Corporate governance and financial distress: lessons learned from an unconventional approach

Alberto Tron (), Maurizio Dallocchio (), Salvatore Ferri and Federico Colantoni ()
Additional contact information
Alberto Tron: Università Commerciale L. Bocconi
Maurizio Dallocchio: Università Commerciale L. Bocconi
Federico Colantoni: University of St. Gallen

Journal of Management & Governance, 2023, vol. 27, issue 2, No 2, 425-456

Abstract: Abstract Using a and a unique set of Italian non-listed Unlikely to Pay (UTP) positions, that consist in the phase that precedes the insolvency but where it is still possible for the company to succeed in restructuring, this paper aims to analyze the relationships between corporate governance characteristics and financial distress status. We compare the performance of corporate governance variables in predicting corporate defaults, using both the Logit and Random Forest models, which previous researchers have deemed to be the most efficient machine learning techniques. Our results show that the use of corporate governance variables – especially with regards to CEO renewal and stability in the composition of the board of directors – increases the accuracy of the Random Forest technique and influences the success of the turnaround process. This paper also confirms the Random Forest technique’s ability to significantly outperform the Logit model in terms of accuracy.

Keywords: Financial distress; Corporate finance; Corporate governance; Corporate default; G32; G33; G34; K22 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10997-022-09643-8 Abstract (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:jmgtgv:v:27:y:2023:i:2:d:10.1007_s10997-022-09643-8

Ordering information: This journal article can be ordered from
http://www.springer. ... 29/journal/10997/PS2

DOI: 10.1007/s10997-022-09643-8

Access Statistics for this article

Journal of Management & Governance is currently edited by Lino Cinquini

More articles in Journal of Management & Governance from Springer, Accademia Italiana di Economia Aziendale (AIDEA) Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().