TY - CHAP
T1 - The effect of good corporate governance on financial distress in companies listed in sharia stock index Indonesia
T2 - Machine learning approach
AU - Rusmita, Sylva Alif
AU - An-Nafis, Moh Saifin Ami
AU - Ramadhani, Indria
AU - Irfan, Mohammad
N1 - Publisher Copyright:
© 2023, IGI Global. All rights reserved.
PY - 2023/1/9
Y1 - 2023/1/9
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85147391932&partnerID=8YFLogxK
U2 - 10.4018/978-1-6684-4483-2.ch014
DO - 10.4018/978-1-6684-4483-2.ch014
M3 - Chapter
AN - SCOPUS:85147391932
SN - 9781668444832
SP - 220
EP - 251
BT - Advanced Machine Learning Algorithms for Complex Financial Applications
PB - IGI Global
ER -