Artículo

Mapping scientific production on predictive models of insolvency in Brazil

Resumen

In Brazil, the most prominent insolvency prediction models are those of Kanitz (1978), Altman (1979), Matias (1978), Elizabetsky (1976) and Silva (1982), which have inspired scientific production in this regard. , especially in the last 20 years. However, the number of literature reviews or bibliometric mappings in this area is still low, with a representation of only 5% of the works gathered in this research. In this context, with the objective of answering about the accuracy of the reliability of Brazilian predictive models indicated in the literature, this research carried out a bibliometric mapping based on the CAPES and SPELL bibliographic databases. Forty-two articles resulting from the search for the terms “insolvency” and “forecasting models” in the aforementioned databases were extracted and analyzed. The results indicate that researchers of predictive models frequently made a relationship between them and the financial and business market; and that most of the researches carried out had as objective the construction of a new predictive model, being the probable reason for this presented in the answer to the question of this article: it was found that the accuracy of the reliability of the already existing predictive models is not satisfactory. Thus, this research, which is qualitative, contributes to the mapping of work on insolvency prediction developed in Brazil and to the understanding of its thematic outline.
https://doi.org/10.29173/cais1841
The Use of Institutional Repositories for Self-Archiving in Canadian Universities
2024
bronze
https://doi.org/10.29173/cais1841
Poppy Riddle; Marc-André Simard; Pallavi Gone; Vinson Li; Philippe Mongeon
School of Information Management, Dalhousie University; École de bibliothéconomie et des sciences de l’information, Université de Montréal
Artículo obtenido de:
OpenAlex
Fuente
0 0 votos
Califica el artículo
Campus Samborondón
Vía Samborondón Km. 13.5
Call Center: 5150400
metricas@ecotec.edu.ec
Facebook Instagram Youtube

Mapa del sitio