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.
|Title of host publication
|Advanced Machine Learning Algorithms for Complex Financial Applications
|Number of pages
|Published - 9 Jan 2023