Delisting of Brazilian Companies: a forecasting model for the period from 2013 to 2018
Objective: The objective of this article is to identify which are the main accounting variables that explain the delisting decision. The decision to close capital has aroused the interest of recent international research, and it was a topic rarely addressed.Method: The survey uses data from companie...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2022 |
| País: | Brasil |
| Institución: | Academia Brasileira de Ciências Contábeis (Abracicon) |
| Repositorio: | Revista de Educação e Pesquisa em Contabilidade |
| Idioma: | portugués inglés |
| OAI Identifier: | oai:ojs.www.repec.org.br:article/2591 |
| Acceso en línea: | https://www.repec.org.br/repec/article/view/2591 |
| Access Level: | acceso abierto |
| Palabra clave: | Delisting; Capital delisting; Cancellation of Registration. Delisting Fechamento de Capital Cancelamento de Registro |
| Sumario: | Objective: The objective of this article is to identify which are the main accounting variables that explain the delisting decision. The decision to close capital has aroused the interest of recent international research, and it was a topic rarely addressed.Method: The survey uses data from companies traded on B3, focusing on the analysis of 126 record cancellations between 2013 and 2018, with separation between groups and application of logistic regression to analyze the forecast of the closing event.Results: The results allow to perceive as main characteristics of the companies that went public: (i) greater cash availability; (ii) lower growth; (iii) less liquidity; (iv) greater concentration of ownership; and (v) larger size, consistent with previous studies. The main accounting variables related to delisting are (i) lower growth, (ii) greater cash availability, and (iii) greater concentration of control.Contributions: The results contribute to the advancement of the modeling employed by Bortolon and Silva Junior (2015), reaching a sensitivity of 100% in cases of delisting. The application of modeling can allow investors to identify the moment when a company tends to close capital. |
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