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All Journal International Journal of Business, Economics, and Social Development International Journal of Global Operations Research International Journal of Quantitative Research and Modeling
Bayyinah, Ayyinah Nur
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Physics Social Sciences

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Bankruptcy Prediction Analysis of Life Insurance Companies Using Altman Z-Score dan Ohlson O-Score Methods Bayyinah, Ayyinah Nur; Helena, Putri Zahra
International Journal of Global Operations Research Vol. 5 No. 4 (2024): International Journal of Global Operations Research (IJGOR), November 2024
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v5i4.335

Abstract

Life insurance is one of the non-bank industries that offers guarantees to overcome risks. In its implementation, life insurance companies need to maintain the survival of the company in the midst of increasing economic competition so that they don't face the threat of bankruptcy. Bankruptcy itself is a legal status of a certain entity or company that cannot pay its debts to creditors and the company's operations cannot be continued due to lack of funds. This study aims to compare the accuracy of the Altman Z-Score and Ohlson O-Score methods in predicting the bankruptcy of life insurance companies in Indonesia, such as PT Axa Financial Indonesia, PT Taspen Life, Asuransi Jiwa Bersama (AJB) Bumiputera 1912, PT Avrist Assurance, and PT Reliance Life Insurance Indonesia. The data used in this study is secondary data in the form of financial statements of life insurance companies taken from the official website of the relevant company. The results showed that the comparison between the two models revealed that the Altman method is better in predicting company bankruptcy. This is because the Altman method has a more detailed classification of conditions compared to the Ohlson method.
Analysis of Financial Distress in Telecommunication Companies in Indonesia Using the Ohlson O-Score and Zmijewski Methods Bayyinah, Ayyinah Nur
International Journal of Business, Economics, and Social Development Vol 6, No 1 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijbesd.v6i1.875

Abstract

Currently, major telecommunication sub-sector companies in Indonesia are experiencing rapid growth and have become dominant players in the market. However, not all telecommunication companies are profitable, as some dominant subsidiaries have experienced declining profits or losses, potentially leading to financial distress. Financial distress is a condition where a company is unable to meet its current obligations, such as trade payables, tax liabilities, and short-term debts. This study aims to analyze and evaluate the accuracy of the Ohlson O-Score and Zmijewski methods in detecting financial distress in telecommunication companies in Indonesia. The data used in this study are historical financial data from several telecommunication companies listed on the Indonesia Stock Exchange. The results show that the Ohlson O-Score is effective in early detection of potential financial distress, while the Zmijewski method is more effective in evaluating companies already in critical financial conditions.
Investment Portfolio Optimization Using Genetic Algorithm on Infrastructure Sector Stocks Based on the Single Index Model Bayyinah, Ayyinah Nur; Riaman, Riaman; Sukono, Sukono
International Journal of Quantitative Research and Modeling Vol 6, No 2 (2025)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v6i2.977

Abstract

Investment is a strategic step in managing assets to gain profits in the future by allocating some funds in the present. However, behind the promising potential returns, investment also contains risks that cannot be ignored. One way to reduce the level of risk in investing is to implement a portfolio diversification strategy, which is to form an optimal portfolio by allocating investments to various stocks. This study aims to identify the stocks that form the optimal portfolio, determine the optimal weight of each stock, and calculate the expected return and risk of the portfolio. The portfolio optimization process is carried out using Genetic Algorithm, with the calculation of expected return and risk using the Single Index Model (SIM) approach. The data used includes data on stocks in the infrastructure sector for the period July 1, 2023 to June 30, 2024. The results showed that there were six stocks selected in forming the optimal portfolio with the weight of each stock: PGEO 15.0023%, ISAT 32.1522%, GMFI 4.7822%, EXCL 15.3236%, JSMR 29.7379, and OASA 3.0018%. This optimal portfolio provides an expected return of 0.1167% with a portfolio risk of 0.0152%.
Co-Authors Helena, Putri Zahra Riaman Riaman, Riaman Sukono .