use of DNNs models for Selic time series forecasting. Our results demonstrate the potential of DNNs with an error rate above 0.00219 and training time above 84.28 s. Our findings open up opportunities for further investigations toward real-time interest rate forecasting, facilitating more reliable and timely forecasting of interest rates for decision-makers and investors.