Anticipating corporate’s distresses

Abstract

Master's degree in Business Administration from the Faculty of Business Graduated in Business Administration and Accounting from the University Center of Patos de Minas (UNIPAM), with a specialization in Business Strategy Management and an MBA in People Management. He is currently a Master's student in Administration at the Faculty of Management and Business (FAGEN) of the Federal University of Uberlândia (UFU). Acts as a substitute professor at the Faculty of Management and Business at the Federal University of Uberlândia, teaching classes at the Undergraduate level. His research interests involve: Third Sector, Organizational Behavior, Consumer Behavior, Business Consulting and Risk Management.

Maternidade de filho com deficiência e mercado de trabalho: vozes de mães que vivenciam essa complexa realidade

“…Na atualidade, "a quantidade de mulheres ativas no mercado de trabalho vem aumentando consideravelmente, elas estão ocupando participações em praticamente todos os seguimentos e buscando sempre a qualificação profissional" (Sales;Farias, 2022, p. 189). A formação escolar, principalmente universitária, é um aspecto fundamental para o desenvolvimento da carreira profissional feminina (Barboza;Gonçalves, 2022), mas ainda assim, apesar de priorizar a qualificação, elas não têm tido o devido reconhecimento social. Nesse sentido, Souza, Fernandes e Gomes (2022, p. 264) ressaltam que "As mulheres são mais escolarizadas, mas há persistência de salários menores quando comparados aos masculinos".…”

A Financial Distress Prediction using a Non-stationary Dataset

“…Table 2 presents these attributes and is organized into three columns: Document indicating the source of information, Indicator representing the information name and Attribute identifying each attribute with a code. These attributes are important to compute a second set of attributes that have been used for other studies based on FDP [Altman 1968, Tomczak 2016, Barboza et al 2017, Shen et al 2020, Liang and Tsai 2020, Barboza et al 2022, Chen et al 2022, Bragoli et al 2022]. The attributes listed in Table 3 were also listed in similar study [Shen et al 2020].…”

“…The FDP using economic-financial indicators has been extensively researched since the late 1960s [Frydman et al 1985, Altman et al 1994, Sun et al 2011, Altman 2013, Clement 2020, Jabeur et al 2021, Duarte and Barboza 2020, Barboza et al 2022]. These indicators come from financial data such as balance sheets, income statements, cash flow statements, accounts receivable aging reports, and budget reports [Ross et al 2012].…”

“…Despite the interest of academics and practitioners in the topic [Kumbure et al 2022], the availability of datasets remains a barrier. The majority of the datasets are not publicly available [Barboza et al 2017, Barboza et al 2022, Bragoli et al 2022, Zou et al 2022, Chen et al 2022, Pilch 2021, while just few are public [Lombardo et al 2022, Liang et al 2016, Zieba et al 2016, Tang et al 2019. Additionally, most of these datasets have considered the data as stationary and have overlooked the time dependence aspect, which has been addressed in recent studies [Sun et al 2019, Shen et al 2020, Kim et al 2022.…”

“…However, after some decades and the evolution of ML models, a new research concluded that these new techniques have overcome those based on statistics [Barboza et al 2017]. Some ML models used for that purpose were Logistic Regression (LR) [Barboza et al 2017], Support Vector Machines [Hui and Sun 2006], Decision Tree [Zibanezhad et al 2011], Random Forest (RF) [Alam et al 2020], eXtreme Gradient Boosting (XGBoost) [Barboza et al 2022], Categorical Boosting (CatBoost) [Martorano 2021] and Neural Networks [Tang et al 2019] and others. Some of them have reached accuracy higher than 90%.…”