Delisting Prediction Model in Sharia Stock Using Logistic Regression - Zulnan Tinggi Hari; Endri Endri; Saiful Anwar (Universitas Muhammadiyah Pontianak, 2019)
This study aims to develop a predicting model for delisting in sharia stock using logistic regression and also to find out the best model in predicting the occurrence of delisting in sharia stocks by comparing the three types of link function models in logistic regression. This study uses financial ratios as predictive variable ROA, ROE, Leverage, Debt to Equity, Quick Ratio, Current Ratio, ROIC, Asset Turn Over, Long Term Debt, and Interest Coverage. The population of this study was 335 Sharia stocks registered with ISSI in the period 2012 - 2018. The samples in this study were 102 companies which consist of share issuers delisting and listing companies from Islamic stock as a comparison. The sampling method in this study is Purposive Judgment Sampling. Comparison of the analytical model used in this study Logistic Regression Results Logit model 93.77%, Normit 93.85% and Gompit 93.62%. From the result, it found that the Normit model is the best model with the highest level of accuracy 93.85%.