A Financial Distress Prediction using a Non-stationary Dataset - Chaves, Rubens Marques; Rossi, André Luis Debiaso; Garcia, Luís Paulo Faina (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC); 2023: Anais do XX Encontro Nacional de Inteligência Artificial e ComputacionalDO - 10.5753/eniac.2023.234013, 2023)
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, F 1 -Score and G mean bring evidences that the dataset is suitable for FDP.