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Article
Publication date: 28 February 2023

Isabel Abinzano, Harold Bonilla and Luis Muga

Using data from business reorganization processes under Act 1116 of 2006 in Colombia during the period 2008 to 2018, a model for predicting the success of these processes is…

211

Abstract

Purpose

Using data from business reorganization processes under Act 1116 of 2006 in Colombia during the period 2008 to 2018, a model for predicting the success of these processes is proposed. The paper aims to validate the model in two different periods. The first one, in 2019, characterized by stability, and the second one, in 2020, characterized by the uncertainty generated by the COVID-19 pandemic.

Design/methodology/approach

A set of five financial variables comprising indebtedness, profitability and solvency proxies, firm age, macroeconomic conditions, and industry and regional dummies are used as independent variables in a logit model to predict the failure of reorganization processes. In addition, an out-of-sample analysis is carried out for the 2019 and 2020 periods.

Findings

The results show a high predictive power of the estimated model. Even the results of the out-of-sample analysis are satisfactory during the unstable pandemic period. However, industry and regional effects add no predictive power for 2020, probably due to subsidies for economic activity and the relaxation of insolvency legislation in Colombia during that year.

Originality/value

In a context of global reform in insolvency laws, the consistent predictive ability shown by the model, even during periods of uncertainty, can guide regulatory changes to ensure the survival of companies entering into reorganization processes, and reduce the observed high failure rate.

Details

The Journal of Risk Finance, vol. 24 no. 3
Type: Research Article
ISSN: 1526-5943

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Access Restricted. View access options
Article
Publication date: 28 February 2023

Isabel Abinzano, Harold Bonilla and Luis Muga

The aim of this paper is to provide an overview of the impact of the implementation of Colombian Corporate Insolvency Act 1116 of 2006 in the period 2008–2018 and to assess the…

156

Abstract

Purpose

The aim of this paper is to provide an overview of the impact of the implementation of Colombian Corporate Insolvency Act 1116 of 2006 in the period 2008–2018 and to assess the relevance of a broad set of financial predictors, as well as variables related to the economic context or the characteristics of the process itself, in explaining the failure of reorganization processes.

Design/methodology/approach

Both logit and probit models are estimated, starting from a large number of variables proposed in the literature which are then narrowed down to a final selection based on their individual significance and machine learning.

Findings

The results show the prevalence of a limited number of financial variables related to equity, indebtedness, profits and liquidity as predictors of the failure of reorganization processes. The use of financial information from the year prior to the completion of the reorganization improves predictive accuracy and reliability. The debt-to-equity indicator provides no significant explanatory power, while voluntary entry into a reorganization process favors its success.

Originality/value

While financial and accounting information is used across the literature to predict insolvency events, it is used here to predict success or failure in reorganization processes under the conditions imposed by a specific legislative act in a Latin American context.

Propósito

Proporcionar una panorámica de la implementación de la Ley 1116 de 2006 a partir de las empresas que suscribieron acuerdos de reorganización en Colombia en el periodo 2008–2018 y evaluar la relevancia de un conjunto amplio de predictores financieros, así como variables relacionadas con el entorno económico o de características del propio proceso, para explicar el fracaso de la reorganización.

Diseño/Metodología/Aproximación

Se han estimado tanto modelos logit como probit, partiendo de un amplio número de variables propuestas en la literatura, que luego se reducen a una selección final basada en su significancia individual y una metodología de machine learning.

Hallazgos

Un número reducido de variables relacionadas con los fondos propios, el endeudamiento, los beneficios y la liquidez prevalecen como predictores financieros del fracaso de los procesos de reorganización. El uso de información del año anterior al cierre del acuerdo mejora la precisión de las predicciones realizadas. El indicador de conversión de deuda en capital no ofrece capacidad explicativa significativa, mientras que la entrada voluntaria a la reorganización favorece su éxito.

Originalidad/Valor

Muchos trabajos han usado información financiera y contable para predecir eventos de insolvencia. En nuestro caso se usa esta información para predecir el éxito o fracaso de los procesos de reorganización bajo una ley específica en el contexto latinoamericano.

Details

Academia Revista Latinoamericana de Administración, vol. 36 no. 1
Type: Research Article
ISSN: 1012-8255

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