The effect of good corporate governance on financial distress in companies listed in sharia stock index Indonesia: Machine learning approach

Sylva Alif Rusmita, Moh Saifin Ami An-Nafis, Indria Ramadhani, Mohammad Irfan

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

5 Citations (Scopus)

Abstract

This study aims to examine the effect of good corporate governance (GCG) on financial distress in companies listed on the Indonesian Sharia Stock Index. The purposive sampling method was used, obtaining 23 samples that met the criteria. Panel regression and machine learning were used to test the hypothesis. Based on the results, the variables of GCG, which consist of institutional ownership (IO), managerial ownership (MO), board of commissioners size (BoC), and proportion of independent commissioners (PI), affect financial distress simultaneously, whereas BoC and PI are partially the most significant variables. The machine learning method shows that extra trees is the best model to analyze financial distress. The model indicates the most significant variable is IO, followed by BoC and PI. From the result, Islamic issuers should manage their GCG by reducing the number of BoC, IO, and adding a proportion of PI to minimize the case of financial distress.

Original languageEnglish
Title of host publicationAdvanced Machine Learning Algorithms for Complex Financial Applications
PublisherIGI Global
Pages220-251
Number of pages32
ISBN (Electronic)9781668444856
ISBN (Print)9781668444832
DOIs
Publication statusPublished - 9 Jan 2023

Fingerprint

Dive into the research topics of 'The effect of good corporate governance on financial distress in companies listed in sharia stock index Indonesia: Machine learning approach'. Together they form a unique fingerprint.

Cite this