Comparing Artificial Neural Network Architectures for Brazilian Stock Market Prediction

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Comparing Artificial Neural Network Architectures for Brazilian Stock Market Prediction Suellen Teixeira Zavadzki de Pauli1   · Mariana Kleina1 · Wagner Hugo Bonat1 Received: 27 January 2020 / Revised: 20 June 2020 / Accepted: 25 June 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020

Abstract Prediction of financial time series is a great challenge for statistical models. In general, the stock market times series present high volatility due to its sensitivity to economic and political factors. Furthermore, recently, the covid-19 pandemic has caused a drastic change in the stock exchange times series. In this challenging context, several computational techniques have been proposed to improve the performance of predicting such times series. The main goal of this article is to compare the prediction performance of five neural network architectures in predicting the six most traded stocks of the official Brazilian stock exchange B3 from March 2019 to April 2020. We trained the models to predict the closing price of the next day using as inputs its own previous values. We compared the predictive performance of multiple linear regression, Elman, Jordan, radial basis function, and multilayer perceptron architectures based on the root of the mean square error. We trained all models using the training set while hyper-parameters such as the number of input variables and hidden layers were selected using the testing set. Moreover, we used the trimmed average of 100 bootstrap samples as our prediction. Thus, our approach allows us to measure the uncertainty associate with the predicted values. The results showed that for all times series, considered all architectures, except the radial basis function, the networks tunning provide suitable fit, reasonable predictions, and confidence intervals. Keywords  Artificial neural network · Forecasting · Time series · Stock market

* Suellen Teixeira Zavadzki de Pauli [email protected] 1



Federal University of Paraná (UFPR), Cel. Francisco H. dos Santos Avenue, 210, Curitiba, PR 81530‑000, Brazil

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Annals of Data Science

1 Introduction The stock market is a popular investment option for investors because of its expected high returns. In contrast, stock market prediction is a complex task to achieve with the help of artificial intelligence. It is due to stock prices depend on many factors, including trends and news in the market [1]. The prediction of financial market times series is challenging. Such information presents high volatility in the observed data, together with the uncertainty inherent in any forecast. Therefore, wrong decisions based on such predictions can have economically catastrophic consequences for individuals, institutions, and nations, as observed in the financial crises [2]. According to [3], financial institutions and banks are among those industries that have relatively complete and accurate data. Typical cases include stock investment, loan payment prediction, credit approval, bankruptcy prediction, and fraud detec