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.
TopIntroduction
The term Good Corporate Governance is commonly referred to as the governance of a company. Good Corporate Governance is regulation for companies helpful in carrying out control to add value-added to the company and its stakeholders. This can happen with Good Corporate Governance, which can form a pattern of management performance that makes management clean, transparent, and professional (Effendi, 2009).
Based on the data from Asian Development bank in 2013, Indonesia is ranked 5th at (54.55), far above the first ranked country, namely Thailand (75.39), Malaysia ranked second (71.69), Singapore ranked third (71.68). Furthermore, the Philippines ranked fourth at (57.99), while Vietnam ranked sixth at (33.87) (Asia Develompent Bank, 2014). Good Corporate Governance is considered increasingly crucial with macro-scale financial problems. Further the data from the Ministry of Finance, state owned enterprises were threatened with financial distress, such as PT. Dirgantara Indonesia (-0.84), PT. Pindad (1.02), PT Indosutri Kereta Api (0.92), PT. Barata Indonesia (0.83), PT. Krakatau Steel (0.47), PT. Doc and Kodja Bahari (-1.72), PT. Dock and Shipping Surabaya (-1.23), PT Industri Kapal Indonesia (0.89), PT PAL Indonesia (-0.1) (Santoso & Kurniawan, 2019). The emergence of various kinds of problems that occur further strengthens the importance of applying good corporate governance.
The concept of corporate governance is promoted to realize corporate governance that is more open to users of financial statements. So, in the long term, the implementation of good corporate governance can affect the company's results, where the company's results will be linked to financial performance. If there is a problem with financial performance, it can damage the company's stability, and in the long run, it can lead to bankruptcy conditions (Ananto et al., 2017).
Symptoms of bankruptcy can be identified by conducting a bankruptcy analysis. Management will be better at minimizing bankruptcy if these symptoms are detected early. One of the symptoms of bankruptcy can be seen from financial distress. Financial distress is a fundamental problem for a company to be aware of. According to Platt & Platt (2002), financial distress is a critical stage in financial decline and can signify more serious financial problems, including bankruptcy.
In this study examine the effect of GCG to financial distress from a group of companies listed on Indonesian Sharia Stock Index (ISSI). Researchers are interested in taking samples from ISSI because the considerations have more complex criteria. The Islamic capital market has its own charm for Muslim investors who avoid shares of companies that operate not according to sharia. This object is attractive because of the very fast development of investors. The shares listed on ISSI so far have more than 350 issuers joining ISSI, so it is interesting for the authors to conduct this research. In addition, ISSI constituents are selected (screening) 2 times a year, ISSI constituents are also determined by Indonesia financial Authority (OJK) that they do not have a non-halal portfolio, not only that, the issuer's financial ratio is a comparison ratio with a total legacy of less than 45% and the ratio of interest income / non-halal is limited to an optimal 10% of the total income and other income. With these criteria, listed companies must implement good corporate governance. This analyze the last 9 years period from 2011-2019. This research was also conducted to ensure the correctness of the inconsistent results between the influences of independent variables on the possibility of financial distress. The writing stage of this research begins with the introduction. The second is research background to describe the related theories. Next is the problem statement of research, which are includes the previous study, issues, problem statement, and the development of hypotheses. In the fourth part is the solutions and recommendations. Then, followed by future research directions. Finally, there is a conclusion.
Key Terms in this Chapter
Board of Commissioners Size: The company's parts are collectively responsible for supervising and advising the board of directors and ensuring that the company implements good corporate governance.
Panel Regression: Quantitative research method that combines time series and cross sections which is useful for knowing the relationship between variables both simultaneously and partially.
Islamic Issuers: Companies that are listed on the stock exchange and comply with sharia principles (financial statements and their business types).
Managerial Ownership: Ownership of company shares owned by the company's own managers.
Institutional Ownership: The proportion of company shares owned by institutions or business entities or organizations.
Risk of Bankruptcy: The risk of failure for the company in carrying out its operations, can be caused by internal and external factors of the company.
Proportion of Independent Commissioners: Members of the commissioners are determined based on the decision of the GMS (General Meeting of Shareholders) by parties affiliated with the president director, board members, or other members of the commission.
Extra Trees: One of the machine learning models, which classifies a type of ensemble learning technique that combines the results of several uncorrelated decision trees collected in the “forest” to produce the results of its classification.