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Prediction of failure in reorganization agreements under Colombia's Corporate Insolvency Act

Prediction of failure in reorganization agreements under Colombia's Corporate Insolvency Act 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/approachBoth 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.FindingsThe 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/valueWhile 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Academia Revista Latinoamericana de Administración Emerald Publishing

Prediction of failure in reorganization agreements under Colombia's Corporate Insolvency Act

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References (41)

Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
1012-8255
DOI
10.1108/arla-12-2021-0230
Publisher site
See Article on Publisher Site

Abstract

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/approachBoth 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.FindingsThe 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/valueWhile 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.

Journal

Academia Revista Latinoamericana de Administración – Emerald Publishing

Published: Mar 6, 2023

Keywords: Insolvency; Reorganization; Failure; Financial predictors; Machine learning; Insolvencia; reorganización; fracaso; predictores financieros; machine learning; C51; G33

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