A Financial Distress Prediction using a Non-stationary Dataset

  • Rubens Marques Chaves Universidade de Brasília
  • André Luis Debiaso Rossi Universidade Estadual Paulista
  • Luís Paulo Faina Garcia Universidade de Brasília


Financial distress prediction (FDP) is crucial to companies, investors, and authorities. However, most FDP studies have been based on stationary models, disregarding important challenges present on financial distress data such as non-stationarity. Therefore, the lack of real-world datasets of economic-financial indicators organized in a timeline manner is a gap to be addressed. This study proposes a comprehensive dataset of 84 economic-financial indicators from the Brazilian Securities and Exchange Commission (CVM) organized in a non-stationary manner and validated by experiments using classification models. The results of the metrics AUC-ROC, AUC-PS, F1-Score and Gmean bring evidences that the dataset is suitable for FDP.

Palavras-chave: Financial Distress, CVM, non-stationary, Machine Learning


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CHAVES, Rubens Marques; ROSSI, André Luis Debiaso; GARCIA, Luís Paulo Faina. A Financial Distress Prediction using a Non-stationary Dataset. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 20. , 2023, Belo Horizonte/MG. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 300-314. ISSN 2763-9061. DOI: https://doi.org/10.5753/eniac.2023.234013.