Impact of the COVID-19 pandemic on IBOVESPA

a statistical analysis with machine learning models PROPHET and AUTOARIMA

Authors

Keywords:

Statistical Modeling, Time series, Forecasting

Abstract

This work analyzed the impact of the covid-19 pandemic in the year 2020 on Brazilian stocks using the Bovespa index and identified outliers in the data. It also observed a trend of stability in the following years, indicating economic recovery. The seasonality in the regular patterns was identified and represented in a line graph, highlighting the lowest medians in June and July. Prophet and autoARIMA models were used for forecasting, and the results were evaluated using various error metrics, including RMSE, MAE, SMAPE, MAPE, MASE, and RSQ. Although the Prophet model performed better with the differentiated data, the AutoARIMA model performed better with the original and log1p transformed data. The study is relevant to understand the impact of the pandemic on Brazilian stocks and how forecasting models can be used to assist in decision-making.

 

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Published

01-07-2024

How to Cite

Silva, A. V. A., Xavier, E. F. M., Barbosa, N. F. M., Júnior, S. F. A. X., & Jale, J. da S. (2024). Impact of the COVID-19 pandemic on IBOVESPA: a statistical analysis with machine learning models PROPHET and AUTOARIMA. Sigmae, 13(2), 57–71. Retrieved from https://publicacoes.unifal-mg.edu.br/revistas/index.php/sigmae/article/view/2158

Issue

Vol. 13 No. 2 (2024): "Don't panic"

Section

Applied Statistics

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